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Efficient detour-aware dynamic routing based on local traffic load in urban road networks
Jie Chen1, Yantao Zhang2, Xin Hong1
1Anhui Normal University, School of Computer and Information, Wuhu 241003, People's Republic of China.
Physical Review. E
|December 23, 2025
Summary
A new decentralized traffic routing strategy using local traffic load (LTL) information significantly improves urban transportation efficiency. This approach balances distance with real-time congestion, enhancing throughput and stability for smarter traffic management.
Area of Science:
- Transportation Engineering
- Complex Systems Science
- Urban Planning
Background:
- Conventional traffic routing relies on static paths or global optimization, often failing to adapt to real-time urban congestion.
- Dynamic traffic management is crucial for improving urban mobility and reducing travel times.
Purpose of the Study:
- To introduce and evaluate a decentralized traffic routing strategy based on local traffic load (LTL) information.
- To assess the strategy's effectiveness in enhancing urban transportation network performance.
Main Methods:
- Simulation of traffic dynamics using a two-dimensional cellular automaton model.
- Evaluation of key performance indicators: average velocity, flow, and arrival rate.
- Testing the strategy's robustness on real-world urban network topologies.
Main Results:
- The LTL-based strategy significantly enhances system throughput and stability across various traffic densities.
- The strategy effectively suppresses congestion buildup and spatial heterogeneity through locally informed detours.
- Improved performance was validated on realistic urban network structures.
Conclusions:
- Decentralized routing strategies leveraging localized feedback offer scalable and adaptive solutions for urban congestion.
- The LTL approach provides a computationally efficient method for dynamic detour decisions.
- This strategy represents a promising advancement for intelligent transportation systems.
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