Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python |
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Authors: | Ron Dalumpines Darren M Scott |
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Institution: | TransLAB (Transportation Research Lab), School of Geography and Earth Sciences, McMaster University, Hamilton, Canada |
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Abstract: | The increasing popularity of global positioning systems (GPSs) has prompted transportation researchers to develop methods that can automatically extract and classify episodes from GPS data. This paper presents a transferable and efficient method of extracting and classifying activity episodes from GPS data, without additional information. The proposed method, developed using Python®, introduces the use of the multinomial logit (MNL) model in classifying extracted episodes into different types: stop, car, walk, bus, and other (travel) episodes. The proposed method is demonstrated using a GPS dataset from the Space-Time Activity Research project in Halifax, Canada. The GPS data consisted of 5127 person-days (about 47 million points). With input requirements directly derived from GPS data and the efficiency provided by the MNL model, the proposed method looks promising as a transferable and efficient method of extracting activity and travel episodes from GPS data. |
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Keywords: | Global positioning systems time-use diary episode extraction multinomial logit travel behavior mode detection |
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