Tonioli Mariotto, Flávio (Universidade Estatual de Campinas), Gaybor Murillo, Miguel Angel (Universidade Estatual de Campinas), Ullon, Alcivar, Ruben Hernan (State University of Campinas), Cortes de Almeida, Madson (University of Campinas)

Fast Processing GNSS Data to Estimate Geodetic Distances in Intelligent Transportation System Contexts

Scheduled for presentation during the Regular Session "Data Management and Geographic Information Systems" (FrBT2), Friday, September 27, 2024, 13:30−13:50, Salon 5

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

Many problems related to ITS (Intelligent Transportation Systems) require the handling of huge GNSS (Global Navigation Satellite System) datasets that contain historical data on vehicles in transportation, and most of them need to calculate distances using the available methods in the literature. In general, the most accurate methods are complex, and for huge datasets, they have very high processing consumption. The less complex methods are simpler to process and usually present for the entire globe with a lower accuracy. However, in huge datasets, they still consume a lot of processing. Therefore, datasets are always becoming bigger, and, even with the increase of processing capabilities, algorithms to estimate GNSS distance drain an important amount of processing in ITS, such as for machine learning methods. This manuscript proposes a new method for estimating the distance between GNSS samples. It is applied to a delimited area of interest and can reduce processing up to three times when compared to the fastest traditional techniques. Although, it can be applied without significant loss of accuracy. For interest areas lower than 50 km in width, it can provide lower mean errors than the Great Circle method in most parts of the Earth.