With the rise of the low-altitude economy, the application scenarios of unmanned aerial vehicle (UAV) continue to expand, particularly playing a significant role in emergency response. Leveraging advantages such as high mobility and remote control, UAV has proven to be powerful tools in disaster monitoring, communication restoration, personnel search and rescue, material delivery, and post-disaster assessment during emergencies such as natural and human-made disasters. This paper aims to provoide a comprehensive review of modeling methods and the latest research progress in UAV path planning for emergency rescue, offering thorough theoretical references and technical guidance for researchers in related fields. It begins by outlining typical emergency rescue scenarios such as earthquakes, fires, and floods, summarizing the application requirements of UAV in different contexts.Then it systematically reviews UAV path planning modeling methods, including dynamic models and task models, with task models further categorized into hierarchical, collaborative, fault-tolerant, real-time, and adaptative dimensions. Subsequently, it comprehensively analyzes path planning optimization algorithms based on three core elements: constraints, optimization objectives, and solution algorithms. Finally, the paper discusses the challenges and opportunities of UAV path planning in emergency rescue, highlighting that technological development, multi-UAV collaboration, and interdisciplinary integration represent future development opportunities. This study provides theoretical support and practical reference for the further development and application of UAV path planning modeling.

With the aggravation of energy crisis and environmental pressure, electric vehicles (EVs) are considered as an effective measure to reduce petrochemical resource dependence, combat climate change, maintain sustainable energy and environmental development and achieve the carbon peaking and carbon neutrality goals. The growing EV market, fueled by policy incentives, has led to increasing charging demands, posing challenges for charging infrastructure deployment and operation. “Range anxiety”caused by limited battery capacity and difficulties in finding available charging stations contribute to low accessibility and inconvenience for users. This issue has become the pain point of the EV industry, hindering the popularization of EVs. However, the maturation of dynamic wireless charging (DWC) technology has brought very promising solutions to the above problems. To provide a more comprehensive review perspective of the research field, this paper provided a comprehensive overview of DWC development and summarized the technical characteristics according to the advantages and their challenges. Then, from the dimensions of modeling method, decision variables, optimization objectives, constraints, model assumptions, solution algorithm and so on, this paper analyzed the optimal configuration model of dynamic wireless charging lanes (DWCLs) and enumerated the research results and test conditions at home and abroad. Finally, it summarized existing methods and technical issues in the dynamic wireless charging domain and offered insights into future prospects, including its impact on the power grid, charging load management, electricity market analysis, charging strategies, battery management systems, battery capacity prediction, battery equalization control, and interdisciplinary integration with autonomous driving technologies.

The inefficient and inaccurate bidirectional search by both ride-hailing drivers and passengers leads to a mismatch between supply and demand. Ride-hailing vehicle repositioning strategy can pre-dispatch vehicles to areas with future demand, improving supply-demand matching. However, existing research mostly uses network grids to represent the urban road environment, lacking geometric topological information and reducing the dispatch accuracy. To address this issue, a ride-hailing vehicle relocation algorithm called GA2C was proposed based on Graph Neural Networks (GNN) and Actor-Critic reinforcement learning algorithm. This algorithm has a smoother learning process and can perform high-dimensional sampling, and it is suitable for learning the best relocation strategy for a large number of ride-hailing vehicles as multi-agent systems. Moreover, the geometric network structure was used to represent the urban road environment by using a GNN as a function approximator to learn the geometric information of the road network. Additionally, an action sampling strategy based on action value function was introduced to increase the randomness of action selection, effectively preventing competition. A ride-hailing vehicle relocation simulation experiment was conducted using Python, and the results are as follows: (ⅰ) the order response rate of the GA2C algorithm is 84.2%, significantly higher than all the comparative experimental results; (ⅱ) in the order distribution comparative experiment, GA2C’s relative improvements in uniform distribution, central distribution layout, block distribution layout, and checkerboard distribution layout are 1.17%, 6.02%, 13.12%, and 14.55%, respectively. The above experimental results demonstrate that the GA2C algorithm can effectively relocate ride-hailing vehicles. When the order distribution presents significant differences, and the distance between different demand areas is relatively close, it can better learn dynamic demand changes, and achieve maximum order response rate by relocating ride-hailing vehicles.