Presentation information

Oral presentation

General Session » [General Session] 2. Machine Learning

[2A4] [General Session] 2. Machine Learning

Wed. Jun 6, 2018 5:20 PM - 7:00 PM Room A (4F Emerald Hall)

座長:椿 真史(産業技術総合研究所)

6:00 PM - 6:20 PM

[2A4-03] A study on traffic flow prediction method using probe information in transportation system

〇Keisuke Ohno1, Eichi Takaya1, Hiroshi Matsumoto2, Tetsuo Morita2, Satoshi Kurihara1 (1. Graduate School of Science and Technology, Keio University, 2. Sumitomo Electric Industries, Ltd)

Keywords: deep leaning, probe information

With the remarkable development of Intelligent Transportation System in recent years, it is possible to easily collect traffic information and various information of the vehicle. Probe information provides more extensive traffic information in addition to the observation information. In this paper, we consider the traffic flow prediction method on urban road using probe information. Accurate and real-time traffic information is indispensable for the deployment of high-performance intelligent transportation systems. Traffic flow is complicated, but by deep learning that can acquire feature quantities automatically, it is possible to express the characteristics of the traffic flow without the prior knowledge such as the characteristics of the site, and it is expected to improve the prediction accuracy. Therefore, in this research, we consider a traffic flow prediction model using deep learning. Also, we compared it with other traffic flow prediction method.