01663995

Component

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.

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

18-06423

English

8p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

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

COPERT (Model); Didi Dache

Shanghai (China)

Energy; Environment; Highways; Vehicles and Equipment

Transportation Research Board Annual Meeting 2018 Paper #18-06423

TRIS, TRB, ATRI

Jan 8 2018 11:39AM