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Real-time OD estimation using automatic vehicle identification and traffic count data

delete2002-12-17
delete79
PRE
AI
D
Dixon, MP
L
Laurence R. Rilett
DOI：10.1111/1467-8667.00248delete
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Abstract

Abstract

En 中文
A key input to many advanced traffic management operations strategies are origin-destination (OD) matricies. In order to examine the possibility of estimating OD matricies in real-time, two constrained OD estimators, based on generalized least squares and Kalman filtering, were developed and tested. A one-at-a-time processing method was introduced to provide an efficient organized framework for incorporating observations from multiple data sources in real-time. The estimators were tested under different conditions based on the type of prior OD information available, the type of assignment available, and the type of link volume model used. The performance of the Kalman filter estimators also was compared to that of the generalized least squares estimator to provide insight regarding their performance characteristics relative to one another for given scenarios. Automatic vehicle identification (AVI) tag counts were used so that observed and estimated OD parameters could be compared. While the approach was motivated using AVI data, the methodology can be generalized to any situation where traffic counts are available and origin volumes can be estimated reliably. The primary means by which AVI data was utilized was through the incorporation of prior observed OD information as measurements, the inclusion of a deterministic link volume component that makes use of OD data extracted from the latest time interval from which all trips have been completed, and through the use of link choice proportions estimated based on link travel time data. It was found that utilizing prior observed OD data along with link counts improves estimator accuracy relative to OD estimation based exclusively on link counts.
Keywords:
ORIGIN-DESTINATION MATRICES
MAXIMUM-LIKELIHOOD
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Journal

C
Computer-Aided Civil and Infrastructure Engineering
IF:
9.1
Papers:
2.0K
Citations:
10.0K

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