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A Novel Method for Traffic Estimation and Air Quality Assessment in California

Author

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  • Jucheol Moon

    (Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA)

  • Jin Gi Hong

    (Department of Civil Engineering and Construction Engineering Management, California State University, Long Beach, CA 90840, USA)

  • Tae-Won Park

    (Department of Earth Science Education, Chonnam National University, Gwangju 61186, Korea)

Abstract

Motor vehicle traffic is recognized as one of the critical factors that causes air pollution; however, the relationship between traffic volume and air pollutant concentrations is unclear, especially at a local level. Traditional traffic volume monitoring systems collect traffic data through counting the number of vehicles, using either sensors or surveillance cameras, but they have clear limitations such as they can only monitor certain areas and specific occasions. To overcome such limitations, we introduce a method of monitoring traffic volume in the local area by collecting estimated travel times for virtual trips in Google Maps. We began collecting the data in January 2020; ironically, the COVID-19 pandemic provided a natural experimental environment of showing unusual trends in traffic volume and unexpected changes in air pollutants. We demonstrate monthly traffic volumes in urban areas and analyze the correlation of traffic volume with typical traffic-related air pollutants using the proposed traffic volume monitoring method.

Suggested Citation

  • Jucheol Moon & Jin Gi Hong & Tae-Won Park, 2022. "A Novel Method for Traffic Estimation and Air Quality Assessment in California," Sustainability, MDPI, vol. 14(15), pages 1-12, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9169-:d:872272
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    References listed on IDEAS

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    1. Sorensen, Paul, 2009. "Moving Los Angeles," University of California Transportation Center, Working Papers qt3zj4m1kf, University of California Transportation Center.
    2. Lee, Munseob & Finerman, Rachel, 2021. "COVID-19, commuting flows, and air quality," Journal of Asian Economics, Elsevier, vol. 77(C).
    3. Noor Ullah Khan & Munam Ali Shah & Carsten Maple & Ejaz Ahmed & Nabeel Asghar, 2022. "Traffic Flow Prediction: An Intelligent Scheme for Forecasting Traffic Flow Using Air Pollution Data in Smart Cities with Bagging Ensemble," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    4. Corinne Le Quéré & Robert B. Jackson & Matthew W. Jones & Adam J. P. Smith & Sam Abernethy & Robbie M. Andrew & Anthony J. De-Gol & David R. Willis & Yuli Shan & Josep G. Canadell & Pierre Friedlingst, 2020. "Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement," Nature Climate Change, Nature, vol. 10(7), pages 647-653, July.
    5. Shi An & Lina Ma & Jian Wang, 2020. "Optimization of Traffic Detector Layout Based on Complex Network Theory," Sustainability, MDPI, vol. 12(5), pages 1-22, March.
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    Cited by:

    1. Jun Zhang & Shenghao Zhao & Chaonan Peng & Xianming Gong, 2022. "Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    2. Fangchen Ma & Jinliang Xu & Chao Gao & Yufeng Bi, 2022. "Study on the Distribution of the Suburban Expressway Hourly Traffic Volume and Modification of the Design Hourly Volume under the Background of the Tourism Economy—Analysis on Design Factors of Normal," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    3. Promporn Sornsoongnern & Suthatip Pueboobpaphan & Rattaphol Pueboobpaphan, 2023. "Innovative Dynamic Queue-Length Estimation Using Google Maps Color-Code Data," Sustainability, MDPI, vol. 15(4), pages 1-15, February.

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