Application of immune clonal annealing algorithm for traffic diversion in traffic accident areas
Year: 2025
Type: article
Abstract: To address the delayed adaptation of conventional traffic diversion methods in dynamic accident scenarios, this study proposes an immune clone annealing algorithm (ICAA)-based framework with three key innovations. First, a residual capacity analysis model is developed to pre-identify high-congestion-risk road sections (threshold: congestion probability CP ≥ 0.65) using directed graph theory, enabling proactive congestion management. Second, an enhanced support vector machine (SVM) classifier is introduced, integrating vehicle speed, density, and occupancy with cuckoo search optimization, achieving a congestion detection accuracy of 93.2%. Third, a throughput-maximizing objective function, resolved by the ICAA, is designed to optimize traffic flow redistribution. Simulation results under peak-hour accident conditions demonstrate that the proposed framework reduces average delays by 37.5% and reduces probability of congestion by 20%. These advancements highlight the framework’s robust adaptability in multi-accident scenarios, and make it a practical solution for real-time traffic management in complex urban networks.
Authors: Manzhi Zheng, Changming Zhao, Chunxu Wu
Institution: Southwest Jiaotong University Hope College
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7092695215
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