原文传递
Improving Traffic Prediction by Including Rainfall Data
| 题名： | Improving Traffic Prediction by Including Rainfall Data |
| 正文语种： | 中文 |
| 作者： | Leong Wai Leong Kelvin Lee Kumar Swapnil Xiao Li Ho Yao Tong Victor Nikola Mitrovic Muhammad Tayyab Asif Justin auwels Patrick Jaillet |
| 作者单位： | School of Electrical and Electronic Engineering, ollege of Engineering,Nanyang Technological University,Singapore ity 639798,Singapore epartment of Electrical Engineering and omputer Science,School of Engineering, ambridge,M 02139,US ;Operations Research enter,Massachusetts Institute of Technology, ambridge,M 02139,US ; enter for Future Urban Mobility,Singapore-MIT lliance for Research and Technology,Singapore ity 138602,Singapore |
| 关键词： | ITS intelligent transportation systems traffic prediction SVM rainfall correlation |
| 摘要： | 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 study we explore the impact of rainfall intensity on low-resolution speed band data.We also test whether this additional information about rainfall may improve the prediction accuracy of data-driven models for individual roads.To do so, we incorporate the information about the rainfall intensity into support vector machine (SVM) prediction algorithm.As a benchmark we only consider temporal features 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. |
| 会议日期： | 20150427 |
| 会议举办地点： | 南京 |
| 会议名称： | 2015年南京第十四届亚太智能交通论坛 |
| 出版日期： | 2015-04-27 |
| 母体文献： | 2015年南京第十四届亚太智能交通论坛论文集 |
相关文献
- Spatio-Temporal Graph Convolutional Networks:A Deep Learning Framework For Traffic Forecasting
- Trajectory Prediction for Vehicle using HMM with Tracking on Traffic Video
- Genetic Algorithms Assisted DDHV Prediction Models Based on Victoria Day Traffic Flows for Major Rural Highways
- HOW BIG DATA MAKES CONSTRUCTION PROJECT RISK INTACT
- A Method of Cleaning and Repairing the Urban Floating Car Data
- Statistical Characteristics of Sample Size of Cellular Handoff Probe System for Freeway Traffic Speed Estimation
检索历史
应用推荐
