When a traffic crash occurs, following vehicles need to change lanes to bypass the obstruction. We define these maneuvers as post-crash lane changes (LCs). In such scenarios, vehicles in the target lane may refuse to yield even after the lane change has already begun, increasing the complexity and crash risk of post-crash LCs. However, the behavioral characteristics and motion patterns of post-crash LCs remain unknown. To address this gap, we construct a post-crash LC dataset by extracting vehicle trajectories from drone videos captured after crashes. Our empirical analysis reveals that, compared to mandatory LCs (MLCs) and discretionary LCs (DLCs), post-crash LCs exhibit longer durations, lower insertion speeds, and higher crash risks. Notably, 79.4% of post-crash LCs involve at least one instance of non-yielding behavior from the new follower, compared to 21.7% for DLCs and 28.6% for MLCs. Building on these findings, we develop a novel trajectory prediction framework for post-crash LCs. At its core is a graph-based attention module that explicitly models yielding behavior as an auxiliary interaction-aware task. This module is designed to guide both a conditional variational autoencoder and a Transformer-based decoder to predict the lane changer's trajectory. By incorporating the interaction-aware module, our model outperforms existing baselines in trajectory prediction performance by more than 10% in both average displacement error and final displacement error across different prediction horizons. Moreover, our model provides more reliable crash risk analysis by reducing false crash rates and improving conflict prediction accuracy. Finally, we validate the model's transferability using additional post-crash LC datasets collected from different sites.

Unintentional speed reductions in bottleneck sections significantly contribute to traffic congestion on freeways. To address this issue, the Moving Light Guidance System (MLGS) has been implemented as a traffic management measure designed to counteract speed reductions and facilitate recovery by adjusting its lighting speed to slightly exceed observed vehicle speeds. This paper investigates the MLGS’s impact on lane-changing behavior. Our findings show that, the number of lane changes higher with MLGS than without MLGS. Furthermore, these results suggest that MLGS contributes to inducing lane changes by improving vehicle speed and its homogenization, as well as enhancing the homogenization of headway distances. Additionally, we explore the relationship between traffic states and lane-changing phenomena. The results suggest that MLGS may facilitate lane changes as drivers seek to maintain their desired speed. Furthermore, we analysed the average headway distance between the new leader and new follower during a lane change. It shows that the mean headway distance is smaller, suggesting that MLGS helps create lane-changeable gaps. In summary, the MLGS appears to improve traffic conditions in the passing lane. Under MLGS there are more lane changes likely to be caused by the availability of gaps based on headway distance and the desire to maintain desired speed. This paper shows the mechanisms of MLGS operations and shows that MLGS hence may help reduce traffic disturbances in the other lane, where merging vehicles frequently enter.

This researchextracts trajectories of non-directional micromobility traffic (pedestrians,cyclists, and mopeds) in a shared right-of-way urban space, applying aVoronoi-based area-weighted framework to construct Network Fundamental Diagrams(NFDs). Using an aggregation technique that weights by link length, we exploretwo Voronoi-based approaches for generating NFDs from microscopic data: astandard mixed-mode approach and a novel mode-isolated approach. Resultsdemonstrate that both methods accurately compute macroscopic traffic measures,and mode-isolated approach, in particular, reveals unique contribution of eachmode to the NFD. Cyclists and mopeds drive performance dominance, shaping theNFD even when pedestrians dominate traffic composition (mode-share dominance).This study empirically validates existence of mixed two-dimensional traffic NFDincluding critical capacities and jam densities and highlights the aggregateimpact of individual modes. The findings underscore the potential ofarea-weighted aggregation to account for heterogeneity in urban mixed traffic,offering insights into capacity and efficiency evaluations for non-motorizedtransport systems.

The field of transportation research addresses the complexities of a particular sociotechnical system. Its usual focus is on human transportation systems, but non-human systems that effect transportation are also abundant in nature. This paper draws an analogy between modern human transportation systems and mycorrhizal networks (MN), the underground networks formed by fungi and plants for resource transportation. By examining MN, the study aims to extract insights applicable to human transport and to explore potential reciprocal learnings about natural systems. The research emphasizes an interdisciplinary approach that acknowledges both the technical and social dimensions of transport. The primary focus is to propose improvements to human transportation by learning from the natural efficiency of MN, thereby fostering a more holistic understanding and implementation of transport solutions.

Multi-modal transport is getting more popular due to the emergence of new traffic
modes. The increase of modes also adds complexity for the transport researchers.
This paper proposes an augmented link-based super-network approach for modeling
multi-modal transport networks, addressing the scalability and versatility issues of
conventional methods. This approach is used to calculate the user equilibrium for
urban transport networks traffic assignment with multiple traffic modes, a difficult
problem due to the intractable enumeration of feasible paths between origindestination
pairs and restricted transfers between different traffic modes. In the supernetwork
representation of multi-modal transport networks, the travel cost of any
feasible route between the origin and destination is formulated as the sum of cost
functions of the augmented links, thus avoiding the enumeration of feasible paths.
Additionally, restrictions on traffic mode transfers can be embedded in the link-based
model by excluding infeasible transfer links or adding penalties for undesired transfers.
The user equilibrium of the augmented link-based super-network model is formulated
as a variational inequality problem, solved using the extra-gradient algorithm. A multimodal
transport network is considered in the case study. Simulation results validate the
effectiveness of the proposed model, demonstrating its scalability and versatility in
addressing complex multi-modal transport networks with diverse traffic modes.
We anticipate that our method can serve as an efficient modeling approach for more
general and complex multi-modal transport networks, facilitating traffic management
and network design.

In traffic flow theory, there is a relationship between the number of vehicles in a certain area and their average speed, the so-called macroscopic fundamental diagram (MFD). Related control concepts involve limiting the inflow into an area, so-called perimeter control. This may increase both the flow inside the controlled area and the outflow rate, reducing overall delays. This control concept is taught in classes on traffic flow theory around the globe. This paper presents an experiment that can be run in class to create the MFD with a group of students. The basic idea is that we have a confined area where participating students have to walk a predefined route. We repeat this experiment several times (runs of 2–4 minutes each), each time with a different, pre-defined number of students being simultaneously in the area (the accumulation). The number of students is controlled by metering the inflow of participants. Exit flow rates are determined for each run by dividing the number of exiting participants by the run time. One run will hence yield one observed combination of number of participants and run time. From this, an accumulation and exit flow rate can be computed. The Macroscopic Fundamental Diagram (MFD) is then created by plotting the exit flow rates and accumulation of all runs. Students can this way derive this MFD from data. Moreover, they also experience during the walking how delays change with accumulation, and thereby the experiment intuitively teaches them the concepts of the MFD and perimeter control. The paper describes the experiment, and provides tools (software, routes) for repeating the experiment with other groups.

The deep understanding of the behaviors of traffic participants is essential to guarantee the safety of automated vehicles (AV) in mixed traffic with vulnerable road users (VRUs). Precise trajectory prediction of traffic participants can provide reasonable solution space for motion planning of AV. Early works mainly focused on handcrafting the feature representation and designing complicated architectures in deep learning-based prediction models. However, these approaches overlooked the fact that different road users perceive the safety of the same interaction differently and also exhibit heterogeneous risk-taking styles. In this paper, we will develop a model for trajectory prediction based on risk-taking styles. The model accounts for the expected positions and occupancy of traffic participants in the surrounding environment. It consists of two sequential steps: risk-taking styles of multi-modal road users under interactive scenes are first clustered, and then reformulated in the heterogeneous graph model for trajectory prediction. The model is validated by the driving data collected on the urban road using a public dataset. Comparative experiments demonstrate that the proposed method can predict the trajectory of traffic participants much more accurately than the state-of-the-art methods.

A lane-changing (LC) maneuver may cause the follower in the target lane (new follower) to decelerate and give up space, potentially affecting crash risk and traffic flow efficiency. In congested flow, a more aggressive LC maneuver occurs where the lane changer is partially next to the new follower and creates negative gaps, namely negative gap forced LC (NGFLC). Although NGFLC forms the foundation of sideswipe crashes, little has been done to address its impacts and the contributing factors. To tackle this issue, a total of 15,810 LC trajectory samples are extracted from three drone videos at different locations. These samples are categorized into NGFLC and normal LC groups for comparative analysis. Five commonly used conflict indicators are extended into two-dimensional to evaluate the crash risk of LC maneuver. The change of time gaps during LC maneuver are examined to quantify the impact of LC on traffic flow efficiency. We find that NGFLCs significantly increase crash risk, reflected by the number of hazardous LC events and potential crash areas compared to normal LC. Additionally, results reveal that both the lane changer and the new follower tend to maintain a larger time gap after NGFLCs. Factors including time headway, relative speed, and historical gaps in the target lane significantly affect NGFLC incidence. Once the movement of the leader in the original lane is taken into account, the prediction accuracy improves from 81% to 91%. The transferability tests indicate that the findings about the negative impact of NGFLC and the accuracy of its prediction model are consistent across different locations. These findings hold implications for driving assistance systems to better predict and mitigate NGFLCs.

The increasing urbanization, combined with shrinking space for transport infrastructure and private parking, significantly challenges urban accessibility. Moreover, the rising number of vehicles exacerbates congestion in city centers, leading to longer commute times, increased noise levels, and greater air pollution. These issues underscore the urgent need for creating low-car urban zones. One promising approach is an integrated traffic management system that considers various modes of transportation—such as cycling, walking, shared mobility, and public transport. However, multi-modal traffic management typically involves diverse stakeholders with potentially conflicting interests, which necessitates a balance of these interests through multi-objective optimization. Traditional approaches often employ a weighted sum method to transform multiple objectives into a single objective. This method significantly constrains the solution space and complicates the assignment of appropriate weights to different objectives. Therefore, generating a Pareto front for multi-modal traffic management could provide decision-makers with a set of efficient solutions, enabling them to select the most suitable option. The ε-constraint method is recognized for its ability to generate a Pareto front. The question we discuss here is whether this method can be effectively applied to managing multi-objective, multi-modal traffic networks. In this study, we answer this question by proposing an augmented ε-constraint-based optimization framework for multi-objective multi-modal traffic management. This framework is bi-level and can accommodate various traffic models and objectives that reflect the diverse interests of multiple stakeholders. Thus the multi-modal traffic management problem can be formulated as a multi-objective nonlinear optimization problem. The augmented ε-constraint method (Mavrotas, 2009) is employed to efficiently address the multiple objectives, and the multi-start sequential quadratic programming method is used to solve the nonlinear optimization problems, such that the Pareto front is obtained. We validate the effectiveness of our framework through a case study, whose preliminary results show that our method improves the traffic performance and provides insights into the trade-off among different objectives.

A substantial number of vehicles nowadays are equipped with adaptive cruise control (ACC), which adjusts the vehicle speed automatically. However, experiments have found that commercial ACC systems which only detect the direct leader amplify the propagating disturbances in the platoon. This can cause severe traffic congestion when the number of ACC-equipped vehicles increases. Therefore, an ACC system which also considers the second leader further downstream is required. Such a system enables the vehicle to achieve multi-anticipation and hence ensure better platoon stability. Nevertheless, measurements collected from the second leader may be comparatively inaccurate given the limitations of current state-of-the-art sensor technology. This study adopts deep reinforcement learning to develop ACC controllers that besides the input from the first leader exploits the additional information obtained from the second leader, albeit noisy. The simulation experiment demonstrates that even under the influence of noisy measurements, the multi-leader ACC platoon shows smaller disturbance and jerk amplitudes than the one-leader ACC platoon, indicating improved string stability and ride comfort. Practical takeaways are twofold: first, the proposed method can be used to further develop multi-leader ACC systems. Second, even noisy data from the second leader can help stabilize traffic, which makes such systems viable in practice.

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty caused by partial observability of all factors that may affect a driver's near-future decisions, the so-called aleatoric uncertainty, and the uncertainty of deploying a model in new scenarios that are possibly not present in the training set, the so-called epistemic uncertainty. They reflect the trade-off between data collection and model improvement In this paper, we propose a new framework to systematically quantify both sources of uncertainty. Specifically, to approximate the spatial distribution of an agent's future position, we propose a 2D histogram-based deep learning model combined with deep ensemble techniques for measuring aleatoric and epistemic uncertainty by entropy-based quantities. The proposed Uncertainty Quantification Network (UQnet) employs a causal part to enhance its generalizability so rare driving behaviours can be effectively identified. Experiments on the INTERACTION dataset show that UQnet is able to give more robust predictions in generalizability tests compared to the correlation-based models. Further analysis presents that high aleatoric uncertainty cases are mainly caused by heterogeneous driving behaviours and unknown intended directions. Based on this aleatoric uncertainty component, we estimate the lower bounds of mean-square-error and final-displacement-error as indicators for the predictability of trajectories. Furthermore, the analysis of epistemic uncertainty illustrates that domain knowledge of speed-dependent driving behaviour is essential for adapting a model from low-speed to high-speed situations. Our paper contributes to motion forecasting with a new framework, that recasts the problem of accuracy improvement in a way that focuses on differentiating between unpredictable components and rare cases for which more and different data should be collected.

For car traffic it was found that a more crowded area leads to a lower speed and a lower arrival rate. The relation between crowdedness and speed (or arrival rate) can be expressed in a network fundamental diagram, or macroscopic fundamental diagram (MFD). Similar concepts have been shown for pedestrian and train traffic. In this paper, we extend the concept to three spatial dimensions. While simulations have explored some concepts, we present for the first time empirical results of the relation between the crowdedness in the air and the performance of the “network.” We base our results on several months of data of airplanes around Amsterdam Schiphol Airport. Similar to car traffic, we observe a reduction in speeds as the number of airplanes in the area increases. However, even at the highest observed densities, we do not see a reduction in flows. This is because of active and intensive management (based on departure/landing possibilities), comparable to perimeter control in traffic, as well as a minimum airplane speed. This paper introduces an interesting concept of applying a MFD to three-dimensional (3D) spaces. We also show to what extent the performance reduction is caused by speed reduction and to what extent it is caused by less efficient routes. The MFD concept can eventually be used to also manage 3D airspaces for applications with less strict microscopic air traffic management than the current management around airports.

Resolving predicted conflicts is vital for safe and efficient autonomous vehicles (AV). In practice, vehicular motion prediction faces inherent uncertainty due to heterogeneous driving behaviours and environments. This spatial uncertainty increases non-linearly with prediction time horizons, leading AVs to perceive more road space occupied by conflicting vehicles. Reacting early to resolve predicted conflicts can ensure safety but may adversely affect traffic efficiency. Therefore, determining how far ahead AVs should start resolving predicted conflicts based on safety and traffic efficiency constraints is crucial. To answer this question, this study proposes a novel approach to explore the trade-off between safety and traffic efficiency considering prediction uncertainty. Firstly, a continuous-time motion prediction framework is proposed for estimating the spatial probability distribution of a vehicle's future position at any moment within the maximum time horizon. Subsequently, average driver space and the corresponding traffic flow are derived from the safety settings of AV and prediction uncertainty. As such, the safety-efficiency trade-off can be quantified. Experiments show that mandatory decision points, high speeds, and traffic state transitions usually cause fast-increasing prediction uncertainty. A case study of Intelligent Driver Models (IDM) shows that traffic efficiency drops rapidly when AVs resolve predicted conflicts longer than 1.5 seconds ahead. AVs can act earlier on motorways for efficiency concerns but must be myopic at urban intersections. Prediction uncertainty fundamentally constrains the safety-efficiency performance of AVs. These findings are instructive for designing traffic-compatible AVs.

Traffic condition forecasting is fundamental for Intelligent Transportation Systems. Besides accuracy, many services require an estimate of uncertainty for each prediction. Uncertainty quantification must consider the inherent randomness in traffic dynamics, the so-called aleatoric uncertainty, and the additional distrust caused by data shortage, the so-called epistemic uncertainty. They together depict how predictable macroscopic traffic is. This study uses deep ensembles of graph neural networks to estimate both types of uncertainty in network-level speed forecasting. Experimental results given by the used model reveal that, although rare congestion patterns arise randomly, the short-term predictability of traffic states is mainly restricted by the irreducible stochasticity in traffic dynamics. The predicted future state bifurcates into congested or free-flowing cases. This study suggests that the potential for improving prediction models through expanding speed and flow data is limited while diversifying data types is crucial.

Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets collected by Autonomous Vehicles (AVs). However, trajectories collected by AVs are noisy and not readily applicable for studying CF behaviour. This paper extracts and enhances two categories of CF data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset. First, CF pairs are selected based on specific rules. Next, the quality of raw data is assessed by anomaly analysis. Then, the raw CF data is corrected and enhanced via motion planning, Kalman filtering, and wavelet denoising. As a result, 29k+ H-A and 42k+ H-H car-following segments are obtained, with a total driving distance of 150k+ km. A diversity assessment shows that the processed data cover complete CF regimes for calibrating CF models. This open and ready-to-use dataset provides the opportunity to investigate the CF behaviours of following AVs vs. HVs from real-world data. It can further facilitate studies on exploring the impact of AVs on mixed urban traffic.

When do traffic jams emerge? Can we predict, given certain demand levels, when queuing will occur, how long the queues will be, how they will propagate in space and time, and how long it takes for the congestion to resolve? Why does an overloaded traffic network underperform? This chapter gives a basic introduction in traffic flow theory which can help to answer these kinds of questions.

The traffic density of small aerial vehicles operating within urban environments is expected to increase significantly in the near future. This urban environment is highly constrained due to being limited to the low-altitude airspace directly above the existing road network. Multiple studies have addressed factors influencing the capacity of urban airspace. These have used simulations of aircraft, yet the empirical nature of these simulations limits their use beyond the specific conditions that have been tested. Analytical models would not have this limitation, but they are only developed for general airspace, while the emergent patterns in constrained urban airspace are different than in general, unconstrained conditions. For instance, queuing and local congestion are patterns that are unique to the heavily-constrained environment. Therefore, in this paper, we derive an analytical model for air traffic in a confined airspace to find the influencing factors for its capacity. By means of a simulation of aerial vehicles, we verify the analytical model and show a relationship between the mean flow rate and mean density in a two-dimensional orthogonal grid network airspace. Results show that the entire airspace can become unstable when the maximum capacity of just one intersection is reached. Furthermore, the maximum airspace density is found to be unaffected by cruise speed. The results demonstrate how the derived analytical model provides an effective tool to predict the effect of several design parameters on the capacity of constrained urban airspace. Moreover, this model can form the basis for further extensions, including the altitude dimension and non-orthogonal or non-four-way intersections.

The macroscopic fundamental diagram (MFD) is developed to describe traffic operations aggregated over an area. The MFD is defined by network traffic states as a relationship between the accumulation of vehicles and flow or speed of vehicles. The concept of the MFD has been applied to model traffic dynamics and to design control strategies. For various applications (e.g., routing and departure time choices), the MFD is often assumed to be of a particular shape, consisting of two branches—a free-flow branch and a congestion branch. However, empirical observations show some inconsistencies between the theoretical and the empirical MFD. First, the empirical MFD only presents free-flow branch, i.e., the congestion branch is missing. Second, the MFD presents as hysteresis loop(s). This paper explores these differences, providing insights into urban network traffic dynamics. This work takes the travelers’ departure time choices and user equilibrium (UE) as starting points. We consider demand to be in UE in terms of departure time choices. Using this property of the demand profile, the paper proposes a closed-form expression for average density and outflow. Finally, we show some insights in the urban traffic dynamics: (i) an explanation of the hysteresis phenomenon solely from the perspective of departure time choices and UE and (ii) an explanation of why we hardly observe the MFD congestion branch in real life even in heavily congested networks. Our study shows that, for management purposes, the missing of congestion branch is a result of UE, rather than an indicator of congestion severity.

Lane-changing (LC) in congested traffic has been identified as a trigger for the sudden deceleration behavior of the new follower in the target lane, leading to severe traffic disturbances. Thus, investigating the response of the new follower to an LC maneuver is an important research topic in the literature. To date, numerous efforts have been devoted to understanding the impact of the lane changer on the new follower after the insertion, while less attention has been given to this influence during the pre-insertion stage (anticipation). Therefore, this paper aims to establish a new car-following (CF) model to capture the new follower's driving behavior during anticipation. Specifically, we introduce an attention mechanism deviating from Newell's CF rules to quantify the impact of anticipation. Then, we apply a neural network with an attention layer to estimate the attention mechanism and incorporate it into the Newell CF model, which yields a new CF model, denoted as CF_Attention. Using real-world trajectory data, we design three experiments and select three representative CF models to validate the CF_Attention. The results indicate that the CF_Attention outperforms the other models in predicting the new follower's trajectory, which is not affected by the heterogeneous behavior of the new follower and the anticipation duration. Additionally, the CF_Attention is proven effective in capturing the speed-space relationship and the formation of oscillation. Finally, our transferability test suggests that the CF_Attention is promising for different locations and times without requiring retraining. The results of this study could advance the integration of the LC impact and CF behavior, and could be implemented into commercial traffic simulation programs to describe vehicle movements in traffic flow more accurately.

Traffic dynamics on freeways are stochastic in nature because of errors in perception and operation of drivers as well as the heterogeneity between and within drivers. This stochasticity is often represented in car-following models by a stochastic term, which is assumed to follow a normal distribution for the convenience of mathematical processing. However, the validity of this assumption has not been studied yet. In this study, we focused on the shape of the distribution of a stochastic term in the car-following model that predicts an acceleration after a time step. Based on vehicle trajectory data on a freeway in Japan, a car-following model is first developed by using data-driven methodology in which long short-term memory (LSTM) network is applied. In this LSTM network, the acceleration value is discretized and the model parameters are trained with the focal loss function. The relationship between the predicted distributions’ modality, standard deviation (SD), and (Formula presented.) with respect to traffic states is then examined. The findings demonstrate that: 1) the developed model can accurately predict the accelerations; 2) a probabilistic distribution tends to have a large SD and multimodality around a merging point and at the beginning of and along stop-and-go waves; and 3) driving behavior can be classed in one of four clusters based on the variation of the percentile value that a driver takes within the probability distribution. The proposed model and the insights are helpful for improving microscopic simulation models when considering new traffic management measures.