Travel-mode classification based on GPS-trajectory data and geographic information using an XGBoost classifier

Abstract

Massive Global Positioning System (GPS) trajectory datasets are being produced owing to the advances in mobile sensors, the Internet, and GPS devices. Accurately inferring travel modes from GPS trajectory data can be helpful in transportation planning and modeling, infrastructure design, etc. However, adverse factors such as data noise, differences in sampling rate, and inadequate features have a negative impact on the results of travel mode classification. In this paper, to address such issues, we first propose a preprocessing workflow, which includes data cleaning, segmentation, and resampling, to preprocess raw trajectories. Then, we add new features related to the road and bus stop information for travel mode classification using an XGBoost (eXtreme Gradient Boosting) classifier, along with various basic features of the trajectories. We conducted a set of experiments on the GeoLife dataset using a group of state-of-the-art methods. The results showed that the proposed methods can improve the classification accuracy by using all the classifiers we compared and the classification accuracy using the XGBoost classifier can reach a maximum of 90.41%.