Integrating Traffic Operation With Emission Impact Using Dual-Loop Data
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Integrating Traffic Operation With Emission Impact Using Dual-Loop Data

Filetype[PDF-1.25 MB]


English
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  • Creators:
  • Corporate Creators:
    University of Cincinnati
  • Corporate Contributors:
    Ohio Transportation Consortium
  • Subject/TRT Terms:
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    Ohio
  • Corporate Publisher:
    Ohio Transportation Consortium
  • Abstract:
    "Transportation contributes great amount of green house gases and other pollutant emissions to the global environment. Localized analysis of on-road traffic source emissions is often required by the U.S. Environmental Protection Agency (EPA) for project-level conformity analysis of transportation projects in accordance with the Phase Implementation Plan (SIP). In public environmental health field, some healthy concerns have been addressed on their association with mobile source air pollution. However, studies on levels of exposures to mobile source emissions and their influence on children’s health obviously lack local micro-scale data to reflect the characterization of variability in emissions under various representative real-world vehicle activities and traffic conditions. The dual-loop data could be utilized as a rich data source for micro-scale emission study. However, little research has been reported on it and one of reasons lies in the incapability of producing accurate traffic inputs for the prevailing emission tool MOVES (Motor Vehicle Emission Simulator) by using current dual-loop models. In this study a methodological framework is developed for integrating the improved dual-loop models and the MOVES emission model to estimate the emission impact of traffic flow operation by utilizing the data source available at dual-loop monitoring stations in highways. Firstly, we apply the framework to eliminate dual-loop data errors by a dual-loop data screening algorithm. There are six filters in the dual-loop data screening algorithm, namely, 0-1 filter, minimum headway filter, pairing filter, sensitivity filter, on-time filter, and median speed filter. The screened dual-loop data is then processed by a traffic flow phase identification algorithm in order to prepare the dual-loop data for the MOVES inputs calculations. The traffic flow phase identification algorithm uses a hybrid model that incorporates the level-of-service based method and K-means clustering method to separate free flow records, free flow to synchronized flow transition records, synchronized flow records, and traffic jam records from the raw dataset. The processed dual-loop data, which carries traffic flow phase information, is used by the MOVES inputs calculation algorithm to generate traffic volume, vehicle composition and operating mode distribution for the MOVES emission analysis. Specifically, a vehicle specific power (VSP) based model is used along with vehicle acceleration data to determine the operating mode distribution in the algorithm. The presented framework is validated using video based vehicle trajectory data. The traffic parameters generated by the framework have the same pattern as it is revealed by the video based ground truth data. A case study is presented to demonstrate the application of the presented framework. It is found that the impact of traffic flow operation on vehicle emission along a specific roadway section can be associated with three quantified traffic flow variables, i.e., operating mode distribution, traffic volume, and traffic fleet composition. On the other hand, the data analysis shows that traffic flow phase can be mathematically featured with those traffic parameters. The connection between traffic operation and vehicle emission impact has been established through the application of the three traffic parameters. This study makes it feasible for a project level mobile source emission impact study to be performed by using microscopic real-world traffic data. In the future, more comprehensive ground truth data are needed to further validate the proposed methodology. It is also desirable to expand the methodology in order to take advantage of other similar traffic data sources such as radar data and video detection data sources."
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