Search Results (7,217)

Urban traffic congestion presents a complex challenge driven by intricate spatial dependencies and non-stationary temporal dynamics. While Multi-Agent Deep Reinforcement Learning has shown promise for Traffic Signal Control, existing approaches often struggle with partial observability and fail to coordinate effectively across large-scale, heterogeneous road networks. In this paper, we propose HydraLight (HYbrid Deep Reinforcement Learning Architecture for Traffic Lights), a novel spatio-temporal framework that integrates Graph Attention Networks and Temporal Transformers. To overcome the localized myopia of standard graph methods, HydraLight introduces a Global Pooling Context module that broadcasts macroscopic, citywide traffic summaries, enabling agents to proactively mitigate systemic gridlock. Furthermore, to facilitate robust multi-scenario training, we introduce a Unified Prioritized Experience Replay (Unified PER) module that normalizes Temporal-Difference errors, preventing task dominance across diverse topologies. Extensive experiments on the RESCO benchmark across five synthetic and real-world networks demonstrate that HydraLight consistently outperforms state-of-the-art baselines (including X-Light and CoSLight).Byreducing traffic congestion, travel delays, and idle waiting times, the proposed framework also contributes to more sustainable urban mobility through improved traffic flow efficiency, lower fuel consumption, and reduced vehicular carbon emissions. Notably, the proposed architecture excels in structurally irregular environments, achieving up to 13.07% reduction in average travel time on complex arterial networks and consistently improving queue stability and waiting-time minimization across both synthetic and real-world RESCO benchmarks compared to state-of-the-art baselines. Full article

Vehicle emission inventories are highly sensitive to vehicle activity data, yet annual vehicle kilometers traveled (VKT) is still commonly represented using generalized default values whose representativeness at the city scale remains uncertain. In this study, large-scale vehicle inspection data from Haikou, China, were used to derive inspection-based VKT estimates and to quantify how activity assumptions affect urban vehicle emission inventories and policy evaluation. By holding vehicle population and emission factors constant across scenarios, we explicitly isolated the effect of activity representation on emission estimates. An inspection-based, age-sensitive VKT framework was further developed to capture within-fleet heterogeneity. The results showed that inspection-derived VKT accounted for only 36–75% of guideline-recommended values across major vehicle categories, with the largest discrepancies observed for diesel freight vehicles. As a result, the use of guideline-based VKT produced higher emission estimates by 34–39% for carbon monoxide (CO) and volatile organic compounds (VOCs) and by approximately 66–67% for nitrogen oxides (NOx) and particulate matter (PM). The influence of activity representation was also evident in policy assessment. In a case study of old diesel vehicle retirement, guideline-based VKT produced estimated emission reduction benefits that were more than 120% higher for most pollutants and nearly 200% higher for NOx than those derived from inspection-based VKT. These findings demonstrate that generalized activity assumptions can substantially affect both emission inventory estimates and policy-oriented assessments. Rather than merely refining a local mileage parameter, this study highlights a potential representativeness limitation of generalized activity assumptions when they are applied to city-specific emission inventories, particularly in medium-sized or geographically constrained urban systems. The inspection-based, age-sensitive approach proposed here provides a practical pathway for improving activity representation in data-rich urban environments, while its transferability should be evaluated according to local fleet structure and transport conditions. Full article

Tourism development can both support and strain regional sustainability. Sustainable tourism matters especially in highly urbanized metropolitan areas, where resident mobility and tourist demand jointly use transport systems. This study evaluates transport infrastructure adequacy and quality under tourism pressure in regions containing Russia’s largest urban agglomerations. Because official tourist-flow statistics are available at the regional rather than agglomeration level, the analysis uses an exploratory regional proxy approach. The methods combine comparative analysis, correlation and regression analysis, index analysis, and sensitivity checks. Tourist flows show the strongest statistical associations with absolute indicators of bus infrastructure. Rail transport, especially commuter rail, also shows a stable positive association, which matters for large metropolitan areas and regions with intensive intermunicipal mobility. Overall, tourist flows in the studied regions correlate primarily with the scale of the existing passenger transport system. Therefore, the results represent diagnostic associations rather than causal estimates of tourist transport behavior. The study proposes a comparative index of tourism transport infrastructure adequacy that characterizes how well the selected territories’ transport systems can absorb tourist traffic under data limitations. The index reveals pronounced differentiation among the Moscow, Saint Petersburg, and Kaliningrad cases. Full article

This study presents a multivocal literature review (MLR) on the implementation of Shared Autonomous Vehicles (SAVs), a relatively new concept in urban mobility that merges autonomous driving with shared transportation. The purpose of this review is to analyse the feasibility, challenges and potential impacts of SAV deployment by aggregating and synthesising insights from the academic literature and grey sources. The review addresses factors influencing deployment, including social acceptance, environmental impact, business models, policy frameworks, needs and barriers, and lessons from existing pilot programmes. The findings reveal that successful SAV implementation depends on combining technology, regulation and infrastructure. Public trust and perception of safety, cost and convenience can also significantly influence the adoption of this technology, as well as potential sustainability benefits (like reduced emissions and fewer private vehicles). Case studies from cities like Phoenix, San Francisco and Singapore show promising results but also context-specific challenges. This study concludes that future research should apply these insights to specific cities, where urban layouts and public transport reliance demand customised approaches to successfully deploy SAVs. Full article

Accurate and efficient vehicle detection from unmanned aerial vehicle (UAV) imagery is essential for intelligent transportation, urban monitoring, and public safety, yet this task remains challenging due to high target density, extreme scale variation, complex backgrounds, and stringent onboard computational constraints. Existing DETR-based detectors model global context through self-attention but incur quadratic $\mathcal{O}\left(N^2\right)$ complexity that is prohibitive for high-resolution UAV images, while CNN-based methods lack the long-range contextual awareness needed for dense small-object scenarios. We propose RSMamDet, an efficient end-to-end detection framework built upon RT-DETR that replaces quadratic self-attention with linear $\mathcal{O}\left(N\right)$ State Space Model scanning. The framework integrates a MobileMamba backbone with a Selective Feature Scanning module for efficient global context modeling, a Dimension-Aware Selective Integration module for adaptive cross-scale feature fusion, a Poly Kernel Inception Network encoder for multi-receptive-field feature enrichment, and an Adaptive Multi-Level Feature Fusion module for content-aware dynamic upsampling, complemented by an Uncertainty-Minimal Composite loss for stable query selection in cluttered aerial scenes. Experiments on DroneVehicle and VisDrone2019 demonstrate that RSMamDet achieves mAP₅₀ of 72.6% and 40.2%, surpassing state-of-the-art methods by 4.1% and 2.2%, respectively, while maintaining real-time inference at 186.2 FPS with only 19.8M parameters and 42.3 GFLOPs, representing a $6.14\times$ reduction in computational cost and a $3.86\times$ reduction in model parameters compared to the strongest baseline. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

Does public transportation infrastructure expansion necessarily improve urban air quality? Using panel data from 168 Chinese cities, this study examines the impact of public transportation infrastructure development on air quality by applying GTWR (Geographically and Temporally Weighted Regression) models to capture spatial–temporal heterogeneity. Partial Dependence Plots (PDPs) are further employed to identify nonlinear relationships, alongside mechanism analysis. The results indicate that the effects of public transportation infrastructure on air quality are significant but highly heterogeneous across cities and over time. Transport development is associated with air quality through channels related to industrial transformation and agglomeration dynamics, with the latter showing a stronger relationship. Moreover, several key variables exhibit nonlinear relationships with identifiable threshold effects. These findings suggest that the environmental benefits of public transportation infrastructure are context-dependent rather than universal. This study provides a more comprehensive understanding of transport–environment linkages and offers policy insights for optimizing urban transport systems and promoting sustainable development. Full article

Free-floating bike-sharing (FFBS) systems play a valuable role in alleviating traffic congestion and reducing carbon emissions, making them vital to sustainable urban transportation. Although extensive research has investigated the relationship between the built environment and cycling behavior, the adverse effects of air pollution and its interaction with the built environment remain insufficiently understood. In this study, multisource data from Shenzhen are used, and an XGBoost–SHAP model is employed to comprehensively investigate the nonlinear associations among the FFBS trip volume, built environment, and air pollution while considering the spatial heterogeneity in interaction effects. The results indicate that population density, road density, building density, and PM2.5 are the most influential factors. In addition, significant temporal heterogeneity is observed between weekdays and weekends. The effects of the built environment variables and their interactions are more pronounced on weekdays than on weekends. More importantly, an interaction analysis reveals that the positive influence of compact urban development on cycling is conditional: in high-density areas with elevated pollution exposure, the health risks associated with air pollution can offset or even outweigh the mobility benefits of compactness. Overall, this study identifies the complex, spatially heterogeneous mechanisms through which the built environment and air quality jointly shape FFBS usage. These findings provide important evidence for integrating environmental health considerations into compact city planning and offer practical insights for promoting cycling and sustainable urban mobility in high-density cities. Full article

Predicting origin–destination flows in high-density bike-sharing systems remains challenging due to the lack of models that jointly capture temporal dynamics and behavioral variability in urban mobility. In this study, we introduce a spatio-temporal optimal transport framework with dynamically calibrated behavioral regularization that integrates physical network costs with historical mobility priors to infer latent behavioral structure in trip patterns. Unlike static or purely predictive approaches, the proposed framework captures temporal spillovers across hourly intervals, allowing for the continuous evolution of mobility flows. We reinterpret the regularization parameter as a behavioral persistence indicator governing the trade-off between cost minimization and prior adherence. This parameter is dynamically calibrated over a 12-month period using Kullback–Leibler divergence from historical priors, enabling a behavioral diagnostic perspective on mobility regimes. Empirically, we uncover statistically significant regime shifts: weekday mobility is dominated by cost-efficient flows, whereas weekend behavior exhibits stronger adherence to historical mobility patterns and greater variability. We further identify systematic weather-related modulation, with adverse conditions associated with reduced behavioral persistence and patterns consistent with a contraction of discretionary mobility. These findings demonstrate that the proposed framework yields an interpretable behavioral metric for urban mobility systems. This has implications for adaptive mobility management, enabling data-driven rebalancing strategies that respond to temporal variation in behavioral regimes. Full article

Improving pedestrian safety at urban intersections is a key challenge for achieving safer and more sustainable urban transport systems. This study develops a multi-criteria decision-making model (MCDM) for selecting the most appropriate traffic signal type at pedestrian crossings in different urban zones. Traffic conditions, illegal pedestrian crossings and the number of traffic accidents were taken into account during the modelling, as well as the characteristics of the urban environment. The research involved 66,616 pedestrians at 22 pedestrian crossings located in three urban zones: school zones, central zones, and non-central zones. The data were aggregated using Bayesian (beta-binomial) and classical statistical methods. The OPA-Group method was then used to develop the model. In the decision-making phase, the Ordinal Priority Approach (OPA) was applied as the core MCDM method. It was then extended to the OPA-Group framework to incorporate group-based evaluation in accordance with the model requirements. Additionally, a comprehensive sensitivity analysis was conducted, confirming the robustness and stability of the proposed model. The results show that traditional traffic signals are most suitable for school and non-central zones, whereas countdown traffic signals are recommended for central zones. Push-button traffic signals were identified as the least efficient solution for regulating pedestrian movement at pedestrian crossings. Full article

Urbanisation has led to dramatic alterations in pre-existing natural environments, resulting in several subsequent phenomena, such as the disappearance of habitats suitable for many plant and animal species and the concurrent arrival of generalist and non-native species, contributing to environmental homogenisation. Towns and cities serve as crossroads for transport, people, and animals, making them susceptible to colonisation by many types of plant species, dispersed either intentionally or unintentionally by these biotic vectors. Abiotic vectors, such as wind and water, also influence the composition of vegetation assemblages. Urban spontaneous vegetation occurs in (1) undisturbed areas, including brownfield sites, commons, and marginal lots, and (2) disturbed sites, such as green areas, parks, lawns (not subject to weeding), ancient monuments and walls, peripheral and industrial areas, and railways. When disturbance occurs, vegetation remains at early successional stages. Within this framework, with the aim of comparing existing contradictions and identifying knowledge gaps, we reviewed the literature on the characteristics of spontaneous plants and vegetation in urban areas, the different habitats in which they grow, the ecosystem services they provide, and management strategies, considering human perception. Our results highlight that studies on spontaneous plants are well-developed in terms of botany and ecology; however, some gaps remain, particularly regarding their integration into urban design and maintenance practices. Concerning public perception and acceptance, cultural and geographical differences emerged that deserve further investigation. In conclusion, spontaneous plants can represent a valuable heritage for cities, helping to address the challenges posed by the climate crisis. Full article

Crowd simulation is essential for applications in evacuation planning, transportation systems, urban analytics, virtual reality, and intelligent mobility. Despite substantial progress, research in this field remains fragmented across diverse modeling paradigms, behavioral abstractions, simulation settings, implementation tools, and evaluation practices. To provide a unified overview, this study conducts a Systematic Mapping Study (SMS) of 54 peer-reviewed primary studies published between 2021 and 2025. Guided by a structured set of 15 research questions, the SMS examines dominant modeling paradigms, associated modeling techniques, spatial representations, behavioral layers, learning methods, and agent capabilities. The study further analyses simulation characteristics—including behavior types, granularity levels, temporal modes, environment types, and application domains—alongside implementation aspects such as programming tools and simulation platforms. Additionally, the mapping covers evaluation practices by identifying reported performance metrics and methodological approaches. Based on the extracted evidence, we propose a comprehensive taxonomy. The results highlight prevailing trends, gaps, and fragmentation in crowd simulation research, including uneven reporting of metrics, limited integration of learning-based methods, and inconsistencies in behavioral modeling. The study also synthesizes key technical challenges and corresponding solutions proposed in recent literature, offering a structured foundation for future research. Full article

While drones play important roles in areas such as communication and logistics delivery, they have certain limitations in emergency rescue scenarios due to their inability to carry passengers. Building on mature drone technologies such as autonomous flight and environmental perception, unmanned passenger Electric Vertical Take-off and Landing (eVTOL) aircraft are designed with a manned cabin, enabling them to operate without an onboard pilot while rapidly transporting injured people. Consequently, eVTOLs can play a significant role in emergency rescue that cargo-only drones cannot fulfill, as they are capable of rapidly reaching emergency scenes, effectively overcoming the delays caused by traditional ground traffic congestion. Despite their potential, eVTOLs still face several critical obstacles, including signal disruption, limited coverage of dispatching centers, mutual authentication among entities, and concerns related to security and privacy preservation. As a remedy, this paper presents a lightweight authentication protocol leveraging drone assistance to overcome these challenges for unmanned eVTOL emergency rescue. In scenarios where an unmanned eVTOL experiences signal blockage due to dense urban high-rise structures, neighboring drones can serve as a transmission relay to assist the unmanned eVTOL and the dispatch center (DC) in completing mutual authentication and session key negotiation, thereby enabling the unmanned eVTOL to safely complete its mission. To enhance security, physical unclonable functions (PUFs) are integrated into unmanned eVTOLs, drones, and the DC, safeguarding sensitive data against side-channel and physical capture attacks while preserving the confidentiality of unmanned eVTOL identities to mitigate privacy risks. Our protocol achieves provable security in the random oracle model while exhibiting strong resistance to various well-known attacks. Comparative analysis with the existing drone authentication and drone-assisted emergency rescue authentication protocols reveals that our protocol not only provides stronger security guarantees but also maintains a low computational overhead. Full article

Under the dual pressures of global climate change and rapid urbanization, the spatial contradiction between urban expansion and flash flood disasters in mountainous dam areas is increasingly evident. However, the mechanisms by which the multi-dimensional characteristics of urban expansion affect regional flash flood susceptibility (FFS) remain unclear, limiting scientific guidance for source-level disaster prevention. This study uses Zhaotong City, a flash flood-prone area in the lower Jinsha River basin of southwestern China, as a case study. Using land use and multi-source remote sensing data from 2000 and 2025, we identify urban expansion patterns and morphological characteristics, apply the XGBoost-SHAP model to evaluate flash flood susceptibility and determine dominant factors, and employ the generalized additive model (GAM) to quantify the nonlinear responses of expansion dimensions to FFS. Results show the following: (1) Urban expansion in Zhaotong City is primarily edge (51%) and leapfrog (46%), clustering along river valleys, dam areas, and transportation corridors. (2) The XGBoost model performs well (AUC = 0.877). Elevation, slope, normalized difference vegetation index (NDVI), and precipitation are the primary natural factors influencing FFS. About 15.66% of the city falls within the high/very high FFS zones, mainly in the Zhaolu Dam area, riverbanks of main and tributary streams, and the urban built-up area. (3) Urban expansion-related indicators explain 28.6% of the spatial variation in FFS, with leapfrog expansion as the primary driver (contribution rate 32.75%). Disorderly urban growth and morphological imbalance significantly increase flash flood susceptibility. This study provides a scientific basis for spatial planning, flash flood prevention and control, and climate-adaptive urban development in similar mountainous dam areas in Southwest China and Asia, supporting regional sustainable development goals. Full article