Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management. Partial observability of vehicle flows complicates this task despite the availability of two privacy-preserving data sources: (i) aggregated vehicle counts from loop detectors near stop lines, and (ii) aggregated floating car data (aFCD) that provide segment-wise average speed measurements. However, how to integrate these sources with differing spatial and temporal resolutions for queue length estimation is rather unclear. Addressing this question, we present Q-Net: a queue estimation framework built upon a state-space formulation. This design addresses key challenges in queue modeling, such as violations of traffic conservation assumptions. Q-Net follows the Kalman predict-update structure and maintains physical interpretability in both the state evolution and measurement models. Q-Net uses an AI-augmented Kalman filter to learn time-varying gain dynamics from data. The framework supports real-time implementation and improves spatial transferability by grouping aFCD measurements into fixed-size local groups, making the number of learnable parameters independent of section length. Evaluations on urban main roads in Rotterdam, the Netherlands, show that Q-Net outperforms baseline methods, tracks queue formation and dissipation accurately, and mitigates aFCD-induced delays. By combining data efficiency, interpretability, real-time applicability, and spatial transferability, Q-Net makes accurate queue length estimation possible without costly sensing infrastructure like cameras or radar.

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.

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.

Understanding and modeling the alighting and boarding process in suburban train services is crucial to optimizing train dwellings. The alighting and boarding process is a bi-directional pedestrian flow through a bottleneck, being the door opening. Pedestrian flows, including alighting and boarding processes, are generally modeled by two-dimensional pedestrian models, such as cellular automata or social force models. These two-dimensional models are calibrated from two-dimensional data sources that are often complicated to access for privacy reasons. The availability of disaggregated passenger counting data led us to propose a different modeling approach based on cumulative flows. The model is a Markov process with variable transition probabilities. Transition probabilities are computed from the remaining number of alighting and boarding via a differential equation based on the pedestrian fundamental diagram and density estimations. The parameters of the differential equation were fitted using disaggregated passenger counting data. The model shows better predictive power than a linear benchmark model calibrated on the same data. The physical parameters of the model are consistent with the existing literature. The proposed approach offers an alternative to commonly used two-dimensional models, providing easier calibration. Such a model will enable the forecasting of alighting and boarding time distributions, facilitating better dwell time planning and train and platform design.

As bicycle use increases, so does the need for formal parking spaces to safely store them while performing other activities at a destination. In the Netherlands, several municipalities have created indoor and outdoor formal parking spaces, which remain underutilised. Instead, many cyclists choose to ‘fly park’, i.e. informally lock their bicycle to objects on the street. This can cause dangerous situations or inconvenience, for example by blocking sidewalks. The discrepancy between the use of formal and informal parking spaces may be attributed to a lack of information provided to cyclists about the available formal parking options. This study investigated the effectiveness of different traffic sign designs in encouraging the use of formal parking spaces. The designs were developed within this research with the intention of capturing different communication strategies, namely hazardous, neutral educative and negative educative. A stated preference choice experiment was then performed to allow the comparison of the effectiveness of the different designs, and thus communication strategies. The responses were analysed using discrete choice modelling. According to the results, traffic signs alerting users to the fact that controls are performed (hazardous communication) are the most effective in the fly parking prevention, especially for frequent bicycle users.

Evacuation from transportation tools is receiving increasing attention due to its high risk and complexity. However, as a crucial travel mode, high-deck coaches, have been overlooked, lacking a dedicated evacuation model, let alone exploratory simulation analyses. This work proposes an innovative high-deck coach evacuation model framework, where three intertwined modules are developed to separately delineate the strategic, tactical and operational passenger evacuation behaviours. In the strategic behaviour module, the Cox-Weibull hazard duration model is introduced to capture the pre-evacuation times of passengers so that both the distribution characteristics and the dependence on the proximity to the target exit are encapsulated. In the tactical and operational behaviour modules, elaborate behavioural rules are designed and coupled with Cumulative Prospect Theory to comprehensively incorporate the typical behavioural characteristics and decision-making factors of passengers. The framework is validated with empirical data from various scenarios and proven to significantly outperform the state-of-the-art passenger evacuation model. It is found that the CWM substantially improves the prediction accuracy of the framework compared with the Weibull probabilistic distribution. Overtaking behaviour significantly affects passenger evacuations, but does not induce any benefit for the overall system. This study offers valuable tools and insights for high-deck coach evacuation simulation and management.

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.

More and more people will be living in urban areas. This requires responsive and inclusive urban planning, to keep the urban areas resilient, inclusive and sustainable. This also affects mobility in cities. Governments promote walking as a healthy and sustainable mode of transportation. However, the pressure on pedestrian infrastructure is rapidly increasing, while large crowds gather more and more frequently. We therefore need to get more insights in what pedestrian planning entails. This chapter covers the conclusions of all contributions to this book, ranging from insights into pedestrian traffic flow through data and insights in behavior to different types of models and crowd management. The chapter ends with an overview of innovations in pedestrian planning and management, and what is needed to keep urban regions sustainable and attractive for pedestrians and crowds.

Data is essential for effective urban planning and management. This chapter provides a comprehensive overview of data and data collection techniques for pedestrian planning, aiming to provide researchers and practitioners insights into selecting suitable data and data collection techniques based on their specific pedestrian planning needs. This chapter begins by outlining the taxonomy of data for pedestrian planning, identifying the types of pedestrian behaviour, data types, and data features that are important for pedestrian planning considerations. It specifically identifies four types of data that are essential for pedestrian planning, namely environmental and infrastructure data, traffic data, personal characteristics, and physiological data. This chapter provides a comprehensive overview of each type of data used in pedestrian planning and where these data can be sourced. Moreover, this chapter provides an in-depth overview of different data collection techniques used in pedestrian planning, including sensors, crowd sourcing, and eXtended Reality. The advantages and limitations of each technique are also discussed, offering practical insights for employing them for data collection purposes. In summary, this chapter serves as a comprehensive guide to understanding the why, what, where, and how of using data to enhance pedestrian planning. It offers the readers the knowledge to collect and use data effectively, which ultimately supports the designing, planning, and management of pedestrian-friendly urban environments.

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.

Physical distancing has been an important asset in limiting the SARS-CoV-2 virus spread during the COVID-19 pandemic. This study aims to assess compliance with physical distancing and to evaluate the combination of observed and self-reported data used. This research shows that it is difficult to operationalize new rules, that context affects compliance, that there needs to be a need for compliance, and that rules require upkeep. From a methodological point of view, this study found that the combined methods provide a comprehensive picture of compliance behaviour, that it is challenging but essential to mitigate response fatigue in long-term monitoring studies, and that it would be interesting in future research to learn how actual behaviour is influenced by personal narratives.

SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics—Virus Spread model (PeDViS): an individual-based model that combines pedestrian behaviour models with virus spread models that incorporate direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark ‘at risk’ interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to build-up of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent.

The Covid-19 pandemic has had a large impact on the world. The virus spreads especially easily among people in indoor spaces such as restaurants. Hence, tools that can assess how different restaurant settings can impact the potential spread of an airborne virus and that can assess the effectiveness of mitigation policies are of high value. Microscopic pedestrian models provide the tools necessary to assess the detailed movements of people in a restaurant and with that the risk of virus transmission. This paper presents the application of a microscopic pedestrian model, including a novel activity choice and scheduling model, to assess virus transmission risks in restaurants. Simulation experiments identify that different factors impact virus transmission risks in a restaurant. Contacts between restaurant staff and customers are the driving factor for virus transmission in a restaurant whereby especially staff presents a big risk. Hence, mitigation policies focussing on these interactions and on preventing staff from transmitting the virus can be highly effective. The results also show that different restaurant layouts and setups lead to distinctly different transmission risks. Therefore, insights obtained from simulating one restaurant cannot be just transferred to any other restaurant. Together, these results show the added value of including pedestrian models in disease transmission risk modelling exercises to mitigate the impact of a pandemic caused by an airborne virus. However, the research also shows that, to better utilize the potential of pedestrian models for disease transmission risk modelling, future research of pedestrian activity scheduling behaviour in indoor spaces is necessary.

Pedestrian tactical choices and operational movement in evacuations essentially pertain to decision-making under risk and uncertainty. However, in microscopic evacuation models, this attribute has been greatly overlooked, even lacking a methodology to delineate the related decision characteristics (bounded rationality and risk attitudes), let alone their effects on evacuation processes. This work presents an innovative two-layer floor field cellular automaton model framework, where three intertwined sub-modules respectively dedicated to modelling the exit choice, the locomotion movement and the exit-choice changing behaviours are proposed and integrated as an entity. By introducing various decision-making elements computed by the proposed algorithm, Cumulative Prospect Theory (CPT) is proposed for the first time to model the exit choice and locomotion decision-making under risk and uncertainty. In the exit-choice changing module, attractive and repulsive forces are invented to jointly describe the tendency to revisit the routing decision. Each sub-module and the whole framework are validated in manifold indoor environments. The simulation results of the modules with CPT accord with the empirics from the evacuation experiments and are superior over those from the state-of-the-art models. The degree of rationality and risk attitudes are proven to have significant impacts on tactical and operational decisions. Furthermore, irrational behaviour in decision-making is not variably detrimental to locomotion efficiency of pedestrians. The proposed framework can serve as an elegant tool to predict pedestrian dynamics. The behavioural findings shed new light on understanding and modelling the tactical and operational decisions in evacuations.