$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Estimating Destination of Bus Trips Considering Trip Type Characteristics 원문보기

Applied sciences, v.11 no.21, 2021년, pp.10415 -   

Lee, Soongbong (Big Data Platform and Data Economy, The Korea Transport Institute, 370 Sicheong-daero, Sejong 30147, Korea) ,  Lee, Jongwoo (Big Data Platform and Data Economy, The Korea Transport Institute, 370 Sicheong-daero, Sejong 30147, Korea) ,  Bae, Bumjoon (Center for Privately-Financed Highway Studies, The Korea Transport Institute, 370 Sicheong-daero, Sejong 30147, Korea) ,  Nam, Daisik (Graduate School of Logistics, Inha University, Incheon 22212, Korea) ,  Cheon, Seunghoon (Big Data Platform and Data Economy, The Korea Transport Institute, 370 Sicheong-daero, Sejong 30147, Korea)

Abstract ▼ AI-Helper 아이콘AI-Helper

Recently, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns or po...

참고문헌 (31)

  1. 10.3390/info9010018 Li, T., Sun, D., Jing, P., and Yang, K. (2018). Smart Card Data Mining of Public Transport Destination: A Literature Review. Information, 9. 

    상세보기 crossref

  2. Hussain Transit OD matrix estimation using smartcard data: Recent developments and future research challenges Transp. Res. Part C Emerg. Technol. 2021 10.1016/j.trc.2021.103044 125 103044 

    상세보기 crossref

  3. Barry Origin and Destination Estimation in New York City with Automated Fare System Data Transp. Res. Rec. J. Transp. Res. Board 2002 10.3141/1817-24 1817 183 

    상세보기 crossref

  4. Munizaga Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile Transp. Res. Part C Emerg. Technol. 2012 10.1016/j.trc.2012.01.007 24 9 

    상세보기 crossref

  5. Shin Inferring the Transit Trip Destination Zone of Smart Card User Using Trip Chain Structure J. Korean Soc. Transp. 2016 10.7470/jkst.2016.34.5.437 34 437 

    원문보기 상세보기 crossref

  6. Li, D., Lin, Y., Zhao, X., Song, H., and Zou, N. Estimating a Transit Passenger Trip Origin-Destination Matrix Using Automatic Fare Collection System. Proceedings of the Integer Programming and Combinatorial Optimization. 

  7. Wang Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems J. Public Transp. 2011 10.5038/2375-0901.14.4.7 14 131 

    상세보기 crossref

  8. Xiaolei, M. (2013). Smart Card Data Mining and Inference for Transit System Optimization and Performance Improvement. [Ph.D. Thesis, University of Washington]. 

  9. Nunes Passenger Journey Destination Estimation From Automated Fare Collection System Data Using Spatial Validation IEEE Trans. Intell. Transp. Syst. 2016 10.1109/TITS.2015.2464335 17 133 

    상세보기 crossref

  10. Kim Public Transportation Alighting Estimation Method Using Smart Card Data J. Korean Soc. Railw. 2017 10.7782/JKSR.2017.20.5.692 20 692 

    원문보기 상세보기 crossref

  11. Alsger Use of Smart Card Fare Data to Estimate Public Transport Origin-Destination Matrix Transp. Res. Rec. J. Transp. Res. Board 2015 10.3141/2535-10 2535 88 

    상세보기 crossref

  12. Chapleau Destination Estimation from Public Transport Smartcard Data IFAC Proc. Vol. 2006 10.3182/20060517-3-FR-2903.00209 39 393 

    상세보기 crossref

  13. Kim Bus OD Matrix Estimation Using Smart Card Data J. Korean Soc. Railw. 2018 10.7782/JKSR.2018.21.11.1155 21 1155 

    상세보기 crossref

  14. Zhao Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems Comput. Civ. Infrastruct. Eng. 2007 10.1111/j.1467-8667.2007.00494.x 22 376 

    상세보기 crossref

  15. Barry Use of Entry-Only Automatic Fare Collection Data to Estimate Linked Transit Trips in New York City Transp. Res. Rec. J. Transp. Res. Board 2009 10.3141/2112-07 2112 53 

    상세보기 crossref

  16. Munizaga Validating travel behavior estimated from smartcard data Transp. Res. Part C Emerg. Technol. 2014 10.1016/j.trc.2014.03.008 44 70 

    상세보기 crossref

  17. Alsger Validating and improving public transport origin-destination estimation algo-rithm using smart card fare data Transp. Res. Part C Emerg. Technol. 2016 10.1016/j.trc.2016.05.004 68 490 

    상세보기 crossref

  18. Nassir Transit Stop-Level Origin-Destination Estimation through Use of Transit Schedule and Automated Data Collection System Transp. Res. Rec. J. Transp. Res. Board 2011 10.3141/2263-16 2263 140 

    상세보기 crossref

  19. Lee, I.M. (2019). Estimation of bus-trip destinations using temporal travel patterns of smart card data. [Ph.D. Thesis, Seoul National University]. 

  20. He Validating and calibrating a destination estimation algorithm for public transport smart card fare collection systems CIRRELT 2015 52 1 

  21. Cheng Probabilistic model for destination inference and travel pattern mining from smart card data Transportation 2021 10.1007/s11116-020-10120-0 48 2035 

    상세보기 crossref

  22. Shin Research of Sparse Dataset Analysis for Estimating Missing Alighting Information of Public Transportation Records: Spatial Density-based Clustering J. Transp. Res. 2020 27 19 

  23. Jung Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data IET Intell. Transp. Syst. 2017 10.1049/iet-its.2016.0276 11 334 

    상세보기 crossref

  24. Assemi Improving alighting stop inference accuracy in the trip chaining method using neural networks Public Transp. 2019 10.1007/s12469-019-00218-9 12 89 

    상세보기 crossref

  25. Yan Alighting stop determination using two-step algorithms in bus transit systems Transp. A Transp. Sci. 2019 15 1522 

  26. Yoo A Study of Estimating the Alighting Stop on the Decision Tree Learning Model Using Smart Card Data J. Korea Inst. Intell. Transp. Syst. 2019 10.12815/kits.2019.18.6.11 18 11 

    원문보기 상세보기 crossref

  27. 10.3390/en13133473 Macioszek, E., and Kurek, A. (2020). The Use of a Park and Ride System-A Case Study Based on the City of Cracow (Poland). Energies, 13. 

    상세보기 crossref

  28. 10.3390/su13074064 Ortega, J., Tóth, J., and Péter, T. (2021). A Comprehensive Model to Study the Dynamic Accessibility of the Park & Ride System. Sustainability, 13. 

    상세보기 crossref

  29. Macioszek P&R Parking And Bike-Sharing System as Solutions Supporting Transport Accessibility of The City Transp. Probl. 2020 10.21307/tp-2020-066 15 275 

    상세보기 crossref

  30. Gordon Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data Transp. Res. Rec. J. Transp. Res. Board 2013 10.3141/2343-03 2343 17 

    상세보기 crossref

  31. Song The Promise of Mobile Phone Signaling Data for Inferring Mobility Patterns Int. J. Highw. Eng. 2018 10.7855/IJHE.2018.20.6.199 20 199 

    상세보기 crossref
LOADING...

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로