Adaptive congestion index-based A* algorithm for dynamic vehicle path planning optimization

Yulun Du, Gang Liu, Yunhao Jiang, Siteng Cai and Jing He

Physica A: Statistical Mechanics and its Applications, 2025, vol. 672, issue C

Abstract: To enhance traffic efficiency of urban transportation networks, it is essential to comprehensively consider factors such as the congestion index and road network topology. The congestion index serves as a core metric for objectively evaluating urban traffic conditions, primarily used to assess the traffic performance of transportation networks. This study introduces the principle of probability density segmentation to address the limitations of existing congestion index models, which inadequately account for the spatiotemporal characteristics of traffic flow speed. By segmenting vehicle speed based on distance and time, a congestion index model with adaptive adjustment capabilities is established. Based on this, an improved A* algorithm (MVPP-ACI-IA) is proposed based on dynamic multi-objective path-planning mechanism and adaptive congestion index. Results demonstrate that, compared to the traditional A* algorithm, the proposed method dynamically adjusts vehicle routes, improving traffic efficiency by approximately 10.95 %. Our approach significantly mitigates road congestion under high traffic load scenarios.

Keywords: Congestion index; Probability density segmentation; Dynamic path optimization algorithm; Optimized A* algorithm; Urban traffic flows; Road traffic efficiency (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:672:y:2025:i:c:s0378437125003541

DOI: 10.1016/j.physa.2025.130702

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