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Road-Section-Based Analysis of Vehicle Emissions and Energy Consumption

Author

Listed:
  • Sunhee Jang

    (Department of Smart City Engineering, Hanyang University ERICA Campus, Ansan 15588, Republic of Korea)

  • Ki-Han Song

    (Department of Civil Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea)

  • Daejin Kim

    (Asia Pacific School of Logistics, Graduate School of Logistics, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea)

  • Joonho Ko

    (Graduate School of Urban Studies, Hanyang University, Seoul 04763, Republic of Korea)

  • Seongkwan Mark Lee

    (College of Engineering, United Arab Emirates University, Al Ain, Abu Dhabi 15551, United Arab Emirates)

  • Sabeur Elkosantini

    (SMART Lab, Faculty of Economics and Management, University of Carthage, Carthage 1054, Tunisia)

  • Wonho Suh

    (Department of Smart City Engineering, Hanyang University ERICA Campus, Ansan 15588, Republic of Korea)

Abstract

To monitor air pollution on roads in urban areas, it is necessary to accurately estimate emissions from vehicles. For this purpose, vehicle emission estimation models have been developed. Vehicle emission estimation models are categorized into macroscopic models and microscopic models. While the calculation is simple, macroscopic models utilize the average speed of vehicles without accounting for the acceleration and deceleration of individual vehicles. Therefore, limitations exist in estimating accurate emissions when there are frequent changes in driving behavior. Microscopic emission estimation models overcome these limitations by utilizing the trajectory data of each vehicle. In this method, the total emissions in a road segment are calculated by adding together the emissions from individual vehicles. However, most research studies consider the total vehicle emissions in a road section without considering the difference in vehicle emissions at different locations of a selected road section. In this study, a road segment between two intersections was divided into sub-sections, and energy consumption and emission generation were analyzed. Since there are unique driving behaviors depending on the section of the road segment, energy consumption and emission generation patterns were identified. The findings of this study are expected to provide more detailed and quantitative data for better modeling of energy consumption and emissions in urban areas.

Suggested Citation

  • Sunhee Jang & Ki-Han Song & Daejin Kim & Joonho Ko & Seongkwan Mark Lee & Sabeur Elkosantini & Wonho Suh, 2023. "Road-Section-Based Analysis of Vehicle Emissions and Energy Consumption," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4421-:d:1085045
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    References listed on IDEAS

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    1. repec:cdl:itsrrp:qt5g29c7pp is not listed on IDEAS
    2. Nicolás Deschle & Ernst Jan van Ark & René van Gijlswijk & Robbert Janssen, 2022. "Impact of Signalized Intersections on CO 2 and NO x Emissions of Heavy Duty Vehicles," Energies, MDPI, vol. 15(3), pages 1-19, February.
    3. Ciyun Lin & Xiangyu Zhou & Dayong Wu & Bowen Gong, 2019. "Estimation of Emissions at Signalized Intersections Using an Improved MOVES Model with GPS Data," IJERPH, MDPI, vol. 16(19), pages 1-15, September.
    4. Tang, Tie-Qiao & Yi, Zhi-Yan & Lin, Qing-Feng, 2017. "Effects of signal light on the fuel consumption and emissions under car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 200-205.
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    1. Haiguang Zhao & Laihua Shi & Xiaoliu Xu & Jinshan Qiu & Lan Li & Junfang Wang & Wenhan Yu & Yunshan Ge, 2024. "Impact of Shortening Real Driving Emission (RDE) Test Trips on CO, NO X , and PN 10 Emissions from Different Vehicles," Sustainability, MDPI, vol. 16(21), pages 1-15, October.

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