Research has shown that GLOSA systems can help reduce the emission of $CO_2$ by motor vehicles and improve flow on urban road networks. However, existing GLOSA systems only work in combination with a limited selection of Traffic Signal Controllers (TSCs) and therefore have not been widely implemented. This research describes and evaluates the design of a system to model the behaviour of the most common types of TSCs using data that is shared by road users, also known as Floating Car Data (FCD). Almost every road user is able to share his location using a smartphone, so this means that a system of this kind can be implemented everywhere in the world where people are willing to participate, without requiring any changes to the infrastructure. It is chosen to model TSC behaviour using Hidden Markov Models (HMMs), because they can be generated from unlabelled data, as opposed to other machine learning techniques. More specifically, Explicit Duration Hidden semi-Markov Models (EDHSMMs) are used, so that timing behaviour can be captured more accurately than with standard HMMS. An adaptation to an existing learning algorithm is made to decrease amount of data required for learning and to make sure the algorithm can cope with the sparse nature of FCD. Additionally, a system is developed to combine generally known intersection data and FCD to generate models that have transition parameters with low entropy and thus high predictive powers. In this way, the system produces models that are accurate enough to be used for the generation of GLOSA from relatively sparse data. In a simulated setting, it is shown that the system can identify the true switching behaviour of the most common types of TSCs and that it produces models whose duration probability distributions converge to the true situation when provided with realistic amounts of data.

A new generation of intelligent Traffic Light Controllers uses Model Predictive Control to minimise delay at signalised intersections. The conventional cycle of set sequences of green phases is dropped for optimisation purposes. This research uses the ethical theories utilitarianism, egalitarianism, sufficientarianism & deontology to define equity. An explicit connection of these ethical theories and technical principles of conventional traffic light controller CCOL & new generation traffic light controller Flowtack, has been provided to identify the change in ethical landscape, from predominantly egalitarian towards relatively utilitarian. Performance indicators for equity in traffic control are defined based on earlier research. Multiple setting changes in Flowtack are proposed and tested in simulation experiments with Aimsun. These experiments show that adjusting Flowtack's objective function can improve the equity scores according to egalitarianism & sufficientarianism, at the cost of the equity score of utilitarianism. The best results are validated using various flow compositions on an alternative intersection. As a result, the gain in equity by the proposed settings becomes greater, as the flow composition becomes more uneven. 

At large-scale events, the large inflow of visitors travelling by car often causes queues on the roads in the direct surroundings of the event. These queues can spill back to the main road network, where through traffic is driving as well: this through traffic will be hindered by the congestion, while those travellers don’t have the event venue as their destination. To prevent or at least reduce this unnecessary hindrance caused by car traffic at events, this Master thesis has researched control methodologies to optimise advice that is given to individual travellers using in-car systems. In individual traffic management (ITM), the event visitors will receive advice for their route and departure time choice. ITM can be used to optimise the decisions of the event visitors, to prevent queue spillback and other traffic issues at events. In this thesis, the design trade-offs for an ITM controller are identified: these trade-offs determine the actual design of an ITM controller aimed specifically at car traffic around large-scale events. Conceptually, the controller design is composed of a one-shot prediction model and mathematical optimisations of the individual route and departure time advice. These optimisations target a constrained system optimal traffic state in the network. Simulations of the designed controller show that ITM has potential to alleviate the congestion (and spillback) hindrance at large-scale events, by distributing event visitors over alternative routes and departure times. The benefits found in the simulations advocate the use of ITM at an event (or event-like conditions) in practice. Therefore, the application of the ITM controller design in practice is also discussed in this research. The discussion identifies the need to develop a (software) tool that is able to execute the controller’s actions in an offline or real-time manner. Furthermore, the possible barriers and opportunities for the implementation of ITM in the current and future traffic system are assessed.

To evaluate traffic signal controllers, and vehicle-actuated traffic signal controllers in particular, in terms of how they are performing with respect to the road authority’s policies on traffic flow and accessibility, traffic safety, and environmental factors, several methods are developed in practice. However, in scientific literature, little attention is paid to this type of traffic signal controller evaluations. Indeed, it is found that a functional, and integral evaluation and diagnosis method for traffic signal controllers, based on a multi-variable assessment, is currently lacking. This thesis tries to fill this gap, by developing, and presenting an integral method, which detects inefficiencies in terms of traffic performance functioning, scores the vehicle-actuated traffic signal controller, diagnoses the cause of the detected inefficiency, and propose countermeasures to improve the traffic performance functioning of the vehicle-actuated traffic signal controller, based on a multi-variable assessment. This resulted in a five-step procedure, in which consecutively (1) the multi-variable performance indicators are selected, (2) the computational models are calibrated, (3) inefficiencies are detected, (4) the problems are diagnosed that caused the inefficient performance, and (5) optimises the traffic signal controller by implementing the countermeasures that aim at mitigating the diagnosed problems. The procedure includes a feedback loop to check whether the proposed countermeasures were effective. The testing of the method in a case study, using simulation data, showed that the method is indeed able to successfully detect inefficiencies, and diagnose the corresponding problems. Although the presented method is not perfect yet, its potential is clear. Therefore, it is recommended to develop the method further in the future, and include the use of data from practice as a way to make the method more widely applicable.

The ongoing increase in urbanization and traffic congestion creates an urgent need to operate our transportation systems with maximum efficiency. Traffic signal control optimization is considered one of the main ways to solve traffic problems in urban networks. In publications in the field of intelligent transportation systems, a vast amount of different optimal traffic light control methods is described. With new optimization methods being developed, it is important to know their performance compared to similar methods. Such a comparison is only possible if the same performance evaluation methodology is applied to all these methods. Most of the studies in the field of intelligent transportation system consider a self-defined evaluation methodology. A general evaluation methodology is developed to objectively evaluate the performance of these optimization methods. The developed general evaluation methodology is used to evaluate the performance of a dynamic programming and Q-learning method.

Current day routing systems use predicted travel times to calculate optimal routes. Some routing algorithms even use predicted traffic lights states to optimize these routes and to improve the expected travel time. But all these methods optimize the expected travel time and do not take reliability into account. Reliability is a major service indicator and with non-flexible departure or arrival constraints, the value of reliability can increase up to three times the value of time (Markovich, Concas, & Kolpakov, 2009). To use a measure of reliability, not the expected travel time, but the travel time distribution should be known. Since reliability is not uniquely defined, an algorithm is designed with an individual cost function, that gives users the freedom to use their own definition of reliability. Since knowing the traffic light states at intersections could improve the travel time and reliability, this should be used in the algorithm as well. Therefore the main goal of this research is: Designing an urban routing algorithm, that incorporates predicted traffic light states, where the routing is based on the probability distribution of the travel time and an individual cost function that expresses the tradeoff between travel time and reliability. For this research only urban areas with traffic lights are considered, other intersections in this network are not taken into account. The traffic lights are assumed to be traffic lights with a changing cycle time. If this algorithmworks for these traffic lights it will also work for fixed time traffic lights and most other type of traffic lights, since these have less degrees of freedom.

Automated vehicle control provides advantages in transport efficiency, redundancy, human-safety, flexibility, and parallelism. Extensive research has been dedicated to direct-following lateral control methods, using a single preview point as reference signal. However, these methods give rise to significant tracking errors. As alternative to a single point, a continuous path can be used as reference signal for vehicle control. Using a continuous reference path, accurate and comfortable control actions can be computed, while avoiding reference tracking errors. Therefore, robust reference path generation is an essential part of lateral vehicle control. The goal of this research is to develop a generic, robust reference path generator for lateral vehicle control. Currently in path generation, the state-of-the-art method is based on repetitive polynomial fitting. This method inherently contains two main weaknesses. Firstly, it is not robust to sensor noise and other real-world disturbances. Secondly, as a result of the repetitive fitting, a discontinuous path is generated. This is undesirable, because it leads to an unfeasible reference path, since vehicles can only produce and track continuous trajectories. Moreover, a discontinuous reference path results in the need for path smoothing when used in comfortable lateral vehicle control. Fundamentally, this smoothing leads to inaccurate control, caused by manipulation of the original, discontinuous reference path. To overcome these weaknesses, a new Model-based Path Generation method is presented. The development of a new, generic method for robust path generation is the main contribution of this research. This new path generation method is capable of producing feasible vehicle trajectories based on unfeasible waypoints. The performance of this method is evaluated in simulations and experiments, benchmarking it against polynomial fitting path generation. Furthermore, path generation robustness is assessed based on the outcome of a disturbance sensitivity analysis. The results are in accordance with the hypothesis stating that the new method outperforms the benchmark method in terms of path accuracy, robustness, continuity, and general applicability.

Traffic signal controllers have been around since the late sixties of the 19th century and since then they have played an increasing role in (urban) traffic management. Over the years many different algorithms have been developed and in Dutch practice actuated control is mostly used. Actuated control and other similar approaches apply variable green times to handle demand fluctuation, which leads to a reduction of the delay for vehicles in comparison to e.g. fixed-time control, however, in actuated control it is difficult to accurately predict control decisions. This study proposes a new traffic signal control algorithm that creates a planning of control decisions based on expected vehicle arrivals. By doing so, predictability of control decisions increases, while no sacrifices need to be made regarding the optimality of the control policy. Since certain errors such as inaccurate queue discharge rates are incorporated in the planning of control decisions, a second algorithm is required that adjusts the planning of control decisions to actual traffic conditions. Using an N65 case study intersection the performance of the new traffic signal control algorithm is compared to other algorithms. Depending on the availability of vehicle arrivals and the composition of traffic flows at the intersection, the new algorithm outperforms standard Dutch actuated traffic signal control in terms of overall delay by 3.1% to 4.7%. A range of improvement possibilities is suggested to ensure a greater reduction of the delay.

Traffic congestion is a challenge that frequently emerges due to changes in the roadways such as tunnels and sags, causing capacity reduction. The capacity drop phenomenon exacerbates traffic congestion, due to decreased queue discharge rates. Among the strategies employed for traffic management, Variable Speed Limit (VSL) control is a common approach to alleviate congestion and mitigate capacity drop. The control is expected to be more powerful when integrated with Connected Vehicles (CVs). However, the intricate interplay between the Market Penetration Rate (MPR) of CVs and the parameters governing VSL remains under-explored. This study seeks to quantify the relationship between the MPR of CVs and the key VSL parameters, encompassing factors like the optimal acceleration length and speed limits. The VSL control is applied to CVs at the road section upstream of a tunnel bottleneck modelled by a continuum car-following model with bounded acceleration. The findings of this research suggest a minimum MPR of CVs essential for preventing capacity drop and reaching the maximal outflow. Intriguingly, this challenges the conventional notion that regulating solely the leading vehicle suffices to govern the behaviours of all following vehicles. The value of the minimum MPR threshold is affected by the acceleration behaviours vehicles exhibit. Furthermore, this study underscores the importance of considering the MPR of CVs when devising the optimal speed limit and acceleration length for the VSL. It shows that while a higher speed limit can lead to higher throughput, the optimal acceleration length increases exponentially with the increasing speed limit, particularly in cases with low MPR of CVs. While adopting a relatively lower speed limit, the acceleration length can be reduced to 0m for all levels of MPR of CVs. In summary, it suggests that when implementing VSL in practice, a balance between the resulted throughput, the required acceleration length and the robustness of VSL across different levels of MPR of CVs should be considered.

Urban mobility is challenged with increasing demand, while at the same time reducing emissions, without leading to an unpleasant living environment. Reducing the number of stops on the urban arterial controlled by the coordinated traffic controller can provide (part of) the solution to these challenges for urban mobility. Coordinated traffic controllers are subject to some limitations, especially regarding coordination between locally optimal signal timing plans and proactive optimization of the coordination with regards to traffic demand. These limitations where explored in this thesis, where a variable speed was proposed to be able to provide coordination between locally optimal signal timing plans and where the usage of predictions with regards to proactive optimizations was tested. A theoretical study showed no realistic potential for coordination between unequal cycle times, however theory does show significant potential of the usage of a variable speed for coordination between locally optimal signal timing plans, when coordinating in two directions. This potential of the variable speed was confirmed by using the MAXBAND model and performing a simulation study, which indicated a significant decrease in stops on the main arterial over optimizing with a fixed speed. Furthermore, the variable speed allowed for a lower network cycle time, which resulted in a decrease in delay on the side directions. Tests of demand predictions in TopTrac yielded no significant improvements of stops nor delay. In the investigated network, control decisions of the coordinated traffic controller did not correlate closely with fluctuating demand, which is needed for a prediction of the demand to produce significant improvements regarding the stops and delay in the network. Future research should focus on the variable speed, evaluating the theoretical applications in other networks and exploring the practical applications, potentially via Intelligent Speed Adaptation (ISA).

Ever increasing traffic load on freeways calls for smart solutions to improve throughput. With the uprise and standardization of C-ITS, accuracy and latency of traffic state measurements are immensely improved, while the span of control is increased. In this paper, a traffic control algorithm that aims to prevent breakdown at recurring bottlenecks on freeways is presented. By using C-ITS to control connected vehicles and manipulate the traffic state upstream of bottlenecks, a breakdown and capacity drop can be prevented. To evaluate the developed control concept, a microscopic simulation study was performed. The controller was evaluated on both reductions in penetration rate and compliance to control signals. The results are promising, but further development is still required before real-world application is possible.

Permitted conflicts at (vehicle-actuated) signalised intersections are widely researched, though with the main focus on the traffic safety implications, because with permitted conflicts, two or more conflicting streams at a signalised intersection receive green at the same time, and thus meet at the conflict zone, as opposed to protected conflicts. The literature pays little attention to the traffic flow efficiency effects. Although some literature introduce some of these traffic flow efficiency effects, no detailed research has been done. This Additional Graduation Work contributes to this knowledge gap by introducing the potential traffic flow efficiency effects of permitted conflicts. In a simulation study, in which the gap acceptance behaviour of the simulation model Vissim is briefly tested, it is found that permitted conflicts (i) reduce the cycle time, (ii) reduce the queue length, and (iii) reduce the delay experienced by various road users at the signalised intersection. However, this comes at the cost of reduced saturation flow. Also, it is hypothesised that there is some turning point in terms of traffic flow volumes, and the distribution of volumes per approach at which the traffic flow efficiency effects become negative. This relates to the various traffic safety conditions that must be met at the signalised intersection with permitted conflicts as well, to ensure a safe conflict handling, which include that the traffic flow volumes may not be too large. Nonetheless, the traffic flow efficiency effects, in terms of intersection throughput and capacity, queues, and travel times and delays, of implementing permitted conflicts, as opposed to implementing protected conflicts on signalised intersections, are positive, in that sense that it resulted in shorter cycle times, less delay, and shorter queues on average. For signal groups with priority, the number of stops also decreased. Although there is a risk of oversaturation, the overall conclusion is that the traffic flow inefficiency effects are positive on average. In short, this comes down to that fewer protected conflicts, equals a higher traffic safety risk, but also a more efficient traffic flow, on average, hence the title of this research.