Improving Vehicle Localization with Two Low-Cost GPS Receivers

A primary concern of Intelligent Traffic Management Systems (ITMSs) is to collect the required traffic data. Vehicle position is one of the most important data types to manage traffic effectively. In this regard, Global Positioning System (GPS) receivers are widely used; however, their estimation accuracy is affected by several parameters, such as signal blockage. Map-matching is one of the most popular approaches to dealing with this challenge. In this study, we investigated the performance of map-matching software and found that it cannot locate the vehicle effectively if the positional data are too noisy. This paper aims to propose a new methodology by integrating cross-GPS validation, interpolation, best-fit, and map-matching techniques to enhance the vehicle localization performance in the presence of GPS signal noise and investigate the methodology with real traffic data from a metropolitan area. Our evaluations indicate that the proposed methodology can significantly improve vehicle self-localization performance