Abstract
Road traffic accident prediction has always been a complex problem for intelligent transportation since it is affected by many factors. However, to simplify the calculation complexity, most of the current research considers the impact of a few key factors and ignores multiple factors’ impact in reality. To address this problem, we propose traffic accident prediction methods based on multi-factor models. The model introduces information including the severity of the traffic accident, the weather in which the accident occurred, and the external geographic environment to construct a multiple factors model to improve the prediction accuracy. Also, we can use more factors to construct the multi-factor model with the enrichment of data information. The multi-factor model can overcome the shortcomings of existing models in filtering data fluctuations and achieve more accurate predictions by extracting time-periodic features in time series. Furthermore, we combine the multi-factor models with different deep learning models to propose multiple traffic accident prediction methods to explore multi-factor models’ effects in traffic accident prediction. The experimental results on the 2004–2018 Connecticut Crash Date Repository data of the University of Connecticut show that the \(C(T)+R+W+RC\) multi-factor model has better prediction performance than other multi-factor models. Moreover, Multi Factors (\(C(T)+R+W+RC\)) Based Bi-LSTM-Attention Method for Traffic Accident Prediction achieved the best performance on this data set.
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Zhao, H., Rao, G. (2021). Traffic Accident Prediction Methods Based on Multi-factor Models. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_4
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DOI: https://doi.org/10.1007/978-3-030-82153-1_4
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