دانلود مقاله انگلیسی رایگان:اشتراک دوچرخه یا تاکسی؟ مدل سازی گزینه های حالت سفر در شیکاگو با استفاده از یادگیری ماشین - 2019
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  • Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning
    Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning

    سال انتشار:

    2019


    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


    منبع:

    Sciencedirect - Elsevier - Journal of Transport Geography, 79 (2019) 102479: doi:10:1016/j:jtrangeo:2019:102479


    نویسنده:

    Xiaolu Zhoua,d, Mingshu Wangb,⁎, Dongying Lic


    چکیده انگلیسی:

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 11
    حجم فایل: 2861 کیلوبایت

    قیمت: رایگان


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