Transportation Modes Detection in Bangkok Using GPS Logger data and GIS Data
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Abstract
Person trips and transportation mode surveys could be in multiple formats, such as telephone interviews and questionnaires. These data collecting methods rely on manual labeling of data after a survey, and thus, it requires more manpower, time and budget. However, the information technology has introduced advanced data collecting methods such as a mobile phone or a data logger device that can easily record travel time and location data of people. This information plays an essential role in transportation surveying. GPS data can be used to find many features involved in travelling, but those data need to be processed to find transportation modes used before further analysis. The main objective of this study is to detect transportation modes used in Bangkok using GPS logger data. Since the transportation modes in Bangkok are unique and various, there are many problems, such as traffic condition and complexity of the transportation network systems. Therefore, it is not very simple to determine transportation modes. GIS data is used to help detecting transportation modes that have specific routes and stations. Random Forest classifier is used for transportation modes detection. Modes considered in this study are walking, 2-wheel vehicles, 4-wheel vehicles, bus, skytrain, subway and boat. Moreover, activities of people in a week were focused on. Such activities include stationary and modes transferring points. The transportation modes could be automatically detected using our algorithms. This method can be applied for other person trip data collected from a mobile phone that can collect huge number of dataset, and the output data can be used for further analysis in transportation surveying and other related topics.
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