Abstract
This study aims to improve the accuracy of a convolutional neural network (CNN) based model. That predicts the likelihood of accidents on a specific road section from the present to 2 h in the future using a wide range of temporal and spatial sensor information developed in previous studies as input to reduce accidents. In addition to previous studies that only used traffic data (i.e., speed, traffic volume, time occupancy, etc.), we considered time data (i.e., day of the week, time of day, etc.) and weather data as additional explanatory variables. Then, using the chi-square test, we selected the information that contributed to improving the accuracy of accident occurrence prediction and added it as input to the CNN-based model. Compared with the base model, the average F1-score of the proposed model was improved by 19.1%.
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Acknowledgments
We thank Prof. Toshio Yoshii and Dr. Takahiro Tsubota of the Faculty of Engineering, Ehime University, for their advisory, Dr. Michael Dziomba of GRID Inc. for his cooperation in this research, and Dr. Katsunori Tanaka of GRID Inc. for his advisory in proofreading.
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Wakatsuki, Y., Tatebe, J. & Xing, J. Improving the Accuracy of Traffic Accident Prediction Models on Expressways by Considering Additional Information. Int. J. ITS Res. 20, 309–319 (2022). https://doi.org/10.1007/s13177-021-00293-y
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DOI: https://doi.org/10.1007/s13177-021-00293-y