01023234

Component

https://doi.org/10.3141/1935-17

http://scholar.google.com...etty&publication_year=2005

A travel time prediction algorithm scalable to large freeway networks with many nodes with arbitrary travel routes is proposed. Instead of constructing separate predictors for individual routes, it first predicts the whole future space–time field of travel times and then traverses the required subsection of the predicted travel time field to compute the travel time estimate for the requested route. Compared with the traditional approach that offers the same flexibility, the proposed method substantially reduces storage and computation time requirements at the relatively small computational cost at the time of actual prediction. It is first established that travel times computed by traversing travel time fields are compatible with more direct measurements of travel times from a vehicle reidentification technique based on electronic toll collection tags. This provides a conceptual justification of the proposed approach. When applied to the loop data from an 8.7-mi section of the I-80 freeway, the proposed approach with a time-varying coefficient (TVC) linear regression model as the component predictor not only improves the baseline historical travel time predictor substantially, with a 40% to 60% reduction in the prediction error, but also improves the traditional whole-route predictor based on the same TVC regression model by 6% to 9%. The result suggests that the proposed algorithm not only achieves the scalability but also improves prediction accuracy, both of which are critical for successful deployment of the advanced traveler information system for large freeway networks.

01023220

English

pp 147-153

2005

Transportation Research Record: Journal of the Transportation Research Board

0309094097

Print

Figures (6) ; References (10) ; Tables (1)

Accuracy; Advanced traveler information systems; Algorithms; Deployment; Freeways; Linear regression analysis; Mathematical prediction; Travel time

Computational efficiency; Freeway networks

Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Apr 24 2006 1:01PM

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