Search Results (1,755)

Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application. Full article

The implementation of safe and reliable Autonomous Driving Stacks in complex urban environments remains a formidable engineering challenge. While classical modular pipelines provide necessary component-level interpretability, they are inherently rigid, often struggling to adapt to novel environments and failing to provide robust scene interpretation in highly interactive scenarios. In this paper, we present a modular End-to-End ROS-based autonomous driving architecture that upgrades a classical modular baseline by injecting learning-based models into its individual processing layers, integrating GaussianCaR and CLIP for dense semantic BEV perception, expanding the Hierarchical Petri Net state space for safe multi-agent reasoning, refining the planning layer with continuous curve optimization, and replacing the previous reactive controller with an Adaptive Nonlinear Model Predictive Control strategy for superior trajectory tracking. Validated in the CARLA simulator across challenging traffic scenarios and adverse environmental conditions, the proposed architecture raises the Driving Score from 53.81% to 66.46% over the previous baseline, driven by a substantial increase in the Infraction Penalty from 0.59 to 0.79, reflecting a fundamental shift towards safer and more conservative driving behavior at the cost of a moderate reduction in route completion. Against pure End-to-End approaches, our architecture achieves the highest Driving Score at 73.9% and the strongest Infraction Penalty at 0.913, demonstrating that modular interpretability and competitive End-to-End performance are not mutually exclusive. Code will be made publicly available online. Full article

Advanced biofuels represent a key option for transport decarbonization, particularly in sectors where electrification is constrained by technical and economic barriers. Their compatibility with existing vehicle fleets and fuel distribution infrastructure enables rapid deployment without the need for major capital investments. In local public transport, biodiesel (FAME), hydrotreated vegetable oil (HVO), and biomethane are mature solutions capable of delivering greenhouse gas emission reductions of 60–90% compared with fossil fuels. Among these, HVO is particularly promising, as an extensive body of literature has consistently shown its potential to significantly reduce engine-out emissions, especially particulate matter (PM) and nitrogen oxides (NO_x). This study reports the results of an experimental campaign carried out on a diesel-powered local public transport bus equipped with a Euro III engine and lacking particulate matter and NO_x after-treatment systems. Emissions were measured using a portable emissions measurement system (PEMS) under real driving conditions, operating the vehicle with neat diesel, a 15% HVO blend, and a 70% HVO blend. Tests were conducted over urban and extra-urban routes. The results show that NO_x emissions decrease proportionally with increasing HVO content, with high-blend ratios (HVO70) yielding estimated reductions of approximately 13–18%, and up to 23% under carefully controlled and comparable urban driving conditions. Based on these findings and the existing literature, HVO proves to be a useful instrument to meet 2025–2030 climate and air quality targets (particularly NO_x and PM emission reductions), alongside electrification and modal shift measures, if used in public transport fleets. Full article

The electrification of urban transport has made battery electric buses (BEBs) an important option for reducing carbon emissions and improving urban air quality. However, the high investment cost of charging infrastructure and the uncertainty in effective usable battery capacity at the day-ahead scheduling stage—caused by accumulated degradation, heterogeneous operating conditions, and imperfect state estimation—create major challenges for charging infrastructure siting and daily bus operations. This study proposes a joint optimization model for infrastructure siting and BEB charging scheduling, in which effective capacity uncertainty is handled using a distributionally robust optimization (DRO) framework. To solve the resulting mixed-integer nonlinear program efficiently, we develop a matheuristic decomposition method that integrates Adaptive Large Neighborhood Search (ALNS) with small gaps relative to a relaxation-based lower bound. Computational experiments based on real-world bus route data indicate that the proposed framework obtains high-quality solutions with small gaps relative to a relaxation-based lower bound, performs better than representative benchmark heuristics, and scales well to large instances. Full article

With the expansion of metro networks and the increasing integration of underground retail and transit facilities, metro-connected underground commercial spaces have become a common yet safety-sensitive urban form. In fire scenarios, evacuation in such environments is constrained not only by enclosure and limited egress capacity, but also by the interaction between smoke spread and strongly coupled pedestrian flows across connected zones. Existing studies have examined smoke propagation or evacuation performance in underground spaces, but fewer have explicitly addressed how smoke constraints reshape node-level safety and the relative effectiveness of different intervention strategies in metro-connected commercial environments. This study investigates smoke-constrained evacuation dynamics in a representative metro-connected underground commercial space in Nanjing, China. A coupled simulation framework integrating PyroSim and Pathfinder is employed to examine multiple fire-source scenarios. Available safe egress time (ASET) at critical evacuation nodes is assessed using tenability criteria including visibility, temperature, and CO concentration, and is then compared with evacuation performance to diagnose hazardous routes and node-level failures. On this basis, three intervention strategies—corridor widening, stair widening, and pedestrian diversion—are comparatively evaluated. The results show that, within the modeled case, visibility most frequently becomes the controlling tenability criterion, and stairway nodes tend to lose safety margins earlier than final exits. This indicates that smoke constraints in connected underground commercial environments can trigger an early node-failure process before overall exit capacity is exhausted. The comparison further shows that behavior-oriented pedestrian diversion is more effective than geometric enlargement alone in reducing critical-node pressure and improving system-level evacuation performance under the modeled conditions. Rather than proposing universally transferable design rules, this study provides case-grounded evidence on how smoke propagation and pedestrian convergence jointly shape evacuation vulnerability in metro-connected underground commercial spaces, and offers a structured basis for critical-node diagnosis and intervention comparison in similarly configured environments. Full article

With the increasing frequency of extreme weather events, the vulnerability of urban power-transportation systems in severe dust storm conditions has become increasingly apparent. Addressing the shortcomings of existing research regarding the quantitative assessment and enhancement of system resilience, this paper proposes a set of strategies and methods for evaluating and improving the resilience of urban power-transportation systems under severe dust storm conditions, taking mobile energy storage into account. The study first establishes a multidimensional failure probability model for severe dust storm conditions: on the power grid side, it comprehensively considers fluctuations in renewable energy output, wind speed variations, and line insulation performance to propose a probabilistic failure model that accounts for the sand accumulation effect; on the transportation side, it considers road visibility and traffic flow to propose an improved BPR traffic flow model, using the Floyd algorithm to plan MES travel routes. Fault scenarios are generated using the Monte Carlo algorithm, and multidimensional system performance metrics for the power grid–road network-coupled nodes are established. A quantification method for resilience metrics applicable to urban power-transportation systems is proposed based on the ΦΛEΠ resilience index. Furthermore, a multi-objective, multi-stage resilience enhancement strategy for urban power-transportation systems that incorporates mobile energy storage is proposed using the NSGA-II algorithm. Finally, the effectiveness of the proposed optimization strategy was verified through coupled simulation cases using the IEEE-33 node test system and the Sioux Falls network. The results demonstrate that the proposed optimization strategy can significantly enhance system resilience under different optimization objectives. Full article

The transition to electric vehicles (EVs) in urban logistics presents complex operational challenges, driven primarily by limited battery capacities, charging station scheduling, and dynamic traffic congestion. This paper introduces a framework to solve the Capacitated Multi-Depot Electric Vehicle Routing Problem (MD-EVRP). We propose a novel Multi-Depot Rotational Sweep Cluster K-means (MD-RSCK) algorithm to partition large-scale spatial data while strictly adhering to vehicle capacity constraints. To optimize intra-cluster routing, we develop an Ant Colony Optimization (ACO) engine augmented with a Time-Dependent Congestion Model. Furthermore, the framework integrates an Energy-Aware Route Refiner (EARR). This architecture utilizes recursive backtracking to ensure battery-feasible routes, adapting to both symmetric Euclidean approximations and real-world asymmetric traffic networks. The framework is evaluated against standard IEEE EVRP benchmarks and a multi-depot urban case study based on the road network of Shanghai, China. Experimental results demonstrate that this integrated architecture achieves competitive distance and cost metrics within a 2.44% optimality gap of state-of-the-art algorithms while ensuring strictly feasible battery states and preventing cyclic entrapment, providing a scalable operational tool for modern sustainable logistics. Full article

Rapid urbanization, uncoordinated land-use growth, and insufficient integration of public transport have led to severe traffic congestion and declining mobility in Peshawar, Pakistan, even after the implementation of a Bus Rapid Transit (BRT) system. The core research problem addressed in this study is the mismatch between growing travel demand and the limited capacity, coverage, and operational efficiency of the existing urban transport network. This research aims to evaluate the current performance of Peshawar’s transport system and to identify integrated, evidence-based strategies to alleviate congestion and enhance urban mobility. Specifically, the objectives are to assess roadway level of service on major corridors, examine public transport user satisfaction with the BRT system, and propose targeted infrastructure and operational improvements. A mixed-methods approach was employed, combining traffic volume and level-of-service (LOS) analysis, public transport user surveys, and field observations at critical intersections. The findings indicate that several key arterial roads operate at LOS E–F during peak hours, and future traffic projections indicate widespread capacity failures under existing road geometries. Survey results reveal significant dissatisfaction with the BRT system, particularly due to limited spatial coverage, inadequate feeder routes, overcrowding, and excessive travel times. Based on these results, the study proposes integrated interventions, including road widening and auxiliary lanes, geometric and signalized junction improvements, expansion of BRT feeder services, development of new arterial and ring roads, and enhanced pedestrian and parking infrastructure. This study links quantitative traffic performance measures with user-perceived service deficiencies. It provides practical, data-driven guidance for policymakers and planners to support a more efficient, accessible, and sustainable urban transport system in Peshawar. Full article

The rapid growth of populations and industrial activities has intensified the need to optimize resource management and reduce environmental impacts. A promising pathway toward sustainable development is the gradual replacement of fossil fuel vehicles with electric vehicles (EVs). However, managing EV operations, particularly regarding depot siting and vehicle routing, is a complex challenge that requires balancing economic, environmental, and social objectives. This research proposes a model for designing an intelligent and sustainable transportation system for waste collection using EV fleets. The model simultaneously determines optimal depot locations from a set of candidates and identifies efficient vehicle routes. Its dual objectives are to minimize total costs, including depot set-up, operation, and travel costs, and to minimize maximum travel time, ensuring equitable workload distribution among drivers. Beyond reducing costs and emissions, the model incorporates social equity considerations in balancing driver travel times. EV limitations, such as restricted range, are explicitly addressed. To solve small-scale instances, the $ϵ$-constraint method was applied, while medium- and large-scale instances were tackled with two multi-objective metaheuristics: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The results demonstrate the model’s sensitivity to system parameters such as vehicle capacity and demand rates. Statistical comparative analysis revealed that both algorithms successfully optimized the primary objective functions without significant differences. However, they exhibited distinct performance metric strengths; NSGA-II demonstrated statistically significant advantages in computational efficiency, solution quantity, and uniform distribution, while MOPSO excelled in convergence quality and closeness to the true Pareto front. Furthermore, the practical applicability of the proposed model is validated through a real-world case study of a municipal solid waste management network in Southern Tehran. This research contributes a comprehensive framework for optimizing EV-based waste collection systems, offering a meaningful step toward sustainable and intelligent urban transportation. The findings provide a theoretical framework and strategic insights for transportation managers and policymakers seeking effective strategies for environmentally responsible and socially equitable waste collection. Full article

This paper investigates the path-planning problem of unmanned aerial vehicles (UAVs) for logistics delivery in urban environments. The impact of real-time obstacle avoidance and path smoothness on the flyability of UAVs remains a challenge in existing research. To address the issue that the path generated by the traditional Rapidly exploring Random Tree (RRT) algorithm exhibits a sudden slope change at connection points, which makes the UAV non-flyable, this paper proposes an improved algorithm that combines the traditional RRT algorithm with Bézier curves. The proposed real-time path generation strategy consists of two stages. The first stage constructs the environment model. The second stage integrates the RRT algorithm with Bézier curves, enabling the generated route to achieve real-time obstacle avoidance while being smooth and free of curvature discontinuities. Simulation experiments compare the improved algorithm with the traditional RRT algorithm and global path optimization methods. The experimental results show that the improved algorithm has the advantage of real-time obstacle avoidance, and the generated route is smooth at connection points with no curvature discontinuities, thereby ensuring good flyability. Full article

Walkability measurement has expanded from a small set of generic neighbourhood-level proxies into a heterogeneous field of indices, scored audits, perceived-environment scales, route-based instruments, and context-specific assessment instruments. This expansion has improved the sensitivity of walkability research, but it has also made it increasingly difficult to judge which instruments are appropriate for different users, walking purposes, and urban or environmental contexts. This review addresses this problem by comparing operational walkability instruments through an adapted walking-needs framework derived from the Expanded Hierarchy of Walking Needs (HoWN). Publications from 1990 to 2025 were identified through a structured search and screening workflow covering general walkability measurement, population-sensitive instruments, purpose-specific instruments, climate- and exposure-sensitive instruments, and urban-form-specific instruments. After eligibility assessment and consolidation, 46 walkability instruments were retained for comparative analysis. For this review, the framework is adapted from a behavioural hierarchy into an instrument-level comparative structure, with feasibility re-specified as basic environmental passability. The instruments are then compared across five tiers: feasibility, accessibility, safety, comfort, and pleasurability. The review shows that walkability measurement remains strongly concentrated in lower-order and functionally measurable dimensions, especially pedestrian infrastructure, destination access, connectivity, and traffic safety. By contrast, comfort, pleasurability, environmental exposure, personal security, and user-specific constraints are less consistently formalised and often appear only in specialised instruments. Population-, purpose-, climate-, and urban-form-sensitive instruments do not merely add indicators; they alter which walking needs become foundational in specific assessment scenarios. This review contributes a fit-for-purpose comparative logic for walkability measurement. It shows how a shared walking-needs framework can be used to diagnose coverage imbalance, identify scenario-specific threshold conditions, and guide the selection, adaptation, and transfer of instruments across different users, walking purposes, environmental exposures, and urban forms. Full article

In recent decades, residential mobility has emerged as a fundamental interpretative key lens for understanding contemporary urban transformations, particularly in polycentric and fragmented urban contexts. Movements between different residential settings reflect economic, social and cultural changes, impacting the organisation of urban spaces, the demand for services and mobility systems. In territories characterised by dispersed settlement patterns and strong functional polarisation, these dynamics tend to promote the intensive use of private means, with consequent negative impacts on environmental sustainability, social equity and economic efficiency. In response to these critical issues, there is growing interest in sustainable mobility models based on proximity and on the integration between daily travel, access to services and the quality of public space. Within this perspective, greenways are configured as hybrid infrastructures, capable of reorganising mobility while contributing to the regeneration of urban spaces. In the Caserta area, in the Campania region, the disused route of the former Alifana railway represents a topic of great interest, both for research and planning. Its potential strategic conversion into a greenway opens a broader perspective than that so far considered at the regional level, which has mainly focused on the infrastructure dimension. The paper analyses the strengths and weaknesses of an approach limited to infrastructural mobility, proposing a comparative evaluation of project scenarios—including the non-intervention hypothesis—both through the application of the MACBETH approach and preliminary parametric estimation of construction costs, in order to emphasise the importance of integrating social and environmental benefits, as well as quality of life, into decision-making processes. Full article

Domestic cats (Felis catus) are ubiquitous companion animals that provide substantial psychological and social benefits to children and adults alike, but they also serve as reservoirs and vectors for a wide range of zoonotic pathogens. Close physical contact between cats and children, frequent use of shared environments such as homes, playgrounds, and sandboxes, and still-developing hygiene behaviours increase opportunities for exposure to protozoa, helminths, bacteria, fungi, and ectoparasite-borne agents. This review synthesizes current evidence on key feline-associated zoonoses of pediatric concern—including Toxoplasma gondii, Toxocara cati, Ancylostoma spp., Dipylidium caninum, Bartonella henselae, Salmonella enterica, Campylobacter jejuni, Pasteurella multocida, Microsporum canis, flea-borne Rickettsia species, and rabies—with emphasis on transmission routes, clinical manifestations, and risk modifiers in children, pregnant women, and immunocompromised individuals. Within a One Health framework, we also summarize global publication trends on feline zoonoses, discuss how urban cat ecology and management (including free-ranging cats in child-frequented environments) may shape pediatric risk, and outline practical prevention strategies centred on hygiene, veterinary care, and targeted education for caregivers and children. Full article

With the rapid growth of urban motorization, personalized travel modes, including taxis and private cars, have expanded considerably. However, conventional public transportation systems, constrained by fixed routes and limited service flexibility, often struggle to satisfy residents’ increasingly diversified and high-quality commuting needs. To address this issue, this study proposes an integrated planning framework for customized bus services using taxi trajectory data. First, passenger origin–destination (OD) information is extracted by detecting changes in the taxi passenger-status field. The extracted OD records are then used to identify potential commuting demand by jointly considering peak-hour travel characteristics and regional OD stability. Second, the identified potential commuting demand is used to generate candidate boarding and alighting stops through an improved DBSCAN-based clustering method, namely IDK-SG. For route planning among the candidate stops, a bi-objective optimization model is developed to simultaneously account for passenger travel-time costs and bus operating costs, and the model is solved using a genetic algorithm. Finally, timetable optimization is formulated as a Markov decision process and solved using a Deep Q-Network (DQN) algorithm. Case studies using taxi GPS trajectory data from Chongqing demonstrate that the proposed framework can effectively identify stable commuting demand, optimize stop layouts and route schemes, and improve vehicle occupancy and service quality. These findings provide practical decision-making support for the operation and dynamic scheduling of customized bus services in urban peak-hour commuting corridors. Full article

Recognizing the limitations of traditional vehicle routing models in urban environments, this work presents the Time-Dependent Truck-Drone Green Vehicle Routing Problem with Pickup and Delivery (TDTDGVRPPD) to simultaneously optimize environmental impact and operational efficiency. We first develop a truck fuel consumption and carbon emission model that accounts for the effects of time-varying speeds and real-time loads during delivery. A nonlinear energy consumption model is then proposed for drones, considering payload weight. Based on these models, a mathematical formulation is developed to minimize the total operational cost, including truck and drone usage costs, truck fuel and carbon emission costs, drone energy consumption costs, truck–drone coordination time costs, and time-window violation penalties. The model also incorporates truck no-entry zones, time-varying speeds, and customers’ simultaneous pickup and delivery demands. An Improved Whale Optimization Algorithm (IWOA) hybridized with Variable Neighborhood Search (VNS) is developed to solve the problem. Simulation results show that the proposed model and algorithm effectively optimize truck departure times to avoid traffic congestion, reduce truck–drone coordination time, and lower total logistics costs and energy consumption, thereby contributing to energy conservation and emission reduction in logistics operations. Full article