Behavioral cycling profiles and their potential as estimators for cycling motives
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Cycling behavioral research is increasingly conducted by means of GPS data. The presence of these data-sets allow for large-scale investigation of complicated travel behavioral aspects such as cycling motives and enables one to enrich raw GPS data-sets with these attributes based on contextual information. Currently, both the differences in cycling behavioral between cyclists with different motives and the extent up to which these differences can be used to estimate cycling motives for raw GPS tracks have received little attention. Even though more insights on these topics can provide useful insights for policymakers and can stimulate travel behavior research by enabling others to enhance their GPS tracks with more accurate cycling motive attribute data. This research tries to tackle both these problems by establishing cycling behavioral profiles based on trip, route and origin-destination behavioral characteristics and subsequently using the differences in these profiles to estimate cycling motives by means of machine learning. In addition to that, multiple machine learning algorithms are assessed to determine the most suitable The results show that there are significant differences in cycling behavioral profiles between motives. Trip, route and origin-destination behavioral characteristics all outperform a standard model for estimating cycling motives, with a combined model including all behavioral characteristics scoring highest (74.0% accuracy versus 51.4% standard model accuracy). Furthermore the results indicate that Random Forest and Gradient Boosting are among the most suitable algorithms for this purpose. Finally, recommendations and potential improvements are provided for future research on cycling behavior and motive estimation.