Harikishan Perugu

Sensitivity Analysis of Project Level MOVES Running Emission Rates for Light and Heavy Duty Vehicles

Abstract: In order to understand how the uncertainties in the output can be apportioned to different sources of uncertainties in its inputs, it is critical to investigate the sensitivity of MOVES model. The MOVES model sensitivity for regional level has been well studied. However, the uncertainty analysis for project level running emissions has not been well understood. In this research, the MOVES model project level sensitivity tests on running emissions were conducted thru the analysis of vehicle specific power (VSP), scaled tractive power (STP), and MOVES emission rates versus speed curves. This study tested the speed, acceleration, and grade-three most critical variables for vehicle specific power for light duty vehicles and scaled tractive power for heavy duty vehicles. For the testing of STP, four regulatory classes of heavy duty vehicles including light heavy duty (LHD), medium heavy duty (MHD), heavy heavy duty (HHD) and bus were selected. MOVES project running emission rates were also tested for CO, PM2. 5, NOx and VOC versus the operating speeds. A Latin Hypercube (LH) sampling based on method for estimation of the “Sobal” sensitivity indices shows that the speed is the most critical variable among the three inputs for both VSP and STP. Acceleration and grades show lower response to the main effects and sensitivity indices. MOVES emission rates versus speeds curves for light duty vehicles show that highest emission occurs at lower speed range. No significant differences on emission rates among the regulatory classes of heavy duty vehicles are identified.

Modeling Roadway Link PM2.5 Emissions with Accurate Truck Activity Estimate for Regional Transportation Conformity Analysis

The impact of fine particulate matter (PM) on public health has long been a concern. The primary mobile sources of fine (PM) (PM2.5) are diesel trucks. In practice, accurate roadway link-based modeling of the truck emissions remains a major challenge because of aggregated and unreliable truck activity data. The advanced emission model MOVES has been recommended by the U.S. Environmental Protection Agency for estimating emission factors, but supplying accurate and detailed truck activity-related inputs has become another challenge. Daily truck traffic activity is usually not estimated accurately and cannot be disaggregated to hourly activity with traditional methods. To address this problem, two innovative econometric methods were successfully enhanced in this study to predict accurate truck activity-based inputs for emission estimation. The models for truck factor spatial panel and multinomial probit hourly vehicle miles traveled were improved and tested with regional traffic data from the greater Cincinnati, Ohio, area. The application of those models indicates that using MOVES default input data underestimates the regional PM2.5 inventory. The proposed methodology enables plotting the spatiotemporal distribution of PM2.5 emissions in a subarea. Such an integrated method provides a useful decision support tool for practitioners because they can also model PM2.5 emissions at a detailed level as required by project-level conformity analysis. The methodology presented is scalable and transferable and holds technical promise in its application to different regions and different pollutants.

Oversaturation Inherence and Traffic Diversion Effect at Urban Intersections through Simulation

Abstract Conventional approaches of signal control optimization and geometric channelization may not be an inappropriate solution to an oversaturated urban intersection, especially where physical expansion is limited by the intensive land-use development around it. Traffic diversion strategy may be an optional solution; however, it is greatly dependant upon identification of the traffic movements that need diversion and the existence of parallel or accessible detour routes that are capable of accommodating the diverted traffic without bring adverse impact on the diversion routes. Obviously, these factors are difficult to be determined through analysis of the field observations alone. This paper presents a study of using simulation-based approach combined with analysis of observed data to identify oversaturated intersection movements needed for diversion and test the traffic diversion effect in the roadways that have potential to bear the diverted traffic. The proposed approach is discussed through a practical case study at the intersection of Galbraith Avenue and Colerain Avenue (US-27), Ohio. The effect of such option is evaluated using a state-of-the-art microscopic traffic simulation system VISSIM, intersection analysis software programs, Highway Capacity Software (HCS 2000), and Synchro. The optimum percentage of traffic that is desired to be diverted at the inlet to minimize the delay while keeping shorter travel time is evaluated for the concerned intersection. The evaluation results imply a successful option that has great potential to put into practice. Finally, signage countermeasures are discussed for implementing the diversion strategy.

A Heuristic Approach for Quantifying Household Travel GHG Emissions Using GPS Survey and Spatial Correlations-The Cincinnati Case Study

Household travel related Greenhouse Gas (GHG) emissions have been identified as one of the major contributors to greenhouse gas emissions. Many studies have suggested that household trips and their associated GHG footprints are pertinent in great part to land use type and socioeconomic of the household. The current practice of GHGs emission laws and regulations recommend using outputs from travel demand model for GHG and other regulated emission analysis. Conventional travel demand forecasting models are aimed at conducting a macroscopic simulation analysis at an area or regional level of the roadway network but it is unable to generate traffic flow operational data at a microscopic level such as speed, acceleration or deceleration at a fine spatiotemporal scale. On the other hand, the household travel GHG emissions, similar to the household location itself, are spatially and temporally dependent. The spatial factors’ role in the modeling of the household travel GHG footprint is unclear. To address the above gaps, this research proposes a robust household travel GHG quantification method with spatial information considered. By utilizing the greater Cincinnati GPS household travel survey data, household travel is accurately mapped to its origin and linked to the household’s socio-economics and demographic characteristics. The regional traffic analysis zone-based GHG emissions generated from the sampled households are, therefore, spatially modeled by using spatial regression models that originated from econometrics. The results showed that the Spatial Durbin Error model fits the data better comparing to other candidate models.

Accurate Truck Activity Estimate for Roadway Link PM2.5 Emissions

Since diesel truck traffic is a major transportation on-road source of particulate matter (PM2.5), roadway link-based modeling of the truck emissions greatly rely on accurate estimate of truck fractions of traffic volumes as partial input to the MOVES model. However, present aggregated traffic volume and unreliable truck activity data provided from todays practice are obviously a concern in estimating the truck-traffic-source emission. The daily truck traffic activity is usually not estimated accurately and cannot be disaggregated to hourly activity using the traditional methods. To address this problem, two innovative econometric methods have been successfully enhanced in this study to enable accurate truck activity based inputs for the emission estimation. The truck factor spatial panel model (TFSP) and multinomial probit hourly VMT (MNP-HVMT) models have been improved and tested using the Greater Cincinnati areas regional traffic data. The application of those models indicates that using MOVES default input data underestimates the regional PM2.5 inventory. The proposed methodology also enables plotting the spatiotemporal distribution of PM2.5 emissions in a subarea. Such an integrated method provides a very useful decision support tool for practitioners since they can also model PM2.5 emissions at a detailed level as required by project-level conformity analysis. The presented methodology is scalable and transferable and holds technical promise in its application for different regions and for different pollutants.