The emerging technology of connected vehicles generates a vast amount of data that could be used to enhance roadway safety. In this paper, we focused on safety applications of a real field connected vehicle data on a horizontal curve. The database contains connected vehicle data that were collected on public roads in Ann Arbor, Michigan with instrumented vehicles. Horizontal curve negotiations are associated with a great number of accidents, which are mainly attributed to driving errors. Aggressive/risky driving is a contributing factor to the high rate of crashes on horizontal curves. Using basic safety message data in connected vehicle data set, this paper modeled aggressive/risky driving while negotiating a horizontal curve. The model was developed using the machine learning method of random forest to classify the value of time to lane crossing (TLC), a proxy for aggressive/risky driving, based on a set of motion-related metrics as features. Three scenarios were investigated considering different TLCs value for tagging aggressive driving moments. The model contributed to high detection accuracy in all three scenarios. This suggests that the motion-related variables used in the random forest model can accurately reflect drivers' instantaneous decisions and identify their aggressive driving behavior. The results of this paper inform the design of warning/feedback systems and control assistance from unsafe events which are transmittable through vehicles-to-vehicles and vehicles-to-infrastructure applications.
Jahangiri, A., Berardi, V., & Machiani, S. G. (2017). Application of real field connected vehicle data for aggressive driving identification on horizontal curves. IEEE Transactions on Intelliginet Transportations Systems, 19(7). doi: 10.1109/TITS.2017.2768527
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