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

Abstract

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.

Microscale traffic simulation and emission estimation in a heavily trafficked roundabout in Madrid (Spain)

“…As shown in the supplementary material maps, this pattern is reproduced along the week with low speed areas after traffic lights, increasing the emissions per grid cell. These results are in agreement with previous microscale emission simulations (Abou-Senna et al, 2013) that highlight the pronounced sensitivity of emissions to stop and go situations and to the associated driving cycles (braking-acceleration) that occurs in congested traffic situations with lower average speeds. In general fluid traffic scenarios as E1 (Fig.…”

“…But also, speed management influences in a complex way vehicle emissions (Int Panis et al, 2006) so that differentiating between congested and non-congested scenarios is useful to suggest control strategies (Papson et al, 2012). Because of that, micro-simulation models are used to generate emission estimations with higher spatial and temporal resolution (Abou-Senna et al, 2013;Den Braven et al, 2012) and have already been used in other cities (Al Alami, 2012).…”

“…They use very short time steps, drivers' reaction times, route selection logic and lane change logic, obtaining detailed trajectories. This results in stop and go cycles that produce braking-acceleration patterns with a huge influence on emissions (Abou-Senna et al, 2013;Joumard et al, 1995). But also, speed management influences in a complex way vehicle emissions (Int Panis et al, 2006) so that differentiating between congested and non-congested scenarios is useful to suggest control strategies (Papson et al, 2012).…”

Vehicle Emission Factors for Particulate and Gaseous Pollutants in an Urban Tunnel in Xi’an, China

“…Furthermore, new techniques have been developed in using simulated EF models such as MOBILE, COPERT, IVE, CEME, and MOVES [22,23]. ese models have been established by European and American researchers, and the EFs can be obtained directly by inputting the required data such as vehicle type, age distribution, driving distance, and ambient temperature [24,25]. To apply these models well and meet the needs of practical conditions, some parameters need to be localized, particularly for an area in China.…”

Developing a 24-Hour Large-Scale Microscopic Traffic Simulation Model for the Before-and-After Study of a New Tolled Freeway in the Washington, DC–Baltimore Region

“…More recently, the EPA's Motor Vehicle Emission Simulator (MOVES) model gains its increasing popularity. Abou-Senna et al (2013) employed MOVES to simulate air pollution emissions for a limited-access highway corridor. Similarly, Xie et al (2012) combined a Paramics model and MOVES to analyze a multimodal corridor to understand the effects of alternative fueled vehicles.…”