01657475

Component

A large number of drivers with different driving characteristics co-exist on the road network. Assessing a person’s driving profile and detecting aggressive and unsafe driving behavior is essential to enhance road safety, reduce fuel consumption and - at a macroscopic level - tackle congestion. Nowadays, driving data can be massively collected via sensors embedded in mobile phones, avoiding the expensive and inefficient solutions of in-vehicle devices. In this paper, these data are used to detect unsafe driving styles based on two-stage clustering approach and using information on harsh events occurrence, acceleration profile, mobile usage and speeding. First, an initial clustering was performed in order to separate aggressive from non–aggressive trips. Subsequently, to distinguish "normal" trips from unsafe trips, a second level clustering was performed. In this way, trips have been categorized into six distinct groups with increasing importance with respect to safety. The further analysis of drivers in relation to the grouping of their trips showed that drivers cannot maintain a stable driving profile through time, but exhibit a strong volatile behavior per trip. Finally, a discussion is provided on the implications of the main findings in research and practice.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

18-01416

English

6p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Aggression; Automatic data collection systems; Automobile drivers; Cluster analysis; Driver performance; High risk drivers; Machine learning; Smartphones; Traffic data; Traffic safety

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment

Transportation Research Board Annual Meeting 2018 Paper #18-01416

TRIS, TRB, ATRI

Jan 8 2018 10:21AM