01362317

Component

https://doi.org/10.3141/2243-15

http://scholar.google.com...Chiu&publication_year=2011

This study presents a methodology for freeway travel time prediction that uses only count data. The proposed models include the generalized N-curve method in conjunction with the k nearest neighbor (kNN) method so that the travel time predicted for traversing a defined freeway segment at a certain departure time is similar to what a driver actually experiences. A real-world traffic network and demand are replicated in a traffic simulation model in which several scenarios are produced to serve as the test bed for evaluation and validation of the proposed algorithms. The proposed single-NN algorithm best predicts travel times for light, free-flow traffic conditions, and the multiple-NN algorithm best predicts travel times for congested traffic conditions. The hybrid-NN algorithm merges the single-NN and multiple-NN algorithms, exploiting each one where most suitable. A numerical analysis concludes the potential of the proposed models.

01362318

11-4060

English

pp 127-137

2011

Transportation Research Record: Journal of the Transportation Research Board

9780309167635

Print

Figures (6) ; References (26) ; Tables (1)

Algorithms; Freeways; Mathematical prediction; Traffic congestion; Traffic simulation; Travel time; Validation

Free flow (Traffic)

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Feb 3 2012 9:00AM

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