SC
S.C. Calvert
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
27 records found
1
Automated driving systems increasingly rely on learning-based components, which makes their safety evaluation challenging. Runtime monitoring provides a complementary safety layer by flagging anomalies before they escalate. However, current monitoring approaches are predominantly rule-based, which face limitations in complex conditions. Recent advances in vision/large language models, with their capacity for reasoning and context-aware scene interpretation, offer a promising alternative. Nevertheless, existing research primarily focuses on monitoring perception and largely neglects the decision-making and planning (DMP) layer, despite its critical importance. There is also a lack of an integrated framework that unifies monitor outputs into actionable severity levels. This paper addresses these gaps with two contributions. First, we propose a conceptual monitoring framework with a minimally intrusive side-car architecture, layered organization, and a unified four-failure-mode semantics. Second, we instantiate this framework for trajectory prediction within the DMP layer and conduct a controlled comparison of three monitoring paradigms: (i) a non-AI statistical baseline, (ii) a fine-tuned CLIP-based vision-language classifier, and (iii) a zero-shot multimodal LLM. Evaluation results on nuScenes dataset reveal that statistical monitoring struggles to detect safety-critical failures, while the vision-language monitor excels at low-to-mid severity detection, and the LLM-based monitor achieves the strongest performance on the most severe failure modes.
https://github.com/HaodongLi-Hub/How_To_Monitor_AV_Using_AI
...
https://github.com/HaodongLi-Hub/How_To_Monitor_AV_Using_AI
...
Automated driving systems increasingly rely on learning-based components, which makes their safety evaluation challenging. Runtime monitoring provides a complementary safety layer by flagging anomalies before they escalate. However, current monitoring approaches are predominantly rule-based, which face limitations in complex conditions. Recent advances in vision/large language models, with their capacity for reasoning and context-aware scene interpretation, offer a promising alternative. Nevertheless, existing research primarily focuses on monitoring perception and largely neglects the decision-making and planning (DMP) layer, despite its critical importance. There is also a lack of an integrated framework that unifies monitor outputs into actionable severity levels. This paper addresses these gaps with two contributions. First, we propose a conceptual monitoring framework with a minimally intrusive side-car architecture, layered organization, and a unified four-failure-mode semantics. Second, we instantiate this framework for trajectory prediction within the DMP layer and conduct a controlled comparison of three monitoring paradigms: (i) a non-AI statistical baseline, (ii) a fine-tuned CLIP-based vision-language classifier, and (iii) a zero-shot multimodal LLM. Evaluation results on nuScenes dataset reveal that statistical monitoring struggles to detect safety-critical failures, while the vision-language monitor excels at low-to-mid severity detection, and the LLM-based monitor achieves the strongest performance on the most severe failure modes.
https://github.com/HaodongLi-Hub/How_To_Monitor_AV_Using_AI
https://github.com/HaodongLi-Hub/How_To_Monitor_AV_Using_AI
Conditionally automated driving systems issue takeover requests (TORs) in situations that exceed their operational capabilities, requiring drivers to promptly resume manual control and maintain safe vehicle operation. A key factor in ensuring the smoothness of such control transitions is the time budget, i.e., the time offered by automation for control transitions. When the time budget is too short to accommodate the required takeover time (ToT, the time drivers need to regain manual vehicle control after receiving a TOR), the risk of accidents increases as drivers may lack adequate time to perceive, assess, and respond to the situation. Conversely, time budgets that substantially exceed the required ToT may also introduce risks: such TORs can be perceived as false alarms, leading to reduced driver attention and potential dangers, particularly when the out-of-capability situations are not readily perceivable to drivers. Therefore, defining and allocating sufficient time budgets is essential to ensure driving safety and user experience in vehicle control transitions.
This thesis systematically develops an adaptive framework for designing takeover time budgets that account for diverse drivers and situational demands. First, a systematic review synthesises the takeover sequence, identifying factors influencing takeover time and performance, and introduces the concept of the takeover buffer as the safety margin between required and allocated takeover time. Building on this foundation, a driving simulator experiment is conducted to collect behavioural, physiological, operational, and subjective data during takeover situations. Using these data, machine learning models are developed to predict takeover time, revealing that drivers’ perceived Spare Capacity provides substantial predictive power, while extensive driver profiling offers limited additional benefit. The thesis then establishes a multidimensional framework for takeover performance assessment, demonstrating that Situational Awareness primarily influences response efficiency, whereas Spare Capacity has a stronger impact on takeover quality. Finally, these insights are integrated into an adaptive time budget framework that combines predicted takeover time with a preferred takeover buffer to dynamically allocate time budgets.
The proposed framework enables personalised takeover time prediction, multidimensional performance evaluation, and adaptive time budget allocation in conditionally automated driving. In practice, these contributions can support cognition-aware vehicle interfaces, personalised takeover assistance systems, and human-centred automated driving design. Together, they contribute to safer, more reliable, and more comfortable control transitions, supporting the broader deployment and acceptance of automated vehicles. ...
This thesis systematically develops an adaptive framework for designing takeover time budgets that account for diverse drivers and situational demands. First, a systematic review synthesises the takeover sequence, identifying factors influencing takeover time and performance, and introduces the concept of the takeover buffer as the safety margin between required and allocated takeover time. Building on this foundation, a driving simulator experiment is conducted to collect behavioural, physiological, operational, and subjective data during takeover situations. Using these data, machine learning models are developed to predict takeover time, revealing that drivers’ perceived Spare Capacity provides substantial predictive power, while extensive driver profiling offers limited additional benefit. The thesis then establishes a multidimensional framework for takeover performance assessment, demonstrating that Situational Awareness primarily influences response efficiency, whereas Spare Capacity has a stronger impact on takeover quality. Finally, these insights are integrated into an adaptive time budget framework that combines predicted takeover time with a preferred takeover buffer to dynamically allocate time budgets.
The proposed framework enables personalised takeover time prediction, multidimensional performance evaluation, and adaptive time budget allocation in conditionally automated driving. In practice, these contributions can support cognition-aware vehicle interfaces, personalised takeover assistance systems, and human-centred automated driving design. Together, they contribute to safer, more reliable, and more comfortable control transitions, supporting the broader deployment and acceptance of automated vehicles. ...
Conditionally automated driving systems issue takeover requests (TORs) in situations that exceed their operational capabilities, requiring drivers to promptly resume manual control and maintain safe vehicle operation. A key factor in ensuring the smoothness of such control transitions is the time budget, i.e., the time offered by automation for control transitions. When the time budget is too short to accommodate the required takeover time (ToT, the time drivers need to regain manual vehicle control after receiving a TOR), the risk of accidents increases as drivers may lack adequate time to perceive, assess, and respond to the situation. Conversely, time budgets that substantially exceed the required ToT may also introduce risks: such TORs can be perceived as false alarms, leading to reduced driver attention and potential dangers, particularly when the out-of-capability situations are not readily perceivable to drivers. Therefore, defining and allocating sufficient time budgets is essential to ensure driving safety and user experience in vehicle control transitions.
This thesis systematically develops an adaptive framework for designing takeover time budgets that account for diverse drivers and situational demands. First, a systematic review synthesises the takeover sequence, identifying factors influencing takeover time and performance, and introduces the concept of the takeover buffer as the safety margin between required and allocated takeover time. Building on this foundation, a driving simulator experiment is conducted to collect behavioural, physiological, operational, and subjective data during takeover situations. Using these data, machine learning models are developed to predict takeover time, revealing that drivers’ perceived Spare Capacity provides substantial predictive power, while extensive driver profiling offers limited additional benefit. The thesis then establishes a multidimensional framework for takeover performance assessment, demonstrating that Situational Awareness primarily influences response efficiency, whereas Spare Capacity has a stronger impact on takeover quality. Finally, these insights are integrated into an adaptive time budget framework that combines predicted takeover time with a preferred takeover buffer to dynamically allocate time budgets.
The proposed framework enables personalised takeover time prediction, multidimensional performance evaluation, and adaptive time budget allocation in conditionally automated driving. In practice, these contributions can support cognition-aware vehicle interfaces, personalised takeover assistance systems, and human-centred automated driving design. Together, they contribute to safer, more reliable, and more comfortable control transitions, supporting the broader deployment and acceptance of automated vehicles.
This thesis systematically develops an adaptive framework for designing takeover time budgets that account for diverse drivers and situational demands. First, a systematic review synthesises the takeover sequence, identifying factors influencing takeover time and performance, and introduces the concept of the takeover buffer as the safety margin between required and allocated takeover time. Building on this foundation, a driving simulator experiment is conducted to collect behavioural, physiological, operational, and subjective data during takeover situations. Using these data, machine learning models are developed to predict takeover time, revealing that drivers’ perceived Spare Capacity provides substantial predictive power, while extensive driver profiling offers limited additional benefit. The thesis then establishes a multidimensional framework for takeover performance assessment, demonstrating that Situational Awareness primarily influences response efficiency, whereas Spare Capacity has a stronger impact on takeover quality. Finally, these insights are integrated into an adaptive time budget framework that combines predicted takeover time with a preferred takeover buffer to dynamically allocate time budgets.
The proposed framework enables personalised takeover time prediction, multidimensional performance evaluation, and adaptive time budget allocation in conditionally automated driving. In practice, these contributions can support cognition-aware vehicle interfaces, personalised takeover assistance systems, and human-centred automated driving design. Together, they contribute to safer, more reliable, and more comfortable control transitions, supporting the broader deployment and acceptance of automated vehicles.
Cyber-physical systems integrate digital control and physical processes and often operate in complex, uncertain environments. The consequence of operational failures in such systems can be catastrophic and may include loss of life, environmental damage, irreparable system damage, and economic disruption. Therefore, safety is a critical concern and traditional engineering approaches that only rely on extensive testing and conservative design margins are insufficient to guarantee safety in the face of uncertainty. The issue is that testing only assesses the performance on a limited number of scenarios or samples, while in practice the collection of possible scenarios is uncountable due to the physical process and uncertainties therein. Formal methods provide a powerful alternative, offering mathematically rigorous verification that accounts for all possible behaviours subject to ranges of disturbances and uncertainties. A key challenge in applying formal methods to stochastic dynamical systems is a fundamental tension between computational tractability and conservatism. Existing approaches either scale poorly with the system dimension and complexity or produce loose bounds on safety probabilities that limit their practical applicability. This creates a critical gap between theory and practice: while formal verification methods exist, their practical applicability to real-world systems remains severely limited. Therefore, the core research question driving this work is: How to efficiently compute tight bounds on the satisfaction probabilities for safety, reachability, and reach-avoid specifications of stochastic systems?
To answer this question and address the gap, the present dissertation develops several complementary approaches for efficiently verifying properties of stochastic systems. The focus is on discrete-time, continuous-space stochastic systems and simple specifications over given sets. The approaches represent points along the spectrum of the scalability-conservatism trade-off and rely on different system assumptions. The methods developed belong to two families: stochastic barrier functions and finite-state abstractions.
Stochastic barrier functions are Lyapunov-like functions that provide certificates of safety by imposing conditions on the expected value of the barrier function along system trajectories. The core idea is that if the composition of the barrier function with the dynamics of the system forms a c-martingale, then the probability of safety can be bounded using martingale inequalities. The main challenge is to construct a barrier that is optimal with respect to the martingale inequalities. Hand-crafting such functions is difficult and time-consuming, and existing synthesis methods are often limited to low-dimensional and simple systems for non-trivial results. To enable efficient synthesis of stochastic barrier functions, we develop multiple synthesis techniques, including a neural network-based method that offers flexibility but requires post hoc verification to confirm correctness. More significantly, we introduce piecewise-constant stochastic barrier function theory and synthesis methods that are guaranteed to asymptotically approach optimality. The synthesis methods include a dual linear programming formulation, a counterexample-guided inductive synthesis with linear programming solvers, and gradient descent optimization; their trade-off is between scalability and required parameter tuning. The theoretical analysis of piece-wise constant barriers reveals deep insights into the relationship between barrier functions and system dynamics, illuminates fundamental sources of conservatism inherent to the approach, and establishes clear connections to Interval Markov Decision Process (IMDP)-based finite-state abstractions. Additionally, we develop a data-driven scenario-theoretic approach for systems with partially unknown dynamics, leveraging scenario theory to handle uncertainty in system models.
Finite-state abstractions, on the other hand, reduce the original continuous-space system to a finite-state model that can be analysed using probabilistic model checking techniques. While abstraction-based methods are exceedingly flexible -- they have been successfully applied to a wide range of systems, including partially-unknown systems, and specifications -- they often suffer from high computational complexity of using probabilistic model checking and scalability issues due to the pervasive state-space explosion problem. To address the first issue, we develop hardware-aware algorithmic innovations for faster model checking of IMDPs via dynamic programming. Dynamic programming over IMDPs relies for efficiency on an algorithm named O-maximization, or order-maximization, which by theoretical analysis is revealed to be composed of two phases: a sorting phase and a cumulative summation phase. We introduce parallel algorithms to both phases, which allows us to exploit modern highly parallel computing architectures to achieve significant speedups in verifying IMDPs. To address the second issue, we introduce a novel finite-state model called factored Interval Markov Decision Processes (fIMDPs) that exploits structural properties of the system dynamics to significantly reduce memory requirements while maintaining formal guarantees. Factored models encode data-dependencies more fine-grained than flat models, which is the key driver for the reduction in memory. Moreover, factored models have been successfully used in the context of abstraction to Markov Decision Processes (MDPs), but have not been applied until now to IMDPs. An insight of abstracting to factored models is that the structural exploitation inadvertently tightens the ambiguity sets that characterize IMDPs conservatism, thereby reducing the pessimism of the bounds.
The methods developed in this dissertation represent significant algorithmic and theoretical advances to address the scalability-conservatism trade-off and enable more efficient computation of tighter safety probability bounds, advancing formal verification of stochastic dynamical systems. By advancing stochastic barrier function synthesis and IMDP-based finite-state abstractions, this work pushes the frontier of formal verification for stochastic systems, providing new tools and insights to bridge the gap between theory and practice. The findings suggest that future progress in scalable safety verification for stochastic systems depends critically on designing algorithms that respect and exploit inherent problem structure, offering a promising, albeit challenging path toward making formal methods practical for real-world stochastic cyber-physical systems. ...
To answer this question and address the gap, the present dissertation develops several complementary approaches for efficiently verifying properties of stochastic systems. The focus is on discrete-time, continuous-space stochastic systems and simple specifications over given sets. The approaches represent points along the spectrum of the scalability-conservatism trade-off and rely on different system assumptions. The methods developed belong to two families: stochastic barrier functions and finite-state abstractions.
Stochastic barrier functions are Lyapunov-like functions that provide certificates of safety by imposing conditions on the expected value of the barrier function along system trajectories. The core idea is that if the composition of the barrier function with the dynamics of the system forms a c-martingale, then the probability of safety can be bounded using martingale inequalities. The main challenge is to construct a barrier that is optimal with respect to the martingale inequalities. Hand-crafting such functions is difficult and time-consuming, and existing synthesis methods are often limited to low-dimensional and simple systems for non-trivial results. To enable efficient synthesis of stochastic barrier functions, we develop multiple synthesis techniques, including a neural network-based method that offers flexibility but requires post hoc verification to confirm correctness. More significantly, we introduce piecewise-constant stochastic barrier function theory and synthesis methods that are guaranteed to asymptotically approach optimality. The synthesis methods include a dual linear programming formulation, a counterexample-guided inductive synthesis with linear programming solvers, and gradient descent optimization; their trade-off is between scalability and required parameter tuning. The theoretical analysis of piece-wise constant barriers reveals deep insights into the relationship between barrier functions and system dynamics, illuminates fundamental sources of conservatism inherent to the approach, and establishes clear connections to Interval Markov Decision Process (IMDP)-based finite-state abstractions. Additionally, we develop a data-driven scenario-theoretic approach for systems with partially unknown dynamics, leveraging scenario theory to handle uncertainty in system models.
Finite-state abstractions, on the other hand, reduce the original continuous-space system to a finite-state model that can be analysed using probabilistic model checking techniques. While abstraction-based methods are exceedingly flexible -- they have been successfully applied to a wide range of systems, including partially-unknown systems, and specifications -- they often suffer from high computational complexity of using probabilistic model checking and scalability issues due to the pervasive state-space explosion problem. To address the first issue, we develop hardware-aware algorithmic innovations for faster model checking of IMDPs via dynamic programming. Dynamic programming over IMDPs relies for efficiency on an algorithm named O-maximization, or order-maximization, which by theoretical analysis is revealed to be composed of two phases: a sorting phase and a cumulative summation phase. We introduce parallel algorithms to both phases, which allows us to exploit modern highly parallel computing architectures to achieve significant speedups in verifying IMDPs. To address the second issue, we introduce a novel finite-state model called factored Interval Markov Decision Processes (fIMDPs) that exploits structural properties of the system dynamics to significantly reduce memory requirements while maintaining formal guarantees. Factored models encode data-dependencies more fine-grained than flat models, which is the key driver for the reduction in memory. Moreover, factored models have been successfully used in the context of abstraction to Markov Decision Processes (MDPs), but have not been applied until now to IMDPs. An insight of abstracting to factored models is that the structural exploitation inadvertently tightens the ambiguity sets that characterize IMDPs conservatism, thereby reducing the pessimism of the bounds.
The methods developed in this dissertation represent significant algorithmic and theoretical advances to address the scalability-conservatism trade-off and enable more efficient computation of tighter safety probability bounds, advancing formal verification of stochastic dynamical systems. By advancing stochastic barrier function synthesis and IMDP-based finite-state abstractions, this work pushes the frontier of formal verification for stochastic systems, providing new tools and insights to bridge the gap between theory and practice. The findings suggest that future progress in scalable safety verification for stochastic systems depends critically on designing algorithms that respect and exploit inherent problem structure, offering a promising, albeit challenging path toward making formal methods practical for real-world stochastic cyber-physical systems. ...
Cyber-physical systems integrate digital control and physical processes and often operate in complex, uncertain environments. The consequence of operational failures in such systems can be catastrophic and may include loss of life, environmental damage, irreparable system damage, and economic disruption. Therefore, safety is a critical concern and traditional engineering approaches that only rely on extensive testing and conservative design margins are insufficient to guarantee safety in the face of uncertainty. The issue is that testing only assesses the performance on a limited number of scenarios or samples, while in practice the collection of possible scenarios is uncountable due to the physical process and uncertainties therein. Formal methods provide a powerful alternative, offering mathematically rigorous verification that accounts for all possible behaviours subject to ranges of disturbances and uncertainties. A key challenge in applying formal methods to stochastic dynamical systems is a fundamental tension between computational tractability and conservatism. Existing approaches either scale poorly with the system dimension and complexity or produce loose bounds on safety probabilities that limit their practical applicability. This creates a critical gap between theory and practice: while formal verification methods exist, their practical applicability to real-world systems remains severely limited. Therefore, the core research question driving this work is: How to efficiently compute tight bounds on the satisfaction probabilities for safety, reachability, and reach-avoid specifications of stochastic systems?
To answer this question and address the gap, the present dissertation develops several complementary approaches for efficiently verifying properties of stochastic systems. The focus is on discrete-time, continuous-space stochastic systems and simple specifications over given sets. The approaches represent points along the spectrum of the scalability-conservatism trade-off and rely on different system assumptions. The methods developed belong to two families: stochastic barrier functions and finite-state abstractions.
Stochastic barrier functions are Lyapunov-like functions that provide certificates of safety by imposing conditions on the expected value of the barrier function along system trajectories. The core idea is that if the composition of the barrier function with the dynamics of the system forms a c-martingale, then the probability of safety can be bounded using martingale inequalities. The main challenge is to construct a barrier that is optimal with respect to the martingale inequalities. Hand-crafting such functions is difficult and time-consuming, and existing synthesis methods are often limited to low-dimensional and simple systems for non-trivial results. To enable efficient synthesis of stochastic barrier functions, we develop multiple synthesis techniques, including a neural network-based method that offers flexibility but requires post hoc verification to confirm correctness. More significantly, we introduce piecewise-constant stochastic barrier function theory and synthesis methods that are guaranteed to asymptotically approach optimality. The synthesis methods include a dual linear programming formulation, a counterexample-guided inductive synthesis with linear programming solvers, and gradient descent optimization; their trade-off is between scalability and required parameter tuning. The theoretical analysis of piece-wise constant barriers reveals deep insights into the relationship between barrier functions and system dynamics, illuminates fundamental sources of conservatism inherent to the approach, and establishes clear connections to Interval Markov Decision Process (IMDP)-based finite-state abstractions. Additionally, we develop a data-driven scenario-theoretic approach for systems with partially unknown dynamics, leveraging scenario theory to handle uncertainty in system models.
Finite-state abstractions, on the other hand, reduce the original continuous-space system to a finite-state model that can be analysed using probabilistic model checking techniques. While abstraction-based methods are exceedingly flexible -- they have been successfully applied to a wide range of systems, including partially-unknown systems, and specifications -- they often suffer from high computational complexity of using probabilistic model checking and scalability issues due to the pervasive state-space explosion problem. To address the first issue, we develop hardware-aware algorithmic innovations for faster model checking of IMDPs via dynamic programming. Dynamic programming over IMDPs relies for efficiency on an algorithm named O-maximization, or order-maximization, which by theoretical analysis is revealed to be composed of two phases: a sorting phase and a cumulative summation phase. We introduce parallel algorithms to both phases, which allows us to exploit modern highly parallel computing architectures to achieve significant speedups in verifying IMDPs. To address the second issue, we introduce a novel finite-state model called factored Interval Markov Decision Processes (fIMDPs) that exploits structural properties of the system dynamics to significantly reduce memory requirements while maintaining formal guarantees. Factored models encode data-dependencies more fine-grained than flat models, which is the key driver for the reduction in memory. Moreover, factored models have been successfully used in the context of abstraction to Markov Decision Processes (MDPs), but have not been applied until now to IMDPs. An insight of abstracting to factored models is that the structural exploitation inadvertently tightens the ambiguity sets that characterize IMDPs conservatism, thereby reducing the pessimism of the bounds.
The methods developed in this dissertation represent significant algorithmic and theoretical advances to address the scalability-conservatism trade-off and enable more efficient computation of tighter safety probability bounds, advancing formal verification of stochastic dynamical systems. By advancing stochastic barrier function synthesis and IMDP-based finite-state abstractions, this work pushes the frontier of formal verification for stochastic systems, providing new tools and insights to bridge the gap between theory and practice. The findings suggest that future progress in scalable safety verification for stochastic systems depends critically on designing algorithms that respect and exploit inherent problem structure, offering a promising, albeit challenging path toward making formal methods practical for real-world stochastic cyber-physical systems.
To answer this question and address the gap, the present dissertation develops several complementary approaches for efficiently verifying properties of stochastic systems. The focus is on discrete-time, continuous-space stochastic systems and simple specifications over given sets. The approaches represent points along the spectrum of the scalability-conservatism trade-off and rely on different system assumptions. The methods developed belong to two families: stochastic barrier functions and finite-state abstractions.
Stochastic barrier functions are Lyapunov-like functions that provide certificates of safety by imposing conditions on the expected value of the barrier function along system trajectories. The core idea is that if the composition of the barrier function with the dynamics of the system forms a c-martingale, then the probability of safety can be bounded using martingale inequalities. The main challenge is to construct a barrier that is optimal with respect to the martingale inequalities. Hand-crafting such functions is difficult and time-consuming, and existing synthesis methods are often limited to low-dimensional and simple systems for non-trivial results. To enable efficient synthesis of stochastic barrier functions, we develop multiple synthesis techniques, including a neural network-based method that offers flexibility but requires post hoc verification to confirm correctness. More significantly, we introduce piecewise-constant stochastic barrier function theory and synthesis methods that are guaranteed to asymptotically approach optimality. The synthesis methods include a dual linear programming formulation, a counterexample-guided inductive synthesis with linear programming solvers, and gradient descent optimization; their trade-off is between scalability and required parameter tuning. The theoretical analysis of piece-wise constant barriers reveals deep insights into the relationship between barrier functions and system dynamics, illuminates fundamental sources of conservatism inherent to the approach, and establishes clear connections to Interval Markov Decision Process (IMDP)-based finite-state abstractions. Additionally, we develop a data-driven scenario-theoretic approach for systems with partially unknown dynamics, leveraging scenario theory to handle uncertainty in system models.
Finite-state abstractions, on the other hand, reduce the original continuous-space system to a finite-state model that can be analysed using probabilistic model checking techniques. While abstraction-based methods are exceedingly flexible -- they have been successfully applied to a wide range of systems, including partially-unknown systems, and specifications -- they often suffer from high computational complexity of using probabilistic model checking and scalability issues due to the pervasive state-space explosion problem. To address the first issue, we develop hardware-aware algorithmic innovations for faster model checking of IMDPs via dynamic programming. Dynamic programming over IMDPs relies for efficiency on an algorithm named O-maximization, or order-maximization, which by theoretical analysis is revealed to be composed of two phases: a sorting phase and a cumulative summation phase. We introduce parallel algorithms to both phases, which allows us to exploit modern highly parallel computing architectures to achieve significant speedups in verifying IMDPs. To address the second issue, we introduce a novel finite-state model called factored Interval Markov Decision Processes (fIMDPs) that exploits structural properties of the system dynamics to significantly reduce memory requirements while maintaining formal guarantees. Factored models encode data-dependencies more fine-grained than flat models, which is the key driver for the reduction in memory. Moreover, factored models have been successfully used in the context of abstraction to Markov Decision Processes (MDPs), but have not been applied until now to IMDPs. An insight of abstracting to factored models is that the structural exploitation inadvertently tightens the ambiguity sets that characterize IMDPs conservatism, thereby reducing the pessimism of the bounds.
The methods developed in this dissertation represent significant algorithmic and theoretical advances to address the scalability-conservatism trade-off and enable more efficient computation of tighter safety probability bounds, advancing formal verification of stochastic dynamical systems. By advancing stochastic barrier function synthesis and IMDP-based finite-state abstractions, this work pushes the frontier of formal verification for stochastic systems, providing new tools and insights to bridge the gap between theory and practice. The findings suggest that future progress in scalable safety verification for stochastic systems depends critically on designing algorithms that respect and exploit inherent problem structure, offering a promising, albeit challenging path toward making formal methods practical for real-world stochastic cyber-physical systems.
Driving Heterogeneity in Traffic Flow Theory
An Action-based Framework for Identification, Modelling, and Simulation
Human driving behaviour is inherently heterogeneous, shaping traffic dynamics and affecting traffic safety, efficiency and sustainbility. This dissertation develops an interpretable, AI-driven framework to identify, model, and simulate heterogeneous driving behaviour using naturalistic data. By analysing action phases, patterns, and behavioural sequences, it reveals how behavioural variability influences traffic performance and supports improved traffic management, personalised driver assistance, and human-aware autonomous vehicle design.
...
Human driving behaviour is inherently heterogeneous, shaping traffic dynamics and affecting traffic safety, efficiency and sustainbility. This dissertation develops an interpretable, AI-driven framework to identify, model, and simulate heterogeneous driving behaviour using naturalistic data. By analysing action phases, patterns, and behavioural sequences, it reveals how behavioural variability influences traffic performance and supports improved traffic management, personalised driver assistance, and human-aware autonomous vehicle design.
Automated vehicles (AVs) are expected to improve road safety, efficiency, and accessibility, yet their behaviour can at times appear overly cautious, rigid, or counter-intuitive, undermining trust and public acceptance. Existing approaches to address this problem, ranging from ethical decision-making models to behaviour imitation and interaction-based design, often lack a principled account of why certain behaviours should occur in specific contexts. This dissertation argues that these limitations stem from the absence of a unified framework that links human reasons to automated-vehicle decision-making in a transparent and evaluable manner.
To address this challenge, the thesis adopts the philosophical framework of Meaningful Human Control (MHC), which requires that automated systems both track relevant human reasons and allow responsibility for outcomes to be meaningfully traced to human agents. While MHC has been widely discussed at a conceptual level, its technical operationalisation in automated driving remains underdeveloped. This dissertation advances MHC by translating its normative principles into an integrated framework that connects ethical reasoning, engineering implementation, and empirical evaluation.
The dissertation first investigates which human reasons are relevant for automated-vehicle manoeuvre planning in ethically ambiguous, everyday traffic situations. Empirical findings from interviews with AV experts show that such reasons are inherently multi-layered, context-dependent, and often simultaneous, spanning normative, strategic, tactical, and operational considerations. Rather than functioning as fixed values or isolated cost terms, human reasons are shown to form context-sensitive relationships between underlying motivations and expected vehicle behaviour. These insights provide an empirically grounded basis for structuring and prioritising human reasons in automated-vehicle decision-making.
Building on this foundation, the dissertation develops a technical approach for embedding human reasons within automated-vehicle control architectures. Human reasons are translated into formal, machine-readable representations by drawing on insights from human-factors research and are integrated through a supervisory evaluation layer that operates alongside existing motion planning and control frameworks. This approach enables transparent trajectory evaluation and adaptive behavioural adjustment without requiring the design of new controllers, thereby demonstrating a practical pathway for operationalising MHC in real-time decision-making systems.
Finally, the dissertation examines whether meaningful human control can be empirically assessed in practice. Qualitative studies with users of partially automated driving systems reveal how the tracking and tracing conditions of MHC manifest dynamically in drivers’ experiences of safety, trust, responsibility, and intervention readiness. Complementary simulator experiments show that objective behavioural telemetry can capture aspects of tracking at the level of concrete interaction events, while tracing cannot be inferred from behaviour alone. Together, these findings demonstrate that meaningful human control is not merely a normative or post-hoc concept, but an empirically observable property of ongoing human–automation interaction when evaluated through a multi-layer framework combining subjective perception and objective data.
Overall, this dissertation advances the technical operationalisation of meaningful human control by systematically linking human reasons, automated-vehicle decision-making, and empirical evaluation. The proposed framework provides researchers, designers, and policymakers with concrete tools to assess and support reason-aligned automated-vehicle behaviour, contributing to the development of automated driving systems whose behaviour is more transparent, context-sensitive, and reasonable in everyday traffic situations.
...
To address this challenge, the thesis adopts the philosophical framework of Meaningful Human Control (MHC), which requires that automated systems both track relevant human reasons and allow responsibility for outcomes to be meaningfully traced to human agents. While MHC has been widely discussed at a conceptual level, its technical operationalisation in automated driving remains underdeveloped. This dissertation advances MHC by translating its normative principles into an integrated framework that connects ethical reasoning, engineering implementation, and empirical evaluation.
The dissertation first investigates which human reasons are relevant for automated-vehicle manoeuvre planning in ethically ambiguous, everyday traffic situations. Empirical findings from interviews with AV experts show that such reasons are inherently multi-layered, context-dependent, and often simultaneous, spanning normative, strategic, tactical, and operational considerations. Rather than functioning as fixed values or isolated cost terms, human reasons are shown to form context-sensitive relationships between underlying motivations and expected vehicle behaviour. These insights provide an empirically grounded basis for structuring and prioritising human reasons in automated-vehicle decision-making.
Building on this foundation, the dissertation develops a technical approach for embedding human reasons within automated-vehicle control architectures. Human reasons are translated into formal, machine-readable representations by drawing on insights from human-factors research and are integrated through a supervisory evaluation layer that operates alongside existing motion planning and control frameworks. This approach enables transparent trajectory evaluation and adaptive behavioural adjustment without requiring the design of new controllers, thereby demonstrating a practical pathway for operationalising MHC in real-time decision-making systems.
Finally, the dissertation examines whether meaningful human control can be empirically assessed in practice. Qualitative studies with users of partially automated driving systems reveal how the tracking and tracing conditions of MHC manifest dynamically in drivers’ experiences of safety, trust, responsibility, and intervention readiness. Complementary simulator experiments show that objective behavioural telemetry can capture aspects of tracking at the level of concrete interaction events, while tracing cannot be inferred from behaviour alone. Together, these findings demonstrate that meaningful human control is not merely a normative or post-hoc concept, but an empirically observable property of ongoing human–automation interaction when evaluated through a multi-layer framework combining subjective perception and objective data.
Overall, this dissertation advances the technical operationalisation of meaningful human control by systematically linking human reasons, automated-vehicle decision-making, and empirical evaluation. The proposed framework provides researchers, designers, and policymakers with concrete tools to assess and support reason-aligned automated-vehicle behaviour, contributing to the development of automated driving systems whose behaviour is more transparent, context-sensitive, and reasonable in everyday traffic situations.
...
Automated vehicles (AVs) are expected to improve road safety, efficiency, and accessibility, yet their behaviour can at times appear overly cautious, rigid, or counter-intuitive, undermining trust and public acceptance. Existing approaches to address this problem, ranging from ethical decision-making models to behaviour imitation and interaction-based design, often lack a principled account of why certain behaviours should occur in specific contexts. This dissertation argues that these limitations stem from the absence of a unified framework that links human reasons to automated-vehicle decision-making in a transparent and evaluable manner.
To address this challenge, the thesis adopts the philosophical framework of Meaningful Human Control (MHC), which requires that automated systems both track relevant human reasons and allow responsibility for outcomes to be meaningfully traced to human agents. While MHC has been widely discussed at a conceptual level, its technical operationalisation in automated driving remains underdeveloped. This dissertation advances MHC by translating its normative principles into an integrated framework that connects ethical reasoning, engineering implementation, and empirical evaluation.
The dissertation first investigates which human reasons are relevant for automated-vehicle manoeuvre planning in ethically ambiguous, everyday traffic situations. Empirical findings from interviews with AV experts show that such reasons are inherently multi-layered, context-dependent, and often simultaneous, spanning normative, strategic, tactical, and operational considerations. Rather than functioning as fixed values or isolated cost terms, human reasons are shown to form context-sensitive relationships between underlying motivations and expected vehicle behaviour. These insights provide an empirically grounded basis for structuring and prioritising human reasons in automated-vehicle decision-making.
Building on this foundation, the dissertation develops a technical approach for embedding human reasons within automated-vehicle control architectures. Human reasons are translated into formal, machine-readable representations by drawing on insights from human-factors research and are integrated through a supervisory evaluation layer that operates alongside existing motion planning and control frameworks. This approach enables transparent trajectory evaluation and adaptive behavioural adjustment without requiring the design of new controllers, thereby demonstrating a practical pathway for operationalising MHC in real-time decision-making systems.
Finally, the dissertation examines whether meaningful human control can be empirically assessed in practice. Qualitative studies with users of partially automated driving systems reveal how the tracking and tracing conditions of MHC manifest dynamically in drivers’ experiences of safety, trust, responsibility, and intervention readiness. Complementary simulator experiments show that objective behavioural telemetry can capture aspects of tracking at the level of concrete interaction events, while tracing cannot be inferred from behaviour alone. Together, these findings demonstrate that meaningful human control is not merely a normative or post-hoc concept, but an empirically observable property of ongoing human–automation interaction when evaluated through a multi-layer framework combining subjective perception and objective data.
Overall, this dissertation advances the technical operationalisation of meaningful human control by systematically linking human reasons, automated-vehicle decision-making, and empirical evaluation. The proposed framework provides researchers, designers, and policymakers with concrete tools to assess and support reason-aligned automated-vehicle behaviour, contributing to the development of automated driving systems whose behaviour is more transparent, context-sensitive, and reasonable in everyday traffic situations.
To address this challenge, the thesis adopts the philosophical framework of Meaningful Human Control (MHC), which requires that automated systems both track relevant human reasons and allow responsibility for outcomes to be meaningfully traced to human agents. While MHC has been widely discussed at a conceptual level, its technical operationalisation in automated driving remains underdeveloped. This dissertation advances MHC by translating its normative principles into an integrated framework that connects ethical reasoning, engineering implementation, and empirical evaluation.
The dissertation first investigates which human reasons are relevant for automated-vehicle manoeuvre planning in ethically ambiguous, everyday traffic situations. Empirical findings from interviews with AV experts show that such reasons are inherently multi-layered, context-dependent, and often simultaneous, spanning normative, strategic, tactical, and operational considerations. Rather than functioning as fixed values or isolated cost terms, human reasons are shown to form context-sensitive relationships between underlying motivations and expected vehicle behaviour. These insights provide an empirically grounded basis for structuring and prioritising human reasons in automated-vehicle decision-making.
Building on this foundation, the dissertation develops a technical approach for embedding human reasons within automated-vehicle control architectures. Human reasons are translated into formal, machine-readable representations by drawing on insights from human-factors research and are integrated through a supervisory evaluation layer that operates alongside existing motion planning and control frameworks. This approach enables transparent trajectory evaluation and adaptive behavioural adjustment without requiring the design of new controllers, thereby demonstrating a practical pathway for operationalising MHC in real-time decision-making systems.
Finally, the dissertation examines whether meaningful human control can be empirically assessed in practice. Qualitative studies with users of partially automated driving systems reveal how the tracking and tracing conditions of MHC manifest dynamically in drivers’ experiences of safety, trust, responsibility, and intervention readiness. Complementary simulator experiments show that objective behavioural telemetry can capture aspects of tracking at the level of concrete interaction events, while tracing cannot be inferred from behaviour alone. Together, these findings demonstrate that meaningful human control is not merely a normative or post-hoc concept, but an empirically observable property of ongoing human–automation interaction when evaluated through a multi-layer framework combining subjective perception and objective data.
Overall, this dissertation advances the technical operationalisation of meaningful human control by systematically linking human reasons, automated-vehicle decision-making, and empirical evaluation. The proposed framework provides researchers, designers, and policymakers with concrete tools to assess and support reason-aligned automated-vehicle behaviour, contributing to the development of automated driving systems whose behaviour is more transparent, context-sensitive, and reasonable in everyday traffic situations.
Road traffic crashes cause over a million deaths and tens of millions of injuries annually, with the majority occurring in complex multi-directional urban traffic interactions such as merging, turning, and crossing, rather than on high-speed motorways. These collisions rarely stem from a single error, but emerge from escalating conflicts, leaving a time window in which proactive intervention is possible. This thesis systematically develops a data-driven methodology to quantify collision risk in multi-directional urban traffic interactions, in a way that is context-aware, generalisable across scenarios, and scalable without relying on crash labels.
The research progresses from foundational measurement to large-scale risk modelling. First, a two-dimensional coordinate transformation is introduced to normalise longitudinal and lateral spacing between road users. This enables consistent microscopic measurement of interactions and macroscopic analysis of required road space via an interaction Fundamental Diagram (iFD). Building on this representation, a unified probabilistic framework for conflict detection is formulated. It conditions collision risk on interaction context, including motion kinematics and environmental factors. A statistical learning pipeline is then proposed to estimate continuous risk scores that generalise across scenarios and capture a long-tailed spectrum from mild conflicts to near-crashes. To scale up without annotated crash or near-crash events, the Generalised Surrogate Safety Measure (GSSM) is developed as a self-supervised approach that learns collision risk from abundant naturalistic driving data. Further, contrastive learning is explored to more effectively exploit fine-grained interaction patterns.
Experiments on real-world datasets show that lateral interactions utilise road space more efficiently than longitudinal ones, and that collision risk forms a continuum without a universal boundary between safe and unsafe interactions. The proposed context-aware methods achieve state-of-the-art risk detection accuracy and alert timeliness. Environmental factors such as rain, lighting, and surface conditions are shown to significantly impact collision risk. With increasing data in training and factors in consideration, extreme conflicts can be inferred more effectively from everyday interactions.
The proposed methods enable consistent measurement of road user interactions, adaptive conflict detection, unified collision risk scoring, and scalable learning in multi-directional traffic. In practice, the results can support applications in traffic management, advanced driving assistance and automated vehicles, real-time risk monitoring, and accelerated road safety policymaking. All these contribute to a broader shift from reactive to proactive road safety, aligning with the vision of eliminating traffic fatalities and creating more resilient urban transportation systems. ...
The research progresses from foundational measurement to large-scale risk modelling. First, a two-dimensional coordinate transformation is introduced to normalise longitudinal and lateral spacing between road users. This enables consistent microscopic measurement of interactions and macroscopic analysis of required road space via an interaction Fundamental Diagram (iFD). Building on this representation, a unified probabilistic framework for conflict detection is formulated. It conditions collision risk on interaction context, including motion kinematics and environmental factors. A statistical learning pipeline is then proposed to estimate continuous risk scores that generalise across scenarios and capture a long-tailed spectrum from mild conflicts to near-crashes. To scale up without annotated crash or near-crash events, the Generalised Surrogate Safety Measure (GSSM) is developed as a self-supervised approach that learns collision risk from abundant naturalistic driving data. Further, contrastive learning is explored to more effectively exploit fine-grained interaction patterns.
Experiments on real-world datasets show that lateral interactions utilise road space more efficiently than longitudinal ones, and that collision risk forms a continuum without a universal boundary between safe and unsafe interactions. The proposed context-aware methods achieve state-of-the-art risk detection accuracy and alert timeliness. Environmental factors such as rain, lighting, and surface conditions are shown to significantly impact collision risk. With increasing data in training and factors in consideration, extreme conflicts can be inferred more effectively from everyday interactions.
The proposed methods enable consistent measurement of road user interactions, adaptive conflict detection, unified collision risk scoring, and scalable learning in multi-directional traffic. In practice, the results can support applications in traffic management, advanced driving assistance and automated vehicles, real-time risk monitoring, and accelerated road safety policymaking. All these contribute to a broader shift from reactive to proactive road safety, aligning with the vision of eliminating traffic fatalities and creating more resilient urban transportation systems. ...
Road traffic crashes cause over a million deaths and tens of millions of injuries annually, with the majority occurring in complex multi-directional urban traffic interactions such as merging, turning, and crossing, rather than on high-speed motorways. These collisions rarely stem from a single error, but emerge from escalating conflicts, leaving a time window in which proactive intervention is possible. This thesis systematically develops a data-driven methodology to quantify collision risk in multi-directional urban traffic interactions, in a way that is context-aware, generalisable across scenarios, and scalable without relying on crash labels.
The research progresses from foundational measurement to large-scale risk modelling. First, a two-dimensional coordinate transformation is introduced to normalise longitudinal and lateral spacing between road users. This enables consistent microscopic measurement of interactions and macroscopic analysis of required road space via an interaction Fundamental Diagram (iFD). Building on this representation, a unified probabilistic framework for conflict detection is formulated. It conditions collision risk on interaction context, including motion kinematics and environmental factors. A statistical learning pipeline is then proposed to estimate continuous risk scores that generalise across scenarios and capture a long-tailed spectrum from mild conflicts to near-crashes. To scale up without annotated crash or near-crash events, the Generalised Surrogate Safety Measure (GSSM) is developed as a self-supervised approach that learns collision risk from abundant naturalistic driving data. Further, contrastive learning is explored to more effectively exploit fine-grained interaction patterns.
Experiments on real-world datasets show that lateral interactions utilise road space more efficiently than longitudinal ones, and that collision risk forms a continuum without a universal boundary between safe and unsafe interactions. The proposed context-aware methods achieve state-of-the-art risk detection accuracy and alert timeliness. Environmental factors such as rain, lighting, and surface conditions are shown to significantly impact collision risk. With increasing data in training and factors in consideration, extreme conflicts can be inferred more effectively from everyday interactions.
The proposed methods enable consistent measurement of road user interactions, adaptive conflict detection, unified collision risk scoring, and scalable learning in multi-directional traffic. In practice, the results can support applications in traffic management, advanced driving assistance and automated vehicles, real-time risk monitoring, and accelerated road safety policymaking. All these contribute to a broader shift from reactive to proactive road safety, aligning with the vision of eliminating traffic fatalities and creating more resilient urban transportation systems.
The research progresses from foundational measurement to large-scale risk modelling. First, a two-dimensional coordinate transformation is introduced to normalise longitudinal and lateral spacing between road users. This enables consistent microscopic measurement of interactions and macroscopic analysis of required road space via an interaction Fundamental Diagram (iFD). Building on this representation, a unified probabilistic framework for conflict detection is formulated. It conditions collision risk on interaction context, including motion kinematics and environmental factors. A statistical learning pipeline is then proposed to estimate continuous risk scores that generalise across scenarios and capture a long-tailed spectrum from mild conflicts to near-crashes. To scale up without annotated crash or near-crash events, the Generalised Surrogate Safety Measure (GSSM) is developed as a self-supervised approach that learns collision risk from abundant naturalistic driving data. Further, contrastive learning is explored to more effectively exploit fine-grained interaction patterns.
Experiments on real-world datasets show that lateral interactions utilise road space more efficiently than longitudinal ones, and that collision risk forms a continuum without a universal boundary between safe and unsafe interactions. The proposed context-aware methods achieve state-of-the-art risk detection accuracy and alert timeliness. Environmental factors such as rain, lighting, and surface conditions are shown to significantly impact collision risk. With increasing data in training and factors in consideration, extreme conflicts can be inferred more effectively from everyday interactions.
The proposed methods enable consistent measurement of road user interactions, adaptive conflict detection, unified collision risk scoring, and scalable learning in multi-directional traffic. In practice, the results can support applications in traffic management, advanced driving assistance and automated vehicles, real-time risk monitoring, and accelerated road safety policymaking. All these contribute to a broader shift from reactive to proactive road safety, aligning with the vision of eliminating traffic fatalities and creating more resilient urban transportation systems.
Autonomous Driving Systems (ADS) are an innovative solution to urban mobility problems and have the potential to transform the Dutch roads radically. However, before these systems can be widely adopted, they must undergo a rigorous certification process to meet safety and reliability standards. Regulatory authorities are currently grappling with the challenge of certifying ADS responsibly. Despite significant technological progress, there is still limited understanding of the root causes behind these certification challenges. Without a clear understanding of these obstacles, finding effective solutions remains difficult. Therefore, this research aims to explore the barriers to responsible ADS certification and provide insights into how these challenges can be overcome.
...
Autonomous Driving Systems (ADS) are an innovative solution to urban mobility problems and have the potential to transform the Dutch roads radically. However, before these systems can be widely adopted, they must undergo a rigorous certification process to meet safety and reliability standards. Regulatory authorities are currently grappling with the challenge of certifying ADS responsibly. Despite significant technological progress, there is still limited understanding of the root causes behind these certification challenges. Without a clear understanding of these obstacles, finding effective solutions remains difficult. Therefore, this research aims to explore the barriers to responsible ADS certification and provide insights into how these challenges can be overcome.
Platooning has become a useful area for better transportation efficiency on highway driving. As Cooperative and Automated Vehicles continue to evolve and integrate , it is important to have insights into their implications, emphasizing the need for rigorous real-world assessments. In general, platoon formation is monitored by Cooperative Adaptive Cruise Control (CACC), which uses real-time vehicle-to-vehicle (V2V) communication to exchange vehicle status information, improving the control reaction as platoon members adjust to their surroundings. Automated systems can normally drive vehicles to perform planned behaviors based on the pre-setting by humans, but if the platoon encounters disturbances, the extent to which the automated system can still follow human intentions is still unknown. This research uses field operational test (FOT) data from the CACC platoon on an arterial corridor to assess the platoon's performance when disrupted during the test. This research applies the concept of meaningful human control (MHC) with focus on tracking condition. Additionally, this study will focus on human 'reasons', both distal and proximal. An evaluation framework for platoons is created by categorizing 'Tracking' into three main metrics: comfort, safety, and local stability. Furthermore, this study demonstrates that disturbance has variable degrees of detrimental impact on the platoon's tracking state, and that these effects may be recovered when the disturbance has concluded; however, different disturbance situations indicate different recoveries. The evaluation methodology of this paper provides insight into the tracking performance of CAVs, which can help road authorities build infrastructure for their wider deployment of CAVs. Last but not least, this study may provide guidance to automation technology organizations and automobile manufacturers on how to develop vehicles so that they follow human reasons more closely.
...
Platooning has become a useful area for better transportation efficiency on highway driving. As Cooperative and Automated Vehicles continue to evolve and integrate , it is important to have insights into their implications, emphasizing the need for rigorous real-world assessments. In general, platoon formation is monitored by Cooperative Adaptive Cruise Control (CACC), which uses real-time vehicle-to-vehicle (V2V) communication to exchange vehicle status information, improving the control reaction as platoon members adjust to their surroundings. Automated systems can normally drive vehicles to perform planned behaviors based on the pre-setting by humans, but if the platoon encounters disturbances, the extent to which the automated system can still follow human intentions is still unknown. This research uses field operational test (FOT) data from the CACC platoon on an arterial corridor to assess the platoon's performance when disrupted during the test. This research applies the concept of meaningful human control (MHC) with focus on tracking condition. Additionally, this study will focus on human 'reasons', both distal and proximal. An evaluation framework for platoons is created by categorizing 'Tracking' into three main metrics: comfort, safety, and local stability. Furthermore, this study demonstrates that disturbance has variable degrees of detrimental impact on the platoon's tracking state, and that these effects may be recovered when the disturbance has concluded; however, different disturbance situations indicate different recoveries. The evaluation methodology of this paper provides insight into the tracking performance of CAVs, which can help road authorities build infrastructure for their wider deployment of CAVs. Last but not least, this study may provide guidance to automation technology organizations and automobile manufacturers on how to develop vehicles so that they follow human reasons more closely.
Optimal re-routing strategy for CAV and HDV mixed traffic under a road closure
A simulation-based method
Against the backdrop of the increasing maturity of connected automatic driving technologies and the gradually expanding market share of CAVs, this thesis explores the optimal traffic management strategies to cope with road closures in the context of Connected and Automated Vehicles (CAVs) and Intelligent Transportation Systems (ITS).
A rerouting strategy is designed based on the rerouting behaviour of vehicles when road closure occurs in life, the control parameters include the control of the CAV's automatic rerouting period, rerouting probability, HDV Knowledge of the time of lane closure, as well as their rerouting probability. The aim of this study is to find the optimal combination of these five parameters. Four levels of CAV penetration (20\%, 40\%, 60\%, and 80\%) are considered with the objective of minimizing the total travel time on a mixed CAV and human-driven vehicle (HDV) traffic flow network. The main question is \textbf{What is the optimal rerouting strategy for CAV and HDV mixed traffic when road closure happens?} and in the process of answering this question, the effects of CAV penetration, individual rerouting parameters and different road closure locations are considered and analyzed.
In this thesis, a simulation-based approach is used to model the traffic flow applying both micro and meso scale models. Then, the simulation is conducted for the predefined scenarios, then the sensitivity analysis of each relevant parameter is performed using a one-factor-at-a-time approach to understand the impact of each parameter on the network traffic condition. Finally, Bayesian optimisation is used to find the optimal rerouting strategy within a certain search range and number of times, where the results obtained from the sensitivity analysis are used to determine the parameter search space.
The grid network and the Sioux Falls network are simulated respectively and the relatively optimal rerouting strategies are found for them. The grid network can be regarded as a local area on the network, while the results of Sioux Falls, as a larger network, can provide some basis for city-level traffic management.
The key findings of this thesis include (1) CAV penetration increases bring reductions in TTT and TWT and increase in TTD to the network, overall, the traffic flow movement improves and severe congestion decreases, and network conditions improve significantly during the growth phases of 20\%-40\% and 60\%-80\%; (2) the importance of each rerouting parameter varies for different networks and at different penetration rates, and the results fluctuate significantly between different test values, with no single increasing and decreasing trend; (3) road closures at entrances and exits located at intersections are more critical and require targeted rerouting strategies; the traffic demand distribution has a significant impact on it; (4) Bayesian optimization can find the optimal rerouting strategy in a finite amount of time, where the specific strategy and the improvement effect varies for different networks and levels of demand. ...
A rerouting strategy is designed based on the rerouting behaviour of vehicles when road closure occurs in life, the control parameters include the control of the CAV's automatic rerouting period, rerouting probability, HDV Knowledge of the time of lane closure, as well as their rerouting probability. The aim of this study is to find the optimal combination of these five parameters. Four levels of CAV penetration (20\%, 40\%, 60\%, and 80\%) are considered with the objective of minimizing the total travel time on a mixed CAV and human-driven vehicle (HDV) traffic flow network. The main question is \textbf{What is the optimal rerouting strategy for CAV and HDV mixed traffic when road closure happens?} and in the process of answering this question, the effects of CAV penetration, individual rerouting parameters and different road closure locations are considered and analyzed.
In this thesis, a simulation-based approach is used to model the traffic flow applying both micro and meso scale models. Then, the simulation is conducted for the predefined scenarios, then the sensitivity analysis of each relevant parameter is performed using a one-factor-at-a-time approach to understand the impact of each parameter on the network traffic condition. Finally, Bayesian optimisation is used to find the optimal rerouting strategy within a certain search range and number of times, where the results obtained from the sensitivity analysis are used to determine the parameter search space.
The grid network and the Sioux Falls network are simulated respectively and the relatively optimal rerouting strategies are found for them. The grid network can be regarded as a local area on the network, while the results of Sioux Falls, as a larger network, can provide some basis for city-level traffic management.
The key findings of this thesis include (1) CAV penetration increases bring reductions in TTT and TWT and increase in TTD to the network, overall, the traffic flow movement improves and severe congestion decreases, and network conditions improve significantly during the growth phases of 20\%-40\% and 60\%-80\%; (2) the importance of each rerouting parameter varies for different networks and at different penetration rates, and the results fluctuate significantly between different test values, with no single increasing and decreasing trend; (3) road closures at entrances and exits located at intersections are more critical and require targeted rerouting strategies; the traffic demand distribution has a significant impact on it; (4) Bayesian optimization can find the optimal rerouting strategy in a finite amount of time, where the specific strategy and the improvement effect varies for different networks and levels of demand. ...
Against the backdrop of the increasing maturity of connected automatic driving technologies and the gradually expanding market share of CAVs, this thesis explores the optimal traffic management strategies to cope with road closures in the context of Connected and Automated Vehicles (CAVs) and Intelligent Transportation Systems (ITS).
A rerouting strategy is designed based on the rerouting behaviour of vehicles when road closure occurs in life, the control parameters include the control of the CAV's automatic rerouting period, rerouting probability, HDV Knowledge of the time of lane closure, as well as their rerouting probability. The aim of this study is to find the optimal combination of these five parameters. Four levels of CAV penetration (20\%, 40\%, 60\%, and 80\%) are considered with the objective of minimizing the total travel time on a mixed CAV and human-driven vehicle (HDV) traffic flow network. The main question is \textbf{What is the optimal rerouting strategy for CAV and HDV mixed traffic when road closure happens?} and in the process of answering this question, the effects of CAV penetration, individual rerouting parameters and different road closure locations are considered and analyzed.
In this thesis, a simulation-based approach is used to model the traffic flow applying both micro and meso scale models. Then, the simulation is conducted for the predefined scenarios, then the sensitivity analysis of each relevant parameter is performed using a one-factor-at-a-time approach to understand the impact of each parameter on the network traffic condition. Finally, Bayesian optimisation is used to find the optimal rerouting strategy within a certain search range and number of times, where the results obtained from the sensitivity analysis are used to determine the parameter search space.
The grid network and the Sioux Falls network are simulated respectively and the relatively optimal rerouting strategies are found for them. The grid network can be regarded as a local area on the network, while the results of Sioux Falls, as a larger network, can provide some basis for city-level traffic management.
The key findings of this thesis include (1) CAV penetration increases bring reductions in TTT and TWT and increase in TTD to the network, overall, the traffic flow movement improves and severe congestion decreases, and network conditions improve significantly during the growth phases of 20\%-40\% and 60\%-80\%; (2) the importance of each rerouting parameter varies for different networks and at different penetration rates, and the results fluctuate significantly between different test values, with no single increasing and decreasing trend; (3) road closures at entrances and exits located at intersections are more critical and require targeted rerouting strategies; the traffic demand distribution has a significant impact on it; (4) Bayesian optimization can find the optimal rerouting strategy in a finite amount of time, where the specific strategy and the improvement effect varies for different networks and levels of demand.
A rerouting strategy is designed based on the rerouting behaviour of vehicles when road closure occurs in life, the control parameters include the control of the CAV's automatic rerouting period, rerouting probability, HDV Knowledge of the time of lane closure, as well as their rerouting probability. The aim of this study is to find the optimal combination of these five parameters. Four levels of CAV penetration (20\%, 40\%, 60\%, and 80\%) are considered with the objective of minimizing the total travel time on a mixed CAV and human-driven vehicle (HDV) traffic flow network. The main question is \textbf{What is the optimal rerouting strategy for CAV and HDV mixed traffic when road closure happens?} and in the process of answering this question, the effects of CAV penetration, individual rerouting parameters and different road closure locations are considered and analyzed.
In this thesis, a simulation-based approach is used to model the traffic flow applying both micro and meso scale models. Then, the simulation is conducted for the predefined scenarios, then the sensitivity analysis of each relevant parameter is performed using a one-factor-at-a-time approach to understand the impact of each parameter on the network traffic condition. Finally, Bayesian optimisation is used to find the optimal rerouting strategy within a certain search range and number of times, where the results obtained from the sensitivity analysis are used to determine the parameter search space.
The grid network and the Sioux Falls network are simulated respectively and the relatively optimal rerouting strategies are found for them. The grid network can be regarded as a local area on the network, while the results of Sioux Falls, as a larger network, can provide some basis for city-level traffic management.
The key findings of this thesis include (1) CAV penetration increases bring reductions in TTT and TWT and increase in TTD to the network, overall, the traffic flow movement improves and severe congestion decreases, and network conditions improve significantly during the growth phases of 20\%-40\% and 60\%-80\%; (2) the importance of each rerouting parameter varies for different networks and at different penetration rates, and the results fluctuate significantly between different test values, with no single increasing and decreasing trend; (3) road closures at entrances and exits located at intersections are more critical and require targeted rerouting strategies; the traffic demand distribution has a significant impact on it; (4) Bayesian optimization can find the optimal rerouting strategy in a finite amount of time, where the specific strategy and the improvement effect varies for different networks and levels of demand.
Car-Free Development in landside airport areas
Towards Low Car(bon) policies for airport commuters
Approximately 200,000 individuals travel to & from Schiphol Airport daily through various means such as cars, taxis, buses, shuttles, trains, motorcycles, scooters, and bicycles. With this perspective, Schiphol Airport is the largest mobility hub in the whole Netherlands, making accessibility a critical aspect. The Schiphol Group is responsible for providing/ensuring accessibility. Their main goal is to alleviate accessibility for Schiphol's customers, including passengers, personnel/commuters, business partners, and cargo, by focusing on different transportation modalities.
To achieve the goal of “the best airport for accessibility and sustainable aviation as well as land-side transport in Europe,” Schiphol Group works toward a car-free, emission-free vision and plans to apply on-site.
This thesis conjugates Schiphol’s and European Union’s (EU) goals (EGD, TULIPS) set for the aviation sector and further investigates the possibility of reaching the “Car-free Schiphol Centrum”. The objectives designated the reach this primary goal.
- A combined method to design and evaluate the car-free Schiphol Airport efficiency
- A system that uses the methods to display the efficiency of the car-free Schiphol Airport.
By exploring these objectives, the thesis aims to contribute to the overall goal of making Schiphol Airport a sustainable, accessible, and car-free hub for all commuters and users. The problem statement and main research question gather around this unifying goal:
“How can Schiphol Airport become car-free in its land-side areas?”
To address this question, the thesis proposes a combined method to design and evaluate the efficiency of a car-free Schiphol Airport. This method considers the goals set by the Schiphol Group and the European Union.
The methodology constitutes the combination of the literature-based frameworks to create anew the thesis as well as finding the best measurement tools to seek the results of a successful car-free Schiphol Centrum. Car-free Development and Transit-oriented development allow for discussing and creating the framework for a car-free Schiphol Airport. The evaluation of this framework will be done by implementing new modalities to the Schiphol to ensure a possible/potential modal shift of the users and assess the walkability of the land-side areas.
Along with the theoretical work, a case study for the thesis is conducted to apply the proposed methodology and evaluate its effectiveness. The case study focuses on the land-side areas of Schiphol Airport and aims to assess the feasibility of implementing car-free measures in these areas.
The case study's findings show that a car-free Schiphol Centrum is feasible and should be implemented in real life. The proposed measures depict such as improving public transportation, promoting cycling and walking, and providing efficient and sustainable alternatives to private cars (micro mobility options), can contribute to reducing car dependency and creating a more sustainable and accessible airport.
Overall, this thesis provides valuable insights and recommendations for achieving a car-free Schiphol Airport. By combining theoretical frameworks, measurement tools, and a practical case study.
...
To achieve the goal of “the best airport for accessibility and sustainable aviation as well as land-side transport in Europe,” Schiphol Group works toward a car-free, emission-free vision and plans to apply on-site.
This thesis conjugates Schiphol’s and European Union’s (EU) goals (EGD, TULIPS) set for the aviation sector and further investigates the possibility of reaching the “Car-free Schiphol Centrum”. The objectives designated the reach this primary goal.
- A combined method to design and evaluate the car-free Schiphol Airport efficiency
- A system that uses the methods to display the efficiency of the car-free Schiphol Airport.
By exploring these objectives, the thesis aims to contribute to the overall goal of making Schiphol Airport a sustainable, accessible, and car-free hub for all commuters and users. The problem statement and main research question gather around this unifying goal:
“How can Schiphol Airport become car-free in its land-side areas?”
To address this question, the thesis proposes a combined method to design and evaluate the efficiency of a car-free Schiphol Airport. This method considers the goals set by the Schiphol Group and the European Union.
The methodology constitutes the combination of the literature-based frameworks to create anew the thesis as well as finding the best measurement tools to seek the results of a successful car-free Schiphol Centrum. Car-free Development and Transit-oriented development allow for discussing and creating the framework for a car-free Schiphol Airport. The evaluation of this framework will be done by implementing new modalities to the Schiphol to ensure a possible/potential modal shift of the users and assess the walkability of the land-side areas.
Along with the theoretical work, a case study for the thesis is conducted to apply the proposed methodology and evaluate its effectiveness. The case study focuses on the land-side areas of Schiphol Airport and aims to assess the feasibility of implementing car-free measures in these areas.
The case study's findings show that a car-free Schiphol Centrum is feasible and should be implemented in real life. The proposed measures depict such as improving public transportation, promoting cycling and walking, and providing efficient and sustainable alternatives to private cars (micro mobility options), can contribute to reducing car dependency and creating a more sustainable and accessible airport.
Overall, this thesis provides valuable insights and recommendations for achieving a car-free Schiphol Airport. By combining theoretical frameworks, measurement tools, and a practical case study.
...
Approximately 200,000 individuals travel to & from Schiphol Airport daily through various means such as cars, taxis, buses, shuttles, trains, motorcycles, scooters, and bicycles. With this perspective, Schiphol Airport is the largest mobility hub in the whole Netherlands, making accessibility a critical aspect. The Schiphol Group is responsible for providing/ensuring accessibility. Their main goal is to alleviate accessibility for Schiphol's customers, including passengers, personnel/commuters, business partners, and cargo, by focusing on different transportation modalities.
To achieve the goal of “the best airport for accessibility and sustainable aviation as well as land-side transport in Europe,” Schiphol Group works toward a car-free, emission-free vision and plans to apply on-site.
This thesis conjugates Schiphol’s and European Union’s (EU) goals (EGD, TULIPS) set for the aviation sector and further investigates the possibility of reaching the “Car-free Schiphol Centrum”. The objectives designated the reach this primary goal.
- A combined method to design and evaluate the car-free Schiphol Airport efficiency
- A system that uses the methods to display the efficiency of the car-free Schiphol Airport.
By exploring these objectives, the thesis aims to contribute to the overall goal of making Schiphol Airport a sustainable, accessible, and car-free hub for all commuters and users. The problem statement and main research question gather around this unifying goal:
“How can Schiphol Airport become car-free in its land-side areas?”
To address this question, the thesis proposes a combined method to design and evaluate the efficiency of a car-free Schiphol Airport. This method considers the goals set by the Schiphol Group and the European Union.
The methodology constitutes the combination of the literature-based frameworks to create anew the thesis as well as finding the best measurement tools to seek the results of a successful car-free Schiphol Centrum. Car-free Development and Transit-oriented development allow for discussing and creating the framework for a car-free Schiphol Airport. The evaluation of this framework will be done by implementing new modalities to the Schiphol to ensure a possible/potential modal shift of the users and assess the walkability of the land-side areas.
Along with the theoretical work, a case study for the thesis is conducted to apply the proposed methodology and evaluate its effectiveness. The case study focuses on the land-side areas of Schiphol Airport and aims to assess the feasibility of implementing car-free measures in these areas.
The case study's findings show that a car-free Schiphol Centrum is feasible and should be implemented in real life. The proposed measures depict such as improving public transportation, promoting cycling and walking, and providing efficient and sustainable alternatives to private cars (micro mobility options), can contribute to reducing car dependency and creating a more sustainable and accessible airport.
Overall, this thesis provides valuable insights and recommendations for achieving a car-free Schiphol Airport. By combining theoretical frameworks, measurement tools, and a practical case study.
To achieve the goal of “the best airport for accessibility and sustainable aviation as well as land-side transport in Europe,” Schiphol Group works toward a car-free, emission-free vision and plans to apply on-site.
This thesis conjugates Schiphol’s and European Union’s (EU) goals (EGD, TULIPS) set for the aviation sector and further investigates the possibility of reaching the “Car-free Schiphol Centrum”. The objectives designated the reach this primary goal.
- A combined method to design and evaluate the car-free Schiphol Airport efficiency
- A system that uses the methods to display the efficiency of the car-free Schiphol Airport.
By exploring these objectives, the thesis aims to contribute to the overall goal of making Schiphol Airport a sustainable, accessible, and car-free hub for all commuters and users. The problem statement and main research question gather around this unifying goal:
“How can Schiphol Airport become car-free in its land-side areas?”
To address this question, the thesis proposes a combined method to design and evaluate the efficiency of a car-free Schiphol Airport. This method considers the goals set by the Schiphol Group and the European Union.
The methodology constitutes the combination of the literature-based frameworks to create anew the thesis as well as finding the best measurement tools to seek the results of a successful car-free Schiphol Centrum. Car-free Development and Transit-oriented development allow for discussing and creating the framework for a car-free Schiphol Airport. The evaluation of this framework will be done by implementing new modalities to the Schiphol to ensure a possible/potential modal shift of the users and assess the walkability of the land-side areas.
Along with the theoretical work, a case study for the thesis is conducted to apply the proposed methodology and evaluate its effectiveness. The case study focuses on the land-side areas of Schiphol Airport and aims to assess the feasibility of implementing car-free measures in these areas.
The case study's findings show that a car-free Schiphol Centrum is feasible and should be implemented in real life. The proposed measures depict such as improving public transportation, promoting cycling and walking, and providing efficient and sustainable alternatives to private cars (micro mobility options), can contribute to reducing car dependency and creating a more sustainable and accessible airport.
Overall, this thesis provides valuable insights and recommendations for achieving a car-free Schiphol Airport. By combining theoretical frameworks, measurement tools, and a practical case study.
Congestion is a major problem on the Dutch road networks, which is a widely researched topic. Multiple factors contribute to this increasing congestion, examples of these are increasing traffic demands and inefficient traffic signal control. The focus of this research is on arterial traffic networks consisting of signalized intersections. The effects of two innovations that aim to reduce congestion have been evaluated, which are intelligent traffic signal control using the data of connected vehicles and truck/vehicle platooning. Research has shown promising results in both areas, but there are still some gaps to be filled. Especially in the area of truck/vehicle platooning on intersections and the combination of intelligent intersection control and truck/vehicle platooning. These two innovations have therefore been combined in this study to develop a dedicated traffic management system that leverages vehicle connectivity and platooning capabilities to control the traffic on a signalized intersection. The objective of this study is to determine the traffic flow effects that this system has in mixed traffic conditions. This leads to the following main research question:
What dedicated traffic management system can potentially improve the traffic flows on a logistic corridor where truck platoons drive in mixed traffic (consisting of regular and connected vehicles) and what are the traffic flow effects on this corridor when the system is implemented?
The effectiveness of this dedicated traffic management has been determined by doing a microsimulation on an existing logistic corridor. This study is commissioned by the Province of Noord-Holland, so the chosen corridor for this is located in Noord-Holland. This corridor is a part of the logistic corridor between the largest flower auction in the world, Royal FloraHolland, and the A4 highway. The used part of the corridor consists of three intersections, from which the intersection between the N201 and Koolhovenlaan has been used to implement the dedicated traffic management system. The currently used signal controller on this intersection is a vehicle-actuated signal controller, which uses measurements done by induction loops to update the signal phase and timings plan. The intersection is also a part of a field experiment where freight traffic is granted priority based on vehicle connectivity…
...
What dedicated traffic management system can potentially improve the traffic flows on a logistic corridor where truck platoons drive in mixed traffic (consisting of regular and connected vehicles) and what are the traffic flow effects on this corridor when the system is implemented?
The effectiveness of this dedicated traffic management has been determined by doing a microsimulation on an existing logistic corridor. This study is commissioned by the Province of Noord-Holland, so the chosen corridor for this is located in Noord-Holland. This corridor is a part of the logistic corridor between the largest flower auction in the world, Royal FloraHolland, and the A4 highway. The used part of the corridor consists of three intersections, from which the intersection between the N201 and Koolhovenlaan has been used to implement the dedicated traffic management system. The currently used signal controller on this intersection is a vehicle-actuated signal controller, which uses measurements done by induction loops to update the signal phase and timings plan. The intersection is also a part of a field experiment where freight traffic is granted priority based on vehicle connectivity…
...
Congestion is a major problem on the Dutch road networks, which is a widely researched topic. Multiple factors contribute to this increasing congestion, examples of these are increasing traffic demands and inefficient traffic signal control. The focus of this research is on arterial traffic networks consisting of signalized intersections. The effects of two innovations that aim to reduce congestion have been evaluated, which are intelligent traffic signal control using the data of connected vehicles and truck/vehicle platooning. Research has shown promising results in both areas, but there are still some gaps to be filled. Especially in the area of truck/vehicle platooning on intersections and the combination of intelligent intersection control and truck/vehicle platooning. These two innovations have therefore been combined in this study to develop a dedicated traffic management system that leverages vehicle connectivity and platooning capabilities to control the traffic on a signalized intersection. The objective of this study is to determine the traffic flow effects that this system has in mixed traffic conditions. This leads to the following main research question:
What dedicated traffic management system can potentially improve the traffic flows on a logistic corridor where truck platoons drive in mixed traffic (consisting of regular and connected vehicles) and what are the traffic flow effects on this corridor when the system is implemented?
The effectiveness of this dedicated traffic management has been determined by doing a microsimulation on an existing logistic corridor. This study is commissioned by the Province of Noord-Holland, so the chosen corridor for this is located in Noord-Holland. This corridor is a part of the logistic corridor between the largest flower auction in the world, Royal FloraHolland, and the A4 highway. The used part of the corridor consists of three intersections, from which the intersection between the N201 and Koolhovenlaan has been used to implement the dedicated traffic management system. The currently used signal controller on this intersection is a vehicle-actuated signal controller, which uses measurements done by induction loops to update the signal phase and timings plan. The intersection is also a part of a field experiment where freight traffic is granted priority based on vehicle connectivity…
What dedicated traffic management system can potentially improve the traffic flows on a logistic corridor where truck platoons drive in mixed traffic (consisting of regular and connected vehicles) and what are the traffic flow effects on this corridor when the system is implemented?
The effectiveness of this dedicated traffic management has been determined by doing a microsimulation on an existing logistic corridor. This study is commissioned by the Province of Noord-Holland, so the chosen corridor for this is located in Noord-Holland. This corridor is a part of the logistic corridor between the largest flower auction in the world, Royal FloraHolland, and the A4 highway. The used part of the corridor consists of three intersections, from which the intersection between the N201 and Koolhovenlaan has been used to implement the dedicated traffic management system. The currently used signal controller on this intersection is a vehicle-actuated signal controller, which uses measurements done by induction loops to update the signal phase and timings plan. The intersection is also a part of a field experiment where freight traffic is granted priority based on vehicle connectivity…
With the steadily increasing road traffic demand, congestion on freeways has become a major problem. With the development of communication technology and automated vehicles, there are more opportunities for DTMs to be studied extensively at the coordination level and cooperation level. In the existing literature, the coordination of RM (ramp metering) + VSL (variable speed limit) + RG(route guidance) has rarely been studied. Besides, some coordination studies have limited discussion on the interaction between DTMs in non-coordinated cases. Based on these research gaps, the research question of this thesis is: What is the impact of coordinating RM, VSL and RG on a road structure where they have potential counter-effects on each other when following local objectives?
This research aims to explore the network performance on the non-coordination cases and coordinated cases. The simulation is built in SUMO, while the DTM control measures are implemented via the Traffic Control Interface (TraCI). In the coordination, all controller parameters (metered flows, speed limits, and split rate) work together to improve traffic in this two-bottleneck system. The coordination strategy has a model predictive control structure. The cell transmission model (CTM) is used as the prediction model. In this case, all
the effects of considered DTM measures can be integrated into the expression of the inflow to the first cell on the basis of certain assumptions. Considering that there are 5 control variables, genetic algorithm (GA) is used for the optimisation process in the MPC control. The objective is maximising the total outflow after bottlenecks on two routes.
The conclusion of the research is that there is benefit for applying DTM coordination on such a road structure with two parallel freeways. However, the improvement caused by a predictive RG is the major part of the benefit. This is because that the simulation cannot capture the capacity drop phenomenon. The simulation results indicate that a predictive RG is more effective compared to coordinated control. It also leads to the best travel time equity among routes than the coordinated control. Based on the findings, it is more prior for this kind of road structure to improve the in-vehicle RG on this road structure than to apply coordinated control.
...
This research aims to explore the network performance on the non-coordination cases and coordinated cases. The simulation is built in SUMO, while the DTM control measures are implemented via the Traffic Control Interface (TraCI). In the coordination, all controller parameters (metered flows, speed limits, and split rate) work together to improve traffic in this two-bottleneck system. The coordination strategy has a model predictive control structure. The cell transmission model (CTM) is used as the prediction model. In this case, all
the effects of considered DTM measures can be integrated into the expression of the inflow to the first cell on the basis of certain assumptions. Considering that there are 5 control variables, genetic algorithm (GA) is used for the optimisation process in the MPC control. The objective is maximising the total outflow after bottlenecks on two routes.
The conclusion of the research is that there is benefit for applying DTM coordination on such a road structure with two parallel freeways. However, the improvement caused by a predictive RG is the major part of the benefit. This is because that the simulation cannot capture the capacity drop phenomenon. The simulation results indicate that a predictive RG is more effective compared to coordinated control. It also leads to the best travel time equity among routes than the coordinated control. Based on the findings, it is more prior for this kind of road structure to improve the in-vehicle RG on this road structure than to apply coordinated control.
...
With the steadily increasing road traffic demand, congestion on freeways has become a major problem. With the development of communication technology and automated vehicles, there are more opportunities for DTMs to be studied extensively at the coordination level and cooperation level. In the existing literature, the coordination of RM (ramp metering) + VSL (variable speed limit) + RG(route guidance) has rarely been studied. Besides, some coordination studies have limited discussion on the interaction between DTMs in non-coordinated cases. Based on these research gaps, the research question of this thesis is: What is the impact of coordinating RM, VSL and RG on a road structure where they have potential counter-effects on each other when following local objectives?
This research aims to explore the network performance on the non-coordination cases and coordinated cases. The simulation is built in SUMO, while the DTM control measures are implemented via the Traffic Control Interface (TraCI). In the coordination, all controller parameters (metered flows, speed limits, and split rate) work together to improve traffic in this two-bottleneck system. The coordination strategy has a model predictive control structure. The cell transmission model (CTM) is used as the prediction model. In this case, all
the effects of considered DTM measures can be integrated into the expression of the inflow to the first cell on the basis of certain assumptions. Considering that there are 5 control variables, genetic algorithm (GA) is used for the optimisation process in the MPC control. The objective is maximising the total outflow after bottlenecks on two routes.
The conclusion of the research is that there is benefit for applying DTM coordination on such a road structure with two parallel freeways. However, the improvement caused by a predictive RG is the major part of the benefit. This is because that the simulation cannot capture the capacity drop phenomenon. The simulation results indicate that a predictive RG is more effective compared to coordinated control. It also leads to the best travel time equity among routes than the coordinated control. Based on the findings, it is more prior for this kind of road structure to improve the in-vehicle RG on this road structure than to apply coordinated control.
This research aims to explore the network performance on the non-coordination cases and coordinated cases. The simulation is built in SUMO, while the DTM control measures are implemented via the Traffic Control Interface (TraCI). In the coordination, all controller parameters (metered flows, speed limits, and split rate) work together to improve traffic in this two-bottleneck system. The coordination strategy has a model predictive control structure. The cell transmission model (CTM) is used as the prediction model. In this case, all
the effects of considered DTM measures can be integrated into the expression of the inflow to the first cell on the basis of certain assumptions. Considering that there are 5 control variables, genetic algorithm (GA) is used for the optimisation process in the MPC control. The objective is maximising the total outflow after bottlenecks on two routes.
The conclusion of the research is that there is benefit for applying DTM coordination on such a road structure with two parallel freeways. However, the improvement caused by a predictive RG is the major part of the benefit. This is because that the simulation cannot capture the capacity drop phenomenon. The simulation results indicate that a predictive RG is more effective compared to coordinated control. It also leads to the best travel time equity among routes than the coordinated control. Based on the findings, it is more prior for this kind of road structure to improve the in-vehicle RG on this road structure than to apply coordinated control.
With the development of Autonomous Vehicles (AVs), a promising future for their implementation becomes increasingly apparent. However, it is essential to acknowledge that the effects of AV deployment are not straightforward, particularly when considering scenarios involving AVs from different manufacturers and various levels of automation. The current research predominantly concentrates on human-driven vehicles(HDVs) and AVs. However, the assumption that AVs exhibit homogeneous behavior is a simplification that does not reflect the actual diversity within this category.
This research is undertaken to evaluate the impact of autonomous vehicle heterogeneity on traffic flow. To assess AV heterogeneity, the initial step involves an examination of the manifestations of AV heterogeneity through data analysis. The data source of the data analysis includes two parts, the Adaptive Cruise Control (ACC) data, and the high-level AV data. For the ACC data, the open ACC dataset is used. As for the high level, the processed Waymo and Lyft 5 datasets are used. These datasets encompass essential information, including the position, acceleration, and speed of the vehicles within the platoon, which is instrumental in identifying and characterizing heterogeneous driving behaviors. The analysis focuses on analyzing parameters such as Time-to-Collision (TTC), time gap, and acceleration/deceleration patterns. As for the time gap, the investigations include the distribution of time gaps under different speed ranges and different acceleration conditions. The results of the analysis contribute to the conclusion that heterogeneity among AVs is evident, not only across various automation levels but also within the same level of AVs.
Given the presence of heterogeneity, characterized by the same or different automation levels with differing behavioral patterns among AVs, the car-following models are employed to capture this heterogeneity. Therefore, these parameters are calibrated using a genetic algorithm and maximum likelihood estimation is applied to determine the best-fit distributions of desired time gaps and maximum accelerations. Calibrated car-following models are then employed to represent the longitudinal behaviors of AVs. The parameters are drawn from distributions, it is expected that AVs will exhibit slightly varying behaviors.
To assess the impact of heterogeneous traffic on traffic flow, various scenarios are constructed and evaluated. The scenarios encompass ACC vehicles, HDVs, and combinations of ACC, highly automated vehicles(HAVs), and HDVs. The first scenario aims to assess the impact of heterogeneity among AVs of the same automation level, so the different shares of ACC vehicles are involved. In contrast, the second scenario involves HAVs and ACC vehicles to evaluate the influence of heterogeneity arising from various AV automation levels.
The ultimate conclusion drawn from this study suggests that heterogeneity negatively impacts traffic efficiency. Specifically, the efficiency gains afforded by vehicles equipped with ACC are offset by the presence of heterogeneous traffic at low penetration rates.
Furthermore, the results obtained from simulation scenario 2 indicate that the introduction of multi-level AVs may have a detrimental effect on traffic efficiency and stability. These findings underscore the need to validate and improve AV performance comprehensively before embarking on large-scale implementation efforts. ...
This research is undertaken to evaluate the impact of autonomous vehicle heterogeneity on traffic flow. To assess AV heterogeneity, the initial step involves an examination of the manifestations of AV heterogeneity through data analysis. The data source of the data analysis includes two parts, the Adaptive Cruise Control (ACC) data, and the high-level AV data. For the ACC data, the open ACC dataset is used. As for the high level, the processed Waymo and Lyft 5 datasets are used. These datasets encompass essential information, including the position, acceleration, and speed of the vehicles within the platoon, which is instrumental in identifying and characterizing heterogeneous driving behaviors. The analysis focuses on analyzing parameters such as Time-to-Collision (TTC), time gap, and acceleration/deceleration patterns. As for the time gap, the investigations include the distribution of time gaps under different speed ranges and different acceleration conditions. The results of the analysis contribute to the conclusion that heterogeneity among AVs is evident, not only across various automation levels but also within the same level of AVs.
Given the presence of heterogeneity, characterized by the same or different automation levels with differing behavioral patterns among AVs, the car-following models are employed to capture this heterogeneity. Therefore, these parameters are calibrated using a genetic algorithm and maximum likelihood estimation is applied to determine the best-fit distributions of desired time gaps and maximum accelerations. Calibrated car-following models are then employed to represent the longitudinal behaviors of AVs. The parameters are drawn from distributions, it is expected that AVs will exhibit slightly varying behaviors.
To assess the impact of heterogeneous traffic on traffic flow, various scenarios are constructed and evaluated. The scenarios encompass ACC vehicles, HDVs, and combinations of ACC, highly automated vehicles(HAVs), and HDVs. The first scenario aims to assess the impact of heterogeneity among AVs of the same automation level, so the different shares of ACC vehicles are involved. In contrast, the second scenario involves HAVs and ACC vehicles to evaluate the influence of heterogeneity arising from various AV automation levels.
The ultimate conclusion drawn from this study suggests that heterogeneity negatively impacts traffic efficiency. Specifically, the efficiency gains afforded by vehicles equipped with ACC are offset by the presence of heterogeneous traffic at low penetration rates.
Furthermore, the results obtained from simulation scenario 2 indicate that the introduction of multi-level AVs may have a detrimental effect on traffic efficiency and stability. These findings underscore the need to validate and improve AV performance comprehensively before embarking on large-scale implementation efforts. ...
With the development of Autonomous Vehicles (AVs), a promising future for their implementation becomes increasingly apparent. However, it is essential to acknowledge that the effects of AV deployment are not straightforward, particularly when considering scenarios involving AVs from different manufacturers and various levels of automation. The current research predominantly concentrates on human-driven vehicles(HDVs) and AVs. However, the assumption that AVs exhibit homogeneous behavior is a simplification that does not reflect the actual diversity within this category.
This research is undertaken to evaluate the impact of autonomous vehicle heterogeneity on traffic flow. To assess AV heterogeneity, the initial step involves an examination of the manifestations of AV heterogeneity through data analysis. The data source of the data analysis includes two parts, the Adaptive Cruise Control (ACC) data, and the high-level AV data. For the ACC data, the open ACC dataset is used. As for the high level, the processed Waymo and Lyft 5 datasets are used. These datasets encompass essential information, including the position, acceleration, and speed of the vehicles within the platoon, which is instrumental in identifying and characterizing heterogeneous driving behaviors. The analysis focuses on analyzing parameters such as Time-to-Collision (TTC), time gap, and acceleration/deceleration patterns. As for the time gap, the investigations include the distribution of time gaps under different speed ranges and different acceleration conditions. The results of the analysis contribute to the conclusion that heterogeneity among AVs is evident, not only across various automation levels but also within the same level of AVs.
Given the presence of heterogeneity, characterized by the same or different automation levels with differing behavioral patterns among AVs, the car-following models are employed to capture this heterogeneity. Therefore, these parameters are calibrated using a genetic algorithm and maximum likelihood estimation is applied to determine the best-fit distributions of desired time gaps and maximum accelerations. Calibrated car-following models are then employed to represent the longitudinal behaviors of AVs. The parameters are drawn from distributions, it is expected that AVs will exhibit slightly varying behaviors.
To assess the impact of heterogeneous traffic on traffic flow, various scenarios are constructed and evaluated. The scenarios encompass ACC vehicles, HDVs, and combinations of ACC, highly automated vehicles(HAVs), and HDVs. The first scenario aims to assess the impact of heterogeneity among AVs of the same automation level, so the different shares of ACC vehicles are involved. In contrast, the second scenario involves HAVs and ACC vehicles to evaluate the influence of heterogeneity arising from various AV automation levels.
The ultimate conclusion drawn from this study suggests that heterogeneity negatively impacts traffic efficiency. Specifically, the efficiency gains afforded by vehicles equipped with ACC are offset by the presence of heterogeneous traffic at low penetration rates.
Furthermore, the results obtained from simulation scenario 2 indicate that the introduction of multi-level AVs may have a detrimental effect on traffic efficiency and stability. These findings underscore the need to validate and improve AV performance comprehensively before embarking on large-scale implementation efforts.
This research is undertaken to evaluate the impact of autonomous vehicle heterogeneity on traffic flow. To assess AV heterogeneity, the initial step involves an examination of the manifestations of AV heterogeneity through data analysis. The data source of the data analysis includes two parts, the Adaptive Cruise Control (ACC) data, and the high-level AV data. For the ACC data, the open ACC dataset is used. As for the high level, the processed Waymo and Lyft 5 datasets are used. These datasets encompass essential information, including the position, acceleration, and speed of the vehicles within the platoon, which is instrumental in identifying and characterizing heterogeneous driving behaviors. The analysis focuses on analyzing parameters such as Time-to-Collision (TTC), time gap, and acceleration/deceleration patterns. As for the time gap, the investigations include the distribution of time gaps under different speed ranges and different acceleration conditions. The results of the analysis contribute to the conclusion that heterogeneity among AVs is evident, not only across various automation levels but also within the same level of AVs.
Given the presence of heterogeneity, characterized by the same or different automation levels with differing behavioral patterns among AVs, the car-following models are employed to capture this heterogeneity. Therefore, these parameters are calibrated using a genetic algorithm and maximum likelihood estimation is applied to determine the best-fit distributions of desired time gaps and maximum accelerations. Calibrated car-following models are then employed to represent the longitudinal behaviors of AVs. The parameters are drawn from distributions, it is expected that AVs will exhibit slightly varying behaviors.
To assess the impact of heterogeneous traffic on traffic flow, various scenarios are constructed and evaluated. The scenarios encompass ACC vehicles, HDVs, and combinations of ACC, highly automated vehicles(HAVs), and HDVs. The first scenario aims to assess the impact of heterogeneity among AVs of the same automation level, so the different shares of ACC vehicles are involved. In contrast, the second scenario involves HAVs and ACC vehicles to evaluate the influence of heterogeneity arising from various AV automation levels.
The ultimate conclusion drawn from this study suggests that heterogeneity negatively impacts traffic efficiency. Specifically, the efficiency gains afforded by vehicles equipped with ACC are offset by the presence of heterogeneous traffic at low penetration rates.
Furthermore, the results obtained from simulation scenario 2 indicate that the introduction of multi-level AVs may have a detrimental effect on traffic efficiency and stability. These findings underscore the need to validate and improve AV performance comprehensively before embarking on large-scale implementation efforts.
Car-following behaviour is a fundamental element for vehicle manoeuvre. The heterogeneity among driving behaviour has gained significant importance since some researchers argued that it might be relevant to capacity drop and traffic oscillations. Driver space is an area around a vehicle, and drivers will feel a rapid increase in discomfort when their comfort boundary is intruded. The response intensity of discomfort caused by spatial intrusion is represented by proximity resistance. With the development of Advanced Driver Assistance Systems (ADAS), it is crucial for drivers to feel comfortable within the implemented system. Therefore, understanding the heterogeneity of driver space has enormous potential to develop better customised ADAS. This report aims to analyse the impacts of heterogeneity on proximity resistance in car-following. The HighD dataset was processed to differentiate car-following driving styles for cars and trucks through the k-means clustering methodology. After successfully inferring the proximity resistance for each driver at each frame, the impacts of heterogeneity on proximity resistance in terms of different traffic states, vehicle types and driving styles were analysed. The results show that these three heterogeneities have different extent impacts on the proximity resistance of different drivers in car following. This study is beneficial for further investigations on the potential reasons for heterogeneous proximity resistance and the development of personalised car-following models.
...
Car-following behaviour is a fundamental element for vehicle manoeuvre. The heterogeneity among driving behaviour has gained significant importance since some researchers argued that it might be relevant to capacity drop and traffic oscillations. Driver space is an area around a vehicle, and drivers will feel a rapid increase in discomfort when their comfort boundary is intruded. The response intensity of discomfort caused by spatial intrusion is represented by proximity resistance. With the development of Advanced Driver Assistance Systems (ADAS), it is crucial for drivers to feel comfortable within the implemented system. Therefore, understanding the heterogeneity of driver space has enormous potential to develop better customised ADAS. This report aims to analyse the impacts of heterogeneity on proximity resistance in car-following. The HighD dataset was processed to differentiate car-following driving styles for cars and trucks through the k-means clustering methodology. After successfully inferring the proximity resistance for each driver at each frame, the impacts of heterogeneity on proximity resistance in terms of different traffic states, vehicle types and driving styles were analysed. The results show that these three heterogeneities have different extent impacts on the proximity resistance of different drivers in car following. This study is beneficial for further investigations on the potential reasons for heterogeneous proximity resistance and the development of personalised car-following models.
New applications of connectivity between vehicles and the infrastructure are developed. One of these applications is providing upstream information on variable speed limits to connected and automated vehicles. It is expected that the connectivity can contribute to safer roads due to better compliance to the posted speed limits. Those expectations are based on microscopic models with the assumption that human drivers behave similarly in mixed traffic as they do in only human driven vehicles traffic. However, few studies have shown that human drivers tend to change their driving behavior when interacting with automated vehicles in mixed traffic. For this reason, a driving simulator experiment is executed to investigate the effect of the penetration rate of connected and automated vehicles and the distance at which the information is provided on the driving behavior of human drivers. The driving behavior was analyzed in terms of longitudinal and lateral behavior in the context of a three-lane motorway. The penetration rate was found to only impact the speed adaptation when combined with a large distance of upstream information. Lower means speeds, lower section entry speeds and increased speed compliance was observed with an increasing level of penetration rate. For the effect of distance of upstream information, a similar effect was observed. When the distance was increased the mean speed lowered, section entry speed lowered, and speed compliance increased. No change was observed regarding the lateral behavior or Time Headway. As a result, it can be concluded that the cooperation between connected and automated vehicles and variable speed limits on motorways can be used to slow down unconnected vehicles more upstream, without inducing aggressive driving behavior in terms Time Headway and lane changing behavior.
...
New applications of connectivity between vehicles and the infrastructure are developed. One of these applications is providing upstream information on variable speed limits to connected and automated vehicles. It is expected that the connectivity can contribute to safer roads due to better compliance to the posted speed limits. Those expectations are based on microscopic models with the assumption that human drivers behave similarly in mixed traffic as they do in only human driven vehicles traffic. However, few studies have shown that human drivers tend to change their driving behavior when interacting with automated vehicles in mixed traffic. For this reason, a driving simulator experiment is executed to investigate the effect of the penetration rate of connected and automated vehicles and the distance at which the information is provided on the driving behavior of human drivers. The driving behavior was analyzed in terms of longitudinal and lateral behavior in the context of a three-lane motorway. The penetration rate was found to only impact the speed adaptation when combined with a large distance of upstream information. Lower means speeds, lower section entry speeds and increased speed compliance was observed with an increasing level of penetration rate. For the effect of distance of upstream information, a similar effect was observed. When the distance was increased the mean speed lowered, section entry speed lowered, and speed compliance increased. No change was observed regarding the lateral behavior or Time Headway. As a result, it can be concluded that the cooperation between connected and automated vehicles and variable speed limits on motorways can be used to slow down unconnected vehicles more upstream, without inducing aggressive driving behavior in terms Time Headway and lane changing behavior.
This research is carried out to determine the effect of truck platooning on traffic flow using empirical data. This research contains two parts, data fusion, and statistical analysis. For data fusion, loop detector data, infrastructure information and weather data will be added to the original data set. For statistical analysis, the time gap distributions under different categories are analyzed to determine the performance of the truck platoon. Additionally, an analysis of the lane change behavior is included.
...
This research is carried out to determine the effect of truck platooning on traffic flow using empirical data. This research contains two parts, data fusion, and statistical analysis. For data fusion, loop detector data, infrastructure information and weather data will be added to the original data set. For statistical analysis, the time gap distributions under different categories are analyzed to determine the performance of the truck platoon. Additionally, an analysis of the lane change behavior is included.
The research of connected automated vehicles (CAVs) is an emerging topic within the field of transport & planning. It is not a question of whether the vehicles will be available for commercial use, but rather a question of when they will arrive. The safety of these vehicles is a necessary and ongoing discussion. In current research, a consensus is reached that crashes occurmainly due to human error. This human error is either due to negligence of the driving activity, like drunk driving or texting while driving, or due to incorrect decision making at critical moments. This study focuses on the topic of traffic safety with the principle of herd immunity in mind. It is theorised that crash risk can behave similarly to how a virus behaves (where crash risk is the chance that a crash occurs at a certain point in time). It spreads and infects vulnerable members of the population. The aim of this study is to determine whether the principle of herd immunity can be applied to car traffic, and if so, to what extent they can be compared through an impact assessment. The research question attached to this is: "How is traffic safety influenced by connected (automated) vehicles considering the concept of herd immunity?"
...
The research of connected automated vehicles (CAVs) is an emerging topic within the field of transport & planning. It is not a question of whether the vehicles will be available for commercial use, but rather a question of when they will arrive. The safety of these vehicles is a necessary and ongoing discussion. In current research, a consensus is reached that crashes occurmainly due to human error. This human error is either due to negligence of the driving activity, like drunk driving or texting while driving, or due to incorrect decision making at critical moments. This study focuses on the topic of traffic safety with the principle of herd immunity in mind. It is theorised that crash risk can behave similarly to how a virus behaves (where crash risk is the chance that a crash occurs at a certain point in time). It spreads and infects vulnerable members of the population. The aim of this study is to determine whether the principle of herd immunity can be applied to car traffic, and if so, to what extent they can be compared through an impact assessment. The research question attached to this is: "How is traffic safety influenced by connected (automated) vehicles considering the concept of herd immunity?"
Modern societies are heavily relied on efficient transportation systems for mobilizing people and goods. These systems are mainly constituted by road traffic networks. Currently, traffic demand is immense and perpetually increasing with unprecedented rates that traffic congestion has become an imminent subsequent. Over time, all this human activity that has established the status-quo of modern societies has been negatively influenced by climate change. Climate deviations are prominent in urban environments with a higher frequency and elongated time scales. Therefore, road traffic systems jeopardizing their robustness, and their resilience is at stake. A fundamental component in road traffic systems is the human factor. Nevertheless, human factors, to the contribution in traffic, are largely neglected. Some other times we consider that humans act rationally. Consequently, engineers seek answers to questions of how to incorporate the human factor into the system to explain the behaviour of human drivers under adverse weather conditions. In this contribution, an exploratory simulation study was used to put into perspective the derived conceptual frameworks and assess their performance in terms of efficiency and safety. Various psycho-cognitive mechanisms were utilized to address the human factor and rationally connected with the vehicle motion to reproduce the traffic phenomena that we observe under the conditions of rain and fog.
...
Modern societies are heavily relied on efficient transportation systems for mobilizing people and goods. These systems are mainly constituted by road traffic networks. Currently, traffic demand is immense and perpetually increasing with unprecedented rates that traffic congestion has become an imminent subsequent. Over time, all this human activity that has established the status-quo of modern societies has been negatively influenced by climate change. Climate deviations are prominent in urban environments with a higher frequency and elongated time scales. Therefore, road traffic systems jeopardizing their robustness, and their resilience is at stake. A fundamental component in road traffic systems is the human factor. Nevertheless, human factors, to the contribution in traffic, are largely neglected. Some other times we consider that humans act rationally. Consequently, engineers seek answers to questions of how to incorporate the human factor into the system to explain the behaviour of human drivers under adverse weather conditions. In this contribution, an exploratory simulation study was used to put into perspective the derived conceptual frameworks and assess their performance in terms of efficiency and safety. Various psycho-cognitive mechanisms were utilized to address the human factor and rationally connected with the vehicle motion to reproduce the traffic phenomena that we observe under the conditions of rain and fog.
Master thesis
(2021)
-
S. Dharaneppanavar, J.W.C. van Lint, S.C. Calvert, L. Ferranti, Jochen Lohmiller
In the future, Human Driven Vehicles (HDV's) are expected to interact with Automated Vehicles (AV’s) and Connected AV’s (CAV’s). Due to the differences in the expected driving behavior of AV’s (ACC) and CAV’s (CACC) compared to HDV’s, the nature of traffic breakdown phenomena in the future can be expected to change. AV’s (ACC) and CAV’s (CACC) refer to AV’s enabled with Adaptive Cruise Control functionality and CAV’s enabled with Co-operative ACC functionality respectively. Currently, there are traffic management measures which address traffic breakdown for the current situation. With the expected changes in breakdown phenomena in the future, will the current measures be effective in addressing the different nature of breakdown in the future? This research answered this question through simulation (Vissim), by focusing into the effectiveness of one of the current measures. The current measure whose effectiveness was analyzed is Variable Speed limits (VSL) applied through the concept of feedback Mainstream Traffic Flow Control, MTFC-VSL, at on-ramp merge sections. Before conducting simulations, it was hypothesized that MTFC-VSL control effectiveness in addressing traffic breakdown increases as CAV’s (with ACC and CACC functionality) penetration rate increases in mixed traffic, because CAV’s can be expected to precisely follow the speed limits. Mixed traffic in this research comprised of HDV’s and CAV’s (with ACC and CACC functionality). CAV’s (with ACC and CACC functionality) implies that CAV’s majorly differ with that of HDV’s in car following behavior and not in lane change behavior. Simulation results and analysis revealed that the hypothesis doesn’t hold good. Improvements in average Travel Time (TT) of mainline vehicles and average network speed due to the presence of MTFC-VSL control compared to the absence of it, deteriorated as penetration rate of CAV’s (with ACC and CACC functionality) increased until 20% in mixed traffic. For further penetration rates the improvements fluctuates. On-ramp vehicles for most of the scenarios of mixed traffic, are better off without MTFC-VSL control as the presence of it increases the vehicles average TT. MTFC-VSL control doesn’t effectively address the capacity drop phenomenon for various scenarios of mixed traffic. Lastly, it was found that for less than 20% CAV’s (with ACC and CACC functionality) penetration rate in mixed traffic, MTFC-VSL control effectiveness can be expected to overall increase if Intelligent Speed Adaptation is installed as an On-Board Unit in HDV’s, as it limits HDV’s exceeding the speed limits.It must be noted that, MTFC-VSL control was set up considering the practical considerations of implementing in real life, which can also be expected to play a significant role in hypothesis not being valid. Given the ineffectiveness of MTFC-VSL control for various scenarios of mixed traffic, the future traffic management measures should focus on the causes of breakdown phenomena which aren’t addressed by MTFC-VSL control. One of the proposed measures is a combination of a merging assistant strategy & MTFC-VSL control to better address traffic breakdown than MTFC-VSL alone. Merging assistant strategy utilizes the connectivity feature of CAV’s to foster smoother merging of on-ramp vehicles which isn’t addressed by MTFC-VSL control.
...
In the future, Human Driven Vehicles (HDV's) are expected to interact with Automated Vehicles (AV’s) and Connected AV’s (CAV’s). Due to the differences in the expected driving behavior of AV’s (ACC) and CAV’s (CACC) compared to HDV’s, the nature of traffic breakdown phenomena in the future can be expected to change. AV’s (ACC) and CAV’s (CACC) refer to AV’s enabled with Adaptive Cruise Control functionality and CAV’s enabled with Co-operative ACC functionality respectively. Currently, there are traffic management measures which address traffic breakdown for the current situation. With the expected changes in breakdown phenomena in the future, will the current measures be effective in addressing the different nature of breakdown in the future? This research answered this question through simulation (Vissim), by focusing into the effectiveness of one of the current measures. The current measure whose effectiveness was analyzed is Variable Speed limits (VSL) applied through the concept of feedback Mainstream Traffic Flow Control, MTFC-VSL, at on-ramp merge sections. Before conducting simulations, it was hypothesized that MTFC-VSL control effectiveness in addressing traffic breakdown increases as CAV’s (with ACC and CACC functionality) penetration rate increases in mixed traffic, because CAV’s can be expected to precisely follow the speed limits. Mixed traffic in this research comprised of HDV’s and CAV’s (with ACC and CACC functionality). CAV’s (with ACC and CACC functionality) implies that CAV’s majorly differ with that of HDV’s in car following behavior and not in lane change behavior. Simulation results and analysis revealed that the hypothesis doesn’t hold good. Improvements in average Travel Time (TT) of mainline vehicles and average network speed due to the presence of MTFC-VSL control compared to the absence of it, deteriorated as penetration rate of CAV’s (with ACC and CACC functionality) increased until 20% in mixed traffic. For further penetration rates the improvements fluctuates. On-ramp vehicles for most of the scenarios of mixed traffic, are better off without MTFC-VSL control as the presence of it increases the vehicles average TT. MTFC-VSL control doesn’t effectively address the capacity drop phenomenon for various scenarios of mixed traffic. Lastly, it was found that for less than 20% CAV’s (with ACC and CACC functionality) penetration rate in mixed traffic, MTFC-VSL control effectiveness can be expected to overall increase if Intelligent Speed Adaptation is installed as an On-Board Unit in HDV’s, as it limits HDV’s exceeding the speed limits.It must be noted that, MTFC-VSL control was set up considering the practical considerations of implementing in real life, which can also be expected to play a significant role in hypothesis not being valid. Given the ineffectiveness of MTFC-VSL control for various scenarios of mixed traffic, the future traffic management measures should focus on the causes of breakdown phenomena which aren’t addressed by MTFC-VSL control. One of the proposed measures is a combination of a merging assistant strategy & MTFC-VSL control to better address traffic breakdown than MTFC-VSL alone. Merging assistant strategy utilizes the connectivity feature of CAV’s to foster smoother merging of on-ramp vehicles which isn’t addressed by MTFC-VSL control.
The impact of regulations for a centralised route guidance system
A simulation study to the effect of a regulated centralised congestion avoiding route guidance system with different penetration rates of automated vehicles on the Milan ring network
This study aims to quantify the impact on the traffic flow performance of different regulation strategies for a centralised route guidance system where road authorities and service providers work together in a coordinated approach. Previous research concentrates on the effect of a centralised route guidance system when every vehicle participates and all vehicles have perfect knowledge of the traffic state. This is not the real case with human drivers and multiple service providers and the impact of cooperation may be limited. This study combines habitual driving behaviour, the effect of the quality of information and a congestion avoiding user optimum algorithm to quantify the impact of a centralised route guidance system. The congestion avoiding user optimum algorithm will add a perceived time penalty to all routes with links above a certain intensity/capacity ratio to avoid choosing the congested route. The cooperation is described by the coordinated approach model of the SOCRATES²·⁰ project. In this model, the cooperation is organised by an intermediary who takes on the management tasks. Because a lack of commitment could be a problem for the success of the system, the services of the intermediary can be regulated with four regulation strategies starting with no regulation to regulation for both service providers and road users. The impact is determined with the dynamic macroscopic traffic model MARPLE. The result shows that without commitment the system does not improve the traffic state. For the maximum potential of the system, it must be fully regulated for both service providers and road users. Although with only the commitment of service providers, there is already a positive impact on the traffic flow and in less complex networks it can already solve all congestion.
...
This study aims to quantify the impact on the traffic flow performance of different regulation strategies for a centralised route guidance system where road authorities and service providers work together in a coordinated approach. Previous research concentrates on the effect of a centralised route guidance system when every vehicle participates and all vehicles have perfect knowledge of the traffic state. This is not the real case with human drivers and multiple service providers and the impact of cooperation may be limited. This study combines habitual driving behaviour, the effect of the quality of information and a congestion avoiding user optimum algorithm to quantify the impact of a centralised route guidance system. The congestion avoiding user optimum algorithm will add a perceived time penalty to all routes with links above a certain intensity/capacity ratio to avoid choosing the congested route. The cooperation is described by the coordinated approach model of the SOCRATES²·⁰ project. In this model, the cooperation is organised by an intermediary who takes on the management tasks. Because a lack of commitment could be a problem for the success of the system, the services of the intermediary can be regulated with four regulation strategies starting with no regulation to regulation for both service providers and road users. The impact is determined with the dynamic macroscopic traffic model MARPLE. The result shows that without commitment the system does not improve the traffic state. For the maximum potential of the system, it must be fully regulated for both service providers and road users. Although with only the commitment of service providers, there is already a positive impact on the traffic flow and in less complex networks it can already solve all congestion.