Abstract
Traffic flow forecasting is a popular research topic of Intelligent Transportation Systems (ITS). With the development of information technology, much historical electronic traffic flow data have been collected. How to take full use of the historical traffic flow data to improve the traffic flow forecasting precision is an important issue. As more history data are considered, more computation cost is incurred. 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 means to improve computation speed. Some feature extraction methods have been proposed, such as PCA, SOM network, and Multidimensional Scaling (MDS). 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Intelligent Transportation Systems Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.