Vehicular Route Prediction In City Environment Based On Statistical Models

Travel route analysis and prediction are essential for the success of many applications in Vehicular Ad-hoc Networks (VANETs). It is challenging to make accurate route prediction for general vehicles in city settings due to several practical issues such as very complicated traffic networks, the highly dynamic real-time traffic conditions and their interaction with driver’s route selections. The traffic conditions on complicated road networks keep changing from time to time. Inspired by the observation that a vehicle often has its own route selection flavour when traversing between its sources and destinations, here it defines a mobility pattern as a consecutive series of road segment selections that exhibit frequent appearance along all the itineraries of the vehicle. Here with the help statistical models like Markov Model with only first order Markov Model (MM) to choose only the next intersection point of the road segment with only one pattern without considering traffic conditions again by using the Hidden Markov Model(HMM) to predict next intersection point by considering traffic conditions with shortest distance and obstacle and Variable-order Markov Model (VMM) to select mobility patterns from the source to destination which is chosen by the driver by considering the traffic conditions . Keywordrs- MM, HMM, VMM, RSU, OBU.