  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 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 spatiotemporal patterns of traffic flows and emissions
and identify emission hotspots at different locations. This study provides an
accessible means of inferring the environmental impact of urban traffic with
multi-source data that are now widely available in urban areas.
