Affiliation:
1. Transportation College, Jilin University, Changchun 130022, China
2. China Academy of Transportation Sciences, Beijing 100029, China
Abstract
The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet, Conv_LSTM, and TCN (temporal convolutional network). This paper proposed the WDA-DBN (water drop algorithm-Deep Belief Network) model and employed DEEPSHAP to identify factors with higher ASS information content. TCN and GRU performed well in the prediction. Compared to the other models, WDA-DBN exhibited the best performance in terms of MSE and MAE. Overall, deep learning models outperform econometric models in terms of information processing. The total time spent processing alarms positively influences ASS, while variables such as fatigue driving occurrences, abnormal driving occurrences, and nighttime driving alarm occurrences have a negative impact on ASS.
Funder
National Key R&D Program of China
Graduate InnovationFund of Jilin University
Reference69 articles.
1. EsoNews (2023, October 03). Major Accidents Involving Buses Worldwide, 2020–2021. Available online: https://www.163.com/dy/article/GGTF05620552ASSI.html.
2. Agency, X.N. (2023, October 04). Changshen High-Speed Jiangsu Wuxi “9–28” Special Major Road Traffic Accident Investigation Report Published, Available online: https://www.gov.cn/xinwen/2020-09/11/content_5542742.htm.
3. U.S. Government Printing Office (2023, October 04). Title 49–Transportation, Available online: https://www.ecfr.gov/current/title-49/subtitle-B/chapter-III/subchapter-B.
4. Mwale, M., Mwangilwa, K., Kakoma, E., and Iaych, K. (2023). Estimation of the completeness of road traffic mortality data in Zambia using a three source capture recapture method. Accid. Anal. Prev., 186.
5. Chen, Z., Zhang, J., Zhang, Y., and Huang, Z. (2021). Traffic Accident Data Generation Based on Improved Generative Adversarial Networks. Sensors, 21.