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.

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.

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.

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.

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.

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.

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.

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.

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.