As automated vehicles (AVs) become more common, it is important to understand how human-driven vehicles (HDVs) would interact with them. This research investigated HDV gap acceptance behavior in mixed traffic with AVs at a priority intersection, focusing on how mixed traffic factors affect this behavior and overall traffic efficiency. Using a driving simulator, four scenarios were tested by varying AV driving style (less defensive, more defensive, and HDV-like) and AV recognizability (distinguishable or not from HDVs). Gap acceptance models were estimated based on the collected trajectory data. These models were then integrated into the SUMO microscopic traffic simulation platform, where a T-intersection network was set up. Simulation runs varied based on AV driving style, recognizability, penetration rate (0-75% in 25% increments), and whether HDV behavioral adaptation was considered. The results indicated increased minor road vehicle delays with higher AV penetration rates. Recognizable less defensive AVs, and more defensive AVs with high penetration rates caused the largest delays for minor road vehicles compared to other conditions. Ignoring behavioral adaptation led to a delay underestimation of up to 75% for minor road vehicles. In conclusion, there is behavioral adaptation in gap acceptance of HDVs in mixed traffic environments. Taking into account the behavioral adaptation is essential for accurately assessing traffic efficiency in mixed traffic conditions, and guiding AV deployment policies.

Driving heterogeneity significantly influences traffic performance, contributing to traffic disturbances, increased crash risks, and inefficient fuel use and emissions. With the growing availability of driving behaviour data, Machine Learning (ML) techniques have become widely used for analysing driving behaviour and identifying heterogeneity. This paper presents a systematic review of current ML-based methods for driving heterogeneity identification. The review organises key concepts and categorisations of driving heterogeneity, highlights strengths and drawbacks of various methods, and outlines applications of identification analysis. Based on the literature review, we propose a structured framework that guides the ML-based identification process. The framework starts with an extensive data collection and rigorous pre-processing process, followed by feature selection techniques that identify features most indicative of driving behaviours. Sophisticated models including supervised, unsupervised, semi-supervised, and reinforcement learning techniques are discussed with multi-perspective performance evaluation. This paper provides a comprehensive reference for researchers and practitioners to understand driving heterogeneity, supporting the development of data-driven solutions for improving traffic management and road safety.

To escape a dangerous building emergency occupants may need to respond quickly, assess the environment, plan their actions and tackle possible problems during evacuation. In this study 147 participants were tested in an experimental evacuation design for the effects of three environmental factors (fire alarm, lighting and emergency exit signs illumination) on problem-solving abilities. The experimental evacuation scenarios consisted of: (1) fire alarm, normal lighting conditions and illuminated emergency exit signs, (2) fire alarm, dark environment and illuminated emergency exit signs and (3) fire alarm, dark environment and not illuminated emergency exit signs. The tested problem-solving abilities were the time to plan actions and number of excess moves on the Tower of London test. The main results indicate that the third experimental evacuation scenario led to a decrease of 25.9% in planning time, compared to the control scenario. Age also had a significant effect on planning time. The oldest participants took or needed on average 42 s more planning time than the youngest participants, an increase of 146.9%. Furthermore, the second and third experimental evacuation scenario led to significant more excess moves, compared to the control scenario. However, the older the participants the less excess moves they had. For gender no significant effects on problem-solving abilities were found. In addition, the relationships between problem-solving abilities and building evacuation time were investigated. Longer planning times were associated with longer evacuation times and more excess moves were associated with shorter evacuation times. Practical implications for building and safety managers are to add training in darkness or assume more evacuation time in darkness or for older aged populations in evacuation plans and drills. Future research should collect more quantitative data about effects of various environmental factors and personal characteristics, such as problem-solving styles, age and gender, on building evacuation behaviour.

Walking is the most fundamental form of human transportation. It is also the most sustainable mode, and very frequently used in a variety of situations. As such, pedestrian behavior influences the design of our infrastructure, the efficiency of transit systems, and even the safety of large crowds. Despite its ubiquity and importance, the study of pedestrian and crowd dynamics remains an evolving field within transport science. This book aims to give readers a comprehensive overview of pedestrian dynamics, covering fundamental theories, modeling approaches, empirical findings, and applications. All in relation to pedestrian planning. This book is divided into two parts. The first part lays the foundations of pedestrian movement dynamics and behavior, while the second part explores applications of the foundational knowledge presented in the first part. By integrating pedestrian flow analysis, human behavior insights, modeling techniques, and applications, this book contributes to a deeper understanding of how to create walkable, resilient, and human-centered urban environments.

Accurate short-term predictions of active mode traffic are crucial for effective urban traffic control and management, helping to reduce delays, stops, and improve travel time reliability, and optimize travel route choice. While most methods focus on motorized traffic, active modes like walking and cycling have been overlooked due to their complex dynamics and sensitivity to external factors like weather and individual choices, making them inherently less predictable. To address this, we propose a Dynamic Attention-based Spatial-Temporal Graph Convolutional Network (DyASTGCN) model that incorporates the impact of weather on graph spatial correlations within the active mode traffic network. Additionally, we introduce a fusion approach to integrate various heterogeneous spatial correlations, aiming to represent the optimal spatial correlations within the active mode network. Experimental results demonstrate that weather changes have a lagging effect on traffic network spatial correlations. Specifically, active mode traffic demonstrates significant sensitivity to precipitation, with notable changes in spatial correlations occurring within 5 minutes. Conversely, it takes approximately 20 minutes for spatial correlations to respond to wind speed influences. By incorporating both precipitation and wind speed with a 20-minute lag, our model outperforms those using only one feature, achieving the best traffic prediction performance. Given the uncertain traffic state and highly sparse nature of active mode data, our fusion approach adeptly captures the essential spatial correlations required for accurate traffic flow prediction. This allows our model to better understand complex graph correlations and traffic patterns, improving prediction accuracy and offering valuable insights into active mode network dynamics.

Mobility is vital for societal wellbeing, economic growth, social inclusion, and access to essential amenities. However, the current system faces significant challenges, including environmental impact, unequal access, and safety concerns. […]

In public spaces such as city centers, train stations, airports, shopping malls, and multi-modal hubs, accurately predicting pedestrian flow is crucial for effective crowd management e.g. congestion prevention and evacuation planning. Traditional microscopic simulation models offer fine-grained insights by simulating each pedestrian individually, but they are computationally intensive and typically used at the planning and design stage, making them unsuitable for real-time interventions in high-demand scenarios. Macroscopic models, on the other hand, reduce computational cost by aggregating pedestrian behavior and solving partial differential equations, but they typically require estimates of traffic states such as density and speed — quantities that are difficult to measure accurately in practice. Additionally, as the complexity of these physics-based models increases, their computational feasibility for real-time use becomes even more limited. Data-driven (machine learning) models provide a computationally efficient alternative, enhancing real-time prediction capabilities. However, they often require large historical datasets to generalize well, and their performance can degrade under out-of-distribution (OOD) conditions. Moreover, most black-box learning models lack interpretability and domain-specific insights, limiting their practical adoption. In this paper, we propose a novel pedestrian flow prediction model based on the theory of crowd diffusion. Our method estimates flow rates directly from sensor-observed data and infers both Origin–Destination (OD) demand and route choice probabilities to support real-time operations. To address the OOD challenge, we incorporate an online learning mechanism that continuously calibrates model parameters based on incoming observations.

Identifying driving heterogeneity plays an important role in improving traffic safety and efficiency. This paper proposes a novel framework to identify driving heterogeneity from the underlying characteristics of driving behaviour. The framework includes three processes: Action phase extraction, Action pattern calibration, and Action pattern classification. The concepts of Action phase and Action patterns are proposed to decipher and interpret driving behaviours. Action phases are extracted by rule-based segmentation methods and Action patterns are calibrated based on an unsupervised learning approach. The extraction and calibration processes provide a rigorous labelling approach for the attention-based LSTM Action pattern classification process. Evaluation of the framework on a large-scale naturalistic driving dataset reveals six distinct Action patterns. The implementation of the attention mechanism to LSTM models significantly enhanced both the accuracy and time efficiency of Action pattern identification. The proposed framework offers benefits in detecting and reducing variability in driving behaviour through ITS applications such as user-based traffic management, personalised Advanced Driver Assistance Systems (ADAS), and advanced autonomous vehicles (AV) design, thereby enhancing road safety and traffic efficiency.

Crowd management plays a vital role in urban planning and emergency response. Accurate crowd prediction is important for venue operators to respond effectively to adverse crowd dynamics during large gatherings. Although many studies have tried to predict crowd densities or movement dynamics with data-driven predictive models, their validation is often limited to data within the same scenario. As a result, the predictability of the data-driven model in unseen scenarios, such as evacuation scenarios, remains unknown due to the challenges of collecting out-of-distribution data regarding emergency conditions. To address this problem, we present an evaluation pipeline to evaluate different kinds of data-driven models. A method is proposed to generate realistic scenarios by simulation and collect synthetic data from these scenarios to acquire a comprehensive dataset. With these synthetic data, we evaluated different predictive models, from traditional machine learning methods to deep learning time-series prediction models, to explore their generalizability. Furthermore, we propose a weighted average metric, which is better suited to determine the performance of forecasting algorithms under adverse conditions. Through extensive experimentation, we showcase the heterogeneity and diversity of the simulation dataset. The evaluation results also revealed that all the data-driven models performed poorly in unseen scenarios, highlighting the urgent need to develop a robust and generalizable model for predicting crowd flow in indoor spaces.

Traffic flow variables are essential for understanding, analysing, and optimising how pedestrians move in urban areas. In this chapter, we introduce the variables that can be used to describe a pedestrian flow. These variables can be microscopic (individual pedestrians), macroscopic (aggregate level), or mesoscopic (distributions of microscopic quantities). We start with the most detailed level of description, a trajectory. The trajectory describes the dynamics of an individual pedestrian as a function of time by its (two-dimensional) position in space. Based on the trajectory, we derive the most relevant microscopic variables, velocity and acceleration. Then, we give different definitions of density, one of the key macroscopic flow variables, by taking a snapshot of a pedestrian traffic situation at a time instant. Density is used to express crowdedness and level-of-service. Next, we look at the space-mean velocity of a pedestrian flow, followed by the third key macroscopic variable, the flow rate. The flow rate is the number of pedestrians passing a cross-section during a certain time period. We end this chapter with the generalised definitions of flow, density, and velocity for a time-space region, and show how they are related.

Although numerous studies used Virtual Reality (VR) to study pedestrian behavior, there is an ongoing debate about the validity of using VR for studying pedestrian behavior. This study aims to contribute toward the validation of immersive VR systems for pedestrian wayfinding behavior studies by conducting a direct comparison of a field experiment and a matched virtual experiment. Both experiments feature three identical wayfinding assignments across multiple floors in a building. To evaluate the ecological validity of VR, three metrics of three different levels of wayfinding behavior are adopted, namely travel time (level: wayfinding performance), wayfinding strategy (level: decision-making), and angular speed of the head (level: observational behavior). Our findings show that VR can be used to study pedestrian wayfinding strategy in buildings with a single floor. However, there are significant differences in pedestrian wayfinding strategy between the field experiment and the VR experiment. Additionally, we found significant differences in the angular speed of the head between the two experiments. It suggests that researchers should take caution when using VR as a research tool to study the wayfinding strategy and the observational behavior of pedestrians in multi-story buildings.

To promote urban sustainability, many cities are adopting bicycle-friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map-matching. Due to GPS device malfunction, road network ambiguity for cyclists, and inaccuracies in publicly accessible streetmaps, existing map-matching methods face challenges in accurately selecting the best-mapped route. In urban settings, these challenges are exacerbated by high buildings, which tend to attenuate GPS accuracy, and by the increased complexity of the road network. To resolve this issue, this work introduces a map-matching method tailored for cycling travel data in urban areas. The approach introduces two main innovations: a reliable classification of road availability for cyclists, with a particular focus on the main road network, and an extended multi-objective map-matching scoring system. This system integrates penalty, geometric, topology, and temporal scores to optimize the selection of mapped road segments, collectively forming a complete route. Rotterdam, the second-largest city in the Netherlands, is selected as the case study city, and real-world data is used for method implementation and evaluation. Hundred trajectories were manually labelled to assess the model performance and its sensitivity to parameter settings, GPS sampling interval, and travel time. The method is able to unveil variations in cyclist travel behavior, providing municipalities with insights to optimize cycling infrastructure and improve traffic management, such as by identifying high-traffic areas for targeted infrastructure upgrades and optimizing traffic light settings based on cyclist waiting times.

Dedicated Lanes (DLs) have been proposed as a potential alternative for the deployment of Connected and Automated Vehicles (CAVs) to facilitate platooning and increase motorway capacity. However, the impact of the presence and utilization policy of such a lane on drivers’ preference to use automation and their behaviour has not yet been thoroughly investigated. In this study, a driving simulator experiment is conducted, where participants drive a CAV in the presence of a DL with different utilization policies. Drivers have the possibility to choose between driving in an automated mode or in a manual mode. In automated mode they could adjust the driving speed and time headway and initiate automated lane changes. Two utilization policies were examined: mandatory versus optional use of DLs when driving in an automated mode. The impact of the presence and utilization policy of the DL on drivers’ preference to use automation and their behaviour in car-following and lane changing are investigated. The study found that while the presence of a DL does not increase drivers’ preference for automation use, it encourages drivers to utilize the DL more when the utilization policy is mandatory (i.e., drivers can only use automation mode when driving on this lane). Furthermore, drivers are more conservative in automated mode and when driving in mixed traffic. However, they perform closer car-following and merge into smaller gaps when driving on DLs which on one hand can increase the capacity of the DLs, but on the other hand can increase the risk of collisions. These results are useful for road operators, and in setting-up a more realistically traffic simulation studies.

As the offer of digital services in transport expands, understanding users’ digital engagement and how it developed over time is important to make informed policy decisions. In particular, we lack an understanding of how both PT (public transport) and car users access and engage with digital technologies and perceive them to be necessary to travel. This article aims at bridging this gap, using a 2022 survey of representative samples from both populations in the Netherlands. There is clear evidence of travellers getting more used to digital technologies over time. In 2022, at most 80% of car and PT users relied at least from time to time on their smartphone to look for travel information. As expected, higher digital skills correlate positively with the likelihood of using smartphone-based travel information. It is worth noting that PT users report higher digital skills than car users, while these samples do not differ significantly in terms of age and education levels. As such, low (perceived) digital skills might be a barrier to switching from the car to public transport. Almost 75% of car and PT users think that travelling is more difficult nowadays without a smartphone, demonstrating a radical shift in societal expectations within a decade and a half. Alternatives like public information displays exist and are still used by the majority, but traditional communication channels are not deemed sufficient anymore to travel worry-free. These perceptions can contribute to shaping reality and may put those with a lower digital access at a disadvantage.

This study proposes a general framework to investigate car-following heterogeneity and its impacts on traffic safety and sustainability. The framework incorporates rigorous driving style classification using a semi-supervised learning technique and a micro-simulation process that includes 66 fine-grained traffic scenarios exhibiting varying degrees of heterogeneity. Validated using two distinct real-world datasets reveals the superiority of S3VM-based classifiers over traditional SVM classifiers in driving style classification. Simulation results show that an increase in driving aggressiveness is correlated with higher safety issues and greater environmental impacts. Further elucidation of these impacts from the mechanism of underlying characteristics of driving behaviour and traffic flow dynamics indicates that less aggressive drivers can lead to the formation of vehicular platoons, thereby encouraging more aggressive drivers to adopt a milder driving style. Importantly, the formation of these platoons is influenced by both the proportion and spatial distribution of less aggressive vehicles. The proposed approach promises advantages in reducing the negative impacts of driving heterogeneity, thus benefiting Intelligent Transportation Systems (ITS) by improving traffic safety and sustainability.

The potential lack of realism in stated-preference surveys is particularly acute in contexts where disaggregate real-world data is challenging to obtain. Mobility-on-Demand (MOD) services present one such context. The MOD context is unique due to factors such as service reliability (difference in stated vs. actual travel and waiting time) and current mode inertia which affect the choice of MOD services and are difficult to infer from revealed preference data. Further, travel mode choices are repetitive and constitute a relatively easy choice situation. Consequently, individuals may utilize simple non-compensatory strategies. In this study, we design a survey to mimic real-world choice sets using a joint revealed and stated- (RP-SP) preference survey approach. We construct the complete journey of individuals taking into account departure time, access and egress mode, current primary mode and origin–destination pair. A Choquet Integral (CI)-based choice model with endogeneity correction is employed, thereby allowing to approximate non-compensatory behaviour. Results confirm the presence of non-compensatory behaviour across all mode users (car, public transport and bike). Reliability and inertia effects are most pronounced for car users including the potential for a combined departure time-mode shift towards MOD. Owing to non-compensatory behaviour and inertia, higher travel costs cannot be fully compensated by shorter waiting and travel times and a differential pricing strategy may be required to increase MOD market share. Failure to account for common unobserved factors between the RP and SP choices results in inflated attribute importance.

Efficient crowd management is essential for optimizing the performance of pedestrian infrastructures, either in terms of crowd flow or pedestrian levels of safety and comfort. This study investigates the impact of one type of crowd management measure, namely lighting, on pedestrian behavior. Using Virtual Reality experiments, the impact of lighting, both the brightness level and the light color, on pedestrian route choice is studied. A virtual maze was designed, featuring 10 T-intersections, where the light conditions are varied at each T-intersection to study its impact on pedestrian route choice. Our study shows that pedestrian route choice is strongly influenced by the light color in a virtual environment. Pedestrians prefer to follow paths with green-colored lights and avoid paths with red-colored lights, irrespective of the light color on the other path. Moreover, pedestrians slightly prefer to use the path with a higher brightness level. Lastly, the results indicate that pedestrians do have a slight right-handed tendency on average, however, this effect cancels out almost completely when other guidance information is present in the scenario. Altogether, the findings suggest that lighting can impact pedestrian route choice behavior.

Building fires can be considered a risk to the health and safety of occupants. Environmental factors in building fires might affect the speed of an evacuation. Therefore, in this study participants (N = 153) were tested in an experimental design for the effects of (1) a fire alarm, (2) darkness and (3) the use of emergency exit signs on building evacuation time. In addition, the effects of age and gender on evacuation time were investigated. The main results indicate that the combination of a fire alarm, darkness and not illuminated emergency exit signs had a significant negative influence on evacuation time, namely an increase in evacuation time of 26.6% respectively 28.1%. Another important finding is that age had a significant negative effect on evacuation time. The increase in evacuation time was at least 30.4% for participants aged 56 years or older compared to participants aged 18–25 years. For gender no significant effect was found. Building and safety managers can use these results by including longer evacuation time considerations – based on darkness and older age – in their evacuation plans. Future research should focus further on investigating the effects of personal and psychological characteristics on evacuation behaviour and evacuation time.

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.

Drivers initiate a discretionary lane change when they perceive an anticipated improvement in their own driving condition from moving to another lane. However, such a lane change can slow down other vehicles on the target lane, and even worse initiate a disturbance. In this work, we argue that the blocking effect triggered by individual lane changes results from the heterogeneity in the desired speeds of vehicles, and thus using desired speed information of vehicles when regulating lane-changing decisions can improve traffic efficiency. In doing so, our work also exemplifies the usefulness of incorporating user preferences into control decisions. The proposed lane guidance system uses an optimization-based approach to update the target range of desired speeds on each lane in real time, and accordingly recommends individual lane changes. The control system coordinates the lane-changing decisions at the link level, for which the road stretch is subdivided into multiple sections that are controlled independently. We evaluate the performance of the lane guidance system in micro-simulation, for different network demands and desired speed distributions. The results highlight that the proposed approach utilizing the desired speed preferences of drivers results in positive efficiency gains for most traffic compositions in free flow. Moreover, the highest gains are expected in medium to high demand, and when the traffic composition includes a higher proportion of vehicles desiring higher speeds. The gains also increase when the desired speeds of vehicles that want to drive fast and those that want to drive slower are more separated.