Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model

Abstract

h i g h l i g h t sWe present a novel method for predicting air pollution emissions using transport data. Study uses measured microscopic transport data and a microscopic emissions model. GPS data from over 15,000 vehicles were analyzed to quantify speeds and accelerations. CO 2 , NO x , VOCs and PM were modeled in high spatio-temporal resolution. Highly localized areas of elevated emissions were identified. a b s t r a c t Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters in high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO 2 ), nitrogen oxides (NO x ), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset. Results demonstrated that highresolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate

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

“…We further compare the proposed emission estimation method with two different methods: (a) one based on flow data from microwave detectors  and the static fleet information (provided by the Bureau of Statistics of Hangzhou), which is similar to the method proposed by Nyhan et al (2016); (b) the other one based on the flow data and uniform dynamic fleet composition, both observed by LPR cameras. As shown in Figure 11, method (a) tends to underestimate the total emissions during the daytime, which is understandable given that the static fleet information may not be an accurate and up-to-date representation of the vehicle fleet during the study period.…”

Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong

“…When work locations were considered in addition to residential, exposures to NOX and NO2 were found to increase by 5 -10 ppb (Shafran-Nathan et al, 2017). Nyhan et al (2016) found significant differences between dynamic and static population-weighted exposures in New York City using cellular data with spatiotemporal PM2.5 concentrations. It was found travelling to work locations, usually in urban areas with higher pollutant levels, contributed to this variation from home concentrations, particularly time spent in transport microenvironments contributed significantly to overall exposure (Dons et al, 2011;de Nazelle et al, 2013).…”

“…In other studies assessing dynamic air pollution exposure, mobility or time-activity data were derived from transport and activity-based simulation models (Setton et al, 2011;Dhondt et al, 2012), GPS (Dons et al, 2011), mobile-based tracking (de Nazelle et al, 2013), travel smartcard (Smith et al, 2016), travel surveys (Saraswat et al, 2016) or cellular network information (Dewulf et al, 2016;Nyhan et al, 2016). These were combined with air pollution modeling to assess personal and population exposure to pollutants.…”

“…These approaches have facilitated detailed spatio-temporal analysis of individual travel behaviors. A number of studies found static estimates underestimated exposure levels (Dhondt et al, 2012;de Nazelle et al, 2013;Dewulf et al, 2016;Nyhan et al, 2016;Saraswat et al, 2016). Simulations based on travel survey and air pollution modeling data found integrating mobility can affect exposure estimates by as much as 30% (Marshall et al, 2006).…”

Emission estimation based on traffic models and measurements

“…In Gately et al, (2017), emissions are estimated by assimilating GPS data with speed and flow data derived from stationary detectors or a traffic model. Nyhan et al, (2016) use GPS trajectory data from taxis, available for a large urban area in Singapore, to estimate emissions. However, they conclude that Singapore is a special case, where taxi data can be used to infer general traffic pattern and the total volume, but this is not necessarily true for other urban areas.…”