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CIVIL & ENVIRONMENTAL ENGINEERING

High-order Markov model for prediction of secondary crash likelihood considering incident duration

ORCID Icon & | (Reviewing editor)
Article: 1978171 | Received 05 Mar 2021, Accepted 07 Aug 2021, Published online: 06 Oct 2021

References

  • Aceto, G., Bovenzi, G., Ciuonzo, D., Montieri, A., Persico, V., & Pescapé, A. (2021). Characterization and Prediction of Mobile-App Traffic Using Markov Modeling. IEEE Transactions on Network and Service Management, 18(1), 907–16. https://doi.org/10.1109/TNSM.2021.3051381
  • Ahmed, M., Abdel-Aty, M., & Yu, R. (2012). Bayesian Updating Approach for Real-Time Safety Evaluation with Automatic Vehicle Identification Data. Transportation Research Record, 2280(1), 60–67. https://doi.org/10.3141/2280-07
  • Ching, W. K., Fung, E. S., & Ng, M. K. (2003). A Higher-Order Markov Model for the Newsboy’s Problem. The Journal of the Operational Research Society, 54(3), 291–298. https://doi.org/10.1057/palgrave.jors.2601491
  • Chow, J. Y. J., & Nurumbetova, A. E. (2015). A multi-day activity-based inventory routing model with space–time–needs constraints. Transportmetrica A: Transport Science, 11(3), 243–269. https://doi.org/10.1080/23249935.2014.958120
  • Djavadian, S., & Chow, J. Y. J. (2017). An agent-based day-to-day adjustment process for modeling ‘Mobility as a Service’ with a two-sided flexible transport market. Transportation Research Part B: Methodological, 104, 36–57. https://doi.org/10.1016/j.trb.2017.06.015
  • Goodall, N. J. (2018). Probability of Secondary Crash Occurrence on Freeways with the Use of Private-Sector Speed Data. Transportation Research Record: Journal of the Transportation Research Board, 2635(1), 11–18. https://doi.org/10.3141/2635-02
  • Hossain, M., & Muromachi, Y. (2012, March). A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways. Accid Anal Prev, 45, 373–381. Epub 2011 Sep 6. PMID: 22269521. https://doi.org/10.1016/j.aap.2011.08.004.
  • Janssen, J., & Manca, R. (2010). Applied semi-Markov processes. Springer. https://doi.org/10.1007/0-387-29548-8
  • Khattak, A., Wang, X., & Zhang, H. (2009). Are Incident Durations and Secondary Incidents Interdependent? Transportation Research Record: Journal of the Transportation Research Board, 2099(1), 39–49. https://doi.org/10.3141/2099-05
  • Khattak, A. J., Wang, X., Zhang, H., & Cetin, M. (2011). Primary and secondary incident management: Predicting durations in real time. In Virginia Center for Transportation Innovation and Research. Report no. VCTIR 11-R11.
  • Ki, S., F. E., C. W., & N. M, K. (2004). Higher-order Markov chain models for categorical data sequences. Naval Research Logistics (NRL), 51(4), 557–574. https://doi.org/10.1002/nav.20017
  • Li, Y., Dong, Y. N., Zhang, H., Zhao, H. T., Shi, H. X., & Zhao, X. X. 2010. “Spectrum Usage Prediction Based on High-order Markov Model for Cognitive Radio Networks. ” Paper presented at the 2010 10th IEEE International Conference on Computer and Information Technology, IEEE, 2010, Bradford, UK.
  • Ng, M., Khattak, A., & Talley, W. (2013). Modeling the time to the next primary and secondary incident: A semi-Markov stochastic process approach. Transportation Research Part B: Methodological, 58, 44-57. https://doi.org/10.1016/j.trb.2013.09.013
  • NHTSA, “USDOT Releases 2016 Fatal Traffic Crash Data” 2016. http://www.nhtsa.gov/press-releases/usdot-releases-2016-fatal-traffic-crash-data
  • Owens, N., Armstrong, A., Sullivan, P., Mitchell, C., Newton, D., Brewster, R., & Trego, T. (2010). Traffic Incident Management Handbook, Publication FHWA-HOP-10-013. FHWA. Department of Transportation.
  • Park, H., Gao, S., & Haghani, A. (2017). Sequential interpretation and prediction of secondary incident probability in real time. Paper presented atTransportation Research Board 96th Annual Meeting Transportation Research Board, 2017, Washington DC, USA. https://trid.trb.org/view/1439495
  • Park, H., Gao, S., & Samuel, S. (2017). Modeling Effects of Forward Glance Durations on Latent Hazard Detection. Transportation Research Record, 2663(1), 90–98. https://doi.org/10.3141/2663-12
  • Park, H., & Haghani, A. (2016). Real-time prediction of secondary incident occurrences using vehicle probe data. Transportation Research Part C: Emerging Technologies, 70, 69–85. https://doi.org/10.1016/j.trc.2015.03.018. 2016/09/01/ 2016.
  • Pugh, N., Park, H., Derjany, P., Liu, D., & Namilae, S. (2021). Deep adaptive learning for safe and efficient navigation of pedestrian dynamics. IET Intelligent Transport Systems, 15(4), 538–548.
  • Raub, R. (1997). Occurrence of Secondary Crashes on Urban Arterial Roadways. Transportation Research Record: Journal of the Transportation Research Board, 1581(1581), 53–58. https://doi.org/10.3141/1581-07
  • Road Crash Statistics” 0000 . http://asirt.org/initiatives/informing-road-users/road-safety-facts.
  • Rossi, P. S., Ciuonzo, D., & Ekman, T. (2015). HMM-based decision fusion in wireless sensor networks with noncoherent multiple access. IEEE Communications Letters, 19.5(5), 871–874. https://doi.org/10.1109/LCOMM.2015.2413407
  • Sayarshad, H. R., & Chow, J. Y. J. (2017). Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem. Transportation Research Part E: Logistics and Transportation, Review, 106, 60–77. https://doi.org/10.1016/j.tre.2017.08.003
  • „Secondary Crashes” 0000 . http://nchrptimpm.timnetwork.org/?page_id=23
  • Singh, G. B., & song, H. (2010). Comparison of Hidden Markov Models and Support Vector Machines for vehicle crash detection. International Conference on Methods and Models in Computer Science (ICM2CS-2010, pp. 1– 6). https://doi.org/10.1109/icm2cs.2010.5706709
  • Singh, G. B., Song, H., & Chou, C. C., “Crash Detection System Using Hidden Markov Models. ”, SAE Technical Paper Series, SAE International. https://doi.org/10.4271/2004-01-1781 2004.
  • Study Shows, N. H. T. S. A. (2015, January 1). Economic and Societal Impacts of Crashes. Public Roads.
  • Traffic Incident Management (TIM) Performance Measurement: On the Road to Success” 0000. http://ops.fhwa.dot.gov/publications/fhwahop10009/tim_fsi.html
  • Wu, M., Shan, D., Wang, Z., Sun, X., Liu, J., & Sun, M., “A Bayesian Network Model for Real-time Crash Prediction Based on Selected Variables by Random Forest. ” 2019 5th International Conference on Transportation Information and Safety (ICTIS), 2019, pp.670–677, Liverpool, United Kingdom. https://doi.org/10.1109/ICTIS.2019.8883694.
  • Xiong, Y., Tobias, J. L., & Mannering, F. L. (2014a). The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity. Transportation Research Part B: Methodological, 67, 109–128. https://doi.org/10.1016/j.trb.2014.04.007
  • Xiong, Y., Tobias, J. L., & Mannering, F. L. (2014b). The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity. Transportation Research Part B: Methodological, 67, 109–128. https://doi.org/10.1016/j.trb.2014.04.007. 2014/09/01/ 2014.
  • Xu, C., Liu, P., Yang, B., & Wang, W. (2016a). Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data. Transportation Research Part C: Emerging Technologies, 71, 406–418. https://doi.org/10.1016/j.trc.2016.08.015
  • Yang, H., Bartin, B., & Ozbay, K. (2014). Mining the Characteristics of Secondary Crashes on Highways. Journal of Transportation Engineering, 140(4), 04013024. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000646
  • Yang, H., Ozbay, K., Morgul, E., Bartin, B., & Xie, K. (2014). Development of Online Scalable Approach for Identifying Secondary Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2470(1), 24–33. https://doi.org/10.3141/2470-03
  • Yang, H., Ozbay, K., & Xie, K. (2014). Assessing the risk of secondary crashes on highways. Journal of Safety Research, 49, 143.e141–149. https://doi.org/10.1016/j.jsr.2014.03.007
  • You, J., Wang, J., & Fang, S. (2016). Real-time freeway crash prediction model by using single ultrasonic detector lane-level data., Fundamental Pavement Design (1st edition), (pp. 1635–1643). https://doi.org/10.1201/9781315643274-181
  • You, J., Wang, J., & Guo, J. (2017). Real-time crash prediction on freeways using data mining and emerging techniques. J. Mod. Transport, 25(2), 116–123. https://doi.org/10.1007/s40534-017-0129-7
  • You, S. I., Chow, J. Y. J., & Ritchie, S. G. (2016). Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics. Transportmetrica A: Transport Science, 12(7), 650–673. https://doi.org/10.1080/23249935.2016.1189723