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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

113 Scopus Citations
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  • Xiao Luo
  • Yi Dou
  • Ning Zhang
  • Jingzheng Ren
  • Ye Li
  • Lu Sun
  • Shengyong Yao


Original languageEnglish
Pages (from-to)926-935
Journal / PublicationJournal of Cleaner Production
Online published1 Jun 2016
Publication statusPublished - 20 Jan 2017
Externally publishedYes


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 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 behaviors. 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 circumstance, this paper analyzed the taxi's 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 9–10AM, the second peak occurred in 7–8PM, which were both the traffic rush period. The lowest activity/emission moment was 3–4AM. 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 insights to policy makers for better understanding on the travel patterns and related environmental implications in Shanghai metropolis, so as to support better planning on infrastructures system, demand side management and the promotion on low-carbon life styles.

Research Area(s)

  • Big data mining, GPS, Shanghai, Spatial-temporal emissions distribution, Taxi travel pattern

Citation Format(s)