Towards improved traffic predictions by incorporating rainfall forecasts
Ho, Victor Yao Tong
Date of Issue2015
College of Engineering
Weather conditions tend to have measurable impact on traffic conditions of the roads. This relationship is commonly studied at the network level without explicit explanation of the link performances. Furthermore, existing studies typically use high resolution traffic data which may not be available across the entire network and especially during the adverse weather conditions. In this project the impact of rainfall intensity is being explored on low-resolution speed band data. This additional information is being tested whether rainfall may improve the prediction accuracy of data-driven models for individual roads. To do so, the information about the rainfall intensity is incorporated into support vector machine (SVM) prediction algorithm. As a benchmark, only temporal features will be considered to predict near future traffic conditions during rainy weather. Numerical results for 616 road segments in Singapore confirm that rainfall impacts traffic conditions in terms of decreasing the driving speed. This reduction increases with the rain intensity. Furthermore, the results show that additional rainfall data enhances the prediction accuracy for certain number of links; while for the others the rainfall information is not that useful.
Final Year Project (FYP)
Nanyang Technological University