Abstract
As a part of the smart city, urban traffic safety has always received strong attention. For urban traffic safety, previous work often relies on some additional features and machine learning models, mainly considering whether accidents can be accurately predicted, but these work cannot be well integrated with smart cities. In order to better apply traffic safety warning to smart cities, we propose a traffic safety warning framework based on Bayesian deep learning - DeepTSW. Specifically, we propose a traffic prediction model based on Bayesian deep learning. The regional collision index (RCI) is proposed as the traffic accident risk evaluation parameter, and the gaussian mixture model (GMM) is used to cluster the traffic data to realize the accident risk grade evaluation. The experimental results of actual traffic data show that our traffic accident prediction model is superior to the four baseline models, and DeepTSW can effectively reflect the actual accident risk.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kong, X., Cao, J., Wu, H., et al.: Mobile Crowdsourcing and Pervasive Computing for Smart Cities. Pervasive Mob. Comput. 61, 101114 (2020)
City Brain: Exploring “Digital Twin Cities”. https://developer.aliyun.com/article/603873
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818 (2017)
Liu, Z., Huang, M., Ye, Z., Wu, K.: DeepRTP: a deep spatio-temporal residual network for regional traffic prediction. In: 15th International Conference on Mobile Ad-Hoc and Sensor Networks, Hong Kong, pp. 291–296. IEEE (2019)
Shekhar, S., Williams, B.M.: Adaptive seasonal time series models for forecasting short-term traffic flow. Transp. Res. Rec. 2024(1), 116–125 (2007)
Kumar, S.V., Vanajakshi, L.: Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur. Transp. Res. Rev. 7(3), 1–9 (2015). https://doi.org/10.1007/s12544-015-0170-8
Tang, J., Chen, X., Hu, Z., Zong, F., Han, C., Li, L.: Traffic flow prediction based on combination of support vector machine and data denoising schemes. Physica A 534, 120642 (2019)
Wang, J., Gu, Q., Wu, J., Liu, G., Xiong, Z.: Traffic speed prediction and congestion source exploration: a deep learning method. In: IEEE 16th International Conference on Data Mining, pp. 499–508. IEEE (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105. ACM (2012)
Kong, X., Tong, S., Gao, H., Shen, G., et al.: Mobile edge cooperation optimization for wearable internet of things: a network representation-based framework. IEEE Trans. Ind. Inf. 1 (2020). https://doi.org/10.1109/TII.2020.3016037
Zhu, L., Li, T., Du, S.: TA-STAN: a deep spatial-temporal attention learning framework for regional traffic accident risk prediction. In: International Joint Conference on Neural Networks, pp. 1–8. IEEE (2019)
Basso, F., Basso, L.J., Bravo, F., Pezoa, R.: Real-time crash prediction in an urban expressway using disaggregated data. Transp. Res. Part C Emerg. Technol. 86, 202–219 (2018)
Wang, J., Kong, Y., Fu, T.: Expressway crash risk prediction using back propagation neural network: a brief investigation on safety resilience. Accid. Anal. Prev. 124, 180–192 (2019)
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C: Emerg. Technol. 54, 187–197 (2015)
Liu, Y., Huang, X., Duan, J., Zhang, H.: The assessment of traffic accident risk based on grey relational analysis and fuzzy comprehensive evaluation method. Nat. Hazards 88(3), 1409–1422 (2017). https://doi.org/10.1007/s11069-017-2923-2
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2014)
Toncharoen, R., Piantanakulchai, M.: Traffic state prediction using convolutional neural network. In: 15th International Joint Conference on Computer Science and Software Engineering, pp. 1–6. IEEE (2018)
Zhu, J., Song, Y., Zhao, L., Li, H.: A3T-GCN: attention temporal graph convolutional network for traffic forecasting. arXiv preprint arXiv:2006.11583 (2020)
Sun, P., Boukerche, A., Tao, Y.: SSGRU: a novel hybrid stacked GRU-based traffic volume prediction approach in a road network. Comput. Commun. 160, 502–511 (2020)
Lu, W., Luo, D., Yan, M.: A model of traffic accident prediction based on convolutional neural network. In: 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE), pp. 198–202. IEEE (2017)
Zhao, H., Cheng, H., Mao, T., He, C.: Research on Traffic Accident Prediction Model Based on Convolutional Neural Networks in VANET. In: 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 79–84. IEEE (2019)
Yuan, Z., Zhou, X., Yang, T.: Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 984–992. ACM (2018)
Ren, H., Song, Y., Wang, J., Hu, Y., Lei, J.: A deep learning approach to the citywide traffic accident risk prediction. In: 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3346–3351. IEEE (2018)
Zhou, Z.: Attention based stack ResNet for citywide traffic accident prediction. In: 20th IEEE International Conference on Mobile Data Management (MDM), pp. 369–370. IEEE (2019)
Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21, 3848–3858 (2019)
Liu, D., Tang, L., Shen, G., Han, X.: Traffic speed prediction: an attention-based method. Sensors 19(18), 3836 (2019)
Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., Zhang, J.: Urban traffic prediction from spatio-temporal data using deep meta learning. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1720–1730. ACM (2019)
Yang, B., Sun, S., Li, J., Lin, X., Tian, Y.: Traffic flow prediction using LSTM with feature enhancement. Neurocomputing 332, 320–327 (2019)
Wang, H., Yeung, D.-Y.: A survey on Bayesian deep learning. ACM Comput. Surv. 53, 1–37 (2020)
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424 (2015)
PMSP Caltrans. http://pems.dot.ca.gov. Accessed 7 Aug 2020
Acknowledgments
The work was partially supported by the National Natural Science Foundation of China under Grant (62072409, 62073295), and was partially supported by Zhejiang Provincial Natural Science Foundation of China under Grant (LR21F020003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shen, G., Guan, L., Tan, J., Kong, X. (2020). DeepTSW: An Urban Traffic Safety Warning Framework Based on Bayesian Deep Learning. In: Ning, H., Shi, F. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2020 2020. Communications in Computer and Information Science, vol 1329. Springer, Singapore. https://doi.org/10.1007/978-981-33-4336-8_5
Download citation
DOI: https://doi.org/10.1007/978-981-33-4336-8_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4335-1
Online ISBN: 978-981-33-4336-8
eBook Packages: Computer ScienceComputer Science (R0)