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A Hierarchical Clustering Based Travel Time Estimation Model in a Connected Vehicle Environment

Abdullah Kurkcu 1 and Kaan Ozbay 2
1. Department of Civil and Urban Engineering & Center for Urban Science + Progress (CUSP), Tandon School of Engineering, New York University (NYU), New York, USA
2. C2SMART Center (A Tier 1 USDOT UTC), Department of Civil and Urban Engineering & Center for Urban Science + Progress (CUSP), Tandon School of Engineering, New York University (NYU), New York, USA

Abstract—The Connected Vehicle (CV) technology has the potential to transform driver behavior and will become a promising real-time data source that provides information required to accurately estimate traffic conditions. The information generated by CVs -including speed, position, and acceleration- can be used to analyze, evaluate, and improve the efficiency of the existing transportation infrastructure. In this study, the hierarchical clustering approach based on Wasserstein distances is used to estimate travel times using simulated CV data in an urban setting. The proposed methodology combines segments within a roadway section that have similar speed profiles into clusters and uses these grouped sections to compute the travel time on an individual section. The Basic Safety Messages (BSM) data are simulated from a calibrated traffic model using the Trajectory Conversion Tool (TCA). The generated messages with 5 and 10% market penetration levels are used as input for the clustering based travel time estimation algorithm. The results show that it is possible to accurately estimate travel time using CV data even with lower market penetration levels.

Index Terms—connected vehicles, hierarchical clustering, travel time estimation, performance measures, CV applications

Cite: Abdullah Kurkcu and Kaan Ozbay, "A Hierarchical Clustering Based Travel Time Estimation Model in a Connected Vehicle Environment," Journal of Traffic and Logistics Engineering, Vol. 5, No. 2, pp. 54-59, December 2017. doi: 10.18178/jtle.5.2.54-59
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