با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Spatial variations in urban public ridership derived from GPS trajectories and smart card data
ترجمه فارسی عنوان مقاله:
تغییرات فضایی در سواری عمومی شهری حاصل از مسیرهای GPS و داده های کارت هوشمند
منبع:
Sciencedirect - Elsevier - Journal of Transport Geography, 69 (2018) 45-57: doi:10:1016/j:jtrangeo:2018:04:013
نویسنده:
Wei Tua,b,⁎, Rui Caod, Yang Yuea,b, Baoding Zhoua,b, Qiuping Lie, Qingquan Lia,b,c
چکیده انگلیسی:
Understanding urban public ridership is essential for promoting public transportation. However, limited efforts
have been made to reveal the spatial variations of multi-modal public ridership (such as buses, metro systems,
and taxis) and the underlying controlling factors. This study explores multi-modal public ridership and compares
the similarities and differences of the associated factors. Daily bus, metro, and taxi ridership patterns are first
extracted from multiple sources of big transportation data, including vehicle (bus and taxi) GPS trajectories and
smart card data. Multivariate regression analysis and geographically weighted regression analysis are used to
reveal the associations between these data and demographic, land use, and transportation factors. An empirical
study in Shenzhen, China, suggests that employment, mixed land use, and road density have significant effects
on the ridership of each mode; however, some effects vary from negative to positive across the city. The results
also indicate that road density, income, and metro accessibility do not have significant effects on metro, transit
or bus ridership. These findings suggest that the effects of the associated factors vary depending on the mode of
travel being considered and that the city should carefully consider which factors to emphasize in formulating
future transport policy.
Keywords: Ridership ، Big data ، Trajectory ، Smart card data ، Geographically weighted regression ، Transit ، Taxi
قیمت: رایگان
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