Traffic Incident Impact Dynamic Prediction with Information Enhancement Cascade Forest
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 7
Abstract
Accurate incident impact prediction is one of the crucial tasks in road safety management that contributes to mitigating the resultant congestion and accelerating emergency rescue. However, the impact of traffic incidents is affected by numerous and redundant factors, and their incompleteness and scalability make the task of impact prediction particularly challenging. To address these issues, a novel multimodule dynamic prediction framework, named information enhancement cascade forest (IECF), is proposed for traffic incident impact prediction in this paper. In the IECF model, XGBoost is adopted to screen the original incident factors to reduce the information redundancy. Additionally, a novel enhancement generative adversarial network (EGAN) was custom designed to complete the missing factors by historical large-sample adversarial training. Then a multigrained permutation cascade forest (GPCF) was constructed to predict the incident impact degree through integrated learning. Furthermore, sufficient comparative experiments were conducted, and the results show the proposed model has better performance on traffic incident impact dynamic prediction than the state-of-art methods, especially in partial-factors-available situations.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is supported by the National Natural Science Foundation of China (Key Program) (52131202) and the Natural Science Foundation of Jilin Province (20190201107JC). The authors would like to thank S. Moosavi et al. for providing the open-source incident data to validate this methodology.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 7 • July 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 26, 2022
Accepted: Jan 9, 2023
Published online: Apr 21, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 21, 2023
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Cited by
- Liuping Cheng, Intelligent Design of Urban Traffic Information Management System, 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), 10.1109/SMARTGENCON60755.2023.10442558, (1-4), (2023).