Jie Ma

This study provides a spatiotemporal characteristic analysis method based on multi-source data fusion and origin–destination (OD) deduction for the field of intelligent bus dispatching. First, it comprehensively considers the impact of data from the foundation layer, interaction layer, and output layer. Combining the Dempster–Shafer theory evidence method with Jensen–Shannon divergence to enhance fusion credibility can ensure the quality of collected data and the accuracy of its fusion. Second, based on multi-source data cross fusion, the critical value of travel time difference is used to solve the number of passengers boarding at each station, and the travel chain method and attraction method are used to solve the number of passengers disembarking at each station. Finally, based on OD information, K-means clustering analysis was used to analyze the characteristics of bus operating time. A spatial delay propagation analysis was conducted by combining the running time between stations, the stopping time at stations, and the waiting time at intersections. This study can optimize the OD attraction intensity and address the issue of low accuracy in cross-layer data fusion. At the same time, the study can comprehensively obtain spatiotemporal distribution characteristics and provide decision-making support for optimizing bus routes.

As global supply chains increasingly prioritize environmental sustainability and operational efficiency, battery electric freight vehicles (EFVs) have emerged as a pivotal alternative to traditional diesel-powered logistics fleets. This paper addresses the integrated planning and scheduling problem for multi-modal logistics systems utilizing EFVs. An integrated model is proposed to determine the number of electric freight vehicles and optimize dispatch and charging schedules, considering deadheading, and time-of-use electricity pricing. The model is formulated as an integer linear programming (ILP) problem solvable by commercial solvers. A branch-and-price framework and a heuristic algorithm are developed to handle large-scale instances. A case study using real data from a logistics provider in China demonstrates that the EFV system achieves a 45.5% reduction in total monthly costs compared to traditional diesel freight vehicle systems, even after accounting for higher vehicle and infrastructure costs. Sensitivity analyses offer practical insights for EFV adoption in multi-modal logistics.

Urban transportation systems are facing increasingly severe threats from extreme weather events such as rainstorms, which can trigger cascading failures and lead to regional traffic paralysis. The strategic location of emergency facilities to enhance system resilience has emerged as a critical proactive prevention strategy. This study proposes a multi-objective hierarchical coverage location model that integrates point and flow demands to improve the resilience of urban road traffic systems under rainstorm conditions. First, the resilience risk levels of road nodes were quantified using an entropy-weighted TOPSIS method that combines topological attributes, traffic flow performance, and indirect propagation intensity. Second, a flow-capturing mechanism was introduced to address the dynamic rescue demands of stranded vehicles in motion, enabling the pre-positioning of “safe havens” along critical travel routes. The model balances two objectives: maximizing the resilience risk value of the covered demands and minimizing facility construction costs. A case study was conducted in Jianghan District, Wuhan, a flood-prone area, and the NSGA-II algorithm was employed to solve the multi-objective optimization problem. The results demonstrate that the proposed model significantly outperforms traditional single-demand location models in terms of coverage effectiveness and cost efficiency, achieving improvements in resilience risk coverage of up to 311.6% and cost reductions of up to 63.6%. This study provides a systems science perspective for pre-disaster emergency resource allocation, shifting the paradigm from infrastructure-centric protection to human-centered rescue.

The electrification of integrated stereoscopic transportation is a critical step toward achieving sustainable urban mobility and addressing the environmental challenges posed by traditional transportation modes. This study focuses on the case of Jiangsu Province, a leading region in China for electric transportation development, and examines the electrification trends in three key transportation sectors: electric public transit, ride-hailing services, and electric Vertical Takeoff and Landing (eVTOL) systems. The objective of this study is to analyze the current state of these sectors, identify major challenges, and evaluate the effectiveness of existing policies in facilitating this transition. A mixed-methods approach was employed, including bibliometric analysis, keyword clustering, and a detailed review of government reports and academic literature. The findings highlight significant environmental, social, and economic benefits of transportation electrification, while also uncovering barriers such as infrastructure limitations, regulatory gaps, and public acceptance issues. Based on these insights, policy recommendations are proposed to address these challenges and accelerate the adoption of electric transportation systems, contributing to Jiangsu Province’s broader sustainable development goals.

Reducing the volatility in the operation of public transport is still an important consideration when seeking to maintain efficient and stable operations. In this study, stations and intersections are considered as a whole, and a self-coordinated holding strategy is developed based on the fluctuation of passenger flows and the characteristics of signals during off-peak periods. On this basis, a collaborative optimization is carried out in combination with signal priority and speed control. The mathematical model is established with the goal of minimizing passenger travel and enterprise operation costs. A section of the K ₂ line in Ganzhou City is selected for a case study. The improved discrete binary particle swarm optimization algorithm (DPSO) is then used for the calculations. The simulation and comparison of the arrival time distribution, headway deviation, and travel time index before and after the optimization show that reliability, stability, and efficiency have been improved by the optimization, which verifies the effectiveness of the optimization method.

In transportation infrastructure systems, feature images and spatial characteristics are generally utilized as complementary elements derived from point clouds for road edge extraction, but the involvement of one or more hyperparameters in each makes the extraction complicated. This study proposes an autotuning hybrid method with Bayesian optimization for road edge extraction in highway systems. The hybrid method combines the strengths of 2D feature images and 3D spatial characteristics while also automatically tuning the hyperparameter combination using Bayesian optimization. The hyperparameters encompass high and low pixel gradient thresholds, neighborhood radius, and normal vector threshold. Later, the point cloud dataset of national highways in Henan Province, China, is taken as the case study to evaluate the performance of the proposed method against three benchmark methods in two typical road scenarios: straight and curved edges. Experimental results show that the proposed method outperforms the benchmarks in detection quality and accuracy. It can serve as a decision-making tool to complement traditional manual road surveying, enabling efficient and automated road edge extraction in highway systems.