Highlights Use taxi GPS and LPR camera data to reconstruct vehicle emission patterns. Recover carbon footprint of urban traffic with data sources widely available. Propose emission expansion coefficient to extrapolate taxi emissions. Consider vehicle count, fleet composition, and emission factors. Identify spatial-temporal emission patterns and hotspots in a case study.
Abstract Vehicle trajectory data collected via GPS-enabled devices have played increasingly important roles in estimating network-wide traffic, given their broad spatial-temporal coverage and representativeness of traffic dynamics. This paper exploits taxi GPS data, license plate recognition (LPR) data, and geographical information for reconstructing the spatial and temporal patterns of urban traffic emissions. Vehicle emission factor models are employed to estimate emissions based on taxi trajectories. The estimated emissions are then mapped to spatial grids of urban areas to account for spatial heterogeneity. To extrapolate emissions from the taxi fleet to the whole vehicle population, we use Gaussian process regression (GPR) models supported by geographical features to estimate the spatially heterogeneous traffic volume and fleet composition. Unlike previous studies, this paper utilizes the taxi GPS data and LPR data to disaggregate vehicle and emission characteristics through space and time in a large-scale urban network. The results of a case study in Hangzhou, China, reveal high-resolution spatio-temporal patterns of traffic flows and emissions, and identify emission hotspots. This study provides an accessible means of inferring the environmental impact of urban traffic with multi-source urban data that are now widely available in urban areas.
Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data
Tramsportation Research, Part C: Emerging Technologies ; 106 ; 145-165
2019-07-10
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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