Travel time is a key transportation performance measure because of its diverse applications. Various modeling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a microsimulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macroscale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in Saint Louis, Missouri, were used to verify the estimated travel times produced by the proposed General Motors travel time estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model out-performed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times.
S. Yang et al., "Estimating Freeway Travel Times using the General Motors Model," Transportation Research Record, vol. 2594, pp. 83-94, National Research Council, Jan 2016.
The definitive version is available at https://doi.org/10.3141/2594-12
Keywords and Phrases
Intelligent Systems; Traffic Control; Aggregated Traffics; Car Following Models; Diverse Applications; Following Vehicle; Intelligent Transportation Systems; Performance Measure; Travel Time Estimation; Travel Time Model; Travel Time
International Standard Serial Number (ISSN)
Article - Journal
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