TANG Weiwen1,2, GUO Shengnan1,2, CHEN Wei1,2, LIN Youfang1,2, WAN Huaiyu1,2
1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044; 2. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044
Abstract Exploring the law of accident occurrence from historical traffic accident data and realizing accurate road level traffic accident risk prediction can improve the travel safety and efficiency effectively. However, road level traffic accident risk prediction is faced with great challenges due to the influence of multiple factors, such as weather and traffic state, the complex temporal and spatial correlation between traffic accidents and the sparsity of accident data. To address these issues, a two-level and multi-view spatial-temporal graph neural network by incorporating temporal knowledge graph(STGN-TKG) is proposed. Firstly, a traffic accident temporal knowledge graph is constructed for the first time, and diachronic embedding for traffic accident temporal knowledge graph is designed to mine the high-order and dynamic correlation between multi-source influencing factor data. Then, a spatial graph convolution attention module and a temporal representation module are employed to fully model the complex spatial-temporal correlations between traffic accidents from two levels and multiple views. Finally, an accident risk propagation strategy is proposed to alleviate the zero-inflated issue. The experimental results on two real-world road level traffic accident risk datasets show that STGN-TKG achieves superior performance on the road level accident risk prediction task.
Fund:Project of Guoneng Railway Equipment Co., Ltd.(No.TZKY-21-16)
Corresponding Authors:
GUO Shengnan, Ph.D., lecturer. Her research interests include spatio-temporal data mining and deep learning.
About author:: TANG Weiwen, master student. His research interests include spatio-temporal data mining and deep learning.CHEN Wei, Ph.D.candidate. His research interests include knowledge graph reasoning and application.LIN Youfang, Ph.D., professor. His research interests include data mining, machine learning, reinforcement learning, complex net-work and intelligent technology and system.WAN Huaiyu, Ph.D., professor. His research interests include spatio-temporal data mining, information extraction and social network mining.
[1] CHEN Q J, SONG X, YAMADA H, et al. Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference// Proc of the 30th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI, 2016: 338-344. [2] CHEN C, FAN X L, ZHENG C P, et al. SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction via Traffic Big Data// Proc of the 6th International Conference on Advanced Cloud and Big Data. Washington, USA: IEEE, 2018: 328-333. [3] ZHOU Z Y, WANG Y, XIE X K, et al. RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework// Proc of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI, 2020: 1258-1265. [4] WANG B B, LIN Y F, GUO S N, et al. GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting// Proc of the 35th AAAI Confe-rence on Artificial Intelligence. Palo Alto, USA: AAAI, 2021: 4402-4409. [5] ZHOU Z Y, WANG Y, XIE X K, et al. Foresee Urban Sparse Tra-ffic Accidents: A Spatiotemporal Multi-granularity Perspective. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3786-3799. [6] YU L, DU B W, HU X, et al. Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction. Neurocomputing, 2021, 423: 135-147. [7] BAO J, LIU P, UKKUSURI S V, et al. A Spatiotemporal Deep Learning Approach for Citywide Short-Term Crash Risk Prediction with Multi-source Data. Accident Analysis and Prevention, 2019, 122: 239-254. [8] WANG S Z, ZHANG J Q, LI J Y, et al. Traffic Accident Risk Prediction via Multi-view Multi-task Spatio-Temporal Networks. IEEE Transactions on Knowledge and Data Engineering, 2021. DOI: 10.1109/TKDE.2021.3135621. [9] SHARMA B, KATIYAR V K, KUMAR K. Traffic Accident Prediction Model Using Support Vector Machines with Gaussian Kernel // Proc of the 5th International Conference on Soft Computing for Problem Solving. Berlin, Germany: Springer, 2015, II: 1-10. [10] CALIENDO C, GUIDA M, PARISI A, et al. A Crash-Prediction Model for Multilane Roads. Accident Analysis and Prevention, 2007, 39(4): 657-670. [11] OLUTAYO V A, ELUDIRE A A. Traffic Accident Analysis Using Decision Trees and Neural Networks. International Journal of Information Technology and Computer Science, 2014, 6(2): 22-28. [12] 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// Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2018: 984-992. [13] WANG Z, ZHANG J W, FENG J L, et al. Knowledge Graph Embedding by Translating on Hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence, 2014, 28(1): 1112-1119. [14] LIN Y K, LIU Z Y, SUN M S, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion. Proceedings of the AAAI Conference on Artificial Intelligence, 2015, 29(1): 2181-2187. [15] JI G L, HE S Z, XU L H, et al. Knowledge Graph Embedding via Dynamic Mapping Matrix// Proc of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Long Papers). Stroudsburg, USA: ACL, 2015: 687-696. [16] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating Embeddings for Modeling Multi-relational Data // Proc of the 26th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2013: 2787-2795. [17] JIANG T S, LIU T Y, GE T, et al. Towards Time-Aware Know-ledge Graph Completion// Proc of the 26th International Confe-rence on Computational Linguistics(Technical Papers). Stroudsburg, USA: ACL, 2016: 1715-1724. [18] DASGUPTA S S, RAY S N, TALUKDAR P P. HyTE: Hyperplane-Based Temporally Aware Knowledge Graph Embedding// Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2018: 2001-2011. [19] MA Y P, TRESP V, DAXBERGER E A. Embedding Models for Episodic Knowledge Graphs. Journal of Web Semantics, 2019, 59. DOI: 10.1016/j.websem.2018.12.008. [20] GOEL R, KAZEMI S M, BRUBAKER M, et al. Diachronic Embedding for Temporal Knowledge Graph Completion. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 3988-3995. [21] LIN J. Divergence Measures Based on the Shannon Entropy. IEEE Transactions on Information Theory, 1991, 37(1): 145-151. [22] BAI L, YAO L N, LI C, et al. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting // Proc of the 34th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2020: 17804-17815. [23] VASWANI A, SHAZEER N, PARMAR N, et al. Attention Is All You Need // Proc of the 31st International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2017: 6000-6010. [24] BAI L, YAO L N, KANHERE S S, et al. STG2seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting// Proc of the 28th International Joint Conference on Artificial Intelligence. Palo Alto, USA: AAAI, 2019: 1981-1987. [25] KINGMA D P, BA J L. Adam: A Method for Stochastic Optimization[C/OL]. [2023-05-21].https://arxiv.org/pdf/1412.6980v9.pdf. [26] MA C, ZHANG Y X, WANG Q L, et al. Point-of-Interest Reco-mmendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence// Proc of the 27th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2018: 697-706. [27] CHUNG J, GULCEHRE C, CHO K H, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[C/OL]. [2023-05-21]. https://arxiv.org/pdf/1412.3555.pdf. [28] SHI X J, CHEN Z R, WANG H, et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting // Proc of the 28th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2015, I: 802-810.
2023, Vol. 36 
