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30-03-2019

Estimation of origin–destination matrices using link counts and partial path data

Authors: Mojtaba Rostami Nasab, Yousef Shafahi

Published in: Transportation | Issue 6/2020

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Abstract

After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a multiobjective model which utilizes combinations of vehicle counts and partial path data. The methods were tested to estimate the origin–destination matrix of a large-scale network from Mashhad City with 163 traffic zones and 2093 links, and the results were compared with the conventional gradient-based algorithm. The results show that the innovative methods performed better as compared to the gradient-based algorithm.

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Title: Estimation of origin–destination matrices using link counts and partial path data
Authors: Mojtaba Rostami Nasab
Yousef Shafahi
Publication date: 30-03-2019
Publisher: Springer US
Published in: Transportation / Issue 6/2020
Print ISSN: 0049-4488
Electronic ISSN: 1572-9435
DOI: https://doi.org/10.1007/s11116-019-09999-1

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