TY - JOUR
T1 - Analysis on spatial-temporal features of taxis' emissions from big data informed travel patterns
T2 - a case of Shanghai, China
AU - Luo, Xiao
AU - Dong, Liang
AU - Dou, Yi
AU - Zhang, Ning
AU - Ren, Jingzheng
AU - Li, Ye
AU - Sun, Lu
AU - Yao, Shengyong
PY - 2017/1/20
Y1 - 2017/1/20
N2 - 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.
AB - 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.
KW - Big data mining
KW - GPS
KW - Shanghai
KW - Spatial-temporal emissions distribution
KW - Taxi travel pattern
UR - http://www.scopus.com/inward/record.url?scp=84973582097&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84973582097&origin=recordpage
U2 - 10.1016/j.jclepro.2016.05.161
DO - 10.1016/j.jclepro.2016.05.161
M3 - RGC 21 - Publication in refereed journal
SN - 0959-6526
VL - 142
SP - 926
EP - 935
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
ER -
