This study employed Virtual Reality (VR) and physiological sensors to study the impact of different urban elements on pedestrian wayfinding behavior and physiological responses during outdoor-to-indoor transitions in a train station context. Three urban elements — greenery, water, and leading pavement — were placed either indoors, outdoors, both, or neither, creating four experimental scenarios. In total, 35 participants completed wayfinding tasks across all four scenarios. Behavioral, physiological, and eye-tracking data were collected and analyzed. The results revealed that outside-only placement of urban elements was associated with the worst wayfinding performance across all metrics, performing worse than even the control condition with no urban elements. Eye-tracking analysis demonstrated that outside-only placement of urban elements actively distracted participants from the indoor navigation path. Inside-only placement supported the most efficient navigation in terms of travel time, while the scenario with elements in both locations produced the most focused gaze behavior and the highest subjective comfort ratings. These findings highlight that the placement location of urban elements is more critical than their mere presence for supporting outdoor-to-indoor wayfinding, with important implications for human-centered design in transport hubs.

As wildfires become increasingly frequent and severe worldwide, at-risk homeowners face greater responsibility in assessing the fire situation and making safety-critical decisions. This requires specific training in situational awareness (SA). However, the effectiveness of conventional wildfire response training (WRT) methods (e.g., videos, brochures) is limited, as they cannot replicate the unpredictability of wildfires nor provide real-world context. This research introduces Personalized Immersive Training (PIT), a novel paradigm designed to embed WRT in real-world contexts. We implemented PIT in Wildfire@Home, intending to increase homeowners' SA capabilities. Learners first use a desktop wildfire simulator to build mental models of how terrain, vegetation, and wind shape fire spread. Then, experience a realistic and immersive 3D rendering of the simulation in a VR wildfire visualizer. Learners can personalize the training scenario by uploading 3D models and geospatial data.

AbstractThe dynamic evolution and heterogeneity of passenger wayfinding decisions in integrated transport hubs have a significant impact on both operational efficiency and user experience. However, existing static models fall short in capturing the temporal variability of passengers’ cognitive states in response to environmental and situational changes. This study develops a virtual reality scenario of an integrated transport hub and conducts non-immersive behavioral experiments to support the construction of a novel dynamic modeling framework–Dynamic Hidden Markov Model-Logit (DHMM-Logit), which integrates a multi-state hidden Markov model with a Logit model. For the first time, decision cascading analysis is introduced into this framework, utilizing mutual information theory to uncover the temporal dependency and decay mechanism of historical decisions on current choices. These insights guide both the hyperparameter setting and discretization of decision sequences in the DHMM-Logit model. The framework comprehensively incorporates spatial syntax metrics, the use of 2D navigation tools and travel purposes to account for spatial and individual heterogeneity. In addition, a graph embedding-based high-order semantic encoding of nodes is introduced as explanatory variables, enhancing the model’s ability to fit and generalize sequential pedestrian decision-making processes. Empirical validation in the Shanghai Hongqiao Integrated Transport Hub demonstrates that the proposed DHMM-Logit model significantly outperforms baseline methods. The findings reveal pronounced latent cognitive state transitions during pedestrian wayfinding, with travel mode and navigation usage exerting significant influence on passengers’ spatial sensitivity and cognitive processes. This research provides a solid theoretical and empirical foundation for the optimization of hub spatial design and the implementation of personalized information guidance strategies.

Integrated transport hubs require reliable, fine-grained forecasts of crowd distribution to safeguard operations and sustainable urban mobility. We present Group Evolution Mechanism Embedded Network (GEME-Net), a passenger flow distribution forecasting architecture that fuses multimodal data, including video-derived counts, digital twin-based mobility chains, and railway/metro operations information, via multi-graph spatial representations and event-aware temporal modules, with a distilled lightweight student model for deployment. In a real-world case at Shanghai Hongqiao, GEME-Net consistently outperforms statistical, convolutional, recurrent, graph-based and Transformer baselines across MAE, RMSE and WMAPE, while retaining inference latency compatible with near-real-time use. Ablations indicate that schedule encoding and event-driven frequency enhancement, together with learned long-range and community graphs, are principal contributors to accuracy. By coupling operational signals with spatial semantics, our approach improves hub-scale situation awareness and short-horizon decision support, offering a practical route to resilient crowd management without asserting broader societal or policy impacts.

The global shift toward sustainable transportation has raised the profile of cycling. Yet cycling safety still faces persistent challenges (e.g., fragmented governance, inequitable infrastructure, scarce research) that are often overshadowed by motorized transport agendas. This paper presents findings from a workshop held at the 12th International Cycling Safety Conference (ICSC2024) in Imabari, Japan, which brought together an interdisciplinary group of 31 experts (researchers, practitioners, and policymakers) to explore prospective research directions for cycling safety over the next two decades. Drawing on submitted abstracts, group dialogues, and post-event reflections, we used participatory methods, speculative exercises, and collaborative discussions to conduct a thematic analysis that organized key factors into five domains: society, policy, infrastructure, vehicles, and road users. This framework supports a long-term research agenda to address the interconnected challenges of cycling safety. Key priorities include: (i) behavioral and societal studies to make cycling safer and more appealing for diverse users; (ii) development of AI-enabled safety technologies; (iii) establishment of international infrastructure standards; and (iv) tools to anticipate risks linked to emerging vehicle technologies. Additional directions involve the use of eXtended Reality (XR) for behavioral research, multimodal integration, and the ethical and privacy dimensions of data collection. Practically, the findings highlight the importance of participatory and multidisciplinary approaches for tackling real-world safety issues and guiding future research.

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.

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.

This chapter aims to provide crowd operators with an overview, including the effects of crowd management strategies on the pedestrian choice behavior and movement dynamics. Crowd management strategies are commonly considered as the deployment of steering mechanism. The overview also includes how the profile of a crowd and environment influences pedestrian choice behavior and movement dynamics. Specifically, the study focuses on the impact of different steering mechanisms and profiling factors on pedestrian walking speed, flow rate, pedestrian route choice behavior, and pedestrian wayfinding performance. This overview is based on a review of the state-of-the-art literature, which is also presented within this chapter. It demonstrates the opportunity to employ particular steering mechanisms to manage crowds within a given environment. However, the overview also highlights some limitations in the state-of-the-art regarding the effects of the steering mechanisms, or even in the broader context of crowd management. Specific challenges for future crowd management research are discussed, which could provide crowd operators with more insights into the quantitative effect of crowd management strategies in a given environment.

This short communication presents the findings of the work conducted by the human behaviour in fire permanent working group of the International Association for Fire Safety Science. Its aim is to identify determinants of research gaps in the field of human behaviour in fire. Two workshops were conducted in 2023 in which research gaps were identified and discussed by twenty experts. The workshops led experts through a series of questions to determine the reasons (or determinants) for these gaps in human behaviour in building fires and wildfires. Through the questions, the primary identified determinants were (1) researchers’ literacy in the variety of methods adopted in the field, (2) difficulties associated with recruitment of study participants, (3) multi-disciplinary barriers across different research sub-domains, and (4) issues in obtaining funding for addressing fundamental human behaviour in fire research questions. Two key issues emerged from an open discussion during the workshops, namely the difficulties in attracting and training new people in the field (given the limited educational offers around the world on the topic) and the need for more regular opportunities for the community to meet.

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.

This study utilized Virtual Reality (VR) experiments to investigate pedestrian-autonomous vehicle interaction in shared spaces. In the VR experiment, pedestrians attempt to cross the road under different conditions, including the presence of another pedestrian, different external Human-Machine-Interfaces, AV driving styles, and road conditions. We employed an innovative VR setup that enabled two pedestrians to interact in real time with physical movements within an immersive VR environment. Overall, we found that the presence of multiple pedestrians significantly influenced pedestrian movement dynamics during road crossing. Additionally, the relative standing position had a significant impact on the distant pedestrians regarding time before crossing and vehicle-gazing behavior. While previous studies predominantly focused on pedestrian-AV interaction with a single pedestrian, this study takes an important step forward in terms of theory, methods, and relevance by considering interactions between multiple pedestrians and AVs. The findings establish a basis for further exploration of pedestrian-AV interaction in shared space.

Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers’ experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge’s effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency.

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.

The emerging use of automated driving systems introduces novel situations that may affect the safety of vulnerable road users such as cyclists. In this paper, we explain and conceptualise the phenomenon of phantom braking – sudden and unexpected deceleration – in automated vehicles. We apply signal detection theory to interpret phantom braking as a by-product of automated decision-making, with the vehicle favouring the avoidance of accidents at the cost of potentially causing rear-end accidents. To illustrate phantom braking and its effects on cyclists, we used a newly developed cycling simulator. An exploratory measurement conducted with a single cyclist participant revealed a possible complacency effect of the cyclist, with the cyclist’s decision-making mirroring the automated vehicle’s decision-making. The findings provide a testament to using cycling simulators for further exploration of the effects of phantom braking on cyclists.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410

DOI: 10.54941/ahfe1005212

This paper proposes a comprehensive approach for modeling pedestrian wayfinding behavior in complex buildings. This study employs two types of discrete choice models (i.e., MNL and PSL) featuring pedestrian route choice behavior, and three multivariate linear regression (MLR) models featuring the overall wayfinding performance and observation behavior (e.g., hesitation behavior and head rotation). Behavioral and questionnaire data featuring pedestrian wayfinding behavior and personal information were collected using a Virtual Reality experiment. Four wayfinding tasks were designed to determine how personal, infrastructure, and route characteristics affect indoor pedestrian wayfinding behavior on three levels, including route choice, wayfinding performance, and observation behavior. We find that pedestrian route choice behavior is primarily influenced by route characteristics, whereas wayfinding performance is also influenced by personal characteristics. Observation behavior is mainly influenced by task complexity, personal characteristics, and local properties of the routes that offer route information. To the best of our knowledge, this work represents the first attempt to investigate the impact of the same comprehensive set of variables on various metrics feature wayfinding behavior simultaneously.

A shared space area is a low-speed urban area in which pedestrians, cyclists, and vehicles share the road, often relying on informal interaction rules and greatly expanding freedom of movement for pedestrians and cyclists. While shared space has the potential to improve pedestrian priority in urban areas, it presents unique challenges for pedestrian-AV interaction due to the absence of a clear right of way. The current study applied Virtual Reality (VR) experiments to investigate pedestrian-AV interaction in a shared space, with a particular focus on the impact of external human-machine interfaces (eHMIs) on pedestrian crossing behavior. Fifty-three participants took part in the VR experiment and three eHMI conditions were investigated: no eHMI, eHMI with a pedestrian sign on the windshield, and eHMI with a projected zebra crossing on the road. Data collected via VR and questionnaires were used for objective and subjective measures to understand pedestrian-AV interaction. The study revealed that the presence of eHMI had an impact on participants' gazing behavior but not on their crossing decisions. Additionally, participants had a positive user experience with the current VR setting and expressed a high level of trust and perceived safety during their interaction with the AV. These findings highlight the potential of utilizing VR to explore and understand pedestrian-AV interactions.

This study utilized Virtual Reality (VR) experiments to investigate pedestrian-autonomous vehicle interaction in shared spaces. In the VR experiment, pedestrians attempt to cross the road under different conditions, including the presence of another pedestrian, different external Human-Machin-Interfaces, AV driving styles, and road conditions. We employed an innovative VR setup that enabled two pedestrians to interact in real time with physical movements within an immersive VR environment. Overall, we found that the presence of multiple pedestrians significantly influenced pedestrian movement dynamics during road crossing. Additionally, the relative standing position had a significant impact on the distant pedestrians regarding time before crossing and vehicle-gazing behavior. While previous studies predominantly focused on pedestrian-AV interaction with a single pedestrian, this study takes an important step forward in terms of theory, methods, and relevance by considering interactions between multiple pedestrians and AVs. The findings establish a basis for further exploration of pedestrian-AV interaction in shared space.

Understanding pedestrian route choice behavior in complex buildings is important to ensure pedestrian safety. Previous studies have mostly used traditional data collection methods and discrete choice modeling to understand the influence of different factors on pedestrian route and exit choice, particularly in simple indoor environments. However, research on pedestrian route choice in complex buildings is still limited. This paper presents a data-driven approach for understanding and predicting the pedestrian decision point choice during normal and emergency wayfinding in a multi-story building. For this, we first built an indoor network representation and proposed a data mapping technique to map VR coordinates to the indoor representation. We then used a well-established machine learning algorithm, namely the random forest (RF) model to predict pedestrian decision point choice along a route during four wayfinding tasks in a multi-story building. Pedestrian behavioral data in a multi-story building was collected by a Virtual Reality experiment. The results show a much higher prediction accuracy of decision points using the RF model (i.e., 93% on average) compared to the logistic regression model. The highest prediction accuracy was 96% for task 3. Additionally, we tested the model performance combining personal characteristics and we found that personal characteristics did not affect decision point choice. This paper demonstrates the potential of applying a machine learning algorithm to study pedestrian route choice behavior in complex indoor buildings.

Virtual Reality (VR) provides the possibility to study pedestrian wayfinding behaviour in multi-level buildings. Although VR has been applied increasingly to study pedestrian behaviour, it has remained unclear how different VR technology would affect behavioural outcomes in a multi-level building. The study compares the adoption of different VR technologies for pedestrian wayfinding studies, via investigating the difference in pedestrian wayfinding behaviour and user experience. Wayfinding experiments with two groups of participants were conducted using either HMD VR or Desktop VR. Pedestrian movement trajectory data were collected via the VR system and user experience data were recorded via a questionnaire. These data allow for direct comparison and detailed analysis of pedestrian behaviour and user experience between the adoption of two VR technologies. The results showed that technological differences have a significant impact on wayfinding task performance and head rotation change. However, the route choice, exit choice and user experience were overall similar between the two groups. These results provide empirical evidence supporting researchers to choose between immersive and non-immersive VR when study pedestrian wayfinding behaviour.

This dissertation is focused on using Virtual Reality to study pedestrian wayfinding behaviour in buildings during both normal and emergency situations, from simple scenarios to complex scenarios. In particular, various empirical datasets featuring pedestrian wayfinding and evacuation behaviour were collected using different VR technologies to understand the usage of VR to investigate pedestrian behaviour and thereby generate new insights into pedestrian wayfinding behaviour in buildings. This thesis shows that different VR technologies (i.e., Mobile VR, HMD VR, Desktop VR) can collect valid behavioural data and study pedestrian wayfinding behaviour in various contexts.