Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26310
Title: Estimation of mean and covariance of stochastic multi-class OD demands from classified traffic counts
Authors: Shao, H
Lam, WHK 
Sumalee, A 
Hazelton, ML
Keywords: Classified traffic counts
Covariance matrix
Multiple vehicle class
OD demand estimation
Issue Date: 2015
Publisher: Pergamon Press
Source: Transportation research. Part C, Emerging technologies, 2015 How to cite?
Journal: Transportation research. Part C, Emerging technologies 
Abstract: This paper proposes a new model to estimate the mean and covariance of stochastic multi-class (multiple vehicle classes) origin-destination (OD) demands from hourly classified traffic counts throughout the whole year. It is usually assumed in the conventional OD demand estimation models that the OD demand by vehicle class is deterministic. Little attention is given on the estimation of the statistical properties of stochastic OD demands as well as their covariance between different vehicle classes. Also, the interactions between different vehicle classes in OD demand are ignored such as the change of modes between private car and taxi during a particular hourly period over the year. To fill these two gaps, the mean and covariance matrix of stochastic multi-class OD demands for the same hourly period over the year are simultaneously estimated by a modified lasso (least absolute shrinkage and selection operator) method. The estimated covariance matrix of stochastic multi-class OD demands can be used to capture the statistical dependency of traffic demands between different vehicle classes. In this paper, the proposed model is formulated as a non-linear constrained optimization problem. An exterior penalty algorithm is adapted to solve the proposed model. Numerical examples are presented to illustrate the applications of the proposed model together with some insightful findings on the importance of covariance of OD demand between difference vehicle classes.
URI: http://hdl.handle.net/10397/26310
ISSN: 0968-090X
DOI: 10.1016/j.trc.2015.05.017
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