Analyzing Spatiotemporal Traffic Line Source Emissions Based on Massive DIDI Car-Hailing Data

Didi car Hailing data is a popular source for analyzing traffic operation, while it hasn’t been applied to detailed traffic emission estimation. This study combined Europe COPERT model with parameters extracted from GPS data in Shanghai to estimate the NOₓ emission rate of road segments. To investigate 24-hour spatial-temporal emitting pattern, temporal Fuzzy C-means clustering and spatial models including geographical detector, MORAN’s I and SARMA regressions were used. FCM was used to classify roads based on emission rate. Geographical detector and MORAN’s I were used to verify the impact of built environment on line source emission and similarity of nearby segments. Then SARMA regression was introduced to testify the selected built environment factors’ impact on emission rate based on the probabilistic result by FCM. The quantitative spatial method was used to analyze the output from the clustering analysis, in order to realize a better spatial-temporal distribution of speed patterns. Finally, the correlation between the monitored NOₓ concentration and the line emission source was calculated, which explained the contribution of on-road emission to atmospheric pollution.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ADC20 Standing Committee on Transportation and Air Quality. Alternate title: Analyzing Spatiotemporal Traffic Line Source Emission Based on DIDI Car-Hailing Data
• Authors:
  • Sun, Daniel (Jian)
  • Kaisheng, Zhang
  • Shen, Suwan
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 8p

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Built environment; Case studies; Cluster analysis; Demand responsive transportation; Exhaust gases; Fuzzy algorithms; Global Positioning System; Nitrogen oxides; Regression analysis; Ridesharing; Spatial analysis; Time domain analysis; Traffic data
• Identifier Terms: COPERT (Model); Didi Dache
• Geographic Terms: Shanghai (China)
• Subject Areas: Energy; Environment; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01663995
• Record Type: Publication
• Report/Paper Numbers: 18-06423
• Files: TRIS, TRB, ATRI
• Created Date: Mar 22 2018 12:03PM