Spatio-Temporal Association Rule Analysis and Its Application in Intelligent Transportation System
Intelligence transportation system is one of the most effective methods of solving the transportation question, and the transportation information association analysis processing is its core question. In the urban road network, traffic flow data have close temporal and spatial association. We can make full use of the junction itself and as well as the spatio-temporal correlation between them to carry out trend analysis and forecasts, and ultimately generate the plans to ease traffic congestion. In this paper, based on spatio-temporal data mining theoretical study, first of all we analyze spatial association, then establish the traffic statistics matrix and get road network node correlation coefficient matrix, so we can determine with a strong spatial association of the key road junctions or sections. On this basis, we present a kind of association rule mining method with time-constrained. After introducing the time constraints into the Apriori algorithm, the method can reflect the characteristics of spatio-te mporal more clearly, and mine useful spatio-temporal knowledge. At last, an example is given to demonstrate the mining process.
Intelligence transportation system spatial association analysis time-constrained association rules
Ying Xia Jun Zhang Yan Li Hae-Young Bae
School of Information Science and Technology, Southwest Jiaotong University, China Sino-Korea Chongq Dept. of Computer Science and Information Engineering, Inha University, Korea
国际会议
重庆
英文
161-165
2010-04-22（万方平台首次上网日期，不代表论文的发表时间）