Citation

Hong Z, Chen Y, Wu Y. Accid. Anal. Prev. 2020; 139: e105460.

Affiliation

Department of Electrical Engineering & Computer Science, Northwestern University, Technological Institute, 2145 Sheridan Road, Evanston, IL 60208, United States. Electronic address: yangwu2015@u.northwestern.edu.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.aap.2020.105460

PMID

32135334

Abstract

As part of the emerging world of intelligent transportation, there is considerable interest in developing connected vehicles that are more capable of identifying and guiding individual drivers' behavior than collecting mileage as a moving cart. The two goals of this study are (a) to build a conceptual framework for driver assessment and (b) develop recommendation systems to evaluate individual driving performance and guide driver behaviors, thus improving the network traffic conditions and individuals' perceived safety. A safety score is defined relatively by comparing a driver's individual pattern to a standard "safe driver" pattern. To elaborate, the proposed system adopts advanced data mining techniques to extract, identify, characterize, and display driving behavior patterns. The scoring system provides a basis of assessing individual drivers, who are then recommended to mimic a nearby "safe" driver in a connected environment. To evaluate and implement the proposed conceptual framework, an anonymous trajectory dataset collected from Pittsburgh urban area is applied to build the scoring system, which is then integrated within a virtually simulated environment. The results show that the proposed behavior assessment and recommendation system framework improves the overall performance of a connected traffic system beyond those attained through baseline connectivity principles.

Copyright © 2020. Published by Elsevier Ltd.

Language: en

Keywords

Assessment system; Connected vehicles; Data mining; Driver behavior; Gaussian mixture model; Recommendation system; Vehicle trajectories