Short-term traffic breakdown prediction using a hidden Markov model

ZHOU Hao; HU Jianming; ZHANG Yi; SHEN Yingzhen

doi:10.16511/j.cnki.qhdxxb.2016.25.043

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Journal of Tsinghua University(Science and Technology) ›› 2016, Vol. 56 ›› Issue (12) : 1333-1340. DOI: 10.16511/j.cnki.qhdxxb.2016.25.043

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Short-term traffic breakdown prediction using a hidden Markov model

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Abstract

Traffic breakdown reduces road capacity as one of the main factors causing congestion on urban expressways. Accurate short-term traffic breakdown predictions on urban expressways are becoming more and more important because of their vital role in traffic management and control. Traffic flow data was obtained from the Caltrans performance measurement system (PeMS) with traffic breakdown states classified by a lane-based method. A Hidden Markov model (HMM) is then established with the traffic breakdown state as the hidden state and the road occupancy as the observed state with the Viterbi algorithm to solve the problem. The traffic breakdowns were successfully predicted to show that the HHM accurately predicts short-term traffic breakdowns.

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traffic breakdown / PeMS / hidden Markov model / Viterbi algorithm

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ZHOU Hao, HU Jianming, ZHANG Yi, SHEN Yingzhen. Short-term traffic breakdown prediction using a hidden Markov model[J]. Journal of Tsinghua University(Science and Technology). 2016, 56(12): 1333-1340 https://doi.org/10.16511/j.cnki.qhdxxb.2016.25.043

References

[1] Dong J, Mahmassani H S. Stochastic modeling of traffic flow breakdown phenomenon:Applimtion to predicting travel time reliability[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(4):1803-1809. [2] 陈喜群. 交通流动态随机演化模型研究[D]. 北京:清华大学, 2012.CHEN Xiqun. Modeling Traffic Flow Dynamic and Stochastic Evolutions[D]. Beijing:Tsinghua University, 2012. (in Chinese) [3] Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E, 2009, 62(2):1805-1824. [4] Sugiyama Y, Fukui M, Kikuchi M, et al. Traffic jams without bottlenecks-Experimental evidence for the physical mechanism of the formation of a jam[J]. New Journal of Physics, 2008, 10(3):033001. [5] Daniel J G, Glaister S. Road traffic demand elasticity estimates:A review[J]. Transport Reviews, 2004, 24(3):261-274. [6] Lee H K, Barlovic R, Schreckenberg M, et al. Mechanical restriction versus human overreaction triggering congested traffic states[J]. Physical Review Letters, 2004, 92(23):238702/1-4. [7] Okamura H, Watanabe S, Watanabe T. An empirical study on the capacity of bottlenecks on the basic suburban expressway sections in Japan[J]. Transportation Research Circular, 2000(E-C018):120-129. [8] Murashige Y. Countermeasure strategies against traffic congestion on motorways in Japan[J]. Procedia Social and Behavioral Sciences, 2011, 16:110-119. [9] Lorenz M R, Elefteriadou L. A probabilistic approach to defining freeway capacity and breakdown[J]. Transportation Research Circular, 2000(E-C018):84-95. [10] Evans J, Elefteriadou L, Gautam N. Probability of breakdown at freeway merges using Markov chains[J]. Transportation Research Part B, 2001, 35(3):237-254. [11] WANG Haizhong, Rudy K, LI Jia, et al. Calculation of traffic flow breakdown probability to optimize link throughput[J]. Applied Mathematical Modeling, 2010, 34(11):3376-3389. [12] Chow A, Lu X, Qiu T. Empirical analysis of traffic breakdown probability distribution with respect to speed an occupancy[C]//12th IFAC Symposium on Control in Transportation Systems. Berkeleys California, USA:Elsevier Science Direct-IFAC Papers Online, 2009, 42(15):472-477. [13] Blain L. Calculating delay on congested links using a state variable[C]//Proceedings of the 7th TRB Conference on the Application of Transportation Planning Methods. Boston, Massachusetts:Transportation Research Board Business Office, 2002:350-355. [14] CHEN Xiqun, LI Meng, ZHANG Liping, et al. Comparison of highway traffic breakdown features between U.S. and China using discrete wavelet transform:An empirical study[C]//Proceedings of the 12th COTA International Conference of Transportation Professionals. Reston, VA:American Society of Civil Engineers, 2012:545-556. [15] Ma D P, Nakamura H, Asano M. Lane based breakdown identification method at diverge sections for modeling breakdown probability[J]. Journal of the Transportation Research Record, 2013, 2395(1):83-92.