161
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Utilizing angle-based outlier detection method with sliding window mechanism to identify real-time crash risk

, , &

References

  • Abdel-Aty, M., Uddin, N., & Pande, A. (2005). Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways. Transportation Research Record: Journal of the Transportation Research Board, 1908(1), 51–58. doi:10.1177/0361198105190800107
  • Abdel-Aty, M., Uddin, N., Pande, A., Abdalla, M. F., & Hsia, L. (2004). Predicting freeway crashes from loop detector data by matched case-control logistic regression. Transportation Research Record: Journal of the Transportation Research Board, 1897(1), 88–95. doi:10.3141/1897-12
  • Ahmed, M. M., & Abdel-Aty, M. A. (2012). The viability of using automatic vehicle identification data for real-time crash prediction. IEEE Transactions on Intelligent Transportation Systems, 13(2), 459–468. doi:10.1109/TITS.2011.2171052
  • Ahmed, M. M., Abdel-Aty, M., & Yu, R. (2012). Bayesian updating approach for real-time safety evaluation with automatic vehicle identification data. Transportation Research Record: Journal of the Transportation Research Board, 2280(1), 60–67. doi:10.3141/2280-07
  • Basso, F., Basso, L. J., Bravo, F., & Pezoa, R. (2018). Real-time crash prediction in an urban expressway using disaggregated data. Transportation research part C: emerging technologies, 86, 202–219.
  • Behara, K. N., Paz, A., Arndt, O., & Baker, D. (2021). A random parameters with heterogeneity in means and Lindley approach to analyze crash data with excessive zeros: A case study of head-on heavy vehicle crashes in Queensland. Accident Analysis & Prevention, 160, 106308. doi:10.1016/j.aap.2021.106308
  • Boquet, G., Morell, A., Serrano, J., & Vicario, J. L. (2020). A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection. Transportation Research Part C: Emerging Technologies, 115, 102622. doi:10.1016/j.trc.2020.102622
  • Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent, Proceedings of COMPSTAT'2010 (pp. 177–186). Springer.
  • Buddhavarapu, P., Scott, J. G., & Prozzi, J. A. (2016). Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data. Transportation Research Part B Methodological, 91, 492–510. doi:10.1016/j.trb.2016.06.005
  • Cai, Q., Abdel-Aty, M., Sun, Y., Lee, J., & Yuan, J. (2019). Applying a deep learning approach for transportation safety planning by using high-resolution transportation and land use data. Transportation Research Part A: Policy and Practice, 127, 71–85. doi:10.1016/j.tra.2019.07.010
  • Fei, T. L., Kai, M. T., & Zhou, Z. H. (2008). Isolation forest. IEEE International Conference on Data Mining IEEE.
  • Gao, Y., Gao, Z., Yu, R., Huang, Z., & Feng, J. (2018). Utilizing multilayer perceptron neural network for crash risk prediction based on a full set of data. In CICTP 2018: Intelligence, Connectivity, and Mobility (pp. 1947–1956). Reston, VA: American Society of Civil Engineers.
  • Geedipally, S. R., Pratt, M. P., & Lord, D. (2019). Effects of geometry and pavement friction on horizontal curve crash frequency. Journal of Transportation Safety & Security, 11(2), 167–188. doi:10.1080/19439962.2017.1365317
  • He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
  • Hensman, P., & Masko, D. (2015). The impact of imbalanced training data for convolutional neural networks. Degree Project in Computer Science. KTH Royal Institute of Technology.
  • Hossain, M., Abdel-Aty, M., Quddus, M. A., Muromachi, Y., & Sadeek, S. N. (2019). Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements. Accident; Analysis and Prevention, 124, 66–84. doi:10.1016/j.aap.2018.12.022
  • Hossain, M., & Muromachi, Y. (2011). Understanding crash mechanisms and selecting interventions to mitigate real-time hazards on urban expressways. Transportation Research Record: Journal of the Transportation Research Board, 2213(1), 53–62. doi:10.3141/2213-08
  • Karimpour, A., Kluger, R., & Wu, Y.-J. (2021). Traffic sensor data-based assessment of speed feedback signs. Journal of Transportation Safety & Security, 13(12), 1302–1325. doi:10.1080/19439962.2020.1731038
  • Katrakazas, C., Quddus, M., Chen, W.-H., & Deka, L. (2015). Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C: Emerging Technologies, 60, 416–442. doi:10.1016/j.trc.2015.09.011
  • Khoda Bakhshi, A., & Ahmed, M. M. (2022). Real-time crash prediction for a long low-traffic volume corridor using corrected-impurity importance and semi-parametric generalized additive model. Journal of Transportation Safety & Security, 14(7), 1165–1200. doi:10.1080/19439962.2021.1898069
  • Krawczyk, B. (2016). Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221–232. doi:10.1007/s13748-016-0094-0
  • Kriegel, H.-P., Schubert, M., & Zimek, A. (2008). Angle-based outlier detection in high-dimensional data. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (444–452). doi:10.1145/1401890.1401946
  • Lange, S., & Riedmiller, M. (2010). Deep auto-encoder neural networks in reinforcement learning. International Joint Conference on Neural Networks IEEE.
  • Li, P., Abdel-Aty, M., & Yuan, J. (2020). Real-time crash risk prediction on arterials based on LSTM-CNN. Accident; Analysis and Prevention, 135, 105371. doi:10.1016/j.aap.2019.105371
  • Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988.
  • Llopis-Castelló, D., Findley, D. J., & Garcia, A. (2021). Comparison of the highway safety manual predictive method with safety performance functions based on geometric design consistency. Journal of Transportation Safety & Security, 13(12), 1365–1386. doi:10.1080/19439962.2020.1738612
  • Ma, X., Lu, J., Liu, X., & Qu, W. (2022). A genetic programming approach for real-time crash prediction to solve trade-off between interpretability and accuracy. Journal of Transportation Safety & Security, 1–23. doi:10.1080/19439962.2022.2076756
  • Oh, C., Oh, J.-S., Ritchie, S., & Chang, M. (2001). Real-time estimation of freeway accident likelihood. In 80th Annual Meeting of the Transportation Research Board, Washington, DC.
  • Roshandel, S., Zheng, Z., & Washington, S. (2015). Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis. Accident; Analysis and Prevention, 79, 198–211. doi:10.1016/j.aap.2015.03.013
  • Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. doi:10.1162/089976601750264965
  • Shi, Q., & Abdel-Aty, M. (2015). Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C: Emerging Technologies, 58, 380–394. doi:10.1016/j.trc.2015.02.022
  • Sun, J., & Sun, J. (2015). A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data. Transportation Research Part C: Emerging Technologies, 54, 176–186. doi:10.1016/j.trc.2015.03.006
  • Wang, L., Abdel-Aty, M., Shi, Q., & Park, J. (2015). Real-time crash prediction for expressway weaving segments. Transportation Research Part C: Emerging Technologies, 61, 1–10. doi:10.1016/j.trc.2015.10.008
  • Wang, X., Zhou, Q., Quddus, M., Fan, T., & Fang, S. (2018). Speed, speed variation and crash relationships for urban arterials[J]. Accident; Analysis and Prevention, 113(APR), 236–243. doi:10.1016/j.aap.2018.01.032
  • World Health Organization. (2018). Global status report on road safety 2018. World Health Organization.
  • Wz, A., & Ww, B. (2019). Learning V2V interactive driving patterns at signalized intersections. Transportation Research Part C: Emerging Technologies, 108, 151–166.
  • Xu, C., Liu, P., Yang, B., & Wang, W. (2016). Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data. Transportation Research Part C: Emerging Technologies, 71, 406–418. doi:10.1016/j.trc.2016.08.015
  • Xu, C., Wang, W., Liu, P., Guo, R., & Li, Z. (2014). Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models. Transportation Research Part C: Emerging Technologies, 38, 167–176. doi:10.1016/j.trc.2013.11.020
  • Yang, K., Wang, X., & Yu, R. (2018). A Bayesian dynamic updating approach for urban expressway real-time crash risk evaluation. Transportation Research Part C: Emerging Technologies, 96, 192–207. doi:10.1016/j.trc.2018.09.020
  • Yu, R., & Abdel-Aty, M. (2013). Utilizing support vector machine in real-time crash risk evaluation. Accident Analysis & Prevention, 51, 252–259.
  • Yuan, J., Abdel-Aty, M., Gong, Y., & Cai, Q. (2019). Real-time crash risk prediction using long short-term memory recurrent neural network. Transportation Research Record: Journal of the Transportation Research Board, 2673(4), 314–326. doi:10.1177/0361198119840611
  • Zhu, B., Jiang, Y., Zhao, J., He, R., Bian, N., & Deng, W. (2019). Typical-driving-style-oriented Personalized Adaptive Cruise Control design based on human driving data. Transportation Research Part C: Emerging Technologies, 100, 274–288. doi:10.1016/j.trc.2019.01.025

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.