Research Article
HMM-Based Traffic State Prediction and Adaptive Routing Method in VANETs
@INPROCEEDINGS{10.1007/978-3-030-67540-0_14, author={Kaihan Gao and Xu Ding and Juan Xu and Fan Yang and Chong Zhao}, title={HMM-Based Traffic State Prediction and Adaptive Routing Method in VANETs}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={VANETs Kalman filter HMM Traffic state prediction}, doi={10.1007/978-3-030-67540-0_14} }- Kaihan Gao
Xu Ding
Juan Xu
Fan Yang
Chong Zhao
Year: 2021
HMM-Based Traffic State Prediction and Adaptive Routing Method in VANETs
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_14
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.
