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Title:

Pattern Recognition Using Clustering Algorithm for Scenario Definition in Traffic Simulation-Based Decision Support Systems

Accession Number:

01518155

Record Type:

Component

Availability:

Transportation Research Board Business Office

500 Fifth Street, NW
Washington, DC 20001 United States

Abstract:

The definition and selection of input scenarios to be tested in connection with a particular study are critically important to the decision outcomes of the evaluation process. This paper presents a scenario clustering approach intended to mine historical data warehouses to identify appropriate scenarios for simulation as a part of an evaluation of transportation projects or operational measures. As such, it provides a systematic and efficient approach to select and prepare effective input scenarios to a given traffic simulation model. The scenario clustering procedure is discussed in connection with two main simulation-based applications: travel time reliability analysis, and traffic estimation and prediction systems. The ability to systematically identify similarity and dissimilarity among weather scenarios can facilitate the procedure of selecting critical scenarios for reliability analysis and studies. It can also support real-time weather-responsive traffic management (WRTM) by quickly classifying a current weather condition into pre-defined weather categories and suggesting relevant WRTM strategies that can be tested via real-time traffic simulation before deployment. A detailed method for clustering weather time series data based on the K-means clustering algorithm is presented. The clustering method is demonstrated using weather scenarios constructed from historical data. The study also performs a cluster analysis based on simulation outputs produced from the given weather scenarios to compare input- and output-based clustering results.

Supplemental Notes:

This paper was sponsored by TRB committee AHB45-1 Traffic Flow Theory and Characteristics Special Paper Review.

Monograph Accession #:

01503729

Report/Paper Numbers:

14-2405

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Chen, Ying
Kim, Jiwon
Mahmassani, Hani S

Pagination:

19p

Publication Date:

2014

Conference:

Transportation Research Board 93rd Annual Meeting

Location: Washington DC
Date: 2014-1-12 to 2014-1-16
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Subject Areas:

Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory; I72: Traffic and Transport Planning

Source Data:

Transportation Research Board Annual Meeting 2014 Paper #14-2405

Files:

TRIS, TRB, ATRI

Created Date:

Jan 27 2014 2:51PM