Journal of Cadastre & Land InformatiX (지적과 국토정보)

LX Spatial Information Research Institute (한국국토정보공사 공간정보연구원)
권호 표지
DOI QR코드

DOI QR Code

A Case Study on the Emission Impact of Land Use Changes using Activity-BAsed Traveler Analyzer (ABATA) System

활동기반 통행자분석시스템(ABATA)을 이용한 토지이용변화에 따른 차량 배기가스 배출영향 사례 분석

  • Eom, Jin Ki (Railroad Policy Research Department, Korea Railroad Research Institute) ;
  • Lee, Kwang-Sub (Railroad Policy Research Department, Korea Railroad Research Institute)
  • 엄진기 (한국철도기술연구원 철도정책연구실) ;
  • 이광섭 (한국철도기술연구원 철도정책연구실)
  • Received : 2023.04.05
  • Accepted : 2023.06.21
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Activity-based modeling systems have increasingly been developed to address the limitations of widely used traditional four-step transportation demand forecasting models. Accordingly, this paper introduces the Activity-BAsed Traveler Analyzer (ABATA) system. This system consists of multiple components, including an hourly total population estimator, activity profile constructor, hourly activity population estimator, spatial activity population estimator, and origin/destination estimator. To demonstrate the proposed system, the emission impact of land use changes in the 5-1 block Sejong smart city is evaluated as a case study. The results indicate that the land use with the scenario of work facility dispersed plan produced more emissions than the scenario of work facility centralized plan due to the longer travel distance. The proposed ABATA system is expected to provide a valuable tool for simulating the impacts of future changes in population, activity schedules, and land use on activity populations and travel demands.

전 세계적으로 가장 널리 사용되고 있는 교통수요모형은 전통적 4단계 교통수요모델이다. 하지만, 기존 분석방법은 시공간적으로 다양한 분석에 제약을 가지고 있으며, 이러한 한계를 극복하기 위해 최근 활동기반 모형 및 시스템이 활발히 연구 개발되고 있다. 이에 본 연구에서는 빅데이터를 활용한 활동기반 통행자분석시스템 ABATA(Activity-Based Traveler Analyzer) 기술개발을 소개한다. 이 시스템은 시간별 총인구 추정, 활동 프로파일 생성, 시간별 활동 인구 추정, 공간 활동 인구 추정 및 출발지·목적지 추정 등의 구성요소를 포함한다. 제안된 시스템을 실증하기 위해 사례연구로 세종시 5-1 블록스마트시티를 대상으로 토지이용변화에 따른 배기가스 배출영향을 평가하였다. 그 결과 업무시설 분산계획 시나리오의 토지이용이 업무시설 집중계획 시나리오보다 이동 거리가 길어 배출량이 더 많이 발생하는 것으로 나타났다. 제안된 ABATA 시스템은 활동 인구 및 통행 수요에 대한 인구, 활동 일정 및 미래 토지이용의 변화 영향을 시뮬레이션하기 위한 유용한 도구를 제공할 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 한국철도기술연구원 기본사업(철도-대중교통 모빌리티 분석 기술 및 정책지원 연구, PK2302B1)의 연구비 지원으로 수행되었습니다.

References

  1. Kang JY, Hwang CS. 2022. Reproducibility and Replicability in the Studies about GIS and Spatial Analysis: The Case Stdy on the Journal of the Korean Geographical Society. Journal of the Korean Geographical Society. 57(5):425-435. 
  2. Go JY, Lee SI. 2010. Developing an urban development and transport infrastructure linking model: The case of Namyangju city. Journal of Korea Planning Association. 45(5):163-179. 
  3. Kim KI, Yi CH, Lee SI. 2010. A scenario analysis on transport energy consumption and carbon emission using DELTA. Journal of Korea Planning Association. 45(6):117-135. 
  4. Kim IK.. 1994. Theoretical comparison of land-use/transportation models. Journal of Korea Planning Association. 29(4):135-155. 
  5. Kim JY, Lee SJ. 2012. A study of operation mechanisms of the integrated land-use-transportation. Seoul Studies. 13(1):99-120.  https://doi.org/10.23129/SEOULS.13.1.201203.99
  6. Kim HG, Ahn JW. 2019. The Analysis of Research Trends in Technology to the Fourth Industrial Revolution using SNA. Journal of Cadastre & Land InformatiX. 49(1):113-121. 
  7. Park YR, Kang YO. 2019. Estimation of Flow Population of Seoul Walking Tour Courses using Telecommunications Data. Journal of Cadastre & Land InformatiX. 49(1):181-195. 
  8. Park JY, Lee JS, Kim YH, Yoo JB. 2012. Forecasting Individual Travel Behavior based on Activitybased Approach. KOTI. 2012-24. 
  9. Shon EY, Kwon BW, Lee MH. 2004. Modelling the subway demand estimation by station using the multiple regression analysis by category. Journal of Korean Society of Transportation. 22(1):33-42. 
  10. Eom JK. 2008. Estimating travel demand by using a spatial-temporal activity presence-based approach. Journal of Korean Society of Transportation. 26(5):163-174. 
  11. Won SH, Hwang CS. 2017. A Study on the Change of Urban Land Use According to the Change of Transportation Accessibility. Journal of Cadastre & Land InformatiX. 47(1):127-142. 
  12. Lee KS, Eom JK, Seong ME, Lee J, Moon DS. 2019. Analysis of hourly transit usage and estimation of hourly service population using mobile phone data - Focusing on Seoul Metropolitan area. Journal of the Korean Society for Railway. 22(12):1008-1017.  https://doi.org/10.7782/JKSR.2019.22.12.1008
  13. Lee BJ, Yoon SY, Lee CY, Oh SH. 2017. Development and Application of Activity-based Simulation Models for Promotion of Transportation Policy Effectiveness(II). KRIHS. 2014-43. 
  14. Lee SI. 2010. Development scheme of a land-use transport model for Korea's large cities toward a low-carbon-energy-saving city. Journal of Korea Planning Association. 45(1):265-281. 
  15. Lee SI, Lee JI, Go JY, Yi CH. 2011. Development and operation of land use and transportation model. Urban Information Service. 356:3-17. 
  16. Lee SI, Yi CH. 2015. Fitting a land use - transport model for its application to the Seoul Metropolitan Area - Calibrating the location sub-model of DELTA. Journal of Korea Planning Association. 50(3):39-54.  https://doi.org/10.17208/jkpa.2015.04.50.3.39
  17. Lee SI, Jang SM. 2018. Applying land-use transport integrated model for analyzing long-term and comprehensive effect of tram in the city of Suwon, Korea. Journal of Korea Planning Association. 53(1):63-84.  https://doi.org/10.17208/jkpa.2018.02.53.1.63
  18. Lee SC, Rhee JH. 2005. Comparative analysis of activity-based model and UTMS model by TRANSIMS and EMME/2 assigned link data. Journal of Transport Research. 12(1):45-58. 
  19. Jeong JC, Lee SH. 2018. Spatial distribution of Particulate Matters in comparison with land-use and traffic volume in Seoul. Republic of Korea. Journal of Cadastre & Land InformatiX. 48(1):123-138. 
  20. Cho SJ, You GH. 2017. Land-use transportation integrated models: A literature review and research directions. The Korea Spatial Planning Review. 94:3-19.  https://doi.org/10.15793/kspr.2017.94..001
  21. Adnan M, Pereira FC, Azevedo CML, Basak K, Lovric M, Eliu SR, Zhu Y, Ferreira J, Zegras C, Ben-Akiva ME. 2016. SimMobility: A multiscale integrated agent-based simulation platform. Transportation Research Board 95th Annual Meeting. Washington 
  22. Arentze TA, Hofma F, Mourik H, Timmermans HJP. 2000. ALBATROSS: Multiagent, rulebased model of activity pattern decisions. Transportation Research Record: Journal of the Transportation Research Board. 1706:136-144.  https://doi.org/10.3141/1706-16
  23. Arentze TA, Timmermans HJP. 2004. A learning-based transportation oriented simulation system. Transportation Research Part B. 38:613-633.  https://doi.org/10.1016/j.trb.2002.10.001
  24. Auld J, Hope M, Ley H, Sokolov V, Zhang K. 2016. POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations. Transportation Research Part C. 64:101-116.  https://doi.org/10.1016/j.trc.2015.07.017
  25. Auld J, Hope M, Ley H, Xu B, Zhang K, Sokolov V. 2013. Modeling framework for regional integrated simulation of transportation network and activity-based demand (Polaris). International Symposium for Next Generation Infrastructure. Wollongong, Australia. 
  26. Bao Q, Kochan B, Bellemans T, Shen Y, Creemers L, Janssens D, Wets G. 2015. Travel demand forecasting using activity-based modeling framework FEATHERS: an extension. International Journal of Intelligent Systems. 30(8): 948-962.  https://doi.org/10.1002/int.21733
  27. Baqueri SFA, Adnan M, Kochan B, Bellemans T. 2019. Activity-based model for medium-sized cities considering external activity-travel: enhancing FEATHERS framework. Future Generation Computer Systems. 96:51-63.  https://doi.org/10.1016/j.future.2019.01.055
  28. Bellemans T, Kochan B, Janssens D, Wets G, Arentze T, Timmermans H. 2010. Implementation framework and development trajectory of FEATHERS activity-based simulation platform. Transportation Research Record: Journal of the Transportation Research Board. 2175:111-119.  https://doi.org/10.3141/2175-13
  29. Ciari F, Balac M, Axhausen KW. 2016. Modeling carsharing with the agent-based simulation MATSim. Transportation Research Record: Journal of the Transportation Research Board. 2564:14-20.  https://doi.org/10.3141/2564-02
  30. Eom JK, Lee KS, Seong ME. 2020. Development and application of the Activity-BAsed Traveler Analyzer (ABATA) system. Future Generation Computer Systems. 106:135-153.  https://doi.org/10.1016/j.future.2019.12.048
  31. Harris B. 1985. Urban simulation models in regional science. Journal of Regional Science. 25(4):545-567.  https://doi.org/10.1111/j.1467-9787.1985.tb00322.x
  32. Lee KS, Eom JK. 2020. Development and spatial transferability of hourly activity space attraction models by activity type at a census block level. Transportation Planning and Technology. 43(2): 188-207.  https://doi.org/10.1080/03081060.2020.1717141
  33. Lee KS, Hobeika AG. 2007. Application of dynamic value pricing through enhancements to TRANSIMS. Transportation Research Record: Journal of the Transportation Research Board. 2003:7-16.  https://doi.org/10.3141/2003-02
  34. Lu Y, Adnan M, Basak K, Pereira FC, Carrion C, Saber VH, Loganathan H, Ben-Akiva ME. 2015. SimMobility mid-term simulator: A state of the are integrated agent based demand and supply model. Transportation Research Board 94th Annual Meeting. Washington 
  35. Rasouli S, Timmermans H. 2014. Activity-based models of travel demand: Promises, progress and prospects. International Journal of Urban Sciences. 18(1):31-60.  https://doi.org/10.1080/12265934.2013.835118
  36. Transportation Research Board. 2015. Activity-based travel demand models: A primer. Washington D.C..
  37. UN. 2017. Principles and recommendations for population and housing censuses. New York, United Nations. 
  38. Waddell P, Wang L., Charlton B, Olsen A. 2010. Microsimulating parcel-level land use and activity-based travel: Development of a prototype application in San Francisco. The Journal of Transport and Land Use. 3(2):65-84.  https://doi.org/10.5198/jtlu.v3i2.124
  39. Wagner P, Wegener M. 2007. Urban land use transport and environment models - Experiences with an integrated microscopic approach. disP. 170(3):45-56.  https://doi.org/10.1080/02513625.2007.10556988
  40. Wegener M. 2004. Overview of land-use transport models. Transport Geography and Spatial Systems, Handbook 5 of the the Handbook in Transport. Pergamon/Elsevier Science. Kidlington, UK.