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.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering