عنوان انگلیسی مقاله:
Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning
ترجمه فارسی عنوان مقاله:
اشتراک دوچرخه یا تاکسی؟ مدل سازی گزینه های حالت سفر در شیکاگو با استفاده از یادگیری ماشین
Sciencedirect - Elsevier - Journal of Transport Geography, 79 (2019) 102479: doi:10:1016/j:jtrangeo:2019:102479
Xiaolu Zhoua,d, Mingshu Wangb,⁎, Dongying Lic
In many big cities, the bike-sharing system (BSS) and taxi play critical roles in transportation services. They both
offer on-demand transportation options and allow flexible riding scheduling and routing. Previous literature has
compared BSS and taxi to other transport modes, such as public transit and private automobile, but little is
known about the spatiotemporal factors that influence travel choices between these two alternatives.
Understanding travel patterns of BSS and taxi is critical in traffic demand analysis and sustainable transportation
planning. Also, an in-depth examination of the patterns of travel behaviors, especially when one would choose
BSS over a taxi, will provide valuable insights on human mobility and active living research. In this study, we
investigated the spatiotemporal patterns of BSS and taxi trips in Chicago from 2014 to 2016. To model travel
choices between BSS and taxi, we applied machine learning techniques to simulate the means of transport based
on environmental and temporal factors. Results show seasonal trip variations of the BSS and a declining trend of
taxi trips. BSS speed is relatively stable while taxi speed varies primarily because of time and locations. Based on
the random forest model, which has demonstrated the best fit with high processing speed, travel distance and the
number of parks and recreational facilities seem to be critical spatial predicting factors of the travel choice.
Given any time and location, the model can recommend the travel choices between BSS and taxis for users. This
study shows the significance of machine learning techniques in urban mobility research. Results of the study may
potentially support peoples transportation decision-making and facilitate sustainable transportation planning.
Keywords: Bike sharing systems | Taxi | Travel mode choice | Machine learning | Geographic information systems