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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2017
عنوان انگلیسی مقاله:
Analysis on spatial-temporal features of taxis emissions from big data informed travel patterns: a case of Shanghai, China
ترجمه فارسی عنوان مقاله:
تجزیه و تحلیل در مورد ویژگی های زمانی- مکانی حرکت تاکسی ها از الگوهای سفر آگاه داده های بزرگ : یک مورد از شانگهای، چین
منبع:
Sciencedirect - Elsevier - Journal of Cleaner Production, 142 (2017) 926-935. doi:10.1016/j.jclepro.2016.05.161
نویسنده:
Xiao Luo a, b, Liang Dong c, d, *, Yi Dou d, e, Ning Zhang f, g, **, Jingzheng Ren h, Ye Li i, Lu Sun d, j, Shengyong Yao k
چکیده انگلیسی:
Air pollutions from transportation sector have become a serious urban environmental problem, espe
cially in developing countries with expending urbanization. Cleaner technologies advancement and
optimal regulation on the transporting behaviors and related design in infrastructures is critical to
address above issue. To understand the spatial and temporal emissions pattern within transportation lays
the foundation for design on better infrastructures and guidance on low-carbon transportation behav
iors. The feasibility of Global Positioning System (GPS) and emerging big data analysis technique enable
the in-depth analysis on this topic, while to date, applications had been rather few. With this circum
stance, this paper analyzed the taxis energy consumption and emissions and their spatial-temporal
distribution in Shanghai, one of the most famous mega cities in China, applying big data analysis on
GPS data of taxies. Spatial and temporal features of energy consumptions and pollutants emissions were
further mapped with geographical information system (GIS). Results highlighted that, spatially, the
energy consumption and emission presented a distribution of dual-core cyclic structure, in which, two
hubs were identified. One was the city center, the other was Hongqiao transport hub, the activities and
emission was more concentrated in the west par of Huangpu River. Temporally, the highest activity and
emission moment was 9e10AM, the second peak occurred in 7e8PM, which were both the traffic rush
period. The lowest activity/emission moment was 3e4AM. Causal mechanism for such distribution was
further investigated, so as to improve the driving behaviors. Through the exploration of spatial and
temporal emissions distribution of taxis via big dada technique, this paper provided enlightening in
sights to policy makers for better understanding on the travel patterns and related environmental im
plications in Shanghai metropolis, so as to support better planning on infrastructures system, demand
side management and the promotion on low-carbon life styles.
Keywords:GPS|Big data mining|Spatial-temporal emissions distribution|Taxi travel pattern|Shanghai
قیمت: رایگان
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