A hidden Markov model combined with RFID-based sensors for accurate vehicle route prediction. (2016)

• Record Type: Journal Article
• Authors: Ye, Ning; Wang, Zhong-qin; Malekian, Reza; Wang, Ru-chuan; Zhao, Ting-ting; Andriukaitis, Darius; Valinevicius, Algimantas; Navikas, Dangirutis; Markevicius, Vytautas
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 23:Number 1/2(2016)
• Page Start: 124
• Page End: 133
• Publication Date: 2016
• Subjects: HMM -- hidden Markov model -- vehicle route prediction -- route filtering -- RFID sensors -- radio frequency identification -- vehicle routing -- road transport -- dangerous goods -- hazardous goods -- probabilistic modelling -- driver habits -- driver behaviour; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

The road transport of dangerous goods (RTDG) arouses more and more attentions in recent years. Vehicle location devices only based on GPS technology have played an important role on the current market. However, there are obvious shortcomings by using a simple GPS method in the aspect of positioning accuracy and coverage. In the blind area of GPS, a vehicle's route could not be detached in real time, which will lead to manage and follow the tracks of vehicle difficultly. In this paper, we propose an approach based on hidden Markov model (HMM) to provide static predictions on driver routes. Our approach is based on building the probabilistic model through observation of the driver's habits from a map database involving RFID information. Before we predict a vehicle's route, we firstly compute the shortest path from starting point to destination point. Then through this path we could filter some redundant data. Finally, experiments demonstrate that we acquire high prediction accuracy under different periods of traffic conditions by training the HMM.