K-Mean Clustering in Transportation: A Work Zone Simulator Case Study
Abstract
Transportation engineering considers many different categories such as accident management, infrastructure management, driver behavior, and traffic management and generates large amount of data. Data mining methods are a common engineering approach to understand data-intensive scenarios and use techniques to extract patterns, correlations, and information from large amounts of data. This research uses a simulator to compare driver patterns and behaviors when comparing reactions to the Missouri Department of Transportation (MoDOT) alternate sign with the Manual on Uniform Traffic Control Devices (MUTCD) current sign. K-mean clustering method is used to cluster driver response to work zone sign configurations presented in the simulator environment and uncover patterns that can assist engineers with usability of work zone signage. Key findings of this research will help the transportation engineering manager make data-driven decisions regarding work zone safety and design.
Recommended Citation
S. Moradpour and S. Long, "K-Mean Clustering in Transportation: A Work Zone Simulator Case Study," American Society for Engineering Management (ASEM), Oct 2017.
Meeting Name
2017 International Annual Conference of the American Society for Engineering Management, ASEM 2017 (2017: Oct. 18-21, Huntsville, AL)
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Data mining; Decision analytics; Pattern recognition; Transportation
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 American Society for Engineering Management (ASEM), All rights reserved.
Publication Date
01 Oct 2017
Comments
This study is sponsored by Missouri Department of Transportation under grant number TR201612.