Fuzzy Logic in VANET context aware Congested Road and Automatic Crash Notification

Abstract

For VANET safety services a context aware system for the Automatic Crash Notification (ACN) is developed while the context aware Congested Road Notification system (CRN) is developed for the convenience services. A simple fuzzy logic model is proposed and compared to different severity estimation models deployed for both systems. The performance of the ACN models is compared using a test collection that is based on nineteen years of real life crash records associated with their severity levels while the performance of the CRN models is tested using nearly 500,000 different urban and rural freeways flow situations associated with their congestion severity levels. The non-binary Spearman correlation coefficient and the Average Distance Measure (ADM) are used to evaluate the performance of the tested models. Results show that the simple fuzzy severity estimation model has a comparable performance to more complicated systems such as the CoTEC (CoOperative Traffic congestion detECtion) fuzzy system and the URGENCY algorithm, and outperforms the binary severity estimation models for the ACN and CRN systems.