Motor Vehicle Emission Simulator Model Research Articles

Development of a Simplified Model of Speed-Specific Vehicle-Specific Power Distribution Based on Vehicle Weight for Fuel Consumption Estimates

The vehicle-specific power (VSP) distribution, as one of the fundamental inputs of VSP-based emission models such as the motor vehicle emission simulator model, is sensitive to vehicle weight. Developing field VSP distributions requires extensive vehicle type-specific trajectory data, which is expensive and time-consuming. On the other hand, estimating fuel consumption accurately by employing VSP distributions for various vehicle types is computationally highly complex. This study aims to develop a simplified model of speed-specific VSP distribution based on vehicle weight for fuel consumption. First, field speed-specific VSP distributions of eight types of vehicles are developed. Second, the Gaussian function is employed to fit the field speed-specific VSP distributions to “change” the discrete VSP distributions into continuous distributions to facilitate quantifying the relationship between VSP distributions and vehicle weights. Third, the relationship between VSP distributions and vehicle weights is quantified by employing polynomial functions. The results indicate the acceptable accuracy of the simplified model, with 93.8% of R2 of the Gaussian function being greater than 0.90. The error in estimating fuel consumption using the simplified model is acceptable. For vehicles weighing 1.5 t (1.5 metric tons), the average error is 6.3%. Besides the “hole filling” of VSP distributions of inaccessible vehicles, the simplified model will reduce the computational complexity of estimating fuel consumption by about 50%, which is beneficial for the realization of real-time online estimates of fuel consumption.

Effects of advanced traffic signal status warning systems on vehicle emission reductions at signalized intersections

ABSTRACTSignalized intersections have been identified as vehicle emission hotspots, where drivers decelerate, idle, and accelerate their vehicles in response to signal changes. Advanced traffic signal status warning systems (ATSSWSs) can be applied to reduce traffic emissions at intersections by mitigating unnecessary braking and acceleration. In this study, two types of ATSSWSs, variable message sign (VMS) based and vehicle-to-infrastructure (V2I) based, were designed, and their environmental effectiveness was evaluated through driving simulator-based experiments. Three scenarios were designed and tested: (1) baseline without an ATSSWS, (2) with the VMS-based ATSSWS, and (3) with the V2I-based ATSSWS. The Motor Vehicle Emission Simulator model was used to evaluate and compare the environmental effectiveness of these two types of ATSSWSs. The results indicate that the proposed ATSSWSs can reduce traffic emissions at signalized intersections. In particular, the V2I-based ATSSWS can substantially reduce CO2, NOx, CO, and HC emissions. The results will help transportation practitioners with implementing advanced driver information systems and decision making on emission reduction policies.Implications: Signalized intersection has been identified as one of hottest spots for vehicle emissions where signal control causes vehicles to frequently decelerate, idle, and accelerate. Advanced Traffic Signal Status Warning Systems (ATSSWS) can be applied to reduce traffic emission at intersections by decreasing vehicles’ unnecessary brakes and accelerations. The results of this study will assist transportation practitioners in implementing advanced driver information systems and making decisions on emission reduction policies.

VSP‐based emission factor calibration and signal timing optimisation for arterial streets

Vehicle delay and traffic emissions have serious influences on traffic flow operations on an arterial street. The emission factors during green and red are calibrated based on vehicle-specific power (VSP), then three optimisation models are formulated by minimising vehicle delay or traffic emissions and by considering left-turn bays and coordinated signals. The field data from Dalian city of China are used to validate these models. Vehicle compositions include car, medium-size vehicle (MSV) and bus. Carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NO x ) emissions are estimated. Three coordinated signal control scenarios are proposed and four simulation experiments are carried out. The results indicate that the emission factor during green is greater than that during red for CO, HC or NO x produced by car, MSV or bus in each lane group on an arterial street; the emissions estimated by the motor vehicle emission simulator model are greater than those calculated by the VSP-based model; and the signal control scenario obtained by minimising total emissions performs better than that obtained by minimising total delay or both of them.

Convex Fuel Consumption Model for Diesel and Hybrid Buses

The concave fuel consumption model may generate unrealistic driving recommendations in a control system; for instance, the model may recommend higher cruise speed to achieve lower fuel consumption levels on steeper roads. To improve the model performance with regard to driving control, the study developed a convex second-order polynomial fuel consumption model for conventional diesel and hybrid-electric buses. The model simultaneously circumvents the bang-bang type of control that implies that drivers would have to accelerate at full throttle or brake at full braking to minimize their fuel consumption levels. Six bus series (four diesel series and two hybrid series), covering a wide range of bus properties, were modeled. The model was developed on the basis of the Virginia Tech comprehensive power fuel-based model (VT-CPFM) framework and, given a lack of readily available data, calibrated by conducting empirical measurements. The model was validated by comparing its estimates against in-field measurements and predictions from the comprehensive modal emissions model, the Motor Vehicle Emissions Simulator model, and the concave VT-CPFM model. The results demonstrate that the convex model generates estimates consistent with field measurements and the predictions of the other models and can provide realistic driving recommendations without significantly sacrificing accuracy relative to the concave model. Optimum fuel economy cruise speed ranges from 39 to 47 km/h for all tested buses on grades ranging from 0% to 8% and decreases with the increase of grade and vehicle load.

Development of Macroscopic Emission Estimation Model Based on Microscopic Operating Modes

This study derived two models to estimate emissions on the basis of macroscopic mobility measures. The developed models can be used as part of signal timing optimization, macroscopic and mesoscopic simulation, cost-benefit analysis, and real-time and off-line estimation of arterial road performance. The derivation relied on output from the Motor Vehicle Emission Simulator model operating mode distribution analysis and used estimates from microscopic simulation model runs as input. Models with various combinations of independent variables were tested and two final models were selected. The first model used vehicle miles traveled (VMT) and vehicle delays as independent variables. The second model used VMT, vehicle delays, stop delays, and number of stops. Both models showed acceptable performance for most of the investigated cases in simulation modeling. A further test was performed using vehicle trajectories from the next generation simulation program databases. The results showed that the model developed on the basis of VMT and total vehicle delays performed well. However, the second model, developed on the basis of VMT, total vehicle delays, total stop delays, and number of stops, did not perform well. This could be because the model required accurate vehicle trajectories, including accurate acceleration and deceleration estimates. The quality of the trajectories in microscopic simulation is expected to affect the accuracy of the second model.

Selection of CO2 mitigation strategies for road transportation in the United States using a multi-criteria approach

Abstract Carbon dioxide emissions from human activities are the primary cause of recent climate change. In the United States, the road transportation sector is one of the largest sources of these emissions. Any policies to reduce emissions must therefore include mitigation strategies for on-road transportation. The aim of this paper is to propose a multi-criteria method, Analytical Hierarchy Process, to rank various on-road emissions mitigation strategies including reduce, avoid, and replace strategies. The method׳s results are obtained based on a survey of transportation and climate science professionals. The Analytical Hierarchy Process was applied to two regional scenarios of a midsize-small city (Lubbock, Texas) and a metropolitan area (Dallas, Texas). To evaluate the effectiveness of these strategies, the Motor Vehicle Emissions Simulator model was run for Dallas. The aim of the model was to estimate the potential carbon dioxide mitigation for a given strategy allocation. Our survey identified no difference between the rankings of reduce, avoid, and replace strategies for our metropolitan and midsize-small city areas. Reduce strategies had the highest preference score of 40% followed by avoid strategies with 36% and replace strategies with 24%. An optimum mixed mitigation scenario would achieve reductions in carbon dioxide emissions of 17% by 2030 from 2010 levels. The contributions of this study are two-fold. First, we evaluate generic scenarios in detail and apply them to a real-world case study. Second, the approach is both simple and generalizable. Applications of this type of platform include ranking transportation strategies on mitigating carbon dioxide emissions, evaluating the strategies, prioritizing budgets, and developing assessments of mitigation potential.

Traffic Emissions and Air Quality near Roads in Dense Urban Neighborhood

A traffic simulation was developed for a dense neighborhood in the city of Montreal, Quebec, Canada (8,656 links), and was linked with a regional traffic assignment model, which was used to determine the travel demand originating in and destined for the study area. With a version of the U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator model fit with local data, traffic emissions for each link were simulated by the use of instantaneous speed profiles. Emissions of greenhouse gases (GHG), oxides of nitrogen (NOx), and carbon monoxide (CO) were modeled under a range of regional and local policies, including fleet renewal, street closures, reduced demand, reduced internal car trips, and reduced through traffic. Several traffic scenarios were modeled in the traffic assignment and simulation models to represent these policies. Because of the high congestion levels in the neighborhood under base case conditions, limited networkwide reductions in emissions were observed, except in the scenario that aimed to reduce through traffic (29% reduction in GHG emissions compared with that in the base case scenario). Significant changes in the spatial patterns of emissions were detected. Average and instantaneous speed-based estimates were also compared; the average speed mode tended to overestimate total emissions as network speeds decreased. Finally, dispersion modeling was conducted along selected corridors to evaluate the effects of different scenarios on air quality. The study found significant increases in air pollution as a result of the street closure scenario and significant decreases with the reduced through traffic scenario.

Greenhouse Gas Emissions and Urban Congestion

The Texas A&M Transportation Institute's Urban Mobility Report (UMR) is acknowledged to be the most authoritative source of information about traffic congestion and its possible solutions. As policy makers from the local to national levels devise strategies to reduce greenhouse gas (GHG) emissions, the level of interest in the environmental impact of urban congestion has increased. To this end, the researchers developed and applied a methodology to determine carbon dioxide (CO2) emissions caused by congestion for inclusion in the UMR. The methodology also estimated fuel consumption on the basis of the CO2 emissions estimates. The researchers developed a five-step methodology with data from three primary data sources: (a) FHWA's Highway Performance Monitoring System, (b) INRIX traffic speed data, and (c) the U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator model. Results were intuitive and reasonable when emission rates (pounds of CO2 per mile) were compared with the emissions inventories in selected cities. The researchers incorporated the new methodology for all urban areas into the 2012 UMR and plan to include the same measures in future releases of the report. The researchers reported that, in 2011, 56 billion pounds of additional CO2 were produced in all 498 urban areas during congestion only; this amount equated to 2.9 billion gallons of wasted fuel. The amount of CO2 produced under free-flow conditions (i.e., absent congestion) was 1.8 trillion pounds in 2011 in all 498 urban areas.

Greenhouse Gas Emission Reductions Through National Ambient Air Quality Standards: The Role of Regional Low Carbon Fuel Standards

Classification of carbon dioxide as a criteria pollutant under the National Ambient Air Quality Syandard (NAAQS) may effectively trigger a form of cap and trade policy that promotes reduction of greenhouse gas (GHG) emissions on a regional basis over time. While economy‐wide cap and trade policy supports abatement at the lowest possible cost, it is not the most effective option for reducing GHG emissions in the transportation sector. Instead, we propose that transportation emission reductions be addressed through a Low Carbon Fuel Standard (LCFS) implemented through State Implementation Plans. Regional LCFS implementations may interact differently in isolation than when they are harmonized. We have modeled LCFS implementation in New Jersey, Oregon, Vermont, and Washington in isolation and as an aggregated region to reflect what could occur under NAAQS‐based LCFS implementation. Results indicate that harmonized standards across regions may substantially ease the burden of transitioning away from gasoline and diesel compared to isolated regional standards. Additionally, the existing implementation of the Environmental Protection Agency’s Motor Vehicle Emissions Simulator model does not account for life‐cycle GHG emissions, meaning that fuel policy will not factor into State Implementation Plans unless modifications to the model are made to account for life‐cycle carbon dioxide emissions associated with fuel types.

Computationally-efficient Approaches to Integrating the MOVES Emissions Model with Traffic Simulators

This paper investigates different approaches to integrating the MOtor Vehicle Emission Simulator model (MOVES) with microscopic traffic simulators to allow for project-level emissions analysis. The integration methodology is based on using the second-by-second vehicle trajectory output from the traffic simulator to define the link drive schedule required to run MOVES. This raises the question of how to define a representative vehicle trajectory for each link, because tracking the emissions for individual vehicles is computationally intractable. In this study, the accuracy of two aggregation methods and one sampling method, for defining the representative trajectory, are compared for both freeway and arterial links. The results indicate that the sampling method outperforms either one of the aggregation methods, and that using as few as five probes can achieve over 95% accuracy in a timely manner.

An Evaluation of Environmental Benefits of Time-Dependent Green Routing in the Greater Buffalo–Niagara Region

This article conducts a realistic assessment, using a real-world case study, of the likely environmental benefits of an intelligent transportation systems (ITS) strategy that provides dynamic route guidance to travelers based on the lowest emission or fuel consumption routes. Several features distinguish this study from previous reported research. First, the research utilizes a realistic case study of a medium-sized metropolitan area in the United States. Second, the research applies the latest state-of-the-art models on both the transportation and the environmental modeling sides, through the development of an integrated model combining the Transportation Analysis and Simulation System (TRANSIMS) model and the Multi-Scale Motor Vehicle Emissions Simulator model (MOVES). The integrated model is then used to approximate “green user equilibrium,” and to investigate the impact of market penetration on the likely environmental benefits of green routing. Third, the article also proposes a “targeted” traveler selection strategy whereby travelers with the greatest potential to reduce emissions or fuel consumption are selected for rerouting. Results indicate that green routing could result in significant reductions in emissions, but that this naturally comes at the expense of an increased travel time. For one scenario that considered assuming a traffic stream of only passenger cars, green routing resulted in an almost 13% reduction in carbon monoxide (CO) emissions, and a corresponding 8% increase in travel time. The results also indicate that tangible emissions reductions are achievable at low to medium market penetration levels for green routing applications, especially when travelers with the largest likely emissions savings are targeted.