This paper suggests a decision support system for vehicle drivers, accessible via mobile phone. Concept behind the system is to help drivers to schedule their activities, best utilizing their time along the way, minimizing the impacts of traffic. This is in contrast to existing approaches focused on controlling traffic in highways. Vehicle is continuously tracked along the journey and information presented to user is adapted according his location and time dimensions. System is based on a decision tree based classification model to predict the future traffic and use those results for decision making. System mines spatio-temporal data to build the decision tree, therefore developed in a distributed architecture to avoid load for a single server. System is exposed to community via existing social networks, bringing social networks into vehicular context. Decision trees to predict traffic are periodically rebuilt using most recent data, therefore this is an intelligent system which learns through empirical data, and best suited for dynamic vehicular environment.
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Perera et al. (2011) studied this question.
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