Abstract
This study aims to identify the patterns of behavior which underlie human mobility. More specifically, we compare commuters who drive in a car with those who use the train in the same geographic region of the Netherlands. We try to understand the mode choices of the commuters based on three factors: the cost of the transport mode, the CO\(_2\) emissions, and the travel time. The analysis has been based on data consisting of travel transactions in the Netherlands during 2018 containing over half a million records. We show how this raw data can be transformed into relevant insights on the three factors. A large difference is observed in terms of CO\(_2\) emissions and cost, a minor difference in speed. Besides, the computation of congestion shows intuitive results. These results can be used to stimulate behavioral change proactively and to improve trip planners.
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Slik, J., Bhulai, S. (2022). Understanding Human Mobility for Data-Driven Policy Making. In: Phillipson, F., Eichler, G., Erfurth, C., Fahrnberger, G. (eds) Innovations for Community Services. I4CS 2022. Communications in Computer and Information Science, vol 1585. Springer, Cham. https://doi.org/10.1007/978-3-031-06668-9_12
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DOI: https://doi.org/10.1007/978-3-031-06668-9_12
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