The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Predicting Carbon Dioxide Emissions of Incoming Traffic Flow at Signalized Intersections by Using Image Detector Data

영상검지자료를 활용한 신호교차로 접근차량의 탄소배출량 추정

  • Taekyung, Han (Dept. of Urban Planning & Eng., Hanyang Univ.) ;
  • Joonho, Ko (Graduate School of Urban Studies, Hanyang Univ.) ;
  • Daejin, Kim (Dept. of Urban Planning & Real Estate, Gangneung-Wonju National Univ.) ;
  • Jonghan, Park (Graduate School of Urban Studies, Hanyang Univ)
  • 한태경 (한양대학교 도시공학과) ;
  • 고준호 (한양대학교 도시대학원 도시.지역개발경영학과) ;
  • 김대진 (강릉원주대학교 도시계획.부동산학과) ;
  • 박종한 (한양대학교 도시대학원 도시.지역개발경영학과)
  • Received : 2022.09.21
  • Accepted : 2022.10.24
  • Published : 2022.12.31
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Abstract

Carbon dioxide (CO2) emissions from the transportation sector in South Korea accounts for 16.5% of all CO2 emissions, and road transportation accounts for 96.5% of this sector's emissions in South Korea. Hence, constant research is being carried out on methods to reduce CO2 emissions from this sector. With the emerging use of smart crossings, attempts to monitor individual vehicles are increasing. Moreover, the potential commercial deployment of autonomous vehicles increases the possibility of obtaining individual vehicle data. As such, CO2 emission research was conducted at five signalized intersections in the Gangnam District, Seoul, using data such as vehicle type, speed, acceleration, etc., obtained from image detectors located at each intersection. The collected data were then applied to the MOtor Vehicle Emission Simulator (MOVES)-Matrix model-which was developed to obtain second-by-second vehicle activity data and analyze daily CO2 emissions from the studied intersections. After analyzing two large and three small intersections, the results indicated that 3.1 metric tons of CO2 were emitted per day at each intersection. This study reveals a new possibility of analyzing CO2 emissions using actual individual vehicle data using an improved analysis model. This study also emphasizes the importance of more accurate CO2 emission analyses.

대한민국에서 수송으로 인해 발생하는 이산화탄소(CO2) 배출량은 전체의 16.5%이며, 이중 도로교통 부문이 96.5%를 차지한다. 따라서 도로교통 부문에서 발생하는 CO2 배출량을 줄이기 위한 노력이 계속되고 있다. 최근 스마트 교차로의 확대에 따라 교차로를 통과하는 개별차량에 대한 모니터링 기회가 점차 늘어나고 있으며, 장래 자율주행차 보급 확산에 따라 개별차량주행 자료 수집 가능성 또한 커지고 있다. 이에 맞춰 강남구 소재 5개 교차로에 설치된 영상검지기를 통해 얻은 차량의 차종, 교통량, 속도, 가속도와 같은 실제 데이터를 활용해 신호교차로에 접근하는 차량에 대한 CO2 배출 현황 조사를 실시했다. 이렇게 수집된 데이터를 초당 데이터를 분석하도록 개량된 MOVES-Matrix 모형에 대입해 2개 대형교차로와 3개 소형교차로에서 하루 동안 발생하는 CO2 배출량은 평균 3.1톤임을 확인했다. 본 연구는 실제 개별차량 데이터와 개량된 모형을 활용한 CO2 배출량 분석의 새로운 가능성을 보여주는 한편, CO2 배출량을 보다 더 정확히 조사해야 하는 필요성을 제시한다.

Keywords

Acknowledgement

이 논문은 2022년도 정부(경찰청)의 재원으로 과학치안진흥센터의 지원을 받아 수행된 연구임 (No.092021C28S01000, 자율주행 혼재 시 도로교통 통합관제시스템 및 운영기술 개발)

References

  1. Abou-Senna, H., Radwan, E., Westerlund, K. and Cooper, C. D.(2013), "Using a traffic simulation modek (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway", Journal of the Air & Waste Management Association, vol. 63, no. 7, pp.819-831. https://doi.org/10.1080/10962247.2013.795918
  2. Afrotey, B., Sattler, M., Mattingly, S. P. and Chen, V. C.(2013), "Statistical model for estimating carbon dioxide emission from a light-duty gasoline vehicle", Journal of Environmental Protection, vol. 04, no. 08, pp.8-15.
  3. Ahn, K., Rakha, H., Trani, A. and Van Aerde, M.(2002), "Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels", Journal of Transportation Engineering, vol. 128, no. 2, pp.182-190. https://doi.org/10.1061/(ASCE)0733-947X(2002)128:2(182)
  4. Chen, Z., Zhang, Y., Lv, J. and Zou, Y.(2014), "Model for optimization of Exodriving at signalized intersections", Transportation Research Record: Journal of the Transportation Research Board, vol. 2427, no. 1, pp.54-62. https://doi.org/10.3141/2427-06
  5. Coelho, M. C., Farias, T. L. and Rouphail, N. M.(2005), "Impact of speed control traffic signals on pollutant emissions", Transportation Research Part D: Transport and Environment, vol. 10, no. 4, pp.323-340. https://doi.org/10.1016/j.trd.2005.04.005
  6. Datondji, S. R., Dupuis, Y., Subirats, P. and Vasseur, P.(2016), "A survey of vision-based traffic monitoring of road intersections", IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 10, pp.2681-2698. https://doi.org/10.1109/TITS.2016.2530146
  7. Government of the Republic of Korea(2020), Driving Strategies for 2050 Carbon Neutralization, p.1.
  8. Greenhouse Gas Inventory and Research Center(2022), 2021 National Greenhouse Gas Inventory Report of Korea, p.132.
  9. Guensler, R., Liu, H., Xu, X., Xu, Y. and 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.
  10. Guensler, R., Liu, H., Xu, Y., Akanser, A., Kim, D., Hunter, M. P. and Rodgers, M. O.(2017), "Energy consumption and emissions modeling of individual vehicles", Transportation Research Record, vol. 2627, no. 1, pp.93-102. https://doi.org/10.3141/2627-11
  11. Han, D., Lee, Y. and Chang, H.(2011), "A Study of Calculation Methodology of Vehicle Emissions based on Driver Speed and Acceleration Behavior", Journal of Korean Society of Transportation, vol. 29, no. 5, pp.107-120.
  12. IPCC(Intergovernmental Panel on Climate Change)(2022), Climate Change 2022: Mitigation of Climate Change, p.252.
  13. Jones, T. R. and Potts, R. B.(1962), "The Measurement of Acceleration Noise-A Traffic Parameter", Operations Research, vol. 10, no. 6, pp.745-763. https://doi.org/10.1287/opre.10.6.745
  14. Kim, D., Ko, J., Xu, X., Liu, H., Rodgers, M. O. and Guensler, R.(2019), "Evaluating the environmental benefits of median bus lanes: Microscopic simulation approach", Transportation Research Record, vol. 2673, no. 4, pp.663-673. https://doi.org/10.1177/0361198119836982
  15. KOTEMS(Korea Transport Emission Management System), https://www.kotems.or.kr/app/kotems/forward?pageUrl=kotems/ptl/Localgov/Co2/KotemsPtlLocalgovCo2Localgovco2AreaVw&topmenu1=03&topmenu2=03&topmenu3=01&PARENT_AREA_CODE=02, 2022.08.10.
  16. Lee, J. and Lee, S.(2011), "Analysis of the Effect of Carbon Dioxide Reduction by Changing from Signalized Intersection to Roundabout using Tier 3 Method", The Journal of the Korea Institute of Intelligent Transport Systems, vol. 10, no. 5, pp.105-112.
  17. Li, M., Boriboonsomsin, K., Wu, G., Zhang, W. and Barth, M.(2009), "Traffic Energy and Emission Reductions at Signalized Intersections: A Study of the Benefits of Advanced Driver Information", International Journal of ITS Research, vol. 7, no. 1, pp.49-58.
  18. Li, S., Chang, F. and Liu, C.(2021), "Bi-Directional Dense Traffic Counting Based on Spatio-Temporal Counting Feature and Counting-LSTM Network", IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 12, pp.7395-7407. https://doi.org/10.1109/TITS.2020.3001638
  19. Liao, T. Y. and Machemehl, R. B.(1998), "Development of an Aggregate Fuel Consumption Model for Signalized Intersections", Transportation Research Record, vol. 1641, no. 1, pp.9-18. https://doi.org/10.3141/1641-02
  20. Lin, C., Zhou, X., Wu, D. and Gong, B.(2019), "Estimation of emissions at signalized intersections using an improved MOVES model with GPS data", International Journal of Environmental Research and Public Health, vol. 16, no. 19, p.3647. https://doi.org/10.3390/ijerph16193647
  21. Liu, H. and Kim, D.(2019). "Simulating the uncertain environmental impact of freight truck shifting programs", Atmospheric Environment, vol. 214, p.116847. https://doi.org/10.1016/j.atmosenv.2019.116847
  22. Liu, H., Guensler, R., Lu, H., Xu, Y., Xu, X. and Rodgers, M. O.(2019), "MOVES-Matrix for High-performance On-road Energy and Running Emission Rate Modeling Applications", Journal of the Air & Waste Management Association, vol. 69, no. 12, pp.1415-1428. https://doi.org/10.1080/10962247.2019.1640806
  23. Monash University Accident Research Centre(2009), Road Design Factors and their Interactions with Speed and Speed Limits, p.17.
  24. Na, D., Lee, S., Cho, K. and Kim, H.(2021), "Predicting a Queue Length Using a Deep Learning Model at Signalized Intersections", The Journal of The Korea Institute of Intelligent Transport Systems, vol. 20, no. 6, pp.26-36. https://doi.org/10.12815/kits.2021.20.6.26
  25. Shirazi, M. S. and Morris, B. T.(2017), "Looking at intersections: A survey of intersection monitoring, behavior and safety analysis of recent studies", IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp.4-24. https://doi.org/10.1109/TITS.2016.2568920
  26. Unal, A., Rouphail, N. M. and Frey, H. C.(2003), "Effect of arterial Signalization and level of service on measured vehicle emissions", Transportation Research Record: Journal of the Transportation Research Board, vol. 1842, no. 1, pp.47-56. https://doi.org/10.3141/1842-06
  27. USEPA(United States Environmental Protection Agency)(1993), User's Guide to MOBILE5 (MOBILE source emission factor model), p.13.
  28. USEPA(United States Environmental Protection Agency)(2005), MOVES2004 Energy and Emission Inputs, EPA420-P-05-003.
  29. USEPA(United States Environmental Protection Agency)(2015), Population and Activity of On-road Vehicles in MOVES2014, EPA-420-R-16-003a.
  30. USEPA(United States Environmental Protection Agency)(2021), Overview of EPA's MOtor Vehicle Emission Simulator (MOVES3), p.6.
  31. Wang, L., Wang, Y. and Bie, Y.(2018), "Automatic estimation method for intersection saturation flow rate based on video detector data", Journal of Advanced Transportation, vol. 2018, pp.1-9.
  32. Wu, G., Boriboonsomsin, K., Xia, H. and Barth, M.(2014), "Supplementary benefits from partial automation in an Exoapproach and departure application at signalized intersections", Transportation Research Record: Journal of the Transportation Research Board, vol. 2424, no. 1, pp.65-75.
  33. Xu, X., Liu, H., Anderson, J. M., Xu, Y., Hunter, M. P., Rodgers, M. O. and Guensler, R. L.(2016), "Estimating project-level vehicle emissions with Vissim and MOVES-matrix", Transportation Research Record, vol. 2570, no. 1, pp.107-117. https://doi.org/10.3141/2570-12
  34. Yudin, D. A., Skrynnik, A., Krishtopik, A., Belkin, I. and Panov, A. I.(2019), "Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection", Optical Memory and Neural Networks, vol. 28, pp.283-295. https://doi.org/10.3103/s1060992x19040118
  35. Zhang, S., Wu, Y., Liu, H., Huang, R., Un, P., Zhou, Y. and Fu, L.(2014), "Real-world fuel comsumption and CO2 (carbon dioxide) emissions by driving conditions for light-duty passenger vehicles in China", Energy, vol. 69, pp.247-257. https://doi.org/10.1016/j.energy.2014.02.103