01556825

Component

https://doi.org/10.3141/2497-03

Historical traffic data are widely used in the estimation of origin–destination (O-D) demand patterns in simulation-based dynamic traffic assignment models, in the prediction of traffic states, and as a basis for defining traffic management scenarios. This study investigated the determination of historical traffic patterns by applying a classical clustering algorithm to a very large data set of sensor observations over an extended period and identified appropriate patterns for use in the application of traffic estimation and prediction systems. Systematic identification of similarity and dissimilarity of traffic flow data can lead to a systematic process for defining critical demand scenarios for traffic state prediction. The objective was to explore the impact of various demand scenarios on real-time traffic estimation and prediction. A detailed procedure for clustering traffic flow data that is based on the K-means clustering algorithm is presented. The procedure was applied to a subnetwork in Salt Lake City, Utah. An O-D estimation procedure was applied to each cluster separately; this procedure repeatedly solved a bilevel optimization problem in which the goal was to minimize the deviation from observations and historical demand. For the evaluation of the effect of selecting a best-matching initial matrix on traffic pattern estimation and prediction quality, a comparative application of initial matrix choices was conducted with an online traffic estimation and prediction system. The results indicate that the clustering process results in better starting matrices matched to the day’s unfolding traffic and weather conditions.

01593848

15-2005

English

pp 23–34

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369473

Print

Figures (9) ; Maps; References (41) ; Tables (1)

Algorithms; Cluster analysis; Data mining; Origin and destination; Traffic data; Traffic estimation; Traffic flow; Travel demand; Travel patterns

Traffic Estimation and Prediction System (TrEPS)

Salt Lake City (Utah)

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

TRIS, TRB, ATRI

Dec 30 2014 12:43PM

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