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Journal Article

Citation

Zheng L, Cao S, Ding T, Tian J, Sun J. Entropy (Basel) 2024; 26(6).

Copyright

(Copyright © 2024, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/e26060434

PMID

38920443

PMCID

PMC11203358

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.


Language: en

Keywords

factor analysis; feature selection; active safety situation; model selection; statistical computing; statistical inference; time series; transportation enterprises

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