Haobing Liu

Estimating Project-Level Vehicle Emissions with Vissim and MOVES-Matrix

Estimating transportation network emissions requires multiplying estimates of on-road vehicle activity (by source type and operating mode) by applicable emission rates for the observed source type and operating conditions. Coupling microsimulation model runs with emissions modeling can make fast assessments possible in transportation air quality planning. This research developed a tool with automated linkage between the Vissim microsimulation model and the Motor Vehicle Emission Simulator (MOVES) model. To link the two models, the research team used MOVES-Matrix, which was prepared by iteratively running MOVES across all possible iterations of vehicle source type, fuel, environmental and operating conditions, and other parameters (hundreds of millions of model runs) to create a multidimensional emission rate lookup matrix. A Vissim simulation of the major arterial roads and freeways at I-85 and Jimmy Carter Boulevard in Gwinnett County, Georgia, provided the case study for this MOVES-matrix application. The researchers present predicted emissions and the results of a sensitivity analysis to identify the potential impacts of various internal Vissim modeling parameters (such as minimum headway, maximum deceleration rate for cooperative braking, and emergency stop distance) on a case study’s emissions outputs. The sensitivity analysis found that internal Vissim parameters impacted emissions and that proper care should be taken in using Vissim for emissions analysis at the corridor and link level. The case study demonstrates that Vissim coupled with MOVES-Matrix can be an effective tool for emissions analysis.

Developing Vehicle Classification Inputs for Project-Level MOVES Analysis

The motor vehicle emission simulator (MOVES) model is the primary regulatory model for estimating automobile emissions in the United States. The model requires refined input data; otherwise, internal model assumptions that are not necessarily representative of the project being modeled can dominate the outputs. For example, project-level on-road fleet composition is highly dependent on local vehicle use; hence, MOVES default inputs and regional distributions are not likely to apply (and MOVES estimates for project-level analyses are especially sensitive to vehicle source type distribution). Unfortunately, developing project-level source type distributions can be challenging for model users. This research proposes a procedure for developing MOVES vehicle source type distribution inputs that uses the FHWA vehicle classification scheme, Environmental Protection Agency certification data, state registration data, along with on-road license plate and video data. A case study of I-85 near Atlanta, Georgia, is presented to illustrate the importance of distinguishing within light-duty vehicle classes for hydrocarbon and carbon monoxide estimations, and between the single-unit heavy-duty truck (HDT) and combination HDT classes for nitrogen oxide and particulate matter estimation. The analysis suggests that the most important work is to generate on-road distributions of HDTs with respect to single-unit and combination trucks rather than to use regional defaults. The case study results show the need for locally derived vehicle class inputs for MOVES for project-level analysis and calls for an alternative MOVES vehicle class input option that uses regulatory class distributions because the default vehicle class distribution embedded in MOVES may sometimes be unrealistic.

Integrating Engine Start, Soak, Evaporative, and Truck Hoteling Emissions into MOVES-Matrix

The MOVES (MOtor Vehicle Emissions Simulator) model was developed by the U.S. Environmental Protection Agency (USEPA) to estimate emissions from mobile sources and is required to be used for regional air quality planning and conformity analysis in all states except California. However, the MOVES interface is complicated, and assessing emissions from dynamic large-scale transportation networks can be difficult. To aid in these analyses, the MOVES-Matrix modeling tool was developed as an alternative to the direct application of the MOVES model. MOVES-Matrix employs a massive multidimensional array of MOVES outputs created by running MOVES with every allowable combination of input variables. Once this output array has been generated, subsequent energy and emissions analyses can be conducted quickly and dynamically. Until recently, MOVES-Matrix has only been used to analyze running exhaust. In this study, MOVES-Matrix has been used expanded to include emissions from engine starts, truck hoteling, evaporative sources, and brake/tire wear as well as running exhaust. A case study is conducted for the metropolitan Atlanta, GA to verify the feasibility of using this expanded version of MOVES-Matrix and to ensure that the approach obtains the exact same results as applying MOVES directly. The travel activity inputs come from regional travel data generated by the Atlanta Regional Commission’s Travel Demand Model. The emission results from MOVES-Matrix were compared with MOVES output to verify the equivalence of this approach.

Evaluation of Transit Ecodriving in Rural, Suburban, and Urban Environments

Ecodriving (also known as eco-driving) is a widely recognized strategy to reduce transit vehicle fuel consumption and emissions. Previous simulation modeling and field studies have demonstrated that ecodriving can reduce fuel consumption by 2%–27%. However, prior studies have typically focused on urban transit operations. More than 1700 transit agencies operate in rural areas within the United States, operating under very different conditions than urban transit systems. Assessing the variability of fuel savings across various transit operating conditions and physical terrain conditions will help transit agencies predict the potential benefits of deploying ecodriving strategies for their systems. The objective of this study is to assess the potential benefits and limitations of deploying ecodriving strategies for different transit services, service areas, fleet composition, and road topographies. There are three preliminary tasks involved in this study. The first element collects and processes the operation data across different transit services. Second-by-second operation data are collected from one urban, suburban, and rural transit agency. The second element evaluates the operation characteristics of each type of transit service. The third element assesses the potential benefits of introducing the transit ecodriving strategy to the operations of these fleets. The fuel consumption is evaluated by matching the second-by-second MOVES model operating mode bin with corresponding emission rates. Overall, the ecodriving strategy can help reduce 1%–5% fuel use across these agencies, and save

Energy Consumption and Emissions Modeling of Individual Vehicles

0.011–

MOVES-Matrix and distributed computing for microscale line source dispersion analysis

0.045 operational cost per mile due to the fuel saving. The actual benefits vary by service, mileage, and road grade impact.

Understanding the emission impacts of high-occupancy vehicle (HOV) to high-occupancy toll (HOT) lane conversions: Experience from Atlanta, Georgia

This study demonstrated an approach to modeling individual vehicle second-by-second fuel consumption and emissions on the basis of vehicle operations. The approach used the Motor Vehicle Emission Simulator (MOVES)–Matrix, a high-performance vehicle emissions modeling system consisting of a multidimensional array of vehicle emissions rates (pulled directly from EPA’s MOVES emissions model) that could be quickly queried by other models to generate an applicable emissions rate for any specified on-road fleet and operating conditions. For this project, the research team developed a spreadsheet-based MOVES-Matrix calculator to simplify connecting vehicle activity data with multidimensional emissions rates from MOVES-Matrix. This paper provides a walk-through of the calculation procedures, from basic vehicle information and driving cycles to second-by-second emissions rates. The individual vehicle emissions modeling framework was incorporated into Commute Warrior, a trademarked travel survey application for Android smartphones, to provide real-time fuel consumption and emissions rate estimates from concurrently obtained GPS-based speed data.

Assessment of alternative fuel and powertrain transit bus options using real-world operations data: Life-cycle fuel and emissions modeling

ABSTRACT MOVES and AERMOD are the U.S. Environmental Protection Agency’s recommended models for use in project-level transportation conformity and hot-spot analysis. However, the structure and algorithms involved in running MOVES make analyses cumbersome and time-consuming. Likewise, the modeling setup process, including extensive data requirements and required input formats, in AERMOD lead to a high potential for analysis error in dispersion modeling. This study presents a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix, a high-performance emission modeling tool, with the microscale dispersion models CALINE4 and AERMOD. MOVES-Matrix was prepared by iteratively running MOVES across all possible iterations of vehicle source-type, fuel, operating conditions, and environmental parameters to create a huge multi-dimensional emission rate lookup matrix. AERMOD and CALINE4 are connected with MOVES-Matrix in a distributed computing cluster using a series of Python scripts. This streamlined system built on MOVES-Matrix generates exactly the same emission rates and concentration results as using MOVES with AERMOD and CALINE4, but the approach is more than 200 times faster than using the MOVES graphical user interface. Because AERMOD requires detailed meteorological input, which is difficult to obtain, this study also recommends using CALINE4 as a screening tool for identifying the potential area that may exceed air quality standards before using AERMOD (and identifying areas that are exceedingly unlikely to exceed air quality standards). CALINE4 worst case method yields consistently higher concentration results than AERMOD for all comparisons in this paper, as expected given the nature of the meteorological data employed. Implications: The paper demonstrates a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix with the CALINE4 and AERMOD. This streamlined system generates exactly the same emission rates and concentration results as traditional way to use MOVES with AERMOD and CALINE4, which are regulatory models approved by the U.S. EPA for conformity analysis, but the approach is more than 200 times faster than implementing the MOVES model. We highlighted the potentially significant benefit of using CALINE4 as screening tool for identifying potential area that may exceeds air quality standards before using AERMOD, which requires much more meteorology input than CALINE4.

Eco-driving for transit: An effective strategy to conserve fuel and emissions

ABSTRACT Converting a congested high-occupancy vehicle (HOV) lane into a high-occupancy toll (HOT) lane is a viable option for improving travel time reliability for carpools and buses that use the managed lane. However, the emission impacts of HOV-to-HOT conversions are not well understood. The lack of emission impact quantification for HOT conversions creates a policy challenge for agencies making transportation funding choices. The goal of this paper is to evaluate the case study of before-and-after changes in vehicle emissions for the Atlanta, Georgia, I-85 HOV/HOT lane conversion project, implemented in October 2011. The analyses employed the Motor Vehicle Emission Simulator (MOVES) for project-level analysis with monitored changes in vehicle activity data collected by Georgia Tech researchers for the Georgia Department of Transportation (GDOT). During the quarterly field data collection from 2010 to 2012, more than 1.5 million license plates were observed and matched to vehicle class and age information using the vehicle registration database. The study also utilized the 20-sec, lane-specific traffic operations data from the Georgia NaviGAtor intelligent transportation system, as well as a direct feed of HOT lane usage data from the State Road and Tollway Authority (SRTA) managed lane system. As such, the analyses in this paper simultaneously assessed the impacts associated with changes in traffic volumes, on-road operating conditions, and fleet composition before and after the conversion. Both greenhouse gases and criteria pollutants were examined. Implications: A straight before-after analysis showed about 5% decrease in air pollutants and carbon dioxide (CO2). However, when the before-after calendar year of analysis was held constant (to account for the effect of 1 yr of fleet turnover), mass emissions at the analysis site during peak hours increased by as much as 17%, with little change in CO2. Further investigation revealed that a large percentage decrease in criteria pollutants in the straight before-after analysis was associated with a single calendar year change in MOVES. Hence, the Atlanta, Georgia, results suggest that an HOV-to-HOT conversion project may have increased mass emissions on the corridor. The results also showcase the importance of obtaining on-road data for emission impact assessment of HOV-to-HOT conversion projects.