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2024 Vol.42, Issue 3 Preview Page

Article

Development of Acceleration Correction Factors for Microscopic Analysis of Automobile Greenhouse Gas Emissions

미시적 수준 도로 수송부문 온실가스 배출량 산정을 위한 감가속 보정계수 개발

Daejin KIM1
,
Soongbong LEE2*
,
Daisik NAM1
,
Gwanyong OH3
,
Hanbyul RYU3
,
Jinjae KIM4
,
Seunghoon CHEON5
김 대진1
,
이 숭봉2*
,
남 대식1
,
오 관용3
,
류 한별3
,
김 진재4
,
천 승훈5
1Assistant Professor, Asia Pacific School of Logistics/Graduate School of Logistics, Inha University, Incheon 22212, Korea
2Associate Research Fellow, Department of Transport Big Data, Korea Transport Institute, Sejong 30147, Korea
3Ph.D. Student, Graduate School of Logistics, Inha University, Incheon 22212, Korea
4Researcher, Department of Transport Big Data, Korea Transport Institute, Sejong 30147, Korea
5Research Fellow, Department of Transport Big Data, Korea Transport Institute, Sejong 30147, Korea
1인하대학교 아태물류학부/물류전문대학원 조교수
2한국교통연구원 AI·빅데이터플랫폼연구팀 부연구위원
3인하대학교 물류전문대학원 박사과정
4한국교통연구원 AI·빅데이터플랫폼연구팀 연구원
5한국교통연구원 AI·빅데이터플랫폼연구팀 팀장
*Corresponding Author
30 June 2024. pp. 268-283
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Abstract

This study aims to develop acceleration correction factors for enhancing the precision of micro-level automobile greenhouse gas (GHG) emissions estimation. By employing MOVES-Matrix, a sophisticated automobile emissions calculation model, this study scrutinizes the sensitivity of GHG emissions to microscopic driving behaviors across various vehicle types. The study findings reveal a general decline in GHG emissions with increasing driving speeds, juxtaposed with an escalation as acceleration rates rise within the same speed range. In light of the results from the sensitivity analysis, the acceleration correction factors are devised for each of the 16 vehicle types, with additional refinements tailored for the generalized vehicle categories (i.e., passenger cars, buses, and freight trucks). Moreover, this study proposes a microscopic GHG emission calculation algorithm, integrating the acceleration correction factors. Scenario analyses demonstrate a discernible 6-15% disparity in GHG emissions upon the application of the acceleration correction factors compared to the scenarios where it is omitted, underscoring the substantive influence of microscopic vehicle driving behaviors, such as instant vehicle speed and acceleration rates, on GHG emissions. Consequently, this study is anticipated to afford a more precise estimation of GHG emissions, considering the microscopic driving behaviors of individual vehicles on roads. This study is anticipated to contribute to the enhancement of precision in estimating GHG emissions from transportation sector and the development of effective transportation policies and operational strategies aimed at mitigating GHG emissions.

Keywords
acceleration/deceleration correction factor
emission factor
greenhouse gas
microscopic analysis
MOVES-Matrix

본 연구는 미시적 수준의 도로 수송부문 온실가스 배출량 산정을 위해 자동차 온실가스 감가속 보정계수를 개발하고 적용 방안을 검토하였다. 본 연구는 미국 조지아공과대학 연구팀에서 개발된 고도화된 자동차 온실가스 배출량 산정 모형인 MOVES-Matrix를 활용하여, 세부 차종/유종별 감가속 주행행태에 따른 온실가스 배출량 민감도 분석을 수행하였다. 분석 결과, 온실가스 단위 배출량은 자동차의 주행속도가 증가함에 따라 전반적으로 감소하는 특성이 있으며, 동일 속도 범위에서 가속도가 증가할수록 증가하는 특성을 확인하였다. 온실가스 배출량 민감도 분석 결과를 바탕으로 세부 16개 차종/유종별 온실가스 감가속 보정계수를 개발하였으며, 국내 교통정보 수집 시스템의 한계를 감안하여 승용차, 버스, 화물차 3개의 차종별 온실가스 감가속 보정계수를 추가적으로 개발하였다. 본 연구는 온실가스 감가속 보정계수를 고려한 미시적 수준의 온실가스 배출량 산정 알고리즘을 제안하였다. 시나리오 분석 결과, 온실가스 감가속 보정계수 적용 시, 미 적용시 대비 약 6-15%의 온실가스 배출량이 차이가 있음을 확인하였다. 이를 통해, 개별 차량의 주행행태(속도, 감가속도 등)가 온실가스 배출량에 상당한 영향력이 있음을 확인할 수 있었다. 본 연구를 통해 도로 구간별 개별 차량의 미시적 주행행태를 반영한 온실가스 배출량의 민감도를 보다 정밀하게 반영할 수 있을 것으로 기대된다. 특히 감가속 주행행태가 빈번히 발생하는 단속류 및 일부 연속류 구간에 대한 정밀한 온실가스 배출량 산정 및 온실가스 배출 저감을 위한 교통운영 전략를 수립하고, 그 효과를 예측하는 데 도움이 될 것으로 기대된다.

키워드
가감속 보정계수
배출계수
온실가스
미시적 분석
MOVES-Matrix
References
1

Ali M., Kamal M. D., Tahir A., Atif S. (2021), Fuel Consumption Monitoring through COPERT Model-A Case Study for Urban Sustainability, Sustainability, 13(21), 11614.

10.3390/su132111614
2

Government of the Republic of Korea (2020), Driving Strategies for 2050 Carbon Neutralization, 1.

3

Greenhouse Gas Inventory and Research Center (2022), 2021 National Greenhouse Gas Inventory Report of Korea, 132.

4

Guensler R., Liu H., Xu X., Xu Y. Rodgers M. O. (2016), MOVES-Matrix: Setup, Implementation, and Application (No.16-6362), Presented at 95th Annual Meeting of the Transportation Research Board, Washington, D.C.

5

Guensler R., Liu H., Xu Y., Akanser A., Kim D., Hunter M. P. et al. (2017), Energy Consumption and Emissions Modeling of Individual Vehicles, Transportation Research Record, 2627(1), 93-102.

10.3141/2627-11
6

Han T., Ko J., Kim D., Park J. (2022), Predicting Carbon Dioxide Emissions of Incoming Traffic Flow at Signalized Intersections by using Image Detector Data, The Journal of The Korea Institute of Intelligent Transport Systems, 21(6), 115-131.

10.12815/kits.2022.21.6.115
7

Hu H., Yoon C., Lee T., Yang I., Sung J. (2013), Instantaneous GHG Emission Estimation Method Considering Vehicle Characteristics in Korea, J. Korean Soc. Transp., 31(6), Korean Society of Transportation, 90-105.

10.7470/jkst.2013.31.6.090
8

Intergovernmental Panel on Climate Change (IPCC) (2022), Climate Change 2022: Mitigation of Climate Change, 252.

10.1017/9781009157926
9

Jang S., Wu S., Kim D., Song K. H., Lee S. M., Suh W. (2022), Impact of Lowering Speed Limit on Urban Transportation Network, Applied Sciences, 12(11), 5296.

10.3390/app12115296
10

Kim D. (2020), Large-Scale, Dynamic, Microscopic Simulation for Region-Wide Line Source Dispersion Modeling, Georgia Institute of Technology, Doctoral Dissertation.

11

Kim D., Ko J., Xu X., Liu H., Rodgers M. O., Guensler R. (2019), Evaluating the Environmental Benefits of Median Bus Lanes: Microscopic Simulation Approach, Transportation Research Record, 2673(4), 663-673.

10.1177/0361198119836982
12

Kim D., Liu H., Xu X., Lu H., Wayson R., Rodgers M. O. et al. (2021), Distributed Computing for Region‐Wide Line Source Dispersion Modeling, Computer‐Aided Civil and Infrastructure Engineering, 36(3), 331-345.

10.1111/mice.12639
13

Kim M. (2016), A Study on the Effects of the Car Idling Control on Greenhouse Gas Emission Reduction. Dong-A University, Master's Thesis.

10.15531/ksccr.2016.7.3.335
14

Koo B. (2013), A Study on the Estimation of Vehicle CO2 Emission using RFID, Sungkyunkwan University, Master's Thesis.

15

Korea Transportation Safety Authority (KTSA) (2021), 2021 Approved National Greenhouse Gas Emission and Absorption Factors. https://www.gir.go.kr/home/board/read.do?menuId=36&boardId=56&boardMasterId=2 (Accessed: Feb 20, 2024).

16

Liu H., Guensler R., Lu H., Xu Y., Xu X., Rodgers M. O. (2019a), MOVES-Matrix for High-performance On-road Energy and Running Emission Rate Modeling Applications, Journal of the Air & Waste Management Association, 69(12), 1415-1428.

10.1080/10962247.2019.164080631291170
17

Liu H., Kim D. (2019), Simulating the Uncertain Environmental Impact of Freight Truck Shifting Programs, Atmospheric Environment, 214, 116847.

10.1016/j.atmosenv.2019.116847
18

Liu H., Rodgers M. O., Guensler R. (2019b), Impact of Road Grade on Vehicle Speed-Acceleration Distribution, Emissions and Dispersion Modeling on Freeways, Transportation Research Part D: Transport and Environment, 69, 107-122.

10.1016/j.trd.2019.01.028
19

National Association of Bus Transportation Operators (2022), 2022 Bus Statistics Yearly Book, https://www.bus.or.kr/ (Accessed: Nov 20, 2023)

20

National Institute of Environmental Research (NIER) (2008), Establishment of Climate Change Responding System for Transportation Sector (I): Development of Bottom-up Type GHGs Emission Factor for Vehicles, NIER No. 2008-27-977.

21

National Institute of Environmental Research (NIER) (2009), Establishment of Climate Change Responding System for Transportation Sector (Ⅱ): Development of Bottom-up Type GHGs Emission Factor for Vehicles, NIER No. 2009-10-1066.

22

National Institute of Environmental Research (NIER) (2010), Establishment of Climate Change Responding System for Transportation Sector (Ⅲ): Development of Bottom-up Type GHGs Emission Factor for Vehicles, NIER No. 2010-64-1239.

23

National Institute of Environmental Research (NIER) (2011), Establishment of Climate Change Responding System for Transportation Sector (Ⅳ): Development of Bottom-up Type Pollutant & GHGs Emission Factor for Vehicles, NIER-RP2011-1306.

24

National Institute of Environmental Research (NIER) (2012), Establishment of Climate Change Responding System for Transportation Sector (Ⅴ): Development of Pollutant & GHGs Emission Factors by bottom-up approach, NIER-RP2012-204.

25

Smit R., Awadallah M., Bagheri S., Surawski N. C. (2022), Real-world Emission Factors for SUVs using On-board Emission Testing and Geo-computation, Transportation Research Part D: Transport and Environment, 107, 103286.

10.1016/j.trd.2022.103286
26

Transportation Statistics Annals (2023), Total Registered Motor Vehicles. https://stat.molit.go.kr/portal/cate/statMetaView.do?hRsId=58 (Accessed: Nov 20, 2023)

27

United States Environmental Protection Agency (USEPA) (2023), Dynamometer Drive Schedules. https://www.epa. gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules (Accessed: Feb 20, 2024).

28

United States Environmental Protection Agency (USEPA) (2024), Previous MOVES Versions and Documentation. https://www.epa.gov/moves/previous-moves-versions-and-documentation (Accessed: Feb 20, 2024).

29

Xu X., Liu H., Anderson J. M., Xu Y., Hunter M. P., Rodgers M. O. et al. (2016) Estimating Project-level Vehicle Emissions with VISSIM and MOVES-Matrix, Transportation Research Record, 2570(1), 107-117.

10.3141/2570-12
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 42
  • No :3
  • Pages :268-283
  • Received Date : 2024-03-05
  • Revised Date : 2024-04-18
  • Accepted Date : 2024-05-21
  • DOI :https://doi.org/10.7470/jkst.2024.42.3.268
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