Search Results (676)

The penetration rate of battery electric buses (BEBs) continues to rise, and the design of BEB networks has become the foundation for establishing efficient and sustainable public transportation systems. Improving the equity of bus network and reducing the total cost of the bus system are taken as the targets, a multi-objective programming model for TNDP is proposed in this study. Among them, the Gini coefficient of bus travel times during peak hours and the direct travel proportion of the elderly during non-peak hours are used to describe the equity of the bus network. When calculating the comprehensive cost, factors such as the fleet size of battery electric buses, charging facilities requirements, and charging costs are taken into account. To enhance the reliability of the obtained results, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to generate the Pareto-optimal solution set. The Mandl’s benchmark network is used for comparative validation, and a case study based on the road network of Zhengzhou is undertaken. Calculation results indicate that the proposed model not only minimizes the total travel costs but also significantly reduces the Gini coefficient of the transportation mode distribution. Under the constraint of overall expenses, it effectively improves the equity and the direct travel proportion of the elderly served by the bus system. The results can provide quantitative support to formulate livelihood transportation policies for local government and optimize the allocation of public transportation resources. Full article

Traditional multimodal public transportation recommenders often overlook in-vehicle crowding, a critical factor that causes passenger discomfort and leads to an inefficient distribution of people across the network that affects its reliability. To address this, we propose a proof of concept for a novel framework that directly integrates crowding into its optimization process, balancing it with user preferences such as travel habits, travel time, and line changes. Built on the Behavior-Enabled IoT (BeT) paradigm, our system is designed to manage the crucial QoE and QoS trade-off inherent in smart mobility. We validate our balanced strategy using real-world data from Lyon, comparing it against two baselines: a QoE-driven model that prioritizes user habits and a QoS-driven model that focuses solely on network efficiency. Our Wilcoxon-based statistical analysis demonstrates that a balanced strategy is the most effective approach for substantially mitigating public transit crowding. Our Wilcoxon-based statistical analysis demonstrates that a balanced strategy is the most effective approach for mitigating public transit crowding, since it leads to a substantial decrease in crowding. Despite a potential increase in travel times, our solution respects user habits and avoids excessive transfers, providing significant operational improvements without compromising passenger convenience. Full article

The growing demand for fast and sustainable urban deliveries has accelerated exploration of the use of Unmanned Aerial Vehicles as viable logistics solutions for the last mile. This study investigates the integration of a distributed multi-agent system with a structured three-dimensional Urban Aerial Network (3D-UAN) for drone delivery operations. The proposed architecture models each drone as an autonomous agent operating within predefined air corridors and communication protocols. Unlike traditional approaches, which rely on simplified 2D models or centralized control systems, this research exploits a multi-layered 3D network structure combined with decentralized decision-making for improving scalability, safety, and responsiveness in complex environments. Through agent-based simulations, this study evaluates the operational performance of the proposed system under varying fleet size conditions, focusing on travel times and system scalability. Preliminary results demonstrate that the potential of this approach in supporting efficient, adaptive, resilient logistics within Urban Air Mobility frameworks depends on both the size of the fleet operating in the 3D-UAN and constraints linked to the current regulations and technological properties, such as the maximum allowed operational height. These findings contribute to ongoing efforts to define robust operational architectures and simulation methodologies for next-generation urban freight transport systems. Full article

To overcome the limitations of traditional methods in emergency response scenarios—such as limited adaptability during the search process and a tendency to fall into local optima, which reduce the overall efficiency of emergency supply distribution—this study develops a Vehicle Routing Problem (VRP) model that incorporates multiple constraints, including service time windows, demand satisfaction, and fleet size. A multi-objective optimization function is formulated to minimize the total travel time, reduce distribution imbalances, and maximize demand satisfaction. To solve this problem, a hybrid deep reinforcement learning framework is proposed that integrates an Adaptive Large Neighborhood Search (ALNS) with Proximal Policy Optimization (PPO). In this framework, ALNS provides the baseline search, whereas the PPO policy network dynamically adjusts the operator weights, acceptance criteria, and perturbation intensities to achieve adaptive search optimization, thereby improving global solution quality. Experimental validation of benchmark instances of different scales shows that, compared with two baseline methods—the traditional Adaptive Large Neighborhood Search (ALNS) and the Improved Ant Colony Algorithm (IACA)—the proposed algorithm reduces the average objective function value by approximately 23.6% and 25.9%, shortens the average route length by 7.8% and 11.2%, and achieves notable improvements across multiple performance indicators. Full article

Wet markets serve as critical infrastructure for access to fresh food for urban residents in China, playing a vital role in daily life and public well-being. However, their accessibility is often shaped by disparities between urban cores and rapidly expanding peripheral districts, raising concerns over spatial equity in the urban food environment. This study investigates these disparities in Shanghai by comparing wet market accessibility in Putuo district (urban core) and Minhang district (periphery). Accessibility is measured using the Gaussian-enhanced two-step floating catchment area (2SFCA) method, incorporating travel time data from the Baidu Map API for multiple transportation modes. The Gini coefficient is further employed to evaluate the equity of accessibility distribution. The results reveal a notable disparity: residents in the periphery (Minhang) experience a higher average level of accessibility, but their access is distributed significantly less equitably compared to those in the traditional urban core (Putuo). These findings underscore a critical trade-off between development efficiency and spatial equity, highlighting the need for targeted planning strategies and policies to address spatial inequalities in fresh food access in rapidly transforming cities. Full article

Addressing the imbalance between supply and demand for urban parks necessitates an assessment of their service accessibility and spatial equity. This study integrates multi-source geographic data, uses multiple data sources to generate a population distribution with high spatial resolution, and constructs park service areas with multiple time thresholds based on travel preference surveys. The network analysis method is used to evaluate the supply–demand ratio and spatial equity by using location entropy, Lorenz curves, and the Gini coefficient to identify the optimal location. The results reveal a significant difference in the supply–demand ratio of parks. Within the 5 min time threshold, only 14.68% of the pixels in the park supply area meet the needs of residents, while the proportions for the 15 min and 30 min time service area expands to 71.74% and 86.34%, respectively. The distribution of parks exhibits apparent spatial inequity. Equity is highest for the 15 min service area (Gini coefficient = 0.25), followed by the 30 min area (Gini coefficient = 0.27) and 5 min areas (Gini coefficient = 0.37). Among the 80 streets in the study area, the per capita green space location entropy of 11 streets is zero. A targeted site selection analysis for areas with park supply deficiencies led to the proposed addition of 11 new parks. After this optimization, the proportion of regions achieving supply–demand balance or better reached 80.38%, significantly alleviating the supply–demand conflict. This study reveals the characteristics of park supply–demand imbalance and spatial equity under different travel modes and time thresholds, providing a scientific basis for the precise planning and equity enhancement of parks in high-density cities. Full article

Given the projection of the impact of climate change and the uncertainty caused by geopolitical volatility, minimising emissions has become an urgent priority for the shipping industry. In this context, the aim of the present study is the calculation and estimation of emissions generated by ship operations within a maritime transportation network, as well as the identification of the optimal route that minimises both emissions and travel time. Emission estimation is carried out using methodologies and assumptions from the Fourth IMO GHG Study. The decision-making, along with the optimisation process, is performed through backward dynamic programming, following a multi-objective optimisation framework. Specifically, the analysis is carried out on both a theoretical and a realistic network. In both cases, various scenarios are examined, including different approaches to vessel speed, some of which incorporate probabilistic speed distributions, as well as scenarios involving uncertainty regarding port availability. Additionally, the resilience of the network is examined, focusing on the additional burden in terms of emissions and travel time when a port is unexpectedly unavailable and a route adjustment is required. The calculations and optimisation are carried out using Excel and the @Risk software by Palisade, with the latter enabling the incorporation of probability distributions and the execution of Monte Carlo simulations. Full article

Background: The pressure on the transport sector to decarbonise intensifies the need to look beyond the usual recommendations (e.g., walking, cycling, technological innovations). Therefore, strategies to avoid or modify commutes to places of work have long been seen as an option to decarbonise. Recognised for achieving an optimal balance between working from home and working in an office, co-working spaces may also minimise the length of commutes and therefore reduce emissions, traffic congestion, road maintenance, stress experienced by drivers, and other negative externalities of traffic. Methods: This study quantifies the above using a digital model of England and Wales. Two distributions of co-working spaces have been compared in this paper (i.e., one co-working space (i) in each Middle-layer Super Output Area or (ii) at the nearest train station). Results: The overall reduction in travel time and distance exceeds 70% if everyone who commutes by car outside their home MSOA drives to a co-working space. Despite a change in the place of work having no impact on the cold start emissions, substantial emission savings can still be achieved. These range from 35.8% to 92.1% depending on the pollutant, scenario, and distribution of co-working spaces. Full article

Accessibility is a key dimension for sustainable transport network management and planning. However, conventional location-based accessibility measures typically rely on average travel times as the sole temporal metric, neglecting detailed travel time distributions. Consequently, these methods yield identical accessibility values for study zones with the same mean travel time but different travel time variations. To overcome this limitation, we developed a novel approach that explicitly integrates the probability density distributions of travel times, modelling the impact of travel time variability on accessibility. We applied the proposed method using GPS data collected from taxis in Harbin, China, and compared its outcomes with those from existing potential accessibility calculations. Across all 103 study zones in Harbin, the existing method underestimated the accessibility by 6–28%, with an average underestimation of 17% when benchmarked against the new method. These inaccuracies also impaired the identification of urban areas with the lowest accessibility levels, leading to the misclassification of 20% of problematic zones. The findings highlight the limitations of existing methods, which produce biassed accessibility estimations and misleading results. In contrast, the proposed travel time variability-integrated accessibility measure demonstrates greater sensitivity to actual traffic conditions, providing a more accurate and objective assessment of network performance. Full article

Mountainous urban areas like Guiyang face unique fire safety challenges due to rugged terrain and complex road networks, which hinder fire station accessibility. This study proposes a GIS-based framework that integrates nighttime light (NPP/VIIRS) and point of interest (POI) data to assess fire risk and accessibility. Kernel density estimation quantified POI distributions across four risk categories, and the Spatial Appraisal and Valuation of Environment and Ecosystems (SAVEE) model combined these with NPP/VIIRS data to generate a composite fire risk map. Accessibility was evaluated using the enhanced two-step floating catchment area (E2SFCA) method with road network travel times; 80.13% of demand units were covered within the five-minute threshold, while 53.25% of all units exhibited low accessibility. Spatial autocorrelation analysis (Moran’s I) revealed clustered high risk in central basins and service gaps on surrounding hills, reflecting the dominant influence of terrain alongside protected forests and farmlands. The results indicate that targeted road upgrades and station relocations can improve fire service coverage. The approach is scalable and supports more equitable emergency response in mountainous settings. Full article

Spatial accessibility is a key principle in urban studies, shaping how people reach amenities and services across cities. While most research concentrates on large metropolitan areas and central urban services, small and medium-sized cities and their main amenities remain less studied. To bridge this gap, this study explores spatial accessibility to public amenities in relation to population density in Odense, a medium-sized city known for its compact layout and robust infrastructure supporting walking, cycling, and public transport. Despite Odense’s proactive planning and multimodal transport network, marked accessibility inequalities still exist, especially in peripheral neighbourhoods. This research uses a data-driven approach combining network-based travel time analysis with grid-cell-based spatial visualisation. Additionally, a multi-criteria accessibility scoring framework is introduced, including indicators such as amenity density, diversity of services, temporal thresholds for walking and cycling, and population distribution. The results show an uneven accessibility landscape, with significant gaps in outer districts, highlighting the limitations of uniform planning thresholds. By applying spatial analytical principles, the study uncovers embedded socio-spatial inequalities in everyday urban access. These insights offer practical guidance for planners and policymakers, underscoring the importance of context-sensitive multimodal infrastructure and decentralised service provision to support sustainable urban growth. Full article

Background/Objectives: The “TROVAILMIOVACCINO” platform was developed to help adults in Italy identify vaccines recommended for them based on individual characteristics, in line with the Italian National Immunization Plan (NIP). The website directs users to an anonymous online questionnaire addressing key factors such as age, sex, pregnancy status, travel history, medical conditions, and risky behaviors. It is intended for adults aged 18 and over and can be filled out either by individuals or by others on their behalf, such as healthcare professionals. The purpose of the study was to assess the platform’s reach, the health status of users, and its ability to inform users. Methods: Data were organized into tables and analyzed using frequencies, percentages, and statistical tests to assess user demographics and health conditions. Significant differences among sociodemographic groups were evaluated using the Chi-square and Fisher’s exact tests. Results: Over 30 months, the website was accessed 1897 times, with 1622 users (85.5%) completing the questionnaire for personal interest. The majority of users were aged 18–49 years (61.5%), with a nearly equal male–female distribution. Healthcare workers represented the most common professional group (29.2%) among users. Older individuals were more likely to have the questionnaire completed by someone else. Among respondents, 25.8% reported having a single medical condition, with cardiovascular diseases (11.9%), diabetes (6.7%), and respiratory diseases (4.8%) being the most frequent. The most common risk condition reported was potential contact with newborns. Conclusions: The findings highlight the value of the platform in reaching diverse user groups and offering tailored vaccine recommendations. Full article

This study aims to use Computational Fluid Dynamics (CFD) analysis to improve inclusion removal efficiency in tundishes used in the steelmaking industry, with the broader goal of promoting more sustainable steel production and supporting circular economy objectives by producing cleaner steel. Inclusions are non-metallic particles, such as alumina, that enter the tundish with the molten steel and travel through it; if not removed, they can exit through the nozzles and adversely affect the mechanical properties of the final product and process yield. An existing tundish design is modified using three passive techniques, including adding a vertical dam, adding a horizontal baffle, and inclining the side walls, to assess their influence on fluid flow behavior and inclusion removal. Residence time distribution (RTD) analysis is employed to evaluate flow characteristics via key metrics such as dead zone and plug flow volume fractions, as well as plug-to-dead and plug-to-mixed flow ratios. In parallel, a discrete phase model (DPM) analysis is conducted to track inclusion trajectories for particles ranging from 5 to 80 μm. Results show that temperature gradients due to heat losses significantly influence flow patterns via buoyancy-driven circulation, changing RTD characteristics. Among the tested modifications, inclining the side walls proves most effective, achieving average inclusion removal improvements of 8% (Case B1) and 19% (Case B2), albeit with increased heat loss due to greater top surface exposure. Vertical dam and horizontal baffle, despite showing favorable RTD metrics, generally reduce the inclusion removal rate, highlighting a disconnect between RTD-based predictions and DPM-based outcomes. These findings demonstrate the limitations of relying solely on RTD metrics for evaluating tundish performance and suggest that DPM analysis is essential for a more accurate assessment of inclusion removal capability. Full article

Maintaining operational efficiency in distributed solar energy systems requires intelligent coordination of inspection tasks and workforce resources to handle diverse fault conditions. This study presents a bi-level multi-objective optimization framework that addresses two tightly coupled problems: dynamic job scheduling and skill-based team formation. The job scheduling component assigns geographically dispersed inspection tasks to mobile teams while minimizing multiple conflicting objectives, including travel distance, tardiness, and workload imbalance. Concurrently, the team formation component ensures that each team satisfies fault-specific skill requirements by optimizing team cohesion and compactness. To solve the bi-objective team formation problem, we propose HMOO-AOS, a hybrid algorithm integrating six metaheuristic operators under an NSGA-II framework with an Upper Confidence Bound-based Adaptive Operator Selection. Experiments on datasets of up to seven instances demonstrate statistically significant improvements ($p<0.05$) in solution quality, skill coverage, and computational efficiency compared to NSGA-II, NSGA-III, and MOEA/D variants, with computational complexity $\mathcal{O}\left(G·N·(M+logN)\right)$ (time complexity), $\mathcal{O}(N·L)$ (space complexity). A cloud-integrated system architecture is also proposed to contextualize the framework within real-world solar inspection operations, supporting real-time data integration, dynamic rescheduling, and mobile workforce coordination. These contributions provide scalable, practical tools for solar operators, maintenance planners, and energy system managers, establishing a robust and adaptive approach to intelligent inspection planning in renewable energy operations. Full article

To improve the uniformity and precision of soybean seeding, this study designed a high-speed airflow-assisted seeding system for the pneumatic drum-type high-speed precision seed-metering device. The system accelerates seed delivery through airflow and ensures precise seed placement using a seed press wheel. Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) coupling simulations were employed to analyze the seed motion trajectory, collision process, and velocity changes. Key design parameters of the airflow-assisted delivery system were optimized, including a tube diameter of 16 mm, a curved section radius of 80 mm, a seed delivery angle of 33.65°, and a press wheel diameter of 254 mm. The simulation results indicated that the relative position between the seed delivery tube and the seed drum significantly impacts seed trajectory and uniformity. Lowering the tube to align with the seed velocity direction minimized collisions and enhanced seed spacing consistency during high-speed operation. Increasing inlet air pressure improved seed distribution uniformity by accelerating seeds within the tube, reducing travel time and collisions; a 500 Pa pressure increase raised the maximum flow velocity by approximately 5 m/s. However, seed acceleration exhibited diminishing returns: pressure increase from 2.5 kPa to 3.5 kPa increased seed speed by 2.1 m/s, while a further increase to 4.5 kPa only added 1.1 m/s. The optimal inlet pressure for efficient energy transfer and seed acceleration was approximately 3.5 kPa. The press wheel played a crucial role by dispersing the impact force when seeds contact the soil, which achieved high capture rates above 94.0% across the seed drum rotary speed range of 11 to 19 rpm. This research provides theoretical and experimental support for the optimization of high-speed airflow-assisted seeding systems, offering significant practical value for large-scale agricultural production by enhancing seeding efficiency and quality. Full article