Traffic Estimation And Prediction Based On Real Time GPS Data

Currently, the growing problem of traffic congestion, air pollution and frequent traffic accidents and other issues have become a serious impediment to the development of the city. The method that using in-car GPS device to obtain real-time data and then combining with the of information urban road traffic network to get real-time traffic conditions, providing people with better traffic information service, has become an effective way to solve traffic problems such as traffic congestion.The Thesis on how low-car GPS-based real-time traffic data to estimate research issues, focusing on the estimation of road traffic speed based on taxi GPS data, short-term traffic forecast and real-time processing for mass traffic flow data based on stream computing and other issues, the main work and results of this are reflected in the following three aspects:1) Design an constraint-based and real-time map-matching algorithm of the reverse checking mechanism to make an exact match of GPS data and the electronic map, and design a method to get valid GPS data and a speed calculation method based on the low sampling frequency GPS date to solve the problem of operational characteristics and the low sampling GPS of the taxi data, characterized by using the lower sampling frequency GPS more to estimate a more accurate state of traffic.2) Improve the non-parametric regression algorithm by the classification of the trend of speed and varying K neighbor precisely search method based on intensity, as well as the improvements in sample database design, the experiments show that the improved algorithm has strong ability to predict traffic state.3) Design a real-timely estimating and short-term forecasting framework based on the Storm stream computing platform to solve the problem of real-time processing for mass GPS flow data, which can provide an effective support for traffic speed estimation and forecast based on large-scale GPS data.