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

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 o...

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Bibliographic Details
Published in:Transportation (Dordrecht) 2020-12, Vol.47 (6), p.2923-2950
Main Authors: Rostami Nasab, Mojtaba, Shafahi, Yousef
Format: Article
Language:English
Subjects:
Algorithms
Automatic vehicle identification systems
Automation
Economic Geography
Economics
Economics and Finance
Engineering Economics
Identification methods
Innovation/Technology Management
Literature reviews
Logistics
Marketing
Matrices
Methods
Multiple objective analysis
Organization
Radio frequency identification
Regional/Spatial Science
Sensors
Traffic
Traffic congestion
Traffic information
Vehicle identification
Vehicles
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Summary: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.
ISSN:0049-4488
1572-9435
DOI:10.1007/s11116-019-09999-1