Implement Spatial-Temporal map-matching algorithm to enhance accuracy of the traffic data extracted low sampling rate GPS trajectories

With the increasing popularity of devices equipped with a Global Positioning System, users will be able to track moving objects such as cars, animals, and people. GPS sensors data has positioning and sampling error. On the other hand, traffic applications, such as traffic data collection requires accurate tracking GPS based on road network. Since most of the percentage of tracking data GPS, particularly GPS modules in smart phones are low-end and low accuracy, a sophisticated and reliable map matching algorithm is crucial for these location-based services.This study aimed to analyze and implement an spatial-temporal map matching algorithm for low sampling rates GPS trajectories. Therefore, in this article GPS tracking data related to eight bus lines in the range of 1 to 4 municipalities with a sampling rate of two minutes will be used.The algorithm employs spatial and temporal analysis to generate a candidate raph, from which a sequence of matched results with highest sum of score is identified as the matching result .The experiment results demonstrate that ST-matching algorithm for low-sampling trajectories significantly performs good and the accuracy is about seventy percent.