Evaluating VANET information retrieval context aware systems using the average distance measure ADM

Abstract

Information Retrieval (IR) techniques are utilized in developing context aware systems for VANET safety and convenience services. For the safety services a context aware system for the Automatic Crash Notification called IR-CAS ACN is developed while the context aware Congested Road Notification system IR-CAS CRN is developed for the convenience services. Different IR models like the vector space, fuzzy logic and binary models are proposed for each of these systems. The performance of the proposed models for IR-CAS ACN is compared using test collections that are based on nineteen years of real life crash records associated with their severity levels while the performance of the IR-CAS CRN is tested using nearly 500,000 different urban and rural freeways flow situations associated with their congestion severity levels. The highway capacity manual (HCM) speed-flow curves along with the Greenshield model are utilized in generating these freeway flow cases and their levels of service. The average distance measure (ADM) is used to evaluate the tested IR models. Results show that using the vector space model for severity estimation by calculating the Manhattan distance between the crash/congestion current context vectors and the severest crash/congestion context vectors outperforms the fuzzy and binary severity estimation models.