Abstract
As the number of vehicles increases, the traffic environment becomes more complicated. It is important to find a routing method for different scenarios in the vehicular ad hoc networks (VANETs). Although there are many routing methods, they rarely consider multiple road traffic states. In this paper, we propose a traffic state prediction method based on Hidden Markov Model (HMM), and then choose different routing methods according to different traffic states. Since we are aware that GPS may cause measurement errors, Kalman Filter is used to estimate the observation, which makes observation more accurate. For different road states, we can make appropriate methods to improve routing performance. When the road is in rush hour, we will use Extended Kalman Filter to predict vehicle information in a short time to reduce the number of broadcasts, which can alleviate channel load. The result show that our method is useful for reducing the number of packets and improving the delivery rate.
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