01658661

Component

In the era of intelligent transport, big data and connected systems, it is important to enable “smart” vehicles to identify and characterize individual drivers’ behavior rather than just collecting the mileage. This study aims to develop a driver scoring system to evaluate individual driving performance and improve the traffic condition and safety from the drivers’ perspective. The proposed scoring system adopts advanced data analytics techniques to extract, identify, characterize, and display driving habits and behavior patterns, including car-following and lane-changing behaviors, from vehicle trajectories. A safety score is developed by comparing a driver’s individual pattern to a standard “safe driver” pattern, defined by mining all drivers’ trajectories. The scores provide a basis for matching individual drivers in a connected environment, and suggesting to drivers an option for following another nearby “safe” driver. To evaluate the scoring system, a sample of trajectory data collected from anonymous drivers are used. In addition, the scoring system is integrated with a micro simulation tool with connected vehicle emulation capability. The results show that the car following recommendation system using the safety score improves overall performance of a connected traffic system beyond those attained through connectivity alone.

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

18-03909

English

0000-0002-8443-8928

7p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References

Behavior; Car following; Connected vehicles; Data analysis; Driver performance; Drivers; Microsimulation; Ratings; Traffic safety; Vehicle trajectories

Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment

Transportation Research Board Annual Meeting 2018 Paper #18-03909

TRIS, TRB, ATRI

Jan 8 2018 10:58AM