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

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...

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Bibliographic Details
Published in:Automation in construction Vol. 72; pp. 237 - 246
Main Authors: Zhan, Xianyuan, Ukkusuri, Satish V., Yang, Chao
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.12.2016
Elsevier BV
Subjects:
Bayesian analysis
Bayesian mixture model
Economic models
EM algorithm
Estimation
Path inference
Probabilistic methods
Probability theory
Scale (ratio)
Short term
Short-term average link travel time estimation
Spatial dependencies
Traffic engineering
Traffic flow
Traffic information
Traffic management
Traffic models
Travel
Travel time
Trip based data with partial information
Trip estimation
Variance (statistics)
Welfare economics
Short-term average link travel time estimation
Bayesian mixture model
EM algorithm
Trip based data with partial information
Path inference
ISSN:0926-5805, 1872-7891
Online Access:Get full text
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Summary: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 30min 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.
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ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2015.12.007