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• W4384936322 endingPage "103669" @default.
• W4384936322 startingPage "103669" @default.
• W4384936322 abstract "Exploring the patterns of urban on-road (OR) emissions and their relationship with built environment (BE) is essential for urban planning oriented by transportation carbon emission reduction. This study quantified the road segment OR emissions with high spatial resolution by incorporating massive vehicle trajectory big data and vehicle type data in the metropolis, Shenzhen city in China. Multi-source big data such as Mobile signaling data, points of interest (POIs) data, were combined to characterize the BE factors at a fine spatiotemporal scale. This was integrated with GWR model to detect the spatial non-stationarity of the BE effects on OR carbon emissions at the transportation analysis zone (TAZ) level, which fills the research gap of the unclear effects of different BE unit on OR emissions reduction. Especially, we firstly assessed the timing feature of the resident movement by developing an improved actual jobs-housing balance (JHB) indicator supported by the mobile signaling data. The results showed that the actual JHB ratio and distance to transit were the most significant positive factors that influenced the emissions of most of the road segments. It provided implications for the policy makers with tradeoff among the instruments of possible BE improvements. Moreover, the TAZs were classified into three groups according to their coefficients in the GWR model. The clustering results have provided zoning policy implication for urban planning." @default.
• W4384936322 created "2023-07-22" @default.
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• W4384936322 date "2023-07-01" @default.
• W4384936322 modified "2023-10-15" @default.
• W4384936322 title "How can urban built environment (BE) influence on-road (OR) carbon emissions? A road segment scale quantification based on massive vehicle trajectory big data" @default.
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• W4384936322 doi "https://doi.org/10.1016/j.jtrangeo.2023.103669" @default.
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