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.

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.

Efficient crowd management is crucial for municipalities to ensure public safety and enhance visitor experience, particularly in tourist-centric areas, such as Scheveningen Beach. Scheveningen Beach faces challenges because of the limited precision of visitor count data and the lack of accurate forecasts. Currently, crowd safety managers use their professional experience to forecast based on factors such as weather, events, and holidays, leading to inaccuracies, highlighting the need for accessible data and advanced analytics to enhance crowd management strategies. This study evaluates machine learning and deep learning models for multi-horizon hourly pedestrian crowd count forecasting, addressing the limitations of current manual prediction methods. Historical crowd data, weather, and holidays were integrated to train eXtreme gradient boosting, categorical boosting (CatBoost), light gradient boosting machine (LightGBM), long short-term memory (LSTM), and Temporal Fusion Transformer models for short-term (1-day), mid-term (7-day), and long-term (30-day) horizons. Models were developed for individual locations and as a unified multilocation approach. Performance was assessed using the coefficient of determination, root mean square error, normalized root mean square error, symmetric mean absolute percentage error, mean absolute error, and normalized mean absolute error metrics. The results showed that CatBoost was best for short-term forecasts, CatBoost and LightGBM for mid-term forecasts, and LSTM and LightGBM for long-term forecasts. Forecast performance decreases over longer time horizons in many locations, suggesting different applications: short-term forecasts for immediate operational decisions and long-term predictions for general trend analysis and strategic planning. Individual location models generally outperformed the unified approach, but at a higher computational cost. This study reveals significant spatial and temporal variability in crowd dynamics, which is crucial for optimizing resource allocation and enhancing preparedness in crowd management at Scheveningen Beach and similar tourist destinations.

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.

This article proposes an evidence-based policy recommendation framework integrating social media data and natural language processing methods, to support inclusive and efficient transport policy-making. Given that current research underscores the crucial role of both external and psychological variables in individual travel decisions, psychological features – such as beliefs, attitudes or values – are frequently used as latent variables for travel behaviour interpretation and travel choice modelling. However, user-centric policy recommendations based on dynamic psychological variables are still limited. Most studies rely on survey data, which neglects the urgent dynamic trend of user perception change and its underlying relationship with travel behaviour. Hence there is a lack of illustration on how these psychological variables can be further used at specific temporal and spatial levels for travel behaviour interpretation. This would be valuable to identify priorities for more targeted (sustainability and other) policies and interventions. In this article, we utilize sentiment analysis and dynamic topic modelling to represent the spatial–temporal variance of psychological features. Integrating with corresponding travel behaviour, we illustrate how these dynamic psychological features can distinguish travel dissonance, identify key motivations, and reflect urgent social demands at precise spatial–temporal levels. We demonstrate these advances in a case study in New York City from 2019 to 2022 using Twitter (X) data. A comparison with existing travel-related policies in the case study validates the feasibility of our framework to support evidence-based policy recommendations. We conclude by discussing the potential of this framework to support sustainable transport promotion.

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.

Bicycle delay is an important variable to assess the performance of the cycling transportation system, especially as an indicator of intersection efficiency. This article estimates a machine learning (ML)-based model for estimating average bicycle delays at signalized intersections. This study evaluates various ML models with regressor features, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks. Sparse GPS cycling data (as reference data) from the Talking Bikes program in the Netherlands and the local control signal and flow detection information from the VLOG data provided by a Dutch city are adopted to train the ML models. The findings illustrate the viability of estimating bicycle delays by considering the interplay among weather conditions, temporal factors, junction topology, and local traffic conditions. The estimation model fit using the best-performing model - random forest - has doubled compared to the case without such additional traffic information, indicating its improved performance. Insights gained from the estimation model emphasize the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

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.

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.

Are you using tools like ChatGPT in your daily life to help write an email or even draft a construction plan? Just ten years ago, these kinds of capabilities would have seemed unimaginable. Today, they’re becoming part of everyday life for ordinary people. Behind these powerful tools are technologies known as Large Language Models (LLMs)—AI systems that can understand and generate human-like text and now even create images and videos. But what exactly are LLMs? Could they help transform fields like transportation and traffic management? Can they really do everything, or are there still limitations? In this article, we’ll walk you through a general introduction to LLMs: what they are, how they work, and what opportunities—and challenges—they bring to the transportation sector.

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.

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.

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. […]

Pre-training is a process used to enhance the learning of deep reinforcement learning (RL) algorithms through initial guidance from an expert demonstrator. This involves training a neural network to replicate the outputs of the selected expert before allowing the RL agent to specialise and develop its own policy. This paper outlines a study that aims to analyse the impact of pre-training on deep RL algorithms used in ramp metering. Specifically, behaviour cloning is performed for increasing lengths of time (0-10,000 epochs), with ALINEA as the chosen expert algorithm guiding a proposed Proximal Policy Optimisation (PPO)-based system. The results confirm that, with the same length of training, some initial guidance through pre-training can significantly improve the system’s effectiveness in reducing congestion compared to no pre-training. Otherwise, excessive pre-training may lead to overfitting and reduced generalisability. Design issues resulting in weak model convergence, however, limit the algorithm’s overall performance in the chosen scenario.

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.

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.

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.

In urban centers, cycling is increasingly popular as an eco-friendly transportation mode and a short-distance transport option, driving higher demand for accurate bicycle travel time estimation. Policymakers need to understand bicycle traffic for urban traffic management and sustainable transport promotion, while cyclists benefit from better route planning and improved network efficiency. However, urban bicycle travel time estimation has not received as much attention as car traffic estimation and presents several challenges: 1) Limited availability of structural cycling data, which can be inaccessible due to privacy concerns and/or severely biased by user demographics. 2) The diverse and complex behaviors of cyclists. 3) The lack of strict road constraints for cyclists and frequent rule violations, complicating the model definition of a comprehensive cycling infrastructure network. This paper presents the first study on urban bicycle travel time estimation using GPS tracking data. Leveraging graph-based deep learning's ability to learn from topological network information, we introduce the Dual Graph-based approach for bicycles (DG4b), which employs two parallel encode-process-decode pipelines: one for a shared undirected road network graph to capture intrinsic road characteristics, and another for a directed trip-specific graph reflecting unique trip features. The outputs are combined to estimate road segment speeds and overall trip travel time. When applied to a real-world dataset from Berlin, our method shows superior accuracy and reliability compared to baseline models, while maintaining low complexity. Our approach provides a novel perspective on integrating bicycling-specific characteristics and aims to inspire more future research in bicycle-related traffic estimation.