Population Origin–Destination Estimation Using Automatic Vehicle Identification and Volume Data

Michael P. Dixon · Laurence R. Rilett

42 citations0/0 files storedJournal of Transportation Engineering

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Transportation Planning and Optimization Traffic control and management Traffic Prediction and Management Techniques

Abstract

The focus of this paper is the use of information from automatic vehicle identification (AVI) systems to help estimate short-term trip origin–destination (OD) matrices in an urban environment. In this paper, a method is proposed for using sample link choice proportions and sample OD matrix information derived from AVI data sampled from a portion of vehicles to estimate population OD matrices with the AVI data collection points acting as the origins and the destinations. This method is extended to estimate ramp-to-ramp freeway OD volumes. The value of such information is demonstrated using a large urban freeway network equipped with an AVI system. Based on a comparison of the OD estimate accuracies, with and without the AVI data, it was found that using AVI data in OD estimation improves significantly the accuracy of the OD matrix estimates.

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References

1971A probabilistic multipath traffic assignment model which obviates path enumeration 1980The most likely trip matrix estimated from traffic counts 1983Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach 1993Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts 1992Estimation of origin-destination matrices from link traffic counts on congested networks 1988A unified framework for estimating or updating origin/destination matrices from traffic counts 1987Recursive estimation of origin-destination matrices from input/output counts 1996Estimation and prediction of time-dependent origin-destination flows

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2015Integrated Determination of Network Origin–Destination Trip Matrix and Heterogeneous Sensor Selection and Location Strategy 2021Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data 2017Enhanced least square based dynamic OD matrix estimation using Radio Frequency Identification data 2019Estimation of origin–destination matrices using link counts and partial path data

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