Open Access

ARTICLE

Dynamic Route Optimization for Multi-Vehicle Systems with Diverse Needs in Road Networks Based on Preference Games

Jixiang Wang¹, Jing Wei², Siqi Chen¹, Haiyang Yu^1,3,4, Yilong Ren^1,3,4,*

1 School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China
2 School of Electrical and Control Engineering, North China University of Technology, Beijing, 100144, China
3 Hangzhou Innovation Institute, Beihang University, Hangzhou, 310023, China
4 The State Key Lab of Intelligent Transportation System, Beijing, 100191, China

* Corresponding Author: Yilong Ren. Email:

Computers, Materials & Continua 2025, 83(3), 4167-4192. https://doi.org/10.32604/cmc.2025.062503

Received 19 December 2024; Accepted 06 March 2025; Issue published 19 May 2025

Abstract

The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation. While existing vehicle-to-infrastructure coordination frameworks partially address congestion mitigation, they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles. To bridge this gap, this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol, explicitly balancing system-wide efficiency (measured by network throughput) with priority vehicle rights protection (quantified via time-sensitive utility functions). The approach innovatively combines (1) a multi-vehicle dynamic routing model with quantifiable preference weights, and (2) a distributed Nash equilibrium solver updated using replicator sub-dynamic models. The framework was evaluated on an urban road network containing 25 intersections with mixed priority ratios (10%–30% of vehicles with priority access demand), and the framework showed consistent benefits on four benchmarks (Social routing algorithm, Shortest path algorithm, The comprehensive path optimisation model, The emergency vehicle timing collaborative evolution path optimization method) showed consistent benefits. Results show that across different traffic demand configurations, the proposed method reduces the average vehicle traveling time by at least 365 s, increases the road network throughput by 48.61%, and effectively balances the road loads. This approach successfully meets the diverse traffic demands of various vehicle types while optimizing road resource allocations. The proposed coordination paradigm advances theoretical foundations for fairness-aware traffic optimization while offering implementable strategies for next-generation cooperative vehicle-road systems, particularly in smart city deployments requiring mixed-priority mobility guarantees.

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Keywords

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