Pattern recognition using clustering algorithm for scenario definition in traffic simulation-based decision support systems

Chen, Ying, Kim, Jiwon and Mahmassani, Hani S. (2014). Pattern recognition using clustering algorithm for scenario definition in traffic simulation-based decision support systems. In: 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014. 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, Qingdao, China, (798-803). 8-11 October 2014. doi:10.1109/ITSC.2014.6957787


Author Chen, Ying
Kim, Jiwon
Mahmassani, Hani S.
Title of paper Pattern recognition using clustering algorithm for scenario definition in traffic simulation-based decision support systems
Conference name 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
Conference location Qingdao, China
Conference dates 8-11 October 2014
Convener IEEE
Proceedings title 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1109/ITSC.2014.6957787
Open Access Status Not Open Access
Start page 798
End page 803
Total pages 6
Language eng
Abstract/Summary 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 has two main 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 selection of critical scenarios for reliability studies. It can also support real-time weather-responsive traffic management (WRTM) by quickly classifying a current or predicted weather condition into pre-defined 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 is presented and demonstrated using historical data. Two clustering algorithms with different similarity measures are compared. Clustering results using a K-means clustering algorithm with squared Euclidean distance are illustrated in an application to travel time reliability.
Keyword K-means clustering
Hierarchical clustering
Similarity measures
Traffic simulation
Scenarios-based approach
Travel time reliability analysis
Classification
Similarity
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Collection: School of Civil Engineering Publications
 
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