Short-term traffic breakdown prediction using a hidden Markov model

doi:10.16511/j.cnki.qhdxxb.2016.25.043

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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.

Keywords traffic breakdown PeMS hidden Markov model Viterbi algorithm

ZTFLH:

U491.1+4

Issue Date: 15 December 2016

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	ZHOU Hao
	HU Jianming
	ZHANG Yi
	SHEN Yingzhen

Cite this article:

ZHOU Hao,HU Jianming,ZHANG Yi, et al. Short-term traffic breakdown prediction using a hidden Markov model[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(12): 1333-1340.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2016.25.043 OR http://jst.tsinghuajournals.com/EN/Y2016/V56/I12/1333

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