[1] LIN L, WAN Q, SADEK A W. A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction[J]. Transportation Research Part C:Emerging Technologies, 2015, 55:444-459. [2] EISENBERG D. The mixed effects of precipitation on traffic crashes[J]. Accident Analysis and Prevention, 2004, 36(4):637-647. [3] TAMERIUS J D, ZHOU X, MANTILLA R, et al. Precipitation effects on motor vehicle crashes vary by space, time, and environmental conditions[J]. Weather, Climate, and Society, 2016, 8(4):399-407. [4] OH J, WASHINGTON S P, NAM D. Accident prediction model for railway-highway interfaces[J]. Accident Ana-lysis and Prevention, 2006, 38(2):346-356. [5] CHEN Q, SONG X, YAMADA H, et al. Learning deep representation from big and heterogeneous data for traffic accident inference[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2016:338-344. [6] CHEN C, FAN X L, ZHENG C P, et al. SDCAE:stack denoising convolutional autoencoder model for accident risk prediction via traffic big data[C]//2018 Sixth International Conference on Advanced Cloud and Big Data (CBD). Piscataway, NJ:IEEE Press, 2018:328-333. [7] YUAN Z N, ZHOU X, YANG T B. Hetero-ConvLSTM:a deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data[C]//KDD'18:Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2018:984-992. [8] REN H L, SONG Y, WANG J W, et al. A deep learning approach to the citywide traffic accident risk prediction[C]//201821st International Conference on Intelligent Transportation Systems (ITSC). Piscataway, NJ:IEEE Press, 2018:3346-3351. [9] ZHOU Z Z, WANG Y, XIE X K, et al. RiskOracle:a minute-level citywide traffic accident forecasting framework[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(1):1258-1265. [10] ZHANG G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50:159-175. [11] YU B, YIN H T, ZHU Z X. Spatio-temporal graph convolutional networks:a deep learning framework for traffic forecasting[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Stockholm:International Joint Conferences on Artificial Intelligence Organization, 2018:3634-3640. |