Lane-Changing Risk Prediction on Urban Expressways: A Mixed Bayesian Approach for Sustainable Traffic Management
Abstract
1. Introduction
- Development of a Bayesian Network Model: This study introduces a Bayesian network model that predicts vehicle lane-changing trends with a high degree of accuracy (86.74%). The model’s strength lies in its ability to handle the uncertainty of input data and its adaptability to varying traffic conditions.
- Integration of Driver Learning Process: Unlike previous probability-based methods, this study’s model takes into account the learning process of drivers during lane-changing maneuvers, providing a more comprehensive understanding of the decision-making process involved in lane changes.
- Enhanced Risk Assessment under High-Density Traffic: This research provides a robust tool for assessing lane-changing risks under high-density traffic conditions, which is crucial for urban expressways where traffic congestion is prevalent. The model’s predictive capabilities can significantly contribute to traffic safety and management strategies.
2. Materials and Methods
2.1. Data Discretization
2.2. Bayesian Network Lane-Changing Model Construction
3. Test Based on the Asia Network Model
4. Results
5. Discussion
6. Conclusions
- Against the background of urban expressways with high load (0.8 ≤ V/C ≤ 0.9), a Bayesian network vehicle lane-changing model based on the I-CH scoring criterion MMHC algorithm was constructed to determine the posterior probabilities of various influencing factors. To verify the predictive accuracy of the lane-changing model, the standard Asia network model was introduced to test the lane-changing model.
- On urban expressways with high load (0.8 ≤ V/C ≤ 0.9), it was found that when the spatial headway between the front and rear vehicles is less than 4.0 m, the vehicle lane-changing success rate is highest when the speed of the vehicle behind in the target lane is between 1.0 and 3.0 m/s; when the spatial headway between the front and rear vehicles is greater than 4.0 m, the vehicle lane-changing success rate is highest when the speed of the vehicle behind in the target lane is between 3.0 and 6.0 m/s.
- For the problem of vehicle lane-changing on high-load urban expressways (with a volume–capacity ratio of 0.8 ≤ V/C ≤ 0.9), the improved Bayesian network model captures the behavioral patterns reflecting drivers’ decisions under varying traffic conditions. Meanwhile, the probability of this model identifying lane-changing vehicles is 80.5%, which is considered to have a certain degree of reliability.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Number | Outcome | |||||
---|---|---|---|---|---|---|
1 | 4 | 4 | 4 | 3 | 1 | 1 |
2 | 4 | 4 | 3 | 3 | 3 | 1 |
3 | 4 | 4 | 3 | 3 | 3 | 1 |
… | … | … | … | … | … | … |
784 | 8 | 8 | 8 | 8 | 0 | 0 |
0.143 | 0.136 | 0.045 | 0.048 | 0.034 | 0.039 | 0.011 | 0.016 | 0.078 | |
0.296 | 0.379 | 0.291 | 0.135 | 0.108 | 0.094 | 0.044 | 0.031 | 0.098 | |
0.265 | 0.212 | 0.367 | 0.304 | 0.148 | 0.125 | 0.066 | 0.047 | 0.078 | |
0.194 | 0.136 | 0.206 | 0.304 | 0.345 | 0.281 | 0.099 | 0.047 | 0.118 | |
0.041 | 0.015 | 0.045 | 0.145 | 0.251 | 0.242 | 0.143 | 0.047 | 0.098 | |
0.031 | 0.015 | 0.01 | 0.029 | 0.074 | 0.07 | 0.286 | 0.156 | 0.137 | |
0.01 | 0.045 | 0.02 | 0.01 | 0.015 | 0.063 | 0.187 | 0.281 | 0.137 | |
0.01 | 0.03 | 0.005 | 0.005 | 0.01 | 0.07 | 0.077 | 0.266 | 0.059 | |
0.01 | 0.03 | 0.01 | 0.019 | 0.015 | 0.016 | 0.088 | 0.109 | 0.196 |
Model | Actual Lane-Change Data | Vehicle Lane-Change Prediction Results | Relative Error |
---|---|---|---|
Bayesian Network Model | 200 successful 94 failed | 239 successful 55 failed | 19.5% |
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Yang, Q.; Li, P.; Yang, F.; Lu, W. Lane-Changing Risk Prediction on Urban Expressways: A Mixed Bayesian Approach for Sustainable Traffic Management. Sustainability 2025, 17, 7061. https://doi.org/10.3390/su17157061
Yang Q, Li P, Yang F, Lu W. Lane-Changing Risk Prediction on Urban Expressways: A Mixed Bayesian Approach for Sustainable Traffic Management. Sustainability. 2025; 17(15):7061. https://doi.org/10.3390/su17157061
Chicago/Turabian StyleYang, Quantao, Peikun Li, Fei Yang, and Wenbo Lu. 2025. "Lane-Changing Risk Prediction on Urban Expressways: A Mixed Bayesian Approach for Sustainable Traffic Management" Sustainability 17, no. 15: 7061. https://doi.org/10.3390/su17157061
APA StyleYang, Q., Li, P., Yang, F., & Lu, W. (2025). Lane-Changing Risk Prediction on Urban Expressways: A Mixed Bayesian Approach for Sustainable Traffic Management. Sustainability, 17(15), 7061. https://doi.org/10.3390/su17157061