A bayesian network approach to traffic flow forecasting

A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2006-03, Vol.7 (1), p.124-132
Main Authors: Sun, S., Zhang, C., Yu, G.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Bayesian analysis
Bayesian methods
Bayesian network
Communication system traffic control
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Control theory. Systems
Exact sciences and technology
expectation maximization algorithm
Forecasting
Gaussian mixture model
Ground, air and sea transportation, marine construction
Information analysis
Intelligent transportation systems
Links
Mathematical models
Maximization
Mean square errors
Networks
Prediction methods
Predictive models
Road transportation and traffic
Roads
Robotics
Software
Studies
Sun
Telecommunication traffic
Traffic control
Traffic flow
traffic flow forecasting
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Summary:A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2006.869623