Zhuo Yao

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.

Developing Operating Mode Distribution Inputs for MOVES with a Computer Vision-Based Vehicle Data Collector

Acquisition of reliable vehicle activity inputs to the U.S. Environmental Protection Agencys MOVES (Motor Vehicle Emission Simulator) model is necessary for maximizing modeling capacity and helping federal and state officials improve the quality of transportation management. For this purpose, rapid and low-cost collection of the operating mode distribution and other traffic activity data for the MOVES model is necessary. In this study, a computer vision–based software tool, Rapid Traffic Emission and Energy Consumption Analysis (REMCAN), is developed to enable a rapid operating mode distribution profiling for the MOVES model. The video-based system provides traffic activity inputs, including vehicle speeds and acceleration and deceleration rates covering the entire vehicle fleet; these may be difficult to extract from traffic data collected by traditional methods. The REMCAN system architecture and vehicle parameter extraction methods are presented. The speed measurement, which is the most critical factor for operating mode profiling, is calibrated with a coefficient that converts screen space to real-world space. Three case studies with different traffic operation scenarios are tested to demonstrate the capability of the REMCAN system. The integration of REMCAN traffic activity data collection and MOVES operating mode distribution generation provides timely, low-cost, and accurate environmental impact assessment compared with traditional data sources for emission estimation analysis.

Estimating Emission Impact of Traffic Flow Operation with Dual-loop Data

While many regional level studies have been reported, project level studies are limited because of lacking microscopic traffic parameters revealing transportation emission changes under varied real-world traffic operations. This paper presents a Vehicle Specific Power (VSP) based model for estimating the emission impact of traffic flow over dual-loop monitoring stations in highways. An innovative algorithm is developed to generate traffic volume, vehicle composition and operating mode distribution from dual-loop data. Those parameters are contributing variables in the VSP based emission model and their accuracy is tested using video-based ground-truth data. A case study using the presented model on an I-71 expressway section shows that traffic flow state is a function of operating mode distribution, traffic volume and vehicle composition. These three traffic parameters dominate mobile source emissions along the specific road section studied. The VSP based model provides a linkage between traffic operation and vehicle emission impact analysis. The presented methodology makes it possible for project level mobile source emission impact studies to be performed using the prevailing microscopic real-world traffic data.

Identifying Characteristics of Freeway Traffic Headway by Vehicle Types Using Video Trajectory Data

The significance of vehicle-type-specific headway studies lies in the application of freeway capacity and safety analysis. However, data reliability plays an important role in the ability to understand the characteristics and microscopic modeling of freeway traffic. This study takes advantage of observation-based video data by using data extracting software VEVID to retrieve reliable vehicle trajectory data. The study focused on uniqueness of vehicle-type-specific headway under specific traffic states, as well as mixed headway. Data examination showed significant differences in vehicle-type-specific headways under average traffic conditions, uncongested flow and congested flow. Headways tend to be minimal in uncongested traffic flow, and maximal in congested traffic flow. In addition, the degree of discreteness by vehicle type headway differs significantly under different traffic conditions. Noticeably, in congested traffic, all types of headways tend to be less than reported in literature review results. In particular, Truck-Car and Truck-Truck headways behave considerably differently when traffic shifts between congested and uncongested conditions. The practical applications of these findings including improvement of microscopic car-following models, traffic state identification and vehicle classification.

Optimized Advance Detector Configuration for Option Zone Protection at High-Speed Signalized Intersections

Advance detection and green extension schemes are widely applied in practice as a typical solution to the safety issues associated with the intersection dilemma zone problem. Most existing detector configurations either were based on the traditional Type I dilemma zone model, in which some critical contributing factors were assumed static, or were based on generic Type II dilemma zones. A comparison analysis based on field-observed trajectory data showed that the option zone model estimated the location of a dilemma zone most accurately of all available dilemma zone models. Recent research on the quantitative modeling of the contributing factors of the option zone made accurate identification of the option zone locations possible. Accurate identification lays a solid foundation for an option zone–based detection scheme for achieving the most effective and efficient dilemma zone protection. An alternative advance detector con-figuration is presented for option zone protection via optimization trials within a calibrated VISSIM simulation model. The optimization objective was to minimize the combined cost of dilemma hazard (safety) and delay (mobility). Dilemma conflict potential, a comprehensive dilemma hazard model, was used to measure the safety performance quantitatively and replaced the traditional measure of number of vehicles in the dilemma zone. The optimal configuration was evaluated and validated through its comparison with four widely applied detector configurations. The results revealed the superiority of the developed optimal detector configuration in terms of the best safety performance and the least combined cost of dilemma hazard and delay of all configurations.

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.

Validating MOVES PM2.5 Emission Factor Empirically by Considering Accumulative Emissions Effect

Estimating the short-term PM2.5 emission factor is a critical subject in analyzing the PM2.5 emission impact of traffic activities and population exposure level to the vehicle emission in the ambience of roadways that bear heavy-duty traffic. In applications of the Motor Vehicle Emission Simulator (MOVES) model, finding out an effective way to validate the MOVES model with respect to short-term emission estimation remains a challenge to practitioners. In order to address this issue, the PM2.5 emission factor in 1-minute intervals is estimated by the MOVES model based on the observed traffic data. On the other hand, the realistic emission factor (or termed as the ground-truth factor) is determined from the monitored minute-by-minute PM2.5 concentration data and its concurrent meteorological data. The validation procedure is undertaken by comparing the estimated emission factor with the ground-truth factor data. The testing result indicates a major inconsistency in the existence of the two datasets. The ground-truth emission factor is found to be about 30 to 50 times larger than the MOVES result. The inconsistency is possibly caused due to neglect of the accumulative effect of the traffic emissions over time. To overcome such an underestimation issue in application of the MOVES model, a modeling methodology is developed in the presented study to take into account the accumulative emission effect. The result from the case study shows a significant increase in the accuracy of estimating the short-term PM2.5 emission factor. The presented modeling methodology lays out a great foundation to develop a computing tool to improve the MOVES estimations.

Modeling ITS data sources for generating realistic traffic operating parameters for project-level conformity analysis

The challenge in characterizing on-road traffic source emissions affected by traffic management or control measures is often the lack of the realistic traffic flow data at the microscopic level for the project-level transportation conformity analysis. With the advancement of Intelligent Transportation Systems (ITS) technologies, more and more ITS devices are deployed for monitoring traffic. Those ITS devices provide promising data sources for inputs to the emission models for the project level analysis. Taking advantage of those data sources, this paper presents an integrated framework to facilitate the on-road transportation emission estimation under various traffic operations which are influenced by the traffic management and control measures. The implementation of the framework is demonstrated by applying the three components of the framework, i.e., traffic flow phase identification module, vehicle classification module and MOVES (Motor Vehicle Emission Simulator) analysis module, in a case study where inductive loop data are utilized. The information provided by the framework can help traffic operation agency improve or develop efficient traffic management or control measures for energy saving and environment protection.