This draft research study is called: “Analysis of Spatial-temporal Features of Taxis’ Emissions from Big Data Informed Travel Patterns: A Case of Shanghai, China” by Luo X, Dong L, Dou Y, Zhang N, Ren J, Li Y, Sun L, Yao S. published in the Journal of Cleaner Production 2016.
The purpose of the study is to understand the spatial and temporal emissions patterns of vehicles in the Shanghai taxi-cab fleet to better guide future infrastructure design within a lower carbon environment.
The feasibility of Global Positioning Systems (GPS) and emerging big data analysis techniques enable the in-depth traffic analysis – and this is one of the first applications of these technologies this area.
Researchers analyzed the taxi fleet’s energy consumption and emissions and their spatial-temporal distribution in Shanghai, applying big data analysis on GPS data of taxies. Spatial and temporal features of energy consumption and pollutant emissions were further mapped with geographical information systems (GIS).