Journal of Cadastre & Land InformatiX (지적과 국토정보)

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A Study on the Optimal Location Selection for Hydrogen Refueling Stations on a Highway using Machine Learning

머신러닝 기반 고속도로 내 수소충전소 최적입지 선정 연구

  • Jo, Jae-Hyeok (School of Business Administration, Kyungpook National University) ;
  • Kim, Sungsu (School of Business Administration, Kyungpook National University)
  • Received : 2021.10.05
  • Accepted : 2021.11.25
  • Published : 2021.12.10
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Abstract

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.

대기오염, 지구온난화 문제 등 환경 문제의 심각성이 대두되면서 청정 연료의 관심이 커지고 있다. 그 중 수소는 기존 화석연료와는 달리 연소 시 부산물로 수분만이 발생하는 대표적인 친환경 에너지원으로 현재 다양한 분야에서 주목을 받고 있다. 물류 분야에서도 수소를 활용한 물류 네트워크를 구축하기 위해 다양한 정책적 노력이 활발히 이루어지고 있다. 이러한 수소 물류 네트워크의 구축에 있어 수소충전소의 입지 결정은 매우 중요한 문제이다. 최근 개발된 수소추진(수소연료전지) 화물차에 수소를 공급하는 충전소는 수소 기반 물류체계가 본격적으로 자리 잡는 데 있어 필수 불가결한 요소이다. 이러한 수소충전소의 최적 입지를 결정하는 선행연구는 대부분 수리적 모형에 기반한 최적화 기법만을 사용하여 수소충전소의 최적 입지를 결정하고자 하였다. 본 연구에서는 기존 연구의 동향과는 차별화하여 최적화 기법의 중요한 투입 변수 중 하나인 충전소 후보지에 대한 공간적 특성을 검토하는 방법으로 머신러닝 모형들을 활용하고 그 적용가능성을 확인하였다. 머신러닝은 다양한 분야에서 우수한 성과를 증명한 기법이지만 수소충전소의 최적 입지를 결정하는 연구 분야에서는 아직 적용된 바가 없다. 이를 위해 본 연구에서는 개별공시지가, 수소공급지와의 거리 등 전국 고속도로 휴게소와 고속도로의 무작위 지점들의 위치와 관련된 변수들을 독립변수로 선정하여 단일 머신러닝 모형과 앙상블 모형을 적용하고 그 성과를 비교하였다. 분석 결과, 랜덤포레스트(Random Forest) 모형이 가장 우수한 성과를 보였으며, 다른 모형들 또한 우수한 분류 성능을 보여 최적 입지 문제에 대해 공간적 특성을 예비적으로 검토하는 방법론으로써 머신러닝의 적용 가능성을 확인할 수 있었다. 따라서 머신러닝 모형은 수소충전소의 최적 입지 결정 분야에서 향후 최적화 기법을 적용한 연구의 예비적 검토 방법론으로 널리 활용할 수 있을 것으로 기대된다.

Keywords

Acknowledgement

이 논문은 2021년 대한민국 교육부와 한국연구재단의 인문사회분야 신진연구자지원사업의 지원을 받아 수행된 연구임 (NRF-2021S1A5A8073366)

References

  1. [http://www.molit.go.kr/USR/NEWS/m_71/dtl.jsp?lcmspage=1&id=95082944]]. Last accessed 20 September 2021.
  2. Kim GJ, Park JS, Go SR. 2019. Location problem of hydrogen refueling station considering hydrid hydrogen supply system. Journal of Transport Research. 26(2):53-70. https://doi.org/10.34143/jtr.2019.26.2.53
  3. Kim NJ, Bae YC. 2018. Status Diagnosis of Pump and Motor Applying K-Nearest Neighbors. The Journal of The Korea Institute of Electronic Communication Sciences. 13(6):1249-1256. https://doi.org/10.13067/JKIECS.2018.13.6.1249
  4. 김봉진, 국지훈, 조상민. 2014. 지리정보시스템을 이용한 고속국도에서의 수소충전소 구축 방:안. 한국 수소 및 신에너지학회 논문집. 25(3): 255-263.
  5. Kim SH, Ryu JH. 2020. A Machine Learning based Methodology for Selecting Optimal Location of Hydrogen Refueling Stations. Korean Chmical Engineering Research. 58(4):573-580.
  6. Kim EK, Jhun MS, Bang SW. 2016. Hierarchically penalized support vector machine for the classi cation of imbalanced data with grouped variables. The Korean Journal of Applied Statistics. 29(5):961-975. https://doi.org/10.5351/KJAS.2016.29.5.961
  7. Kim EM, Kim SB, Cho ES. 2020. Using Mechanical Learning Analysis of Determinants of Housing Sales and Establishment of Forecasting Models. Journal of Cadastre & Land InformatiX. 50(1): 181-200. https://doi.org/10.22640/LXSIRI.2020.50.1.181
  8. Kim JH. 2020. Air Pollutant Reduction Effect on Road Mobility in Hydrogen Economy Era. Transactions of the Korean Hydrogen and New Energy Society. 1(6): 522-529. https://doi.org/10.7316/KHNES.2020.31.6.522
  9. Kim HM. 2020. Predictive Analysis of Ethereum Uncle Block using Ensemble Machine Learning Technique and Blockchain Information. Journal of Digital Convergence. 18(11):129-136. https://doi.org/10.14400/JDC.2020.18.11.129
  10. [https://www.korea.kr/special/policyCurationVie w.do?newsId=148857966] Last accessed 22 September 2021.
  11. Min SH. 2015. Investigating Dynamic Mutation Process of Issues Using Unstructured Text Analysis. Journal of Intelligence and Information Systems. 22(1): 139-157. https://doi.org/10.13088/JIIS.2016.22.1.139
  12. Park JH. Choi BI, Rhee CH. 2006. Density based Fuzzy Support Vector Machines for multicategory Pattern Classification. Proceedings of KFIS Autumn Conference. 16(2):251-254.
  13. Park JM, Cho DY, Lee SS, Lee MS, Nam HS, Yang HR. 2018. A Study on The Methodology of Extracting the vulnerable districs of the Aged Welfare Using Artificial Intelligence and Geospatial Information. Jounral of Cadastre & Land InformatiX. 48(1):169-186.
  14. 외교부. 2015. 기후변화협상. [https://www.mofa. go.kr/www/wpge/m_20150/contents.do]. Last accessed 1 September 2021.
  15. Yoo JE. 2015. Random forest, an alternative data mining technique to decision tree. Journal of Educational Evaluation. 28(2):427-448.
  16. Lee YJ, Sung JW. 2020. Predicting Highway Concrete Pavement Damage using XGBoost. Korean journal of construction engineering and management. 21(6):46-55. https://doi.org/10.6106/KJCEM.2020.21.6.046
  17. Lee WJ, Jeon JH. 2021. A Study on Prediction of the Location of Public Bicycle Rental Stations Using Machine Learning. Journal of The Korea Society of Information Technology Policy & Management. 13(4):2553-2559.
  18. Lee CH, Sung CJ. 2018. Accuracy Analysis of Topographic Survey Data for the Official Land Price Appraising. Journal of Cadstre & Land InformatiX. 48(1):153-167.
  19. Jeon BU, Kang JS, Chung KY. 2021. AutoML and CNN-based Soft-voting Ensemble Classification Model For Road Traffic Emerging Risk Detection. Journal of Convergence for Information Technology. 11(7):14-20. https://doi.org/10.22156/CS4SMB.2021.11.07.014
  20. 정기대. 2019. 수소경제의 경제적.기술적 이슈 - Value Chain 5단계 중심 -. 10(5):1-12.
  21. Jeng YC, Ryu HY, Lee SJ, Seo DJ, Park CG. 2021. Identification recidivism risk factors study based on machine learning: Using decision tree analysis and random forest algorithm. Korean Police Studies Review. 20(1):323-350. https://doi.org/10.38084/2021.20.1.14
  22. Alazemi J, Andrews J. 2015. Autumotive hydrogen fuelling station: An international review. Renewable and Sustainable Energy Reviews, 48:483-499. https://doi.org/10.1016/j.rser.2015.03.085
  23. Bergstra J, Bengio Y. 2012. Random search for hyper-parameter optimization. Journal of machine learning research. 13(2):281-305.
  24. Brandon N P, Kurban Z. 2017. Clean energy and the hydrogen economy. Philosophical Transactions of the Royal Society A: Mathematical. Physical and Engineering Sciences. 375(2098): 1-17.
  25. Buhlmann P. 2012. Bagging, boosting and ensemble methods. In Handbook of computational statistics. USA: Springer, 985-1022,
  26. Cervantes J, Garcia-Lamont F, Rodriguez-Mazahua L, and Lopez A. 2020. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408:189-215. https://doi.org/10.1016/j.neucom.2019.10.118
  27. Dagdougui H. 2012. Models, methods and approaches for the planning and design of the future hydrogen supply chain. International Journal of hydrogen Energy. 37(6):5318-5327. https://doi.org/10.1016/j.ijhydene.2011.08.041
  28. Damavandi H, Abdolvand N, Karimipour F. 2019. Utilizing location-based social network data for optimal retail store placement. Earth Obsevation and Geomatics Engineering. 3(2): 77-91.
  29. Elloumi S, Labbe M, Pochet Y. 2004. A new formulation and resolution method for the p-center problem. INFORMS Journal on Computing. 16(1):84-94. https://doi.org/10.1287/ijoc.1030.0028
  30. Frade I, Ribeiro A, Goncalves G, Antunes A P. 2011 Optimal location of charging stations for electric vehicles in a neighborhood in Lisbon, Portugal. Transportation Research Record. 2252(1):91-98. https://doi.org/10.3141/2252-12
  31. Friedl M A, Brodley C E 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment. 61(3):399-409. https://doi.org/10.1016/S0034-4257(97)00049-7
  32. Gaikwad D P, Thool R C. 2015. Intrusion detection system using bagging ensemble method of machine learning. In 2015 international conference on computing commnication control and automation, 291-295.
  33. Gandhi I, Pandey M. 2015. Hybrid ensemble of classifiers using voting. In 2015 international conference on green computing and Interne of Things (ICGCIoT), 399-404.
  34. Gazalba I, Reza N G I. 2017. Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. In 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). 294-298.
  35. Gielen D, Gorini R, Wagner N, Leme R, Gutierrez L, Prakash G, Renner M. 2019. Global energy transformation: a roadmap to 2050. International Renewable Energy Agency (IRENA). 1-52.
  36. Hu L Y, Huang M W, Ke S W, Tsai C F, 2016. The distance Function effect on k-nearest neighbor classification for medical datasets, SpringerPlus. 5(1):1-9. https://doi.org/10.1186/s40064-015-1659-2
  37. Ozbas E E, Aksu D, Ongen A, Aydin M A, and Ozcan H K. 2019. Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms. International Journal of Hydrogen Energy. 44(32): 17260-17268. https://doi.org/10.1016/j.ijhydene.2019.02.108
  38. Hosseini M, MirHassni S A. 2015. Refuelingstation location problem under uncertainty. Transportation Research Part E: Logistics and Transportation Review. 84:101-116. https://doi.org/10.1016/j.tre.2015.10.009
  39. Jeong I J. 2017. An optimal approach for a set covering version of the refueling-station location problem and its application to a diffusion model. International Journal of Sustainable Transportation. 11(2):86-97. https://doi.org/10.1080/15568318.2016.1193780
  40. Kaur H, Pannu H S, and Malihi A K. 2019. A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Computing Surveys (CSUR). 52(4):1-36
  41. Kim H C, Pang S, Je H M, Kim D, and Bang S Y. 2002. Support vector machine ensemble with bagging. Berlin, Heidelberg: Springer, p. 397-408.
  42. Kim J H, Ki B S, Savarese S. 2012. Comparing image classification methods: K-nearestneighbor and support-vector-machines. In Procddings of the 6th WSEAS international conference on Computer ENginering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics. 1001:48109-2012.
  43. Kluschke P, nann T N, Plotz P, Wietschel M. 2019. Market diffusion of alternative fuels and powertrains in heavy-duty vehicles: a literature review. Energy Report. 5: 1010-1024. https://doi.org/10.1016/j.egyr.2019.07.017
  44. Ko J, Gim T H T, Guensler R. 2017. Locating refuelling stations for alternative fuel vehicles: a review on models and applications. Transport Reviews. 37(5): 551-570. https://doi.org/10.1080/01441647.2016.1273274
  45. Kuby M, Lines L, SchultzR, Xie Z, Kim J G, Lim S. 2009. Optimization of hydrogen stations in Florida using the flow-refueling location model. International journal of hydrogen energy. 34(15):6045-6064. https://doi.org/10.1016/j.ijhydene.2009.05.050
  46. Li L, Manier H, Manier M A. 2019. Hydrogen supply chain network design: An optimizationoriented review. Renewable and Sustainable Energy Reviews. 103:342-360. https://doi.org/10.1016/j.rser.2018.12.060
  47. Lin R H, Ye Z Z, Wu B D. 2020. A review of hydrogen station location models. International Journal of Hydrogen Energy. 45(39):20176- 20183. https://doi.org/10.1016/j.ijhydene.2019.12.035
  48. Lin Z, Ou S, Elgowainy A, Reddi K, Veenstra M, Verduzco L. 2018. A Method for determining the optimal delivered hydrogen pressure for fuel cell electric vehicles. Applied Energy. 261(15):183-194.
  49. Muratori M, Bush B, Hunter C, Melaina M W. 2018. Modeling hydrogen refueling infrastructure to support passenger vehicles. Energies. 11(5): 1-14.
  50. Pradhan A. 2012. Support vector machine-a survey. International Journal of Emerging Technology and Advanced Engineering. 2(8):82-85.
  51. Priyam A, Abhijeeta G R, Rathee A, Srivastava S. 2013. Comparative analysis of decision tree classification algorithms. International Journal of current engineering and technology. 3(2):334-337.
  52. Shah S A A, Aziz W, Arif M, Nadeem M S A. 2015. Decision trees based classification of cardiotocograms using bagging approach. In 2015 13th international conference on frontiers of information technology(FIT). 12-17.
  53. Sharma H, Kumar S. 2016. A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR). 5(4):2094-2097.
  54. Sharma D, Kumar N. 2017. A review on machine learning algorithms, tasks and applications. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET). 6(10):1548-1552.
  55. Teng X, Gong Y. 2018. Research on application of machine learning in data mining. In IOP conference series: materials science and engineering. 392(6):1-5.
  56. Wang H, Ma C, Zhou L. 2009. A brief review of machine learning and its application. In 2009 international conference on information engineering and computer science, 1-4.
  57. Zabinsky Z B. 2009. Random search algorithms. USA: Department of Industrial and Systems Engineering, University of Washington. p. 1-16.
  58. Zaheer N, Hassan S U, Ali M, Shabbir M. 2021. Optimal school site selection in Urban areas using deep neural networks. Journal of Ambient Intelligence and Humanized Computing. 1-15.