دانلود مقاله انگلیسی رایگان:الگوهای مختلف تفریحی حمل و نقل ریلی و روابط آنها با عوامل محیطی ساخته شده در مقیاس خوب: تجزیه و تحلیل داده های بزرگ از گوانگژو - 2020
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  • The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou
    The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou


    ترجمه فارسی عنوان مقاله:

    الگوهای مختلف تفریحی حمل و نقل ریلی و روابط آنها با عوامل محیطی ساخته شده در مقیاس خوب: تجزیه و تحلیل داده های بزرگ از گوانگژو


    منبع:

    Sciencedirect - Elsevier - Cities, 99 (2020) 102580: doi:10:1016/j:cities:2019:102580


    نویسنده:

    Shaoying Lia, Dijiang Lyub, Xiaoping Liuc, Zhangzhi Tand,e, Feng Gaoa, Guanping Huanga, Zhifeng Wua,⁎


    چکیده انگلیسی:

    Investigating the varying ridership patterns of rail transit ridership and their influencing factors at the station level is essential for station planning, urban planning, and passenger flow management. Although many studies have investigated the associations between rail transit ridership and built environment, few studies combined spatial big data to characterize the built environment factors at a fine scale and linked those factors with the varying patterns of rail transit ridership. In this study, we characterized the fine-scale built environment factors in the central urban area of Guangzhou, China, by integrating multi-source geospatial big data including Tencent user data, building footprint and stories, points of interest (POI) data and Google Earth high-resolution images. Six direct ridership models (DRMs) based on the backward stepwise regression method were built to compare the different effects between daily, temporal and directional ridership. The results indicated that number of station entrances/exits and transfer dummy, were positively associated with rail transit ridership, while connecting bus station sites and the parking lots were not significantly related to ridership. Population density and common residences land were found to be dominating factors in promoting morning boarding & evening alighting ridership, which implied that these two factors should be focused on to encourage commuting-purpose rail transit usage. However, the indistinct effect of urban villages on rail transit ridership suggested planners to pay more attentions on urban regeneration at the pedestrian catchment areas (PCAs) with urban villages. High employment density and a large FAR were suggested at the employment-oriented areas owing to their importance in promoting rail transit ridership, especially the morning alighting & evening boarding ridership. Moreover, educational research land use significantly affected weekday ridership while sports land use positively influenced weekend ridership, which suggested planners to pay more attention on the non-commuting trips. The different influencing mechanisms of various types of rail transit ridership highlighted the need to consider land use balance planning and trip demand optimization in highly urbanized metropolises in developing countries.
    Keywords: Rail transit ridership | Big data | Fine-scale | Built environment | Guangzhou


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 3436 کیلوبایت

    قیمت: رایگان


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