原文传递
Traffic Incident Impact Dynamic Prediction with Information Enhancement Cascade Forest
| 题名： | Traffic Incident Impact Dynamic Prediction with Information Enhancement Cascade Forest |
| 正文语种： | eng |
| 作者： | Haitao Li;Qian Cao;Xianmin Song;Hongyu Hu |
| 作者单位： | College of Transportation Jilin Univ. Changchun 130022 China;College of Transportation Jilin Univ. Changchun 130022 China;College of Transportation Jilin Univ. Changchun 130022 China;College of Automotive Engineering Jilin Univ. Changchun 130022 China |
| 关键词： | Traffic incident; Incident impact prediction; Generative adversarial network; Cascade forest |
| 摘要： | 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. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 7 |
| 页码： | 04023049.1-04023049.15 |
相关文献
- Prediction of Traffic Incident Duration Using Clustering-Based Ensemble Learning Method
- Analyzing Freeway Traffic Incident Clearance Time Using a Deep Survival Model
- Modeling of Freeway Real-Time Traffic Crash Risk Based on Dynamic Traffic Flow Considering Temporal Effect Difference
- Modeling Framework to Identify an Affected Area for Developing Traffic Management Strategies
- Traffic State Prediction for Urban Networks: A Spatial-Temporal Transformer Network Model
- A Transfer Learning-Based LSTM for Traffic Flow Prediction with Missing Data
检索历史
应用推荐
