Estimation of vehicular emissions by capturing traffic variations

Nesamani, K S; Chu, Lianyu; McNally, Michael G.; Jayakrishnan, R.

doi:10.1016/j.atmosenv.2006.12.027

Cited by 74 publications

(33 citation statements)

References 11 publications

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“…Sierra Research found that most drivers spend about 2% of total driving time in aggressive mode, which contributes about 40% of total emissions (Samuel et al, 2002). Nesamani et al (2007) proposed an intermediate model component that can provide better estimates of link speeds by considering a set of emission-specific characteristics (ESC) for each link. The intermediate model was developed using multiple linear regression and evaluated using a microscopic traffic simulation model.…”

Section: Literature Reviewmentioning

confidence: 99%

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway

Abou-Senna

Radwan

Westerlund

et al. 2013

Journal of the Air & Waste Management Association

107

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The Intergovernmental Panel on Climate Change (IPCC) estimates that baseline global GHG emissions may increase 25-90% from 2000 to 2030, with carbon dioxide (CO 2 ) emissions growing 40-110% over the same period. On-road vehicles are a major source of CO 2 emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, for example, carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). Therefore, the need to accurately quantify transportation-related emissions from vehicles is essential. The new U.S. Environmental Protection Agency (EPA) mobile source emissions model, MOVES2010a (MOVES), can estimate vehicle emissions on a second-by-second basis, creating the opportunity to combine a microscopic traffic simulation model (such as VISSIM) with MOVES to obtain accurate results. This paper presents an examination of four different approaches to capture the environmental impacts of vehicular operations on a 10-mile stretch of Interstate 4 (I-4), an urban limited-access highway in Orlando, FL. First (at the most basic level), emissions were estimated for the entire 10-mile section "by hand" using one average traffic volume and average speed. Then three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link drive schedules (LDS), and second-by-second operating mode distributions (OPMODE). This paper analyzes how the various approaches affect predicted emissions of CO, NOx, PM2.5, PM10, and CO 2 . The results demonstrate that obtaining precise and comprehensive operating mode distributions on a secondby-second basis provides more accurate emission estimates. Specifically, emission rates are highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach.Implications: Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways.

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Section: Literature Reviewmentioning

confidence: 99%

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway

Abou-Senna

Radwan

Westerlund

et al. 2013

Journal of the Air & Waste Management Association

107

View full text Add to dashboard Cite

show abstract

“…Hatzopoulou & Miller (2010) estimated average hourly concentration contours on the regional scale in the Greater Toronto Area by simulating pollutant dispersion using activity-based travel demand modelling, incorporating the use of EMME, an average speed based travel demand model. Similarly, Nesamani, et al (2007) Amirjamshidi, et al (2012) investigated emissions for the downtown Toronto Waterfront network using a combination of traffic microsimulation model and emissions model; a simplified Gaussian plume dispersion model was applied to estimate roadside emissions that could not simulate the complexity of flow in urban areas. Karim & Matsui (1998) and Karim, et al (1998) developed and evaluated a mathematical model that predicted pollutant concentrations for CO and NO x in roadway micro-environments.…”

Section: Significance and Potentialmentioning

confidence: 99%

An integrated modelling approach to estimate urban traffic emissions

Misra

Roorda

MacLean

2013

Atmospheric Environment

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Vehicular emissions contribute significantly to poor air quality in urban areas. An integrated modelling approach is adopted to estimate microscale urban traffic emissions. The modelling framework consists of a traffic microsimulation model, a microscopic emissions model, and two dispersion models. This framework is applied to a traffic network in downtown Toronto to evaluate summer time morning peak traffic emissions during weekdays for carbon monoxide and nitrogen oxide. The model predicted results are validated against sensor observations with a reasonably good fit. Availability of local estimates of ambient concentration is useful for accurate comparisons of total predicted concentrations with observed concentrations. Both predicted and observed concentrations are significantly smaller than the National Ambient Air Quality Objectives established by Environment Canada. Sensitivity analysis is performed on a set of input parameters and horizontal wind speed is found to be the most influential factor in pollutant dispersion.iii

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“…[53][54][55][56]. Information on the amount of consumption and emissions associated with the links of a traffic network can be used in applications of energy-efficient route guidance, also called "eco-routing".…”

Section: Applicability To On-line Energy-efficient Route Guidancementioning

confidence: 99%

Impact of Synchronised Flow in Oversaturated City Traffic on Energy Efficiency of Conventional and Electrical Vehicles

Hemmerle¹,

Koller²,

Hermanns³

et al. 2016

Coll Dyn

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In this study of city traffic, we show that empirical synchronised flow patterns, which have been revealed recently in oversaturated traffic, exhibit considerable impact on the energy efficiency of vehicles. In particular, we have found out that energy consumption in oversaturated city traffic can decrease considerably when the oversaturated city traffic consists of synchronised flow patterns rather than consisting of moving queues of the classical traffic flow theory at traffic signals. Using empirical GNSS data measured by navigation devices on two different road sections in Düsseldorf, Germany, we show that synchronised flow patterns and moving queues differ in their cumulated vehicle acceleration (a sum of positive speed differences along a vehicle trajectory) despite similar mean speeds. Energy efficiency in return is dependent on the cumulated vehicle acceleration. We consider both the fuel consumption of conventional vehicles with combustion engines and the energy balance of electrical vehicles.

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Estimation of vehicular emissions by capturing traffic variations

Cited by 74 publications

References 11 publications

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway

An integrated modelling approach to estimate urban traffic emissions

Impact of Synchronised Flow in Oversaturated City Traffic on Energy Efficiency of Conventional and Electrical Vehicles

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