Abstract
Traffic prediction is a vital part of intelligent transportation systems. The ability of traffic risk prediction is of great significance to prevent traffic accidents and reduce the damages in a proactive way. Because of the complexity, uncertainty and dynamics of spatiotemporal dependence of traffic flow, accurate traffic state prediction becomes a challenging issue. Most neural networks are compute intensive and memory intensive, making them hard to deploy on embedded systems with limited hardware resources. A real-time and high-compressed video object detection structure is proposed. For traffic prediction, many previous studies only explore the utility of a single factor in their prediction and a few multi-factor researches are conducted. Other studies focus on the temporal distribution of traffic flow, ignoring the spatial correlation. And some methods based on graph convolutional networks (GCNs) do not consider the dynamics of graph structure which is a crucial factor to traffic prediction. In this paper, we analyze and process the onboard video captured by the dashboard camera real time. A high accurate deep learning model called varying spatiotemporal graph-based convolution model (VSTGC) is proposed to express the spatiotemporal structures and forecast future traffic safety trends from previous traffic flow. The traffic detailed features (such as vehicle type, braking state, whether changing lanes or not) and external variables (such as weather, time and road condition) are extracted from our big datasets. We conduct extensive experiments to evaluate the VSTGC model on real-world traffic datasets. Experiments on our real traffic dataset show that the proposed model performs competitive performances over the other state-of-the-art approaches.
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References
Organization WH (2018) The top 10 causes of death. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death/. Accessed June 2019
Park Sh, Kim Sm, Ha Yg (2016) Highway traffic accident prediction using VDS big data analysis. J Supercomput 72(7):2815
Keller CG, Dang T, Fritz H, Joos A, Rabe C, Gavrila DM (2011) Active pedestrian safety by automatic braking and evasive steering. IEEE Trans Intell Transp Syst 12(4):1292
Journet BA, Bazin G (1998) Laser rangefinders for autonomous intelligent cruise control systems. In: Intelligent transportation systems, vol 3207. International Society for Optics and Photonics, Bellingham, pp 65–71
Pilutti T, Ulsoy AG (1999) Identification of driver state for lane-keeping tasks. IEEE Trans Syst Man Cybern Part A Syst Hum 29(5):486
Gandhi T, Trivedi MM (2007) Pedestrian protection systems: issues, survey, and challenges. IEEE Trans Intell Transp Syst 8(3):413
Geronimo D, Lopez AM, Sappa AD, Graf T (2009) Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans Pattern Anal Mach Intell 7:1239
Hariyono J, Hoang VD, Jo KH (2014) Moving object localization using optical flow for pedestrian detection from a moving vehicle. Sci World J 2014:196415
Administration FH (2015) How do weather events impact roads? https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm/. Accessed June 2019
Olutayo V, Eludire A (2014) Traffic accident analysis using decision trees and neural networks. Int J Inf Technol Comput Sci 2:22
Lin L, Wang Q, Sadek AW (2015) A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction. Transp Res Part C Emerg Technol 55:444
Bergel-Hayat R, Debbarh M, Antoniou C, Yannis G (2013) Explaining the road accident risk: weather effects. Accid Anal Prev 60:456
Caliendo C, Guida M, Parisi A (2007) A crash-prediction model for multilane roads. Accid Anal Prev 39(4):657
Oh J, Washington SP, Nam D (2006) Accident prediction model for railway-highway interfaces. Accid Anal Prev 38(2):346
Zeng Z, Liang N, Yang X, Hoi S (2018) Multi-target deep neural networks: theoretical analysis and implementation. Neurocomputing 273:634
Zhang J, Zheng Y, Qi D, Li R, Yi X, Li T (2018) Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif Intell 259:147
Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: Thirty-Second AAAI Conference on Artificial Intelligence
Xia D, Wang B, Li H, Li Y, Zhang Z (2016) A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting. Neurocomputing 179:246
Hong WC (2011) Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74(12–13):2096
Wu C, Peng L, Huang Z, Zhong M, Chu D (2014) A method of vehicle motion prediction and collision risk assessment with a simulated vehicular cyber physical system. Transp Res Part C Emerg Technol 47:179
Ba Y, Zhang W, Wang Q, Zhou R, Ren C (2017) Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system. Transp Res Part C Emerg Technol 74:22
Coughlin JF, Reimer B, Mehler B (2011) Monitoring, managing, and motivating driver safety and well-being. IEEE Pervas Comput 10(3):14
Arbabzadeh N, Jafari M (2017) A data-driven approach for driving safety risk prediction using driver behavior and roadway information data. IEEE Trans Intell Transp Syst 19(2):446
Meiring G, Myburgh H (2015) A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors 15(12):30653
Jafari SA, Jahandideh S, Jahandideh M, Asadabadi EB (2015) Prediction of road traffic death rate using neural networks optimised by genetic algorithm. Int J Inj Contr Saf Promot 22(2):153
Dunne S, Ghosh B (2013) Weather adaptive traffic prediction using neurowavelet models. IEEE Trans Intell Transp Syst 14(1):370
Davis GA, Nihan NL, Hamed MM, Jacobson LN (1990) Adaptive forecasting of freeway traffic congestion. Transportation Research Record 1287
Leshem G, Ritov Y (2007) Traffic flow prediction using adaboost algorithm with random forests as a weak learner. In: Proceedings of World Academy of Science, Engineering and Technology, vol 19. Citeseer, pp 193–198
Jin X, Zhang Y, Yao D (2007) Simultaneously prediction of network traffic flow based on PCA-SVR. In: International Symposium on Neural Networks, Springer, pp 1022–1031
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A et al (2017) Mastering the game of go without human knowledge. Nature 550(7676):354
Lv Y, Duan Y, Kang W, Li Z, Wang FY (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865
Ma X, Dai Z, He Z, Ma J, Wang Y, Wang Y (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4):818
Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191
Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C Emerg Technol 54:187
Kamarianakis Y, Shen W, Wynter L (2012) Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO. Appl Stoch Models Bus Ind 28(4):297
Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emerg Technol 79:1
Yu R, Li Y, Shahabi C, Demiryurek U, Liu Y (2017) Deep learning: a generic approach for extreme condition traffic forecasting. In: Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, pp 777–785
Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7):1501
Redmon J, Farhadi A (2018) YOLOv3: An incremental improvement. arXiv:1804.02767
Zhou S, Wu Y, Ni Z, Zhou X, Wen H, Zou Y (2016) DoReFa-Net: training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv:1606.06160
Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2017) Quantized neural networks: training neural networks with low precision weights and activations. J Mach Learn Res 18(1):6869
De Brabandere B, Neven D, Van Gool L (2017) Semantic instance segmentation with a discriminative loss function. arXiv:1708.02551
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861
Henaff M, Brunav J, LeCun Y (2015) Deep convolutional networks on graph-structured data. arXiv:1506.05163
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp 844–3852
Hammond DK, Vandergheynst P, Gribonval R (2011) Wavelets on graphs via spectral graph theory. Appl Comput Harmon Anal 30(2):129
Jain A, Zamir AR, Savarese S, Saxena A (2016) Structural-RNN: deep learning on spatio-temporal graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5308–5317
Dai X, Fu R, Lin Y, Liv L, Wang FY (2017) DeepTrend: a deep hierarchical neural network for traffic flow prediction. arXiv:1707.03213
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp 3104–3112
Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional neural network: a deep learning framework for traffic forecasting. arXiv:1709.04875
Acknowledgements
The authors gratefully acknowledge the support of the Shanghai Key Science and Technology Project (19DZ1208903), National Natural Science Foundation of China (Grant Nos. 61572325 and 60970012), Ministry of Education Doctoral Fund of Ph.D. Supervisor of China (Grant No. 20113120110008), Shanghai Key Science and Technology Project in Information Technology Field (Grant Nos. 14511107902 and 16DZ1203603), Shanghai Leading Academic Discipline Project (No. XTKX2012) and Shanghai Engineering Research Center Project (Nos. GCZX14014 and C14001).
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Wang, J., Chen, Q. A traffic prediction model based on multiple factors. J Supercomput 77, 2928–2960 (2021). https://doi.org/10.1007/s11227-020-03373-0
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DOI: https://doi.org/10.1007/s11227-020-03373-0