A Discrete Markov Model of Vehicular Traffic

Michael C. Dunne, Richard W. Rothery and Renfrey B. Potts
Additional contact information
Michael C. Dunne: General Motors Research Laboratories, Warren, Michigan
Richard W. Rothery: General Motors Research Laboratories, Warren, Michigan
Renfrey B. Potts: University of California, Berkeley, California

Transportation Science, 1968, vol. 2, issue 3, 233-251

Abstract: For a number of years the Poisson process has been used as a model to describe the time series of events of vehicles passing a point on a roadway. In its simplest form the Poisson model does not take into account the finite size of the vehicles nor does it properly describe the characteristic bunching of traffic that commonly occurs, for example, on a two-lane road when passing is hindered. To give a more comprehensive description of traffic, various extensions of the Poisson model and a variety of other models have been proposed, with varying success. In this paper a discrete Markov model of traffic is proposed. The model is an extension of the simple binomial model and accounts for the bunching tendency of traffic by assuming correlations between successive vehicles. A description is given of the time-arrival experiments that were carried out to test the model and the statistical analysis of the results of these experiments is discussed. In addition, several theoretical aspects of the model are examined.

Date: 1968
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://dx.doi.org/10.1287/trsc.2.3.233 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:2:y:1968:i:3:p:233-251

Access Statistics for this article

More articles in Transportation Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().