Traffic Flow Forecasting Based on Combination of Multidimensional Scaling and SVM

Scaling and SVM Zhanquan Sun, Geoffrey Fox a. Key Laboratory for Computer Network of Shandong Province, Shandong Computer Science Center, Jinan, Shandong, 250014, China b. School of Informatics and Computing, Pervasive Technology Institute, Indiana University Bloomington, Bloomington, Indiana, 47408, USA Abstract: Traffic flow forecasting is a popular research topic of Intelligent Transportation Systems (ITS). With the development of information technology, lots of history electronic traffic flow data are collected. How to take full use of the history traffic flow data to improve the traffic flow forecasting precision is an important issue. More history data are considered, more computation cost should be taken. In traffic flow forecasting, many traffic parameters can be chosen to forecast traffic flow. Traffic flow forecasting is a real-time problem, how to improve the computation speed is a very important problem. Feature extraction is an efficient method to improve computation speed. Some feature extraction methods have been proposed, such as PCA, SOM network, and Multidimensional Scaling (MDS) and so on. But PCA can only measure the linear correlation between variables. The computation cost of SOM network is very expensive. In this paper, MDS is used to decrease the dimension of traffic parameters, interpolation MDS is used to increase computation speed. It is combined with nonlinear regression Support Vector Machines (SVM) to forecast traffic flow. The efficiency of the method is illustrated through analyzing the traffic data of Jinan urban transportation.