Traffic control for air quality management and congestion mitigation in complex urban vehicular tunnels

Tan, Zhouliang; Gao, H. Oliver

doi:10.1016/j.trc.2015.06.004

Cited by 17 publications

(1 citation statement)

References 22 publications

(28 reference statements)

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“…In recent years, there has been an increasing number of institutions and researchers focusing their research on the issue of vehicle emission and urban air pollution (Gühnemann et al, 2004;Zhang et al, 2008;WTO, 2014aWTO, , 2014bTan et al, 2015;Nyhan et al, 2016;Ma et al, 2018). One way to investigate the environmental impact of road traffic is to derive emission inventories, which are used to estimate the total traffic emissions in a specified area, global (Butler et al, 2008; or regional (Zhang et al, 2008), within a specified time span (usually an annual year).…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data

Liu

Han

Chen

et al. 2019

Transportation Research Part C: Emerging Technologies

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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 spatio-temporal 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.

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Section: Introductionmentioning

confidence: 99%

Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data

Liu

Han

Chen

et al. 2019

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

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Spatial-temporal prediction of the environmental conditions inside an urban road tunnel during an incident scenario

Ding

et al. 2022

Building and Environment

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Survey and measurement of the vehicle pollutant emission in urban underground bifurcate tunnel, China

Chen

2019

Sustainable Cities and Society

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Traffic control for air quality management and congestion mitigation in complex urban vehicular tunnels

Cited by 17 publications

References 22 publications

Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data

Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data

Spatial-temporal prediction of the environmental conditions inside an urban road tunnel during an incident scenario

Survey and measurement of the vehicle pollutant emission in urban underground bifurcate tunnel, China

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