Bayesian Dynamic Linear Model with Switching for Real-Time Short-Term Freeway Travel Time Prediction with License Plate Recognition Data

AbstractThis paper presents a Bayesian inference-based dynamic linear model (DLM) with switching based on three-phase traffic flow theory to predict online short-term travel time with plate recognition data. The proposed method combines the DLM model with a Hidden Markov Model (HMM) to capture the probability of flow breakdown and delays associated with congestion. By viewing travel time fluctuations as a time-varying stochastic process due to unforeseen events (e.g., incidents, accidents, or bad weather), the proposed dynamic linear model with Markov switching (SDLM) employs the HMM to determine the optimal traffic state sequence corresponding to a given travel time and flow rate observation sequence. The experimental results based on automatic license plate recognition data of a Jingtong Expressway stretch in Beijing City suggest that the proposed method can provide accurate and reliable travel time prediction under various traffic conditions.

[1]  H. J. Van Zuylen,et al.  Bayesian committee of neural networks to predict travel times with confidence intervals , 2009 .

[2]  Boris S. Kerner,et al.  Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory , 2009 .

[3]  Antony Stathopoulos,et al.  Real-Time Traffic Volatility Forecasting in Urban Arterial Networks , 2006 .

[4]  Xiaoyan Zhang,et al.  Short-term travel time prediction , 2003 .

[5]  Sam Yagar,et al.  Exploration of the Breakdown Phenomenon in Freeway Traffic , 1998 .

[6]  Lily Elefteriadou,et al.  Travel time estimation on a freeway using Discrete Time Markov Chains , 2008 .

[7]  Chung-Cheng Lu,et al.  A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction , 2011 .

[8]  Nathan H. Gartner,et al.  Traffic Flow Theory - A State-of-the-Art Report: Revised Monograph on Traffic Flow Theory , 2002 .

[9]  C. Granger,et al.  A long memory property of stock market returns and a new model , 1993 .

[10]  Mecit Cetin,et al.  Short-Term Traffic Flow Prediction with Regime Switching Models , 2006 .

[11]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

[12]  Eleni I. Vlahogianni,et al.  Memory properties and fractional integration in transportation time-series , 2009 .

[13]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[14]  Eleni I. Vlahogianni,et al.  Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume , 2006 .

[15]  Hani S. Mahmassani,et al.  Flow Breakdown and Travel Time Reliability , 2009 .

[16]  R. Shumway,et al.  Dynamic linear models with switching , 1991 .

[17]  Lily Elefteriadou,et al.  Probabilistic nature of breakdown at freeway merge junctions , 1995 .

[18]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[19]  Chang‐Jin Kim,et al.  Dynamic linear models with Markov-switching , 1994 .

[20]  Lily Elefteriadou,et al.  Probability of breakdown at freeway merges using Markov chains , 2001 .