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Abstract

Despite the considerable progress on estimation of arterial travel time that traffic researchers have made in recent years, many issues remain. This paper presents a stochastic model that integrates the classic queue model and probability theories in order to investigate the estimation of the time-varying arterial travel time and its variability. Such information would be valuable to motorists so that they could best select their daily departure time and routes while contending with recurrent or nonrecurrent congestion. The proposed model, which uses only link detector data, has promising properties on the basis of an extensive evaluation with field data from the Eastern Shore of Maryland and will serve as the core module for ongoing development of a real-time arterial travel time prediction system.

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Stochastic Model for Estimation of Time-Varying Arterial Travel Time and Its Variability with Only Link Detector Data

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