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Real-time hazardous traffic condition warning system: framework and evaluation

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3 Author(s)
Cheol Oh ; Center for Adv. Transp. Technol., Korea Transp. Inst., Kyonggi-do, South Korea ; Jun-Seok Oh ; Ritchie, S.G.

This study presents a warning information system based on an innovate methodology to estimate accident likelihood in real time. Bayesian modeling approach implemented by the probabilistic neural network (PNN) is conducted to identify hazardous traffic conditions leading to potential accident occurrence. The proposed system displays warning signs to call drivers' attention for safer and careful driving once hazardous traffic conditions are observed by evaluating accident likelihood. It is believed that the proposed system to produce effective warning information for real-time safety enhancement could be a valuable tool to highway users and operators.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:6 ,  Issue: 3 )