Abstract
Travel mode detection has been a hot topic in the field of GPS trajectory-related processing. Former scholars have developed many mathematical methods to improve the accuracy of detection. Among these studies, almost all of the methods require a ground truth dataset for training. A large amount of the studies chose to collect the GPS trajectory dataset for training in their customized ways. In this chapter, we provide a standard process for travel mode detection, including ground truth data collection and model training. Firstly, we introduce a ground truth data collection process. The dataset is collected by seven independent volunteers in Japan and covers the time period of a complete month. The travel mode ranges from walking to the railway. A part of routines is traveling repeatedly in different time slots to experience different road and travel conditions. We also provide a case study to distinguish the walking and bike trips in a massive GPS trajectory dataset.
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