Conference Agenda

This work proposes a semantic-enhanced dynamic spatiotemporal model that integrates temporal attention, dynamic graph learning, and semantic module to better capture the complexity of human mobility. By combining dynamic adjacency learning with geographic and semantic structures, the model identifies both physical and functional relationships between zones. Results on TELUS mobility data demonstrate that semantic-enhanced graph construction improves prediction accuracy and robustness, offering a more meaningful representation of urban mobility dynamics and providing a strong foundation for future mobility forecasting and city-scale analytics.

Urban quality of life (QoL) assessments often rely on objective spatial indicators such as infrastructure access, land use, and environmental conditions. However, these metrics may overlook how residents subjectively perceive their surroundings. This disconnect reflects a methodological gap in urban studies: the lack of frameworks that integrate both objective and perceptual aspects of urban quality. In response, this study introduces a Perception-Based Urban Quality of Life Index (PUQLI) derived from street view imagery and deep learning and compares it with a composite objective indicator built from 13 spatially measured indicators across seven QoL domains. Rather than replacing conventional QoL assessments, PUQLI is intended to capture the visual-perceptual or experiential dimension of urban quality as inferred from street-level imagery. Each indicator was normalized and spatially joined to a hexagonal grid system. Pearson correlation revealed only modest associations between PUQLI and the objective indicators, indicating that subjective and objective urban quality are related but not equivalent. A mismatch index was then computed to quantify perception–provision gaps, revealing statistically significant and spatially patterned divergences (t = –10.535, p < 0.0001). Positive mismatch clustered in mixed-use urban centers, whereas negative mismatch aligned with documented environmental and infrastructural stressors; together with the significantly negative mean mismatch, this indicates a structural perception–provision gap in which measurable provision does not always translate into favorable lived experience. These findings highlight the need to integrate subjective perception into urban quality assessment and position the mismatch index as a practical diagnostic tool for perception-informed urban planning.

This research develops a pedestrian behavior model that incorporates the existence and dynamics of pedestrian groups. Using high-precision laser sensor data collected in the atrium of a hospital, the research first defines spatiotemporal parameters representing interpersonal distance, relative speed, and walking direction between pedestrians. Based on these parameters, machine learning techniques, including Support Vector Machine (SVM) and Random Forest algorithms, were employed. The SVM demonstrated superior accuracy and stability, successfully identifying groups even under complex walking conditions. Building on these results, the pedestrian behavior model described by psychological stress factors, such as stress from other pedestrians, obstacles, and group dispersion, is improved to account for the behavior of pedestrian groups. Model parameters were calibrated using laser sensor trajectory data with individual attributes (sex, staff, mobility aid usage). The proposed model accurately reproduced observed walking trajectories, with errors within 80 cm for approximately 80% of pedestrians. Finally, the model was applied to evaluate pedestrian spaces by mapping spatial distributions of psychological stress. Pedestrian stress was highest around reception areas, while group dispersion stress was greater in low-density zones where groups tend to spread out. These findings demonstrate that incorporating group behavior enhances the realism and applicability of pedestrian models for evaluating and designing public spaces. Future work will focus on applying the model to diverse facilities and pedestrian environments.

This study presents a workflow that links roadside LiDAR sensing with the modelling of pedestrian crossing behavior. Using self-collected LiDAR data from an informal mid-block crossing in Munich, the workflow includes object detection, tracking, trajectory reconstruction, event extraction, and contextual feature engineering. Behaviour-based yielding and stepping-out moments are used to identify pedestrian decision moments, which are subsequently labelled as gap-accepted or gap-rejected according to gap-acceptance theory. For each decision moment, features describing pedestrian state, social context, and vehicle context are extracted from the reconstructed trajectories. A logistic regression classifier is applied as an interpretable baseline to estimate gap-acceptance decisions under varying traffic conditions. The preliminary results indicate satisfactory predictive performance and show intuitive coefficient patterns, highlighting the influence of vehicle time gaps, pedestrian standing position, and peer presence. Overall, the study demonstrates the effectiveness of LiDAR-derived trajectories as a behavioral sensing foundation for modelling pedestrian crossing decisions.

This study proposes a ring-based SpatialTransformer to learn how building uses at different distances from a railway station interact to generate pedestrian flow. Concentric ring buffers at 100-meter intervals up to 800 meters were defined around 100 randomly selected stations in Tokyo, treating each ring as a spatial token. Self-Attention was applied to learn inter-zone interactions directly from data, without prior structural assumptions. GPS-derived walking trip counts served as the target variable and Geographically Weighted Regression as the baseline. Across 30 independent trials, the SpatialTransformer consistently outperformed GWR in predictive accuracy. SHAP analysis revealed that mid-to-outer distance zone features dominate pedestrian flow prediction, while features from the 0-100m zone contributed little. The attention matrix showed that each distance zone attends most strongly to spatially distant zones, demonstrating that pedestrian flow is regulated by structural interactions across the entire catchment area rather than by any single zone in isolation. These findings challenge the compact city assumption that station-proximate development maximizes pedestrian flow, and suggest that land use distribution across the full walkable catchment area deserves greater consideration in urban planning practice.

Urban accessibility is a key factor in understanding spatial inequality, as it conditions residents’ ability to reach essential services and urban opportunities. This study analyses accessibility in the intermediate city of Los Ángeles, Chile, characterized by a centralized concentration of services and expanding peripheral residential areas. Accessibility to educational, healthcare, and commercial facilities was evaluated using approximate travel times generated through the TravelTime API, considering walking, public transport, and private vehicle modes. Travel times were calculated from the centroids of residential census blocks, and opportunity-based accessibility was assessed using travel-time thresholds to identify the range of accessible commercial establishments.The results reveal marked spatial disparities. Central areas exhibit the highest levels of accessibility due to the density and diversity of amenities, with walking emerging as the most efficient mode for short distances. In contrast, peripheral neighbourhoods show limited access to healthcare and educational facilities and depend largely on private vehicles to reach central services, despite having higher population densities. Commercial accessibility in these areas is primarily restricted to small-scale neighbourhood establishments. These findings indicate that accessibility is influenced not only by travel time and transport networks but also by the spatial distribution and variety of urban functions. The study highlights the usefulness of routing APIs as an alternative methodological tool for accessibility analysis in contexts where official mobility data are outdated or incomplete, offering valuable insights for urban planning and policies aimed at reducing spatial inequalities.

Rapid urbanization is accelerating the fragmentation of blue–green spaces and the degradation of ecosystem services, while widening inequalities in environmental exposure and access to ecological benefits. Taking the “Geo-information Town” as a case study, this paper develops an integrated 3D framework linking urban form, human behavior and spatial interactions. First, UAV oblique images are semantically segmented to identify blue–green–grey features and to jointly assess and filter image quality. Second, multi-source spatial data, including Gaode POIs, nighttime lights, urban land use, OSM road networks, vector base maps and Baidu heat maps, are used to characterize urban functions and vitality patterns related to catering, sightseeing, shopping and cultural–educational services. Third, social media check-in data from Xiaohongshu and Weibo are incorporated to capture residents’ subjective evaluations and place preferences for different spatial units. An XGBoost–SHAP modelling framework is employed to quantify the relationships between these subjective evaluations and blue–green–grey indicators, and to interpret the marginal contributions of different environmental and functional attributes. The results reveal how perceived landscape qualities and service functions jointly shape spatial attractiveness and human–landscape interactions at the neighborhood scale. Finally, we discuss future research on 3D indicator systems, fine semantic segmentation of blue–green spaces, multi-source big data fusion and perception–behavior–function coupling, providing methodological support for perception-oriented assessment of residential environmental quality and optimization of blue–green urban landscapes.

Active Mobility Accessibility Index (AMAI) quantifies the competitiveness of walking and cycling relative to driving using travel-time and distance ratios on identical sampled origin-destination pairs, reflecting network structure rather than destination choice. AMAI combines time parity and distance parity in a simple diagnostic score, using equal weights as a default specification for interpretation and policy use. Applied across the five Tyne and Wear local authorities, it demonstrates that cycling is more competitive than walking against driving. The median origin-level cycling AMAI is 0.820 and the median walking AMAI is 0.645. Parity remains limited where the share of origins at or above parity is 10.0% for cycling and 1.7% for walking. Initial API-based tests suggested that time-of-day effects are limited for the short local trips studied here, supporting development of a scalable in-house routing workflow for the main analysis. Validation against OA-level Census 2021 mode shares, with controls for terrain gradient and commute-distance composition, suggests that AMAI captures a relevant behavioural signal, while its main value lies in diagnosing local network competitiveness for policy and planning.

Understanding pedestrian behaviors is the foundation of simulation for space planning. However, conventional behavior modeling methods are insufficient for learning detailed interactions, and deep learning methods often lack interpretability. This study aims to develop a pedestrian trajectory modeling approach based on discovering causal relationships among pedestrians. The proposed method consists of two parts: analyzing causal relationships among pedestrians using statistical causal discovery methods and predicting trajectories using attention-based deep learning methods. The first part employs a semi-parametric method to identify the causal relationships underlying observed pedestrian behavior and construct a spatial-temporal graph based on these causal relationships. The second part primarily uses the graph attention network to learn interactions among pedestrians. The experimental results demonstrate that the proposed method achieves a good balance between prediction accuracy and interpretability, while also identifying limitations, including at low-density scenes and due to causal model assumptions.