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Estimating Regional Air Quality Vehicle Emission Inventories: Constructing Robust Driving Cycles

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  • Jie Lin

    (Center for the Environment, Harvard University, 19 Oxford Street, Cambridge, Massachusetts 02138)

  • Debbie A. Niemeier

    (Department of Civil and Environmental Engineering, One Shields Avenue, University of California, Davis, California 95616)

Abstract

Mobile emission inventories are constructed by multiplying a pollutant emission factor by a travel activity (e.g., number of trips, vehicle miles traveled, etc.). To create emission rates, vehicles are tested on dynamometers using driving cycles, or speed-time traces. The process currently used to create the driving cycles is deterministic. However, if we examine the data and data collection techniques closely, it is clear that an observed speed, v(t) , represents one of the many possible values that true speed, V(t) , may take on at a given time t . With an ordered set of random variables { V ( t )} and associated probability distributions, driving cycles should be defined by a stochastic process. In this study, we propose a new approach for constructing driving cycles using Markov process theory. The new approach not only provides an important statistical foundation for drive cycle estimation, it also overcomes several key limitations of the current driving cycle construction methodologies. For example, we use a maximum likelihood estimation (MLE) partitioning algorithm that enables us to associate a segment with a specific modal operating condition, (e.g., cruise, idle, acceleration, or deceleration), which, in turn, preserves finely resolved driving variability. We apply the new method to the data used to construct EPA's new regulatory facility-specific driving cycles. Comparisons with these cycles indicate relatively similar global results (e.g., average speeds) under uncongested conditions. However, the new cycles tend to contain a higher frequency of small scale acceleration and deceleration modal events than are represented in the EPA cycles. For congested conditions, in addition to greater frequencies of acceleration and deceleration modal events, the new cycles tend to have higher speeds and harder accelerations. Overall, the improvements in the new method represent significant advances in the development of stochastic driving cycle construction methods.

Suggested Citation

  • Jie Lin & Debbie A. Niemeier, 2003. "Estimating Regional Air Quality Vehicle Emission Inventories: Constructing Robust Driving Cycles," Transportation Science, INFORMS, vol. 37(3), pages 330-346, August.
  • Handle: RePEc:inm:ortrsc:v:37:y:2003:i:3:p:330-346
    DOI: 10.1287/trsc.37.3.330.16045
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    References listed on IDEAS

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    1. Holmén, Britt & Niemeier, Debbie, 1998. "Characterizing the Effects of Driver Variability on Real-World Vehicle Emissions," Institute of Transportation Studies, Working Paper Series qt5bc5c8dk, Institute of Transportation Studies, UC Davis.
    2. MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-198, January.
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    Cited by:

    1. Cui, Yuepeng & Zou, Fumin & Xu, Hao & Chen, Zhihui & Gong, Kuangmin, 2022. "A novel optimization-based method to develop representative driving cycle in various driving conditions," Energy, Elsevier, vol. 247(C).
    2. Iwona Komorska & Andrzej Puchalski & Andrzej Niewczas & Marcin Ślęzak & Tomasz Szczepański, 2021. "Adaptive Driving Cycles of EVs for Reducing Energy Consumption," Energies, MDPI, vol. 14(9), pages 1-18, May.
    3. Jiankun Peng & Jiwan Jiang & Fan Ding & Huachun Tan, 2020. "Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China," Sustainability, MDPI, vol. 12(17), pages 1-19, September.

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