Search Results (917)

The role of pedestrian movement in urban environments is often overlooked, despite its critical importance in supporting effective city functioning and long-term sustainability. While there has been growing scholarly interest in this area, research on pedestrian mobility remains fragmented across various disciplines and lacks a unified framework. For urban planners and designers to collaborate more effectively, a clearer understanding of the key themes shaping pedestrian mobility is needed. This paper addresses that gap by organizing and analysing existing research through a bibliometric review of 1934 articles published between 1994 and 2023 in the Web of Science database. This article explores the evolution of pedestrian mobility research between 1994 and 2023, highlighting key topics and potential future directions. The bibliometric analysis draws on a range of indicators, including published papers, citation data, journal impact factors, h-index scores, top-cited authors and papers, and regional trends in research output. Most importantly, science mapping was conducted using the SciMAT software, with co-occurrence networks helping to reveal how research themes have evolved over time. The extensive body of work on pedestrian mobility made it possible to develop a conceptual map that traces the field’s intellectual development. From this analysis, five key thematic areas were identified: health, methods, environmental–social, city, and mobility. Full article

Person re-identification (re-ID) aims to match pedestrian images across disjoint camera views. Existing multi-source unsupervised domain adaptation (UDA) re-ID methods still face two critical issues: they fail to effectively balance domain-invariant feature learning and domain-specific style preservation and cannot adequately model the implicit correlations among diverse source domains, resulting in limited cross-domain generalization performance. To address these challenges, this paper proposes a novel multi-source UDA re-ID framework equipped with a Mixture of Experts feature extraction (MEFE) network and a Graph-Based Relation (GBR) module. Specifically, the MEFE network integrates mixed Instance and Batch Normalization (MIBN) to extract robust domain-invariant features, while the embedded domain-specific style information (DSI) module compensates for lost domain-specific style details at the feature level. Furthermore, the cascaded Graph Attention and Graph Convolution Networks (GATs/GCNs) in the GBR module adaptively explore implicit feature correlations and achieve effective multi-source feature fusion. Center maximum mean discrepancy loss is adopted to further reduce cross-domain distribution discrepancies. Extensive experiments on large-scale datasets demonstrate that the proposed method achieves state-of-the-art performance and substantially outperforms mainstream UDA re-ID approaches. Full article

Background. First-mile walking to mass rapid transit (MRT) has two methodological problems. Composite walkability scores blur which features drive walking. And because walking to transit is itself transport physical activity (PA), linking it to total PA is circular. Both issues are sharper in tropical Asian cities. Methods. We surveyed 378 adults within a 1 km network distance of 20 stations on Bangkok’s Pink Line MRT. Walkability was measured with NEWS-A (aggregate and eight subscales); PA with the GPAQ. Binary logistic regression with station-cluster-robust standard errors tested which NEWS-A subscales predict first-mile walking and whether walkers meet the WHO PA guideline (≥150 min/week MVPA). A tautology sensitivity test removed transport PA from the outcome. Results. Walkers were 71.7% of the sample. Disaggregating NEWS-A improved fit; two subscales were the dominant predictors: pedestrian infrastructure and traffic safety. Walkers were 30.6 percentage points more likely to meet the overall PA guideline; with transport PA removed, the gap was 17.5 points and still significant. The pedestrian infrastructure effect was strongest 201–1000 m from a station, not at the immediate frontage. Conclusions. Perceived pedestrian infrastructure quality and perceived traffic safety drive first-mile walking in suburban Bangkok. The walking–PA link is not entirely a measurement artefact. The 201–1000 m ring is a plausible priority for pedestrian investment. Full article

The proliferation of IoT-based surveillance has caused a sharp rise in video data, straining network bandwidth and cloud storage. Conventional video compression exploits pixel-level redundancy but ignores the semantic importance of content, transmitting large volumes of redundant background. This paper proposes a semantic-aware video offloading framework that improves bandwidth efficiency in cloud-based surveillance. DeepLabV3+ with a ResNet-50 backbone performs semantic segmentation at the edge to extract relevant foreground objects (e.g., pedestrians and vehicles) while suppressing static background. A background reference caching mechanism transmits the static scene once and reuses it at the cloud for full-frame reconstruction, minimizing redundant transmission. On a dataset of 12 surveillance sequences (self-captured videos plus sequences from the CDnet 2014 benchmark), the method achieves up to 74.63% reduction in transmitted data, a 33% improvement in storage efficiency, and a compression ratio of 2.88×, while maintaining an average PSNR of 44.92 dB. Paired t-tests ($p<0.001$) and sensitivity analysis across varying scene dynamics and semantic configurations confirm the robustness of the approach, and comparisons indicate clear gains over conventional motion-based offloading in bandwidth efficiency and reconstruction fidelity. Full article

Rail-transit station areas concentrate commercial services, employment, and intensive land development, but their vitality is shaped by multiple built-environment conditions rather than rail accessibility alone. Focusing on 20 stations along the Osaka Metro Midosuji Line in Japan, this study uses Japanese chome units, which are small neighborhood-level address and statistical units, within an 800 m pedestrian catchment as analytical units and measures commercial-service agglomeration intensity, employment intensity, and commercial–employment composite vitality. The composite indicator measures the static co-concentration of commercial-service provision and employment carrying capacity, with pedestrian flow, consumption activity, and dwell time treated as separate dimensions of station-area vitality. Ten station-area environmental variables are examined using ordinary least squares (OLS), Lasso, Random Forest, Back-Propagation (BP) Neural Network, and extreme gradient boosting (XGBoost) models, with Shapley additive explanations (SHAP) applied to interpret variable contributions and nonlinear responses. Results show that nonlinear models generally outperform linear models. Development intensity, officially assessed land price, and network distance to the nearest metro station are the most influential variables, showing threshold, marginal, and non-monotonic effects. Split models indicate that commercial-service agglomeration is more sensitive to rail proximity and street-network conditions, whereas employment intensity is more associated with development intensity and land price. These findings support fine-grained station-area renewal and mixed-function planning. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era. Full article

Improving peri-urban accessibility involves enhancing infrastructure and connectivity for diverse populations. This study proposes a design-led framework for a multiscalar landscape approach to improve peri-urban accessibility using GIS to assess corridor impacts on (i) pedestrian access to green areas, and (ii) cycling access to semi-natural areas, enhancing daily use and socioecological connectivity. It examines how a continuous, safe network of routes can reduce barriers between urban cores, peri-urban belts, and natural spaces, facilitating recreational access. The network improves accessibility to Madrid’s natural and peri-urban landscapes, intersecting with existing networks and reinforcing city structure. New corridors significantly alter 15 min walk and cycling accessibility to green spaces. Findings suggest that Madrid’s peri-urban landscape access can be improved via a corridor network, linking the green belt to the city at walkable and cyclable distances. This strategy promotes sustainable land use by focusing recreation on resilient routes, buffering habitats, and aligning public preferences for beauty with ecological health. Full article

High-precision indoor trajectory estimation using pure Inertial Measurement Units (IMUs) remains challenging due to severe cumulative drift and the complexity of modeling nonlinear dynamics. This paper proposes LKAN, a novel end-to-end framework that integrates the Kolmogorov–Arnold Network (KAN) with Long-History Statistical Regularization (LHSR). We design the KANmer encoder, which fuses Multi-Head Self-Attention with KAN to explicitly capture long-range temporal dependencies and intricate nonlinear features from IMU data. To enhance model robustness, a training-only Long-History Statistical Regularization mechanism is introduced; it effectively suppresses feature distribution drift by enforcing historical statistical consistency. Extensive evaluations on three public datasets demonstrate that LKAN significantly outperforms state-of-the-art methods in IMU-only pedestrian localization. Specifically, on the iIMU-TD dataset, LKAN achieves an Absolute Trajectory Error (ATE) of 2.04 m and a Relative Trajectory Error (RTE) of 2.72 m, representing a reduction of 33.8% and 31.1%, respectively, compared to the second-best ResT-IMU. Results on the RoNIN dataset further validate the superiority of LKAN. These findings confirm that LKAN effectively mitigates error accumulation, providing a reliable, high-precision solution for real-time IMU-based positioning in complex indoor environments. Full article

Road network extraction and updating are crucial for urban development, map updating, and mobility applications. Existing trajectory-based methods often underutilize grid-level semantic information and neighborhood context, thereby limiting their robustness to noisy, heterogeneous, and cross-city trajectory conditions. This study proposes a supervised framework for trajectory-driven road network extraction by coupling intra-grid movement semantics with inter-grid neighborhood context. Multi-level features, including convex-hull shape descriptors, directional clustering, DTW-based (Dynamic Time Warping) heterogeneity, and neighborhood density differences, are used to train a Random Forest classifier for key-grid detection. The detected key grids are further processed through morphology-aware thinning and Kalman smoothing to generate a topology-preserving and vectorization-ready road skeleton. The model is trained on pedestrian trajectories from Shenzhen and directly transferred to vehicle trajectories in Wuhan and Changsha under a zero-shot setting. Experimental results show that the proposed method achieves longer correctly extracted road length and competitive length-based precision compared with raster-based reference methods, while feature-importance and ablation analyses confirm the complementary role of neighborhood context. The proposed pipeline is scalable, interpretable, and transferable, supporting trajectory-based road map updating and urban network analysis. Full article

Text-to-Image Person Re-Identification (TI-ReID) aims to retrieve target pedestrians from large-scale image galleries using natural language descriptions. Despite recent progress achieved by dual-tower architectures based on vision-language pre-training, these methods remain susceptible to semantic misalignment and noise induced by occlusions, background clutter, and fine-grained attribute distractions. To mitigate these issues, we propose a Global Collaborative Discriminative Denoising Network (GCDD), a dual-tower fine-tuning framework built upon a CLIP visual encoder and a BERT text encoder. Specifically, GCDD introduces three complementary branches for robust feature enhancement. First, Discriminative Token Selection (DTS) performs adaptive hard filtering to suppress low-informative tokens. Second, Global-Guided Feature Adaptation (GFA) leverages modality-specific global semantics to recalibrate local features. Third, Query-Driven Aggregation (QDA) constructs more discriminative global representations via attentive pooling, where the backbone global feature serves as the query. The outputs of the three branches are fused through a parameter-free averaging strategy to produce the final representation. Extensive experiments on three standard TI-ReID benchmarks demonstrate that GCDD achieves strong competitive performance, validating the effectiveness of the proposed feature enhancement framework. Full article

Electric scooters (e-scooters) have rapidly integrated into university transportation networks; however, there is limited empirical understanding of users’ and non-users’ perceptions, which is essential for developing effective and inclusive policies. This study addresses this gap by analyzing the differential perceptions of e-scooter adoption, safety, and policy preferences at Louisiana State University (LSU). A quantitative, cross-sectional survey was administered to 1036 respondents (592 users and 444 non-users). Statistical analyses, including Chi-square tests and Binary Logistic Regression, were used to identify key perceptual differences and behavioral predictors of e-scooter usage. Results show that users were predominantly male undergraduates, with speed (90%) and convenience (61%) as the primary motivators. Users were over 12 times more likely to perceive e-scooters as safer than walking. In contrast, non-users cited frequent scooter misplacement (84%) as their top barrier to adoption. Logistic regression confirmed that concern about misplacement (Odds Ratio = 0.076) and support for restrictive policies were strong negative predictors of use, while belief in safety and low cost were positive predictors. These findings may help inform campus micromobility policy discussions. The strong negative perceptions associated with scooter misplacement suggest that designated parking hubs and geofencing strategies could help improve campus operations and pedestrian accessibility. In addition, because safety perception was identified as an important predictor of e-scooter use, targeted safety awareness and educational initiatives may help improve rider behavior and address perceived operational safety concerns. This strategy ensures a balance between user adoption incentives and the safety/accessibility needs of the entire university community. Full article

In coastal residential areas, the combined effects of high temperature, high humidity, and weak wind conditions during summer intensify outdoor heat exposure and reduce pedestrian thermal comfort. To investigate the influence mechanisms of tree height and spatial layout on pedestrian-level thermal comfort, this study selected a typical residential community in Chengyang District, Qingdao, as the research site. Based on field meteorological observations, an ENVI-met model was established and validated. Using the existing composite greening scenario as the baseline, three tree layout types (row, cluster, and free layouts) and four height scenarios (4 m, 6 m, 8 m, and 10 m) were configured to quantitatively compare variations in physiological equivalent temperature (PET) under different planting schemes. The results indicate that tree configuration significantly affects summer thermal comfort. Its regulatory mechanism is governed not only by air temperature reduction but also by shortwave radiation interception, longwave radiation accumulation, and shading continuity. Although low-to-medium height trees can reduce local air temperature through transpiration, their limited canopy height and shading continuity restrict their ability to effectively attenuate direct shortwave radiation at pedestrian level, and in some cases may even increase mean radiant temperature (Tmrt) and PET. In contrast, 10 m tall trees arranged in row and cluster layouts can form continuous shaded cores, with the 10 m cluster layout demonstrating the best overall performance by significantly reducing Tmrt and PET. The free layout, characterized by dispersed canopies and fragmented shading, provides relatively limited thermal comfort improvement. The findings suggest that residential greening optimization should strengthen the coordination between tree height, canopy structure, and activity spaces. Tall trees should be prioritized in children’s play areas, elderly resting areas, residential entrances, main pedestrian pathways, and west-facing sun-exposed zones, while integrating building shadows and road orientation to create a continuous yet not overly enclosed shading network, thereby enhancing summer thermal adaptability in residential areas. Full article

In this paper, we introduce an agent-based pedestrian simulation with aerosol modeling, which we use for analyzing the risk of infection with airborne diseases, with special attention to indoor scenarios and the corresponding geometry. For our analysis, we simulate a realistic supermarket scenario, and analyze the influence of geometric factors for the risk of infection regarding aerosol concentration. Using such a defined set of geometry allows for a targeted analysis of risk factors. Specifically, we examine if angular structures bear higher viral loads than flat structures, which is confirmed by our experiments. An artificial neural network (ANN) specifically trained on simulation data is able to identify adjacent geometric structures based on aerosol concentration with up to 94% accuracy. Full article

The spatial heterogeneity of crowd distributions poses significant challenges for density estimation. Dense regions exhibit high local information entropy due to severe occlusion and feature ambiguity, while sparse regions and backgrounds carry progressively lower informational complexity. To address this, we propose an entropy-inspired crowd density estimation framework that allocates computational attention in proportion to the local information complexity of crowd regions. A Density-Guided Map (DGMap), constructed from nearest-neighbor distance statistics of head annotations, serves as a proxy for local information entropy, enabling the model to differentiate among dense, sparse, and isolated pedestrian regions. The proposed network, termed DGCC-Net, comprises four components: a Twins-Transformer backbone for hierarchical feature extraction, a Local Attention Module (LAM) that enhances high-resolution features through multi-scale receptive fields and rotational attention, a Multi-Level Feature Fusion Module (MLFM) with cross-scale dense connectivity and learnable branch weights for integrating semantic and spatial information, and a Density Guidance Module (DGM) supervised by the entropy-inspired DGMap to achieve density-adaptive feature refinement. Extensive experiments on four benchmark datasets (ShanghaiTech PartA, UCF-QNRF, UCF_CC_50, and JHU-Crowd++) demonstrate that DGCC-Net achieves competitive or state-of-the-art performance, validating the effectiveness of entropy-inspired attention allocation in heterogeneous crowd scenarios. Full article