Browse > Article
http://dx.doi.org/10.6109/jkiice.2015.19.12.2779

A Short-Term Vehicle Speed Prediction using Bayesian Network Based Selective Data Learning  

Park, Seong-ho (Information Technology Center, Pusan National University)
Yu, Young-jung (Department of Computer Engineering, Busan University of Foreign Studies)
Moon, Sang-ho (Department of Computer Engineering, Busan University of Foreign Studies)
Kim, Young-ho (Department of Road Transport Research, The Korea Transport Institute)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.19, no.12, 2015 , pp. 2779-2784 More about this Journal
Abstract
The prediction of the accurate traffic information can provide an optimal route from the place of departure to a destination, therefore, this makes it possible to obtain a saving of time and money. To predict traffic information, we use a Bayesian network method based on probability model in this paper. Existing researches predicting the traffic information based on a Bayesian network generally used to study the data for all time. In this paper, however, only data corresponding to same time and day of the week to predict selectively will be used for learning. In fact, the experiment was carried out for 14 links zone in Seoul, also, the accuracy of the prediction results of the two different methods should be tested with MAPE (Mean Absolute Percentage Error) which is commonly used. In view of MAPE, experimental results show that the proposed method may calculate traffic prediction value with a higher accuracy than the method used to learn the data for all time zones.
Keywords
Short-term Vehicle Speed Prediction; Urban Road; Bayesian Network; Selective Data Learning;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Yu and M. G. Cho, "A Short-Term Prediction Model for Forecasting Traffic Information Using Bayesian Network," Third 2008 International Conference on Convergence and Hybrid Information Technology, pp. 247-253, 2008.
2 G. Q. Yu, J. M. Hu, C. S. Zhang, etc. "Short-Term Traffic Flow Forecasting based on Markov Chain Model," Proc. IEEE Intelligenct Vehicles Symp., Columbus, OH, 2003.
3 J.W.C van Lint, S.P. Hoogendoorn, and H.J. van Zuylen, "Robust and Adaptable Travel Time Prediction with Neural Networks," Proc. 6th Annual ransport, 2000.
4 Rui Wang, Hideki Nakamura, "Short erm Prediction Works in Traffic Engineering: The State-Of-The-Art," ITS World Congress, 2002.
5 M. G. Cho, Y. Yu and S. Kim, "The System for Predicting the Traffic Flow with Real-Time Traffic Information," Lecture Note in Computer Science, Vol. 3980, 2006.
6 M. A. Rasyidi, J. Kim and K. R. Ryu, "Short-erm Prediction of Vehicle Speed on Main City Roads using the k-Nearest Neighbor Algorithm," Journal of Intelligence and Information Systems, Vol. 20, No. 1, pp. 121-131, 2014.   DOI
7 Cheol Oh, "A Study Toward Developing a Framework for Intelligent Transport Systems(ITS) Audit," KOTI, 2005.
8 Shiliang Sun, Changshui Zhang, Guoqiang Yu, "A Bayesian Network Approach to Traffic Flow," IEEE Transaction on Intelligent Transportation Systems, Vol. 7, No. 1, 2006.
9 Hironobu Kitaoka, Takahiro Shiga, Hiroko Mori etc., "Development of a Travel Time Prediction Method for the TOYAA G-BOOK Telematics Services," R&D Review of Toyata CRDL, Vol. 41, No. 4, 2007.