Application of immune clonal annealing algorithm for traffic diversion in traffic accident areas

• Published in: Journal of Applied Science and Engineering Vol. 29; no. 4; pp. 835 - 841
• Main Authors: Manzhi Zheng, Changming Zhao, Chunxu Wu
• Format: Journal Article
• Language: English
• Published: Tamkang University Press 01.10.2025
• Subjects: accident section; cuckoo search algorithm; immune clone annealing algorithm; improve support vector machines; traffic diversion; traffic network model
• ISSN: 2708-9967; 2708-9975
• DOI: 10.6180/jase.202604_29(4).0007

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.