The deployment of automated vehicles (AVs) on public roads remains limited due to concerns about their interaction with human-driven vehicles (HDVs) in mixed traffic. While previous studies suggest that AVs influence HDV behaviour, the nature of this influence is still not well understood. This study examines how AVs affect HDV car-following behaviour in mixed traffic conditions. Empirical data were collected through a driving simulator experiment in which participants followed a lead vehicle in four scenarios varying in vehicle appearance (AV or HDV) and driving style (AV-like or HDV-like). Car-following behaviour was analysed using the Intelligent Driver Model (IDM) and an extended version (IDM+). The results show that HDVs adapt their behaviour when following AVs, exhibiting smaller jam spacing distances and shorter safe time headways compared to following HDVs. These findings support more accurate assessments of traffic safety and efficiency and contribute to the safe integration of AVs into mixed traffic.

At the end of 2019, SARS-CoV-2 rapidly spread across the globe within a few months. Since then, tackling the virus has been high on national agendas for over three years. As with other respiratory viruses, physical distancing (i.e., requiring sufficient space between individuals) became a key measure to prevent airborne virus transmission between individuals. However, this measure significantly reduces the capacity of pedestrian infrastructure, as more space is needed between people. This paper develops a capacity framework designed to calculate the capacity of pedestrian infrastructure, evaluate its state, and propose tailored interventions when physical distancing regulations are enforced. The framework is founded on the current state-of-the-art in pedestrian operational movement dynamics and determines capacity using three independent key performance indicators: flow rate, density, and interactions. Through two case studies from the COVID-19 pandemic, this paper demonstrates how the framework identifies when and why pedestrian infrastructures become unsafe and enables targeted interventions. The framework's relevance extends beyond the COVID-19 pandemic, offering insights into crowd management and resilient pedestrian infrastructure design for future airborne disease outbreaks.

Lighting is an integral element of every pedestrian environment, making it a promising tool for crowd management. However, limited knowledge exists on how different lighting conditions shape pedestrian choice behavior. This study systematically examines how both light intensity and light color influence pedestrian exit choice using data from a large field experiment in which varying light settings were applied to two building exits. Two multinomial logit (MNL) models, a light-intensity model and a light-color model, were estimated to quantify these effects. Findings indicate that only a limited subset of light-intensity and light-color conditions meaningfully influence pedestrian exit choice, with Off-Neutral, Bright-Neutral, White-Green, and Red-Green showing moderate, time-dependent effects. At the same time, contextual factors such as origin, local density, and time of day remain far stronger predictors of behavior. Moreover, learning effects emerge selectively and often counterintuitively, with pedestrians increasingly favoring the darker or red-lit exits in conditions where opposite directional responses are expected. The MNL models suggest that lighting can modestly influence pedestrian routing, provided it is applied with careful attention to contextual conditions and time of day.

The escalating demand for urban mobility has significantly contributed to increased CO₂ emissions, necessitating a shift towards sustainable, low-carbon transportation solutions. Emerging modes and concepts such as micro-mobility, shared mobility, electric mobility and mobility hubs offer promising pathways to reduce vehicle CO₂ emissions. This review explores the role of these modes in emission reduction, with particular attention to the integrative function of mobility hubs. This review synthesized current knowledge on the role of emerging transport modes in reducing urban CO₂ emissions. Our analysis through the Life-Cycle Assessment framework and Dynamic Mitigation Model demonstrates that while these modes can lower emissions by facilitating a shift away from private cars, their success is not a guaranteed outcome. Instead, their environmental benefit depends on managing the balance between modal substitution, operational logistics, and vehicle life-cycles. Mobility hubs are a pivotal strategy for mitigating the life cycle emissions associated with shared transport modes by enhancing integration and minimizing indirect emissions. Therefore, the review argues that advancing shared mobility from a niche option to a mainstream solution, supported by strategically implemented mobility hubs, is essential for achieving significant climate benefits. Prioritizing the coordinated deployment of emerging modes and hubs can capture their synergistic advantages, minimizing life-cycle CO₂ emissions and advancing the transition toward sustainable urban transport.

Future traffic will include automated vehicles (AVs) that will interact with other road users, including cyclists. These interactions need to be safe for AVs to be accepted by society. To accomplish this, the interaction process needs to be studied from both the AV’s point of view (AV’s passenger) and cyclists’ point of view. Insights from current interactions between drivers of conventional vehicles (CVs) and cyclists, and the factors contributing to safe interactions, can inform industry of the design of AVs to interact safely and in socially acceptable ways with cyclists. This paper provides a synthesis of the current literature on the interactions between AVs/CVs and cyclists, from four different points of view: 1) from CV drivers’ point of view when interacting with cyclists; 2) from cyclists’ point of view when interacting with CVs; 3) from AVs driver-seat passengers’ point of view when interacting with cyclists; and 4) from cyclists’ point view when interacting with AVs. The literature review included publications between the years 2015-2025 and resulted in 89 relevant scientific papers. Fifty-one papers focused CVs and cyclists interactions, at intersections, and in overtaking maneuvers, while thirty-eight papers focused on cyclists and AVs interactions. Key factors that influence AV-cyclist interactions were identified, including infrastructure, environment, factors influencing vehicle and cyclist behaviors, and rules and regulations. These elements and the factors influencing them were summarized in a conceptual framework. Future research directions are proposed based on the literature review and knowledge gaps identified and were structured following the proposed conceptual framework.

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.

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.

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.

Bicycle transportation, a low-carbon option, is essential for promoting sustainable urban mobility. However, predicting bicycle traffic is challenging due to limited investments in data collection, especially in smaller cities. This paper proposes a multi-source transfer learning spatial-temporal graph neural network (Multi-TLSTGCN) for accurate bicycle traffic prediction in target cities with limited available data. This study first examines how to transfer knowledge from single source domain to the target domain while mitigating the risk of negative transfer. Following this, a multi-source adaptive transfer learning approach is developed to optimize traffic prediction in the target domain by adaptively integrating knowledge from multiple sources. Finally, the performance of the Multi-TLSTGCN model is evaluated under various levels of target data scarcity and compared with models that do not incorporate source domain knowledge. The experimental results demonstrate several key insights: 1) Models fine-tuned with a single-cluster pre-trained source model where the clusters are formed based on similar traffic patterns are more effective at minimizing negative knowledge transfer than those fine-tuned with single-city pre-trained source models. 2) The proposed Multi-TLSTGCN outperforms baseline models in bicycle traffic prediction, showing promise for accurate predictions in data-scarce environments; and 3) The Multi-TLSTGCN model remains robust across varying levels of data scarcity, exhibiting only a slight decrease in accuracy as the availability of target data decreases, in contrast to models relying solely on target domain data. These findings highlight the Multi-TLSTGCN model as an effective and promising solution for bicycle traffic prediction with limited data availability.

Car dominance in urban landscapes poses environmental, health, and congestion challenges. This comprehensive study examines the potential of shared mobility in car-free areas. Specifically, it investigates the mobility behaviour of inner-city older adult residents (50 + ), traditionally heavy car users through a case study of small-medium-sized Dutch cities and a stated preference experiment. This study applies a Latent Class model to analyse the heterogeneity in passengers’ preferences, identifying four distinct groups: Price Sensitive & Private Car Enthusiasts, Time-Conscious Travellers, Pro-Cycling & Conventional travellers, and Micromobility Enthusiasts. The model predicts class membership based on travel behaviour data from the stated choice experiment and examines the role of key factors such as travel cost, travel time, and walking distance in shaping mode choices across five transport options: bike, e-bike, e-scooter, e-Brommobiel, and e-car. The findings reveal that a significant portion of travellers recognise the value of shared mobility options in reducing private car dependency, underscoring the need for targeted interventions to address barriers and enhance accessibility to promote shared mobility adoption. Based on these distinct passenger segments, the study proposes specific policy measures that not only enhance transport planning but also address existing challenges and user concerns in sustainable urban mobility.