Search Results (213)

The 2026 opening of the Gordie Howe International Bridge between Windsor, Ontario, Canada and Detroit, Michigan, USA, with its multi-use trail for cyclists and pedestrians, is projected to catalyze cross-border trail tourism and help further revitalize these two border cities. Both Windsor and Detroit have unique, extensive trail systems with compelling destinations. However, cross-border trail tourism institutionalization needs improvement. Tourism, greenway, and destination partners should explore creating a boundary organization to foster and market cross-border trail tourism. Recommendations from a 2024 cross-border trail tourism conference include: develop strategies for community engagement and storytelling to enhance cultural connections between regions; strengthen ties between trail groups and environmental organizations to provide trail experiences that reconnect people with the river and other natural resources; support the region’s efforts to obtain a UNESCO World Heritage Site designation for the Underground Railroad and support the Canadian federal designation of Windsor’s Ojibway National Urban Park; strengthen collaborations between tourism and cycling partners to promote and market cross-border trail tourism; institutionalize greenway assessments (every 5–10 years) to evaluate trail segment completions, gaps, potential route improvements, safety improvements, equity considerations, etc., and to keep greenways in the public consciousness; and measure and broadly communicate the economic impact of cross-border trail tourism resulting from the bridge. Full article

Citywalk, a recently emerging form of leisure-oriented mobility, reflects increasing public interest in healthier lifestyles and more immersive urban experiences in Chinese metropolises. This study develops an integrative assessment framework combining Importance–Performance Analysis (IPA) and the Kano model to examine both functional and affective dimensions of pedestrian satisfaction. Based on a multidimensional assessment model that integrates 25 factors of pedestrian perception, large-scale surveys were conducted in Beijing, Shanghai, and Guangzhou, focusing on perceptions of spatial form, sensory atmosphere, accessibility, safety, and supporting facilities. A Satisfaction Scale (SS) and an Importance Scale (IS) were used to quantify responses, and the Kaiser–Meyer–Olkin (KMO) statistic indicated adequate sampling reliability. Results show that in Beijing and Shanghai, Biodiversity Visibility and Ecological Connection (LE5 = 3.706) and Street Furniture and Urban Amenity Integration (IN2 = 3.255) respectively rank first in terms of satisfaction, while Guangzhou showed highest satisfaction ratings with Pedestrian Infrastructure and Circulation Systems (IN3 = 3.206). It concludes that accessibility, safety, and route signage represent basic expectations whose absence substantially lowers satisfaction, and integration of cultural narratives were identified as effective design features associated with improved comfort and engagement. The framework and findings provide an empirical basis for analyzing pedestrian experience and identifying priorities for improving citywalk environments under different urban conditions. Full article

Optimizing the siting and servicing of urban facilities is a core operations research problem that must reconcile heterogeneous demand, spatial constraints, and network-realistic travel. We present GD-ARISE, a GIS-integrated and data analytics pipeline that maintains a pedestrian–road network metric from demand inference through siting to routing. The workflow has three modules: (i) GIS integration that unifies spatial layers on one network and distance metric; (ii) data analytics that builds multi-criteria suitability via the Analytic Hierarchy Process (AHP) and maps scores to adaptive service radii; (iii) optimal location-and-routing that selects nonoverlapping sites with a transparent greedy rule (SCASS) and computes depot-to-depot routes via simulated annealing on the same metric. A case study in Seoul’s Gangnam District yields a high-coverage portfolio and feasible collection routes. We add a theoretical framework that casts SCASS as a conflict-graph problem, document the AHP elicitation with consistency checks, and report robustness analyses including sensitivity to AHP weights and to radius bounds. Results indicate that core hotspots remain stable to weighting, whereas mid-range corridors shift as criteria priorities or spatial parameters change. Full article

Rapid population aging calls for a shift from static facility configuration toward understanding how spatial structures coordinate with everyday behavior. This study develops a structure–behavior coordination framework to examine how the spatial embedding of community service centers and surrounding facilities aligns with older adults’ mobility and activity chains. Using Guangzhou as a case, three representative facility aggregation forms—clustered, linear, and patchy—were identified through POI-based spatial analysis. Behavioral mapping supported by Public Participation GIS (PPGIS) and social network analysis captured facility co-use and path continuity, while rank-based measures (Rank-QAP and Rank-Biased Overlap) evaluated correspondence between structural and behavioral centralities. Findings show form-sensitive rather than typological coordination: the clustered case (FY) exhibits compact, mixed-use integration; the linear case (DJ) requires ground-level access along main pedestrian corridors; and the patchy case (LG) relies on a few highly accessible dual-core nodes where improved connectivity strengthens cohesion. Everyday facilities such as markets, parks, and plazas act as behavioral anchors linking routine routes. The framework offers a transferable tool and comparable metrics for diagnosing alignment between built structure and everyday behavior, guiding adaptive, evidence-based planning for age-friendly community systems. Full article

Proximity-oriented planning aims to deliver everyday services within a short walk, yet closeness does not guarantee usable access for all residents. This study quantifies the gap between spatial proximity and functional accessibility in Timișoara, România, focusing on people with mobility and visual impairments. A three-stage analysis was conducted to evaluate accessibility to public amenities. First, (1) a survey was conducted with 605 respondents to identify distinct accessibility priorities based on 15-Minute City core dimensions defined by Carlos Moreno and adapted afterwards to the city context and needs. In the second stage (2), GIS mapping (radial buffers and isochrones) revealed major disparities among non-disabled residents and residents with mobility and visual impairments. Coverage decreased substantially across amenities under reduced-speed scenarios and after excluding wheelchair-inaccessible destinations. In the third stage (3), field-observed pedestrian routes in three areas of Timișoara were examined against the top-ranked criteria for each group, using the items sourced from the previous survey and grounded in the 15-Minute City concept. The route scoring is exploratory and specific to this context. The findings confirmed recurrent functional barriers, especially for vulnerable groups. These results expose a proximity-accessibility gap, where apparent nearness masks physical or sensory barriers. A shift toward experience-based accessibility planning is needed to ensure that proximity is not only spatial, but also usable by all and inclusive. Full article

Communications in intelligent transportation systems (ITS) face explosive data growth from applications such as autonomous driving, remote diagnostics, and real-time monitoring, imposing severe challenges on limited spectrum, bandwidth, and latency. Reliable semantic image reconstruction under noisy channel conditions is critical for ITS perception tasks, since noise directly impacts the recognition of both static infrastructure and dynamic obstacles. Unlike traditional approaches that aim to transmit all image data with equal fidelity, effective ITS communication requires prioritizing task-relevant dynamic elements such as vehicles and pedestrians while filtering out largely static background features such as buildings, road signs, and vegetation. To address this, we propose an Offline Knowledge Base and Attention-Driven Semantic Communication (OKBASC) framework for image-based applications in ITS scenarios. The proposed framework performs offline semantic segmentation to build a compact knowledge base of semantic masks, focusing on dynamic task-relevant regions such as vehicles, pedestrians, and traffic signals. At runtime, precomputed masks are adaptively fused with input images via sparse attention to generate semantic-aware representations that selectively preserve essential information while suppressing redundant background. Moreover, we introduce a further Bi-Level Routing Attention (BRA) module that hierarchically refines semantic features through global channel selection and local spatial attention, resulting in improved discriminability and compression efficiency. Experiments on the VOC2012 and nuPlan datasets under varying SNR levels show that OKBASC achieves higher semantic reconstruction quality than baseline methods, both quantitatively via the Structural Similarity Index Metric (SSIM) and qualitatively via visual comparisons. These results highlight the value of OKBASC as a communication-layer enabler that provides reliable perceptual inputs for downstream ITS applications, including cooperative perception, real-time traffic safety, and incident detection. Full article

Enhancing the vitality of historic districts is a key challenge in China’s urban regeneration. This study takes Shanghai’s Zhangyuan Historic District as a case, constructing a framework with six spatial indicators—width-to-height ratio (W/H), interface transparency, connectivity, integration, Universal Thermal Climate Index (UTCI), and mean radiant temperature (MRT)—across spatial morphology, path accessibility, and thermal comfort. Using Grey Relational Analysis, the study quantitatively examines how these factors affect spatial vitality and pedestrian behavior. Findings indicate that, overall, W/H and connectivity are the primary drivers of vitality in plazas and alleys, while thermal comfort (MRT, UTCI) strongly affects stationary behaviors. By typology, plazas exhibit the strongest association with interface transparency (grey relational grade = 0.870), demonstrating that open sightlines and permeable interfaces promote pedestrian flow and staying. North–south alleys show pronounced associations with thermal comfort (MRT = 0.918; UTCI = 0.874), suggesting microclimate-friendly environments can substantially enhance vitality in linear walking spaces. East–west alleys are dominated by connectivity (0.831) and W/H (0.849), whereas integration shows a low grade (0.512), revealing weaker configurational coherence for this spatial type. At the micro-scale, connectivity outperforms integration in predicting pedestrian route choices, reflecting actual movement preferences. The study highlights the combined effects of multidimensional built environment factors and provides a scientific basis for targeted spatial optimization, sustainable renewal, and vitality-oriented design in historic urban areas. Full article

The study investigates the disconnect between formal urban planning standards and experiential walkability outcomes in Viranşehir, a planned neighborhood in Mersin, Türkiye. Although the area complies with national regulations on the provision of public services, it exhibits systemic limitations, including car-oriented street layouts, fragmented pedestrian networks, and underutilized public spaces. Employing a mixed-methods case study, the research integrates archival sources (aerial imagery, zoning plans, satellite data) with field observations to assess pedestrian environments. A light coding of sidewalk continuity, crossings, and edge conditions indicates that many streets are bounded by extensive inactive walls, protected crossings are absent along critical routes such as the school–park axis, and sidewalks are frequently narrow, obstructed, or discontinuous. These built-form features undermine safety, comfort, and social interaction despite formal regulatory compliance. The findings demonstrate how grid-pattern street systems prioritize vehicular mobility, while gated developments restrict permeability and diminish everyday encounters. In response, the study proposes a hierarchy of interventions: immediate measures such as school streets, protected crossings, and traffic calming, followed by medium- to long-term strategies including shaded seating, sidewalk widening, and participatory design guidelines. By linking statutory standards with lived experience, the paper conceptualizes walkability not only as a technical planning requirement but also as a socio-cultural right, offering transferable insights for the creation of more inclusive urban environments. Full article

Remote Sighted Assistance (RSA) systems provide visually impaired people (VIPs) with real-time guidance by connecting them with remote sighted agents to facilitate daily travel. However, unfamiliar environments often complicate decision-making for agents and can induce anxiety in VIPs, thereby reducing the effectiveness of the assistance provided. To address this challenge, this paper proposes a video-based map assistance method. By pre-recording pedestrian path videos and aligning them with geographic locations, the system enables route preview and enhances navigation guidance. This study introduces a factor graph optimization (FGO) algorithm that integrates Global Navigation Satellite System (GNSS) and pedestrian dead reckoning (PDR) data for pedestrian positioning. It incorporates road-anchor constraints, a turning-point-based anchor-matching method, and a coarse-to-fine optimization strategy to improve the positioning accuracy. GNSS provides global reference positions, PDR offers precise relative motion constraints through accurate heading estimation, and anchor factors further enhance localization accuracy by leveraging known geometric features. We collected data using a smartphone equipped with a four-camera module and conducted tests in representative urban environments. Experimental results demonstrate that the proposed anchor-aided FGO-GNSS/PDR algorithm achieves robust and accurate positioning, effectively supporting video-based map construction in complex urban settings. With anchor constraints, the mean horizontal positioning error was reduced by 42% to 65% and the maximum error by 38% to 76% across all datasets. In this study, the mean horizontal positioning error was 1.36 m. Full article

The Integrated Choice and Latent Variable (ICLV) model has been widely applied in travel behavior studies, yet its use in understanding pedestrian route choice remains very limited. This paper seeks to address this gap by analyzing data from a series of controlled pedestrian route choice experiments. Four groups of experimental runs were designed, each involving two route options. The first three groups introduced specific controls: bottlenecks, distance constraints, and extra rewards, while the fourth group, without any imposed control, focused on the influence of route geometry (lengths and widths). For each group, we developed measurement and structural models, followed by three comparative models: a binary logit model using only measured variables (MV model), a model using only latent variables (LV model), and the ICLV model that integrates both. Across all the four scenarios, the adjusted R² values have been improved from 0.286/0.135/0.108/0.035 (MV model) to 0.329/0.161/0.111/0.056 (ICLV model), and the ICLV model can provide interpretable results. These findings highlight the value of incorporating latent constructs based on Structural Equation Modelling (SEM), which enhances the explanatory power of pedestrian route choice models. Moreover, the differences in significant latent variables across various experimental settings offers further insights into the distinct mechanisms underlying pedestrian decision-making under varying conditions. Full article

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article

As human–robot shared service environments become increasingly common, autonomous navigation in narrow space environments (NSEs), such as indoor corridors and crosswalks, becomes challenging. Mobile robots must go beyond reactive collision avoidance and interpret surrounding risks to proactively select safer routes in dynamic and spatially constrained environments. This study proposes a deep reinforcement learning (DRL)-based navigation framework that enables mobile robots to interact with pedestrians while identifying and traversing open and safe spaces. The framework fuses 3D LiDAR and RGB camera data to recognize individual pedestrians and estimate their position and velocity in real time. Based on this, a human-aware occupancy map (HAOM) is constructed, combining both static obstacles and dynamic risk zones, and used as the input state for DRL. To promote proactive and safe navigation behaviors, we design a state representation and reward structure that guide the robot toward less risky areas, overcoming the limitations of traditional approaches. The proposed method is validated through a series of simulation experiments, including straight, L-shaped, and cross-shaped layouts, designed to reflect typical narrow space environments. Various dynamic obstacle scenarios were incorporated during both training and evaluation. The results demonstrate that the proposed approach significantly improves navigation success rates and reduces collision incidents compared to conventional navigation planners across diverse NSE conditions. Full article

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

Historical districts face pressing disaster preparedness challenges due to their special spatial properties—risks compounded by static approaches that overlook dynamic fire–pedestrian interactions. This study employs an agent-based model (ABM) for fire simulations and AnyLogic pedestrian dynamics to address these gaps in Dukezong Ancient Town, Yunnan Province, China, considering diverse ignition points, seasonal temperatures, and wind conditions. Dynamic simulations of 16 scenarios reveal critical spatial impacts: within 30 min, ≥28% of streets became impassable, with central ignition points causing faster obstructions. Static models underestimate evacuation durations by up to 135%, neglecting early stage congestions and detours caused by high-temperature zones. Congestions are concentrated along main east–west arterial roads, worsening with longer warning distances. A mismatch between evacuation flows and shelter capacity is found. Thus, a three-stage interaction simplification is derived: localized detours (0–10 min), congestion-driven delays on critical roads (11–30 min), and prolonged structural damage afterward. This study challenges static approaches by highlighting the “fast alert-fast congestion” paradox, where rapid alerts overwhelm narrow pathways. Solutions prioritize multi-route guidance systems, optimized shelter access points, and real-time information dissemination to reduce bottlenecks without costly infrastructure changes. This study advances disaster modeling by bridging disaster development with dynamic evacuation, offering a replicable framework for similar environments. Full article

This study proposes a novel framework integrating Building Information Modeling (BIM) and Geographic Information Systems (GIS) with real-time crowd analytics from Closed-Circuit Television (CCTV) for quantitative walkability assessment. The framework extends open data standards (IFC and CityGML) to model infrastructural and pedestrian flow attributes comprehensively. A walkability scoring mechanism quantifies route quality based on accessibility, efficiency, and physical comfort, differentiating among pedestrian groups, such as individuals sensitive to weather conditions or carrying belongings. Implemented at the Hong Kong University of Science and Technology (HKUST), results indicate that the framework effectively captures variations in walkability scores due to directional differences (uphill vs. downhill), crowd conditions, and operational constraints like facility closures. Statistical tests confirm significant differences in walking costs across these scenarios with variations of up to 30%, demonstrating the framework’s robustness and practical utility for real-time, human-centric urban infrastructure planning. Full article