@article{ref1,
title="Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python",
journal="Transportation planning and technology",
year="2017",
author="Dalumpines, Ron and Scott, Darren M.",
volume="40",
number="5",
pages="523-539",
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.<p /> <p>Language: en</p>",
language="en",
issn="0308-1060",
doi="10.1080/03081060.2017.1314502",
url="http://dx.doi.org/10.1080/03081060.2017.1314502"
}