# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 1 Introduction  
The development of smart cities and intelligent transportation has made multi-objective path planning a key technology to meet the demands of efficient traffic management and personalized travel in urban environments. Intelligent transportation systems require path planning to support traffic management, autonomous driving, logistics, and public transportation in finding efficient routes that alleviate traffic congestion, enhance transportation efficiency, and reduce energy consumption. Consequently, real-time dynamic planning, multi-modal travel integration, personalized demand, and big data support are essential requirements for path planning in smart cities. In modern transportation systems, multi-objective path planning can provide customized travel solutions tailored to diverse user needs, enabling them to make optimal choices by balancing factors such as time, cost, comfort, and environmental impact. Additionally, multi-objective path planning offers a scientific basis for the design of public transportation systems, road layouts, and infrastructure planning. By considering the needs of different groups and environmental impacts, urban planners can allocate resources more effectively to improve accessibility and equity in urban transportation, reduce congestion, and mitigate environmental damage. In recent years, path planning has achieved significant advances across various domains, including intelligent transportation, robotics, logistics management, and urbanplanning. Traditional algorithms, such as Dijkstra's and A*, remain foundational; however, their applications are limited in complex and dynamic environments. Researchers have worked to optimize these algorithms to enhance computational efficiency and adaptability. For instance, [PERSON] et al. proposed a multi-modal path-planning approach based on a multi-objective A* algorithm [1], meeting the complex requirements of multi-modal transportation. [PERSON] et al. applied real-coded genetic algorithms in simulation-based optimization for autonomous transportation systems, demonstrating higher accuracy in solving large-scale optimization problems [2]. [PERSON] et al. introduced a 2.5D multi-objective path-planning method based on deep reinforcement learning, achieving an effective trade-off between distance and energy consumption for vehicles in off-road terrain environments [3]. [PERSON] et al. integrated D* Lite with an improved A* algorithm to develop an incremental multi-objective path-planning algorithm [4], addressing efficiency issues in dynamic environments. [PERSON] et al. presented a method that combines an improved A* algorithm with a multi-objective cellular genetic algorithm aimed at emergency resource allocation and path planning in natural disaster chains [5]. [PERSON] et al. developed a multi-objective evolutionary optimization approach for coverage path planning in precision agriculture, aiming to increase field coverage efficiency while reducing energy consumption [6]. [PERSON] et al., [PERSON] et al., and [PERSON] et al. employed the ant colony optimization algorithm [7; 8; 9], while [PERSON] et al. used the artificial bee colony algorithm [10] to address the multi-objective path-planning challenges for mobile robots. [PERSON] et al. utilized the ant colony optimization algorithm to enhance each Pareto solution, evaluating the optimal solution using a Mamdani fuzzy inference system to achieve multi-objective path planning for unmanned surface vehicles [11]. [PERSON] et al. proposed an innovative path-planning framework for unmanned surface vessels, which integrates multi-objective optimization with a perceptual vector re-planning strategy [12]. [PERSON] et al. utilized an improved genetic algorithm for path planning of unmanned surface vehicles, achieving an optimal balance between path length and time cost through a multi-domain inversion algorithm [13]. [PERSON] et al.