Analysis on spatial-temporal features of taxis' emissions from big data informed travel patterns: a case of Shanghai, China

摘要: Abstract Air pollutions from transportation sector have become a serious urban environmental problem, especially in developing countries with expending urbanization. Cleaner technologies advancement and optimal regulation on the transporting behaviors related design infrastructures is critical to address above issue. To understand spatial temporal emissions pattern within lays foundation for better guidance low-carbon behaviors. The feasibility of Global Positioning System (GPS) emerging big data analysis technique enable in-depth this topic, while date, applications had been rather few. With circumstance, paper analyzed taxi's energy consumption their spatial-temporal distribution Shanghai, one most famous mega cities China, applying GPS taxies. Spatial features consumptions pollutants were further mapped geographical information system (GIS). Results highlighted that, spatially, emission presented dual-core cyclic structure, which, two hubs identified. One was city center, other Hongqiao transport hub, activities more concentrated west par Huangpu River. Temporally, highest activity moment 9–10AM, second peak occurred 7–8PM, which both traffic rush period. lowest activity/emission 3–4AM. Causal mechanism such investigated, so as improve driving Through exploration taxis via dada technique, provided enlightening insights policy makers understanding travel patterns implications Shanghai metropolis, support planning system, demand side management promotion life styles.