Mining of spatio-temporal patterns of congested traffic in urban areas from traffic sensor data

2016 Fiscal Year Final Research Report

• Project/Area Number: 26630233
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Civil engineering project/Traffic engineering
• Research Institution: Tohoku University
• Principal Investigator: Ryo Inoue 東北大学, 情報科学研究科, 准教授 (60401303)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 渋滞波及過程 / データマイニング / 車両感知器データ / 可視化

Outline of Final Research Achievements

Road traffic condition in cities are complicated by the daily, weekly, seasonally, and weather-induced traffic demand fluctuations and the effects caused by the control of traffic signals. Therefore, it is difficult to quantitatively analyze typical traffic congestion patterns that are represented by the time and place of occurrence, the process of propagation and diminution, duration time, and many others. This study proposed a method to enumerate traffic congestion patterns from traffic sensor data based on frequent pattern mining developed in information science to understand the present situations of traffic congestion in cities. The feasibility and effectiveness of the proposed method have been evaluated through the analysis of typical congestion patterns using the traffic sensor data in Okinawa, Japan.

Free Research Field

地理情報科学