01023231

Component

https://doi.org/10.3141/1935-04

http://scholar.google.com....+Oh&publication_year=2005

Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature–based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on suggestions for applying prediction techniques effectively.

01023220

English

pp 28-36

2005

Transportation Research Record: Journal of the Transportation Research Board

0309094097

Print

Figures (6) ; References (38) ; Tables (3)

Accuracy; Kalman filtering; Mathematical prediction; Neural networks; Test beds; Travel time

Data aggregation

Adaptive exponential smoothing; Autoregressive models

Irvine (California)

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

TRIS, TRB, ATRI

Apr 24 2006 11:33AM

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