A Bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data

Abstract Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture model to estimate short-term average urban link travel times using large-scale trip-based data with partial information. Unlike typical GPS trajectory data, trip-based data from taxies or other sources provide limited trip level information, which only contains the trip origin and destination locations, trip travel times and distances, etc. The focus of this study is to develop a robust probabilistic short-term average link travel time estimation model and demonstrate the feasibility of estimating network conditions using large-scale trip level information. In the model, the path taken by each trip is considered as latent and modeled using a multinomial logit distribution. The observed trip data given the possible path set and the mean and variance of the average link travel times can thus be characterized using a finite mixture distribution. A transition model is also introduced to serve as an informative prior that captures the temporal and spatial dependencies of link travel times. A solution approach based on the expectation–maximization (EM) algorithm is proposed to solve the problem. The model is tested on estimating the mean and variance of the average link travel times for 30 min time intervals using a large-scale taxi trip dataset from New York City. More robust estimation results are obtained owing to the adoption of the Bayesian framework.

[1]  Hesham A. Rakha,et al.  Estimating Path Travel-Time Reliability , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[2]  J. Y. Yen.  Finding the K Shortest Loopless Paths in a Network , 1971 .

[3]  Pravin Varaiya,et al.  Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors , 2009 .

[4]  Arnaud Doucet,et al.  Sequential Monte Carlo Methods to Train Neural Network Models , 2000, Neural Computation.

[5]  P. Abbeel,et al.  Path and travel time inference from GPS probe vehicle data , 2009 .

[6]  Ruimin Li,et al.  Lane-based real-time queue length estimation using license plate recognition data , 2015 .

[7]  Wu Chen,et al.  Positioning and tracking construction vehicles in highly dense urban areas and building construction sites , 2007 .

[8]  Alexandre M. Bayen,et al.  Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment , 2009 .

[9]  Michael J. Cassidy,et al.  Vehicle reidentification and travel time measurement on congested freeways , 2002 .

[10]  Dongjoo Park,et al.  Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks , 1998 .

[11]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

[12]  Satish V. Ukkusuri,et al.  Urban Link Travel Time Estimation Using Large-scale Taxi Data with Partial Information , 2013 .

[13]  Jitamitra Desai,et al.  A discrete optimization approach for locating Automatic Vehicle Identification readers for the provision of roadway travel times , 2006 .

[14]  Ruimin Li,et al.  Incorporating uncertainty into short-term travel time predictions , 2011 .

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

[16]  Fangfang Zheng,et al.  Urban link travel time estimation based on sparse probe vehicle data , 2013 .

[17]  Minglu Li,et al.  HERO: Online Real-Time Vehicle Tracking , 2009, IEEE Transactions on Parallel and Distributed Systems.

[18]  Fangfang Zheng,et al.  Uncertainty and Predictability of Urban Link Travel Time , 2010 .

[19]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[20]  Takashi Matsumoto,et al.  Bayesian on-line learning: a sequential Monte Carlo with importance resampling , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

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

[22]  Alexandre M. Bayen,et al.  Estimating arterial traffic conditions using sparse probe data , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

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