Abstract
The ATIS (Advanced Traveler Information System) provides travelers with real-time and precise information about the shortest path to the destination, the traffic condition, travel time estimation, and so on. To offer these services, we have to collect the speed data which are necessary to ATIS. However many data are lost due to communication or sensor errors during collecting the data. In order to provide accurate service, the lost data have to be compensated. Thus, a lot of prediction methods have been proposed to compensate the lost speed data. In this paper, we propose new prediction method adopting the back propagation neural network under neighboring links’ speed as well as weekday and time. Experimental results show that our method reduces prediction error up to 41.8 % compared to the previous method.
This research was supported by the Ubiquitous Computing and Network (UCN) Project, the Ministry of Information and Communication (MIC) 21st Century Frontier R&D Program in Korea and the MIC(Ministry of Information and Communication), Korea, under the ITFSIP(IT Foreign Specialist Inviting Program) supervised by the IITA(Institute of Information Technology Assessment).
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© 2007 Springer Berlin Heidelberg
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Lee, EM., Kim, JH., Yoon, WS. (2007). Traffic Speed Prediction Under Weekday, Time, and Neighboring Links’ Speed: Back Propagation Neural Network Approach. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_62
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DOI: https://doi.org/10.1007/978-3-540-74171-8_62
Publisher Name: Springer, Berlin, Heidelberg
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