[1] REN H L, SONG Y, WANG J W, et al. A deep learning app-roach to the citywide traffic accident risk prediction[C]//Proceedings of the 21st International Conference on Intel-ligent Transportation Systems, Maui, Nov 4-7, 2018. Pisca-taway: IEEE, 2018: 3346-3351.
[2] CHEN C, FAN X, ZHENG C, et al. SDCAE: stack denoising convolutional autoencoder model for accident risk prediction via traffic big data[C]//Proceedings of the 2018 International Conference on Advanced Cloud and Big Data, Lanzhou, Aug 12-15, 2018. Piscataway: IEEE, 2018: 328-333.
[3] YUAN Z N, ZHOU X, YANG T B, et al. Hetero-ConvLSTM: a deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 984-992.
[4] BAO J, LIU P, UKKUSURI S V, et al. A spatiotemporal deep learning approach for citywide short-term crash risk predic-tion with multi-source data[J]. Accident Analysis & Preven-tion, 2019, 122: 239-254.
[5] CALIENDO C, GUIDA M, PARISI A, et al. A crash-prediction model for multilane roads[J]. Accident Analysis & Prevention, 2007, 39(4): 657-670.
[6] OLUTAYO V A, ELUDIRE A A. Traffic accident analysis using decision trees and neural networks[J]. International Journal of Information Technology & Computer Science, 2014, 6(2): 22-28.
[7] LYU Y S, TANG S M, ZHAO H X, et al. Real-time highway traffic accident prediction based on the k-nearest neighbor method[C]//Proceedings of the 2009 International Confer-ence on Measuring Technology and Mechatronics Automation, Zhangjiajie, Apr 11-12, 2009. Piscataway: IEEE, 2009: 547-550.
[8] CHEN Q J, SONG X, YAMADA H, et al. Learning deep representation from big and heterogeneous data for traffic accident inference[C]//Proceedings of the 30th AAAI Con-ference on Artificial Intelligence, Phoenix, Feb 12-17, 2016. Menlo Park: AAAI, 2016: 338-344.
[9] ZHOU Z Y, WANG Y, XIE X K, et al. RiskOracle: a minute-level citywide traffic accident forecasting framework[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, the 32nd Innovative Applications of Artificial Intelligence Conference, the 10th AAAI Symposium on Educational Advances in Artificial Intelligence, New York, Feb 7-12, 2020. Menlo Park: AAAI, 2020: 1258-1265.
[10] GUO S, LIN Y, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow fore-casting[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 922-929.
[11] PAN S R, HU R Q, LONG G D, et al. Adversarially regu-larized graph autoencoder for graph embedding[C]//Procee-dings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Jul 13-19, 2018: 2609-2615.
[12] KIPF T, WELLING M. Semi-supervised classification with graph convolutional networks[C]//Proceedings of the 5th Inter-national Conference on Learning Representations, Toulon, Apr 24-26, 2017: 1-14.
[13] BRUNA J, ZAREMBA W, SZLAM A, et al. Spectral net-works and locally connected networks on graphs[C]//Pro-ceedings of the 2nd International Conference on Learning Representations, Banff, Apr 14-16, 2014: 1-14.
[14] DEFFERRARD M, BRESSON X, VANDERGHEYNST P, et al. Convolutional neural networks on graphs with fast loca-lized spectral filtering[C]//Proceedings of the 29th Annual Conference on Neural Information Processing Systems, Barcelona, Dec 5-10, 2016. Red Hook: Curran Associates, 2016: 3844-3852.
[15] HAMILTON W, YING Z, LESKOVEC J, et al. Inductive representation learning on large graphs[C]//Proceedings of the 30th Annual Conference on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates, 2017: 1024-1034.
[16] VELICKOVIC P, CUCURULL G, CASANOVA A, et al. Graph attention networks[C]//Proceedings of the 6th Interna-tional Conference on Learning Representations, Vancouver, Apr 30-May 3, 2018: 1-12.
[17] WU Z H, PAN S R, LONG G D, et al. Graph WaveNet for deep spatial-temporal graph modeling[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019: 1907-1913.
[18] SONG C, LIN Y, GUO S, et al. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, the 32nd Innovative Applications of Artificial Intelligence Conference, the 10th AAAI Symposium on Educational Advances in Artificial Intelligence, New York, Feb 7-12, 2020. Menlo Park: AAAI, 2020: 914-921.
[19] BAI L, YAO L, KANHERE S S, et al. STG2seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019: 1981-1987.
[20] MA C, ZHANG Y X, WANG Q L, et al. Point-of-interest recommendation: exploiting self-attentive autoencoders with neighbor-aware influence[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 697-706.
[21] CHEN T Q, GUESTRIN C. XGBoost: a scalable tree Boos-ting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 785-794.
[22] CHUNG J, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv:1412.3555, 2014.
[23] SHI X J, CHEN Z R, WANG H, et al. Convolutional LSTM network: a machine learning approach for precipitation now-casting[C]//Proceedings of the 28th Annual Conference on Neural Information Processing Systems, Montreal, Dec 7-12, 2015. Red Hook: Curran Associates, 2015: 802-810. |