A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs - Zhaohui Wang; Meng Zhang; Shanqing Liang; Shuang Yu; Chengchun Zhang; Sheng Du (Algorithms)
With the development of smart cities and intelligent transportation systems, path planning in multi-scenario urban mobility has become increasingly complex. Traditional path-planning approaches typically focus on a single optimization objective, limiting their applicability in complex urban traffic systems. This paper proposes a multi-objective vehicle path-planning approach tailored for diverse scenarios, addressing multi-objective optimization challenges within complex road networks. The proposed method simultaneously considers multiple objectives, including total distance, congestion distance, travel time, energy consumption, and safety, and incorporates a dynamic weight-adjustment mechanism. This allows the algorithm to provide optimal route choices across four application scenarios: urban commuting; energy-efficient driving; holiday travel; and nighttime travel. Experimental results indicate that the proposed multi-objective planning algorithm outperforms traditional single-objective algorithms by effectively meeting user demands in various scenarios, offering an efficient solution to multi-objective optimization challenges in diverse environments.