Application of prediction-error minimization and maximum likelihood to estimate intersection O-D matrices from traffic counts

Nancy L. Nihan, Gary A. Davis

Research output: Contribution to journalArticlepeer-review

92 Scopus citations

Abstract

A maximum likelihood estimator for situations when full information on turning movement counts is available is derived and used as a component for a maximum likelihood algorithm which only requires entering and exiting counts. Several algorithms based on minimizing the error between observed and predicted exiting counts are also developed. Some actual traffic data are collected and used to develop realistic simulations for evaluating the various estimators. Generally, the maximum likelihood algorithm produced biased but more efficient estimates, while prediction error minimization approaches produced unbiased but less efficient estimates. Constraining the recursive version of the ordinary least-squares estimator to satisfy natural constraints did not affect its long run convergence properties.

Original languageEnglish (US)
Pages (from-to)77-90
Number of pages14
JournalTransportation Science
Volume23
Issue number2
DOIs
StatePublished - Jan 1 1989

Fingerprint Dive into the research topics of 'Application of prediction-error minimization and maximum likelihood to estimate intersection O-D matrices from traffic counts'. Together they form a unique fingerprint.

Cite this