Predictive Vehicle Route Optimization in Intelligent Transportation Systems

Abstract

Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.