Abstract
Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users’ transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of application difficult if peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using grid probability distribution function. Second, we introduce an algorithm for the change point–based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models.
Highlights
Understanding the mobility patterns of a user by analyzing the Global Positioning System (GPS) trajectory data is an important research problem, providing the rich context information that can be utilized for traffic management and city transport planning.[1,2] Transportation experts can develop the strategies to reduce traffic congestion, air pollution, cost and time for people based on the inference of transportation modes from GPS trajectories, and the existing transportation system can be improved.[3,4,5] A GPS trajectory is a path of GPS points generated in sequence by any moving object
We considered four baseline segmentation methods including Fixed Length Segment (FLS), Fixed Duration Segment (FDS) and the method of Liang et al.[18] and Zheng et al.[11] to compare the performance of our segmentation method
If all points of GPS trajectory are retrieved as change points, it will give 100% recall, and all the segments contain only one GPS point that does not give meaningful information about the segment for prediction
Summary
Understanding the mobility patterns of a user by analyzing the Global Positioning System (GPS) trajectory data is an important research problem, providing the rich context information that can be utilized for traffic management and city transport planning.[1,2] Transportation experts can develop the strategies to reduce traffic congestion, air pollution, cost and time for people based on the inference of transportation modes from GPS trajectories, and the existing transportation system can be improved.[3,4,5] A GPS trajectory is a path of GPS points generated in sequence by any moving object. Each GPS point can be represented by a tuple (x, y, t), where x, y and t are latitude, longitude and timestamp of a GPS point, respectively.[6] Transportation mode inference aims to infer modes of transport of a user from the user’s data obtained through multiple sensor types including accelerometer, Global System for Mobile Communications (GSM) or GPS sensors. Learning the transportation modes such as walk, bike, bus and car from trajectory data is of great significance for both the users and service providers, because the learned user’s behaviors can be used in many applications for multiple purposes like flow prediction[7,8] traffic congestion[9,10] etc.
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