Conventional campus lighting relies on static illumination, causing energy inefficiency and safety concerns. To address this, this study proposes a smart lighting framework integrating Space Syntax analysis with computer vision and Internet of Things (IoT) technologies for real-time pedestrian sensing. Using National Taipei University of Technology (NTUT) as a case study, pathway accessibility values were combined with pedestrian density to develop a dynamic control model using a cloud-based microservice architecture that supports data processing, adaptive dimming, and visualization. Field experiments demonstrate that lighting allocation based solely on spatial integration reduces energy consumption by approximately 27%. Incorporating pedestrian flow dynamics achieves an additional 9% to 12% reduction without compromising visual requirements or perceived safety. The results indicate that spatial configuration provides a robust quantitative basis for adaptive strategies, extending Space Syntax to infrastructure control while integrating human-centered design, energy efficiency, and smart city technologies in campus and urban environments.

This study investigates the influence of built environment factors on pedestrian route choices in an urban context using passively collected mobile phone trajectory data from Sydney, Australia. We estimate and compare multiple discrete choice models including C-Logit, Path Size Logit (PSL), and Error Component (EC) models to quantify associations between pedestrian route choices and route characteristics such as distance, slope, turns, crossings, amenities, and greenery. The models are applied to a high-resolution sidewalk network to simulate pedestrian flows across the city. Our findings are broadly consistent with existing literature, highlighting the importance of route simplicity, directness, and terrain in walking behavior. A key contribution of this study is the integration of passively collected GPS trajectories with route choice modeling and network-level flow assignment, demonstrating a scalable framework for understanding and forecasting pedestrian behavior. The approach enables city-scale assessments of pedestrian infrastructure and offers valuable insights for data-driven planning of walkable urban environments.

Inferring crowd crush accidents in typical high-density pedestrian movement zones via the vision-trajectory fusion neural network.

Pedestrian dynamics in flooding disasters: an experimental study of unidirectional pedestrian flow under multiple water depths

Modeling pedestrian flows in station complexes, considering the available width of functional areas

Deep learning-driven digital twin system for pedestrian tracking and evacuation load assessment in public spaces

Understanding the dynamics of pedestrian flow in urban areas is crucial for decision-making in urban planning and marketing strategies. Previous methods for analyzing pedestrian flow can be divided into data-driven approaches and simulation-driven approaches. While data-driven approaches effectively capture actual patterns of pedestrian flow, they face the challenge of data incompleteness. On the other hand, simulation-driven approaches can generate complete data on a computer, but they only consider some of the factors determining human behavior, resulting in deviations from actual pedestrian flow. Each approach has its own limitations, yet combining them can mutually resolve these shortcomings. This paper proposes a method that applies data assimilation, a fusion technique of data-driven and simulation-driven approaches, to agent-based simulation. Combining these approaches allows for the collection of more comprehensive pedestrian flow data that better represents real-world human behavior. We conducted an evaluation experiment to assess the effectiveness of the proposed method in addressing three types of incompleteness in pedestrian flow data. The results indicate that the proposed method can effectively address data incompleteness. These findings provide guidelines for supplementing sparse measurement data in real-world environments.

Rapid urbanization has intensified the need for vibrant, walkable, and socially sustainable urban environments, particularly within mixed-use and commercial districts. The way buildings and streets are spatially configured in these districts plays a critical role in shaping pedestrian movement, spatial accessibility, commercial vitality, and social interaction within these environments. This paper investigates the role of spatial configuration in shaping the resilience and sustainability of urban commercial districts through a comprehensive review of recent space syntax applications. The review synthesizes methodological approaches for examining spatial structures, urban morphology, spatial accessibility, and urban activity patterns, including segment-based spatial analysis, visibility graph analysis, agent-based modeling, and predictive spatial simulation. This study consolidates recent methodological developments in spatial analytics and identifies key analytical trends that clarify how spatial configuration contributes to urban vitality and sustainability in commercial districts. Particular attention is given to the methodological evolution of space syntax research and its increasing integration with complementary datasets and analytical frameworks for evaluating urban vitality. Across the reviewed studies, highly integrated and spatially accessible street networks were consistently associated with higher pedestrian flow, greater commercial density, stronger land-use clustering, and improved walkability, particularly within compact, mixed-use urban districts. Movement-based metrics such as integration and Normalized Angular Choice (NACH) repeatedly emerged as dominant predictors of pedestrian movement, land-use intensity, and commercial concentration. Despite significant methodological advances in spatial analysis, a persistent gap remains in linking configurational metrics with lived human experience and broader social sustainability outcomes. Overall, the findings demonstrate that spatial configuration is a fundamental driver of walkability, commercial vitality, and socio-spatial interaction, reinforcing the growing role of space syntax as a framework for evidence-based and sustainable urban design.

Mass gatherings at sporting events pose critical risk for crowd safety, especially in venues with limited history of hosting high-demand events or insufficient data to inform evidence-based interventions. Ahead of the UEFA Europa League (UEL) Final 2024–2025, San Mamés Stadium implemented staged access protocols across league matches. These real-world experiences provide a timely case study on crowd management strategies and operational planning in urban environments. This work presents a simulation-based study of the pedestrian ingress dynamics during fan entry to the stadium under high-attendance settings. The agent-based modeling framework integrates a Social Force Model that considers the pedestrian’s limited visual range, queuing behavior, and where path-finding is computed from the numerical solution of the Laplace equation. Using empirical data of the geometrical constraints of the stadium and its surrounding areas and timestamped turnstile access records from pre-final league matches, the model reproduces the pedestrian flow under every access protocol implemented during the tournament. Quantitative analysis of ingress efficiency, access rates, and pedestrian flow patterns reveals specific stadium-adjacent zones susceptible to undesired counterflow and overcrowding. Simulations indicate that the suggested access guidelines can streamline crowd movement and lower density levels by roughly 20%, while delaying individual screening at the fenced security perimeter by 2.5 s can decrease the maximum average local density by nearly 80%. These findings emphasize the role of computational modeling as a decision-support tool, allowing the evaluation of alternative crowd management strategies before their implementation in real-world events.

Despite extensive research on sustainable urban mobility, non-designated crossings remain underexplored, particularly in low- and middle-income countries where they are highly prevalent. This study applies agent-based simulation to analyze pedestrian crossing behavior in commercial streets. We adopted a mixed-methods approach, combining video recordings, field observations, and structured questionnaires to capture physical conditions and user perceptions in a case in Cairo. The collected data were spatially analyzed using a Geographic Information System (GIS) to identify key spatial and behavioral variables influencing crossing decisions. These variables were then incorporated into an Agent-Based Model developed using the GAMA platform to simulate pedestrian–vehicle interactions. The simulation assessed pedestrian flow, non-designated crossing rates, average vehicle speed, and traffic volume. Results indicate strong relationships between pedestrian flow and non-designated crossings, and moderate associations between increased pedestrian activity and reduced vehicle speeds, while traffic volume shows weak correlations with pedestrian-related indicators. The model reveals distinct patterns of pedestrian crossing behavior, shaped by street configuration and traffic dynamics, and highlights critical risk points in commercial streets. Based on these findings, the study proposes seven actionable strategies to enhance pedestrian safety while supporting a more sustainable urban mobility.

This study examines how high-speed rail (HSR) hubs shape public space vitality in central-city station areas, using Shapingba Station (Chongqing, China) as a representative case of station–city integration. We delineated pedestrian catchments using Baidu Map walking isochrones (300–1200 s) and integrated multi-source data, including Public Space Public Life (PSPL) field observations (eight monitoring points, 07:00–24:00), Baidu heat maps, point-of-interest (POI) records, streetscape semantic segmentation, and a perception questionnaire. Indicators were synthesized via entropy weighting, and multivariate associations between perceived vitality and environmental variables were examined using Mantel tests. Pedestrian flow exhibits a clear double-peak pattern (09:00–11:00 and 15:00–16:00), averaging 42,248 pedestrians per day (2347 per hour) and showing strong spatial heterogeneity across monitoring points. POIs show a pronounced core–periphery structure: totals increase from 803 (300 s) to 4365 (600 s) and 7539 (1200 s), while overall density declines from 7477 to 2492 POIs/km2, highlighting a 600 s core where accessibility and functional agglomeration are most strongly coupled. Overall, this study contributes a replicable multi-source evaluation framework and quantitative evidence on accessibility–function coupling and micro-scale design effects in HSR station areas, enabling theory-informed comparisons across station typologies and urban contexts.

The conversion of sidewalks into parking areas and informal activity spaces remains a persistent issue that reduces the quality of pedestrian facilities and disrupts their primary function as safe, comfortable, and barrier-free public spaces. This study examines the impact of such conversions on sidewalk operational performance through field observations using a quantitative approach, including measurements of pedestrian flow, walking speed, density levels, and movement space across three different time periods. The results indicate a significant decline in service levels, characterized by increased movement barriers, reduced effective lane width, and decreased accessibility. The variables of travel time and density also show reduced performance in segments with higher economic activity. These findings highlight the need for spatial reorganization through infrastructure improvements, regulation of informal activities, and consistent enforcement of policies. Inclusive design approaches and strategies such as street diets, as recommended in international studies, can serve as references for improving sidewalk quality to ensure they remain safer, more functional, and more equitable.

Inferring the structure of pedestrian flows at a transportation hub.

Disaster evacuation of the old city of Nicosia

Complex dynamics of single-file pedestrian flow under varying flood water depths

Safety-oriented passenger flow control at a congested metro hub: A microscopic approach.

Collective fs movement is a hallmark of complex systems, exhibiting emergent order across contexts from pedestrian flows to biological collectives. In high-speed, directional settings, alignment ensures efficient navigation, whereas in low-speed, undirectional, socially engaged contexts, alignment arises from interpersonal interaction rather than locomotion goals. Using high-resolution spatial and orientation data from preschool classrooms, we uncover a sharp distance-dependent transition in pairwise alignment that reflects a spontaneous symmetry breaking between behavioral phases: Below a threshold of ~0.65 meters, side-by-side orientations dominate, while face-to-face orientations prevail at larger distances. This transition stems from a distance-dependent competition among three alignment mechanisms: parallelization, opposition, and reciprocation, whose interplay generates a bifurcation structure in the effective interaction potential. Fourier decomposition of orientation distributions reveals these mechanisms, enabling a minimal pseudopotential model that captures the transition as a nonequilibrium phase change. Monte Carlo simulations using inferred interaction terms reproduce empirical patterns, establishing a quantitative framework for social alignment with implications for biological collectives and artificial swarms.

Urban transport (traffic), for many reasons, is becoming a bigger and bigger problem in today's world. Although it is a type of problem that arises, transport solves many important social issues, such as economic problems, unemployment, and poverty, and it raises the general standard of modern living. It is in this paper that the pedestrian flows in the city of Bitola, at two of the busiest locations along the pedestrian street Shirok Sokak, will be considered. The behavior of pedestrians when crossing a pedestrian crossing, whether they cross legally or illegally, as well as a comparative analysis with two software packages, is the key objective of this paper. The use of software packages is of great importance for traffic engineers, as it allows for real-time simulation of traffic participants, perception of problems, and provision of suggested solutions.

Capturing stochastic variabilities in pedestrian flows: A dynamic continuum modeling approach

Modeling proactive avoidance behaviors in pedestrian flows considering congestion anticipation