Integrating Traffic Data Sources and Advanced Data Collection Technologies for Facilitating Project-Level Transportation Conformity Analysis
When applying MOVES (MOtor Vehicle Emission Simulator) in project-level PM (particulate matter) conformity analysis, link-based traffic characteristics are required at local roadway level so as to ensure realistically representation of the local scale air quality modeling assessments. Fractions of the link traffic volume and average speed are represented by each vehicle type, which is defined as an emission source type in MOVES. Heavy truck traffic has been recognized one of the major PM emission sources (PM2.5 or PM10), and on-road traffic source emissions vary with traffic operation conditions (e.g., speed, acceleration or deceleration) and fleet compositions (i.e., percentage of vehicle types in traffic streams). Operating mode distribution is one of critical variables representing the attribute of a link traffic activity. However, locally acquiring accurate fleet composition and relevant traffic operation data is always a challenge at various categories of roadways. And also little practical experiences are reported on MOVES model calibration and validation of the link-based emission factors. No outcomes from traffic simulation models are viewed reliable unless passing the calibration and validation tests. The goal of the proposed research is to explore and develop an integrated method and relevant models to extract required datasets from the existing traffic data sources and/or using advanced data collection techniques to be compatible with the project-level transportation conformity analysis and validation need. The following research activities are proposed: 1. To investigate applied traffic data sources and collection techniques applied in a number of selected states in the US for all possible categories of roadway infrastructures. Two major types of data courses will be investigated: 1) data sources and technologies for generating transportation activity relevant inputs for the project-level PM2.5 conformity by using MOVES, and 2) data sources and techniques for generating required data for validations of MOVES and traffic simulation model. Major traffic flow characteristics, such as traffic flow speed, vehicle classification, acceleration/deceleration, and road slope as well, are required to be obtained simultaneously for concerned periods of time. Data collection techniques that are commonly used in different states will be investigated and identified to be suitable for the conformity analysis. The techniques may include manual, portable (road tube), loop, video detection, and permanent Automatic Traffic Recorders (ATR), cellular movement data, and other intelligent transportation systems (ITS) methods as well. 2. Models and algorithms for generating data from the identified data sources to be easily applicable to project-level conformity analysis will be developed based on the result from the above activity. A computing aid tool will then be developed to generate the transportation activity relevant datasets from the identified data sources and/or by applying the identified data collection technologies, or apply simulation-based operational data projection functionality to forecast required traffic operational data for all principle categories of roadway infrastructures. 3. Model validation procedure will be recommended for applying the identified traffic data and emission monitoring data along with microscopic traffic simulation for project-level transportation conformity analysis to improve the accuracy of local scale air quality modeling assessments. The case study sites will be selected in a nonattainment and maintenance (N&M) area. The PM2.5 N&M areas are identified through evaluating regional level data acquired at a local metropolitan planning organization (MPO) and county environmental service agency. Traffic data and PM2.5 as well as meteorological data will be collected simultaneously at the study sites. Proper design of applying the identified traffic data collection technique and/or data sources will be involved in this activity.
The research result will better clarify the localized traffic data barrier in project-level conformity analysis and provide a proof-of-concept framework for developing solutions by taking advantage of the existing traffic data sources and collection techniques. More significantly, a real-world case study for validating MOVES and traffic simulation models in an integrated manner will be provided to facilitate the application of the involved traffic and emission related models in the project-level conformity analysis. Convincible model validation procedure is a key factor to ensure the reliability and quality control of conformity analysis when MOVES and traffic simulation models are applied. Finally, recommendation about utilizing regional level data as part of project-level analysis will be expected to maximize the data sources at traffic monitoring stations and environmental services while reducing the data collection burden at the microscopic level.
The University of Cincinnati
June 17, 2014
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