Paratactic Spatial-Temporal Two Dimension Data Fusion Based on Support Vector Machines for Traffic Flow Prediction of Abnormal State

Abstract:

This paper presents a paratactic spatial-temporal 2dimension data fusion model based on support vector machines (SVM) for traffic volume prediction of the abnormal state. Time and space SVM operates respectively in two parallel operating system models to reduce the time cost. By comparing the prediction results with which obtained by the multiple regression prediction method, the prediction accuracy is greatly improved by utilizing the paratactic spatial-temporal dimension data fusion model. Especially in the abnormal state caused by unexpected events (such as: traffic accidents, traffic jam etc), the proposed method can also significantly avoid structural system error of one-dimensional time source data fusion.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1225-1229
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1225
• Online since: June 2012
• Authors: Liang Chen, Qiao Ru Li, Xiao Yong Tian, Xiang Shang Chen, Rong Xia Wang
• Keywords: Abnormal State, Spatial-Temporal 2Dimension Data Fusion, Support Vector Machine (SVM), Traffic Flow Prediction

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