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

  • 제19권1호
  • /
  • Pages.32-43
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  • 2020
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  • 1738-0774(pISSN)
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  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
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심층신경망 모형을 활용한 대중교통 이용자의 환승시간 추정에 관한 연구

A study on Estimating the Transfer Time of Transit Users Using Deep Neural Network Models

  • 이경재 (홍익대학교 도시계획과) ;
  • 김수재 (홍익대학교 도시계획과) ;
  • 문형택 (홍익대학교 도시계획과) ;
  • 한재윤 (홍익대학교 도시계획과) ;
  • 추상호 (홍익대학교 도시계획과)
  • 투고 : 2020.02.10
  • 심사 : 2020.02.20
  • 발행 : 2020.02.28
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초록

환승시간은 대중교통계획 및 정책 수립에 있어서 중요한 요소이다. 이에 본 연구에서는 교통카드 이용자료를 활용하여 대중교통 이용자의 환승시간 영향요인을 규명하고, 딥러닝 기법인 심층신경망 모형을 이용한 환승시간을 추정하였으며 이를 전통적인 회귀모형과 비교 분석하였다. 먼저 환승시간 영향요인의 경우, 주변 버스의 배차간격과 버스 정류장까지의 거리가 버스 환승시간에 양의 영향을 주었으며, 버스 노선수는 반대로 음의 영향을 주었다. 또한 지하철역이 속해있는 자치구에 따라서도 환승시간에 영향을 주는 것으로 나타났다. 도출된 환승시간 영향요인을 통해 딥러닝 모형을 구축하고 성능을 비교한 결과, 회귀모형보다 딥러닝 모형의 성능이 보다 우수하였다. 본 연구의 결과는 지역별 환승허용시간의 차등 적용 등 대중교통 환승정책의 기초 자료로 활용될 수 있을 것으로 판단된다.

The transfer time is an important factor in establishing public transportation planning and policy. Therefore, in this study, the influencing factors of the transfer time for transit users were identified using smart card data, and the estimation results for the transfer time using the deep learning method such as deep neural network models were compared with traditional regression models. First, the intervals and the distance to the bus stop had positive effects on the subway-to-bus transfer time, and the number of bus routes had a negative effect. This also showed that the transfer time is affected by the area in which the subway station exists. Based on the influencing factors of the transfer time, the deep learning models were developed and their estimation results were compared with the regression model. For model performance, the deep learning models were better than those of the regression models. These results can be used as basic data for transfer policies such as the differential application of transit allowance times according to region.

키워드

참고문헌

  1. Baek J. H. and Sohn K. M.(2017), "Deep-Learning Architectures to Forecast Bus Ridership at the Stop and Stop-To-Stop Levels for Dense and Crowded Bus Networks," Applied Artificial Intelligence, vol. 30, no. 9, pp.861-885. https://doi.org/10.1080/08839514.2016.1277291
  2. Duan Y., Lv Y., Liu Y. and Wang F.(2016), "An efficient realization of deep learning for traffic data imputation," Transportation Research Part C, vol. 72, pp.168-181. https://doi.org/10.1016/j.trc.2016.09.015
  3. Han G. I. and Sohn K. M.(2016), "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B, vol. 83, pp.121-135. https://doi.org/10.1016/j.trb.2015.11.015
  4. Huang W., Hong H., LI M. and Hu W.(2013), "Deep Architecture for Traffic Flow Prediction," ADMA2013, vol. 8347, no. 2, pp.165-176.
  5. Hwang J. H.(2014), "Analyzing Factors Affecting Public Transit Transfer Volume: Focused on Daegu City," Journal of Korean Society of Transportation, vol. 32, no. 3, pp.179-186. https://doi.org/10.7470/jkst.2014.32.3.179
  6. Jang W. J.(2010), "Travel Time and Transfer Analysis Using Trnasit Smart Card Data," Transportation Research Record: Journal of the Transportation Research Board, vol. 2144, pp.142-149. https://doi.org/10.3141/2144-16
  7. Jung J. Y. and Sohn K. M.(2017). "Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data," The Institution of Engineering and Technology, vol. 11, no. 6, pp.334-339.
  8. Lee M. S. and Sohn K. M.(2015), "Inferring the Route-Use Patterns of Metro Passengers Based Only on Travel-Time Data Within a Bayesian Framework Using a Reversible-Jump Markov Chain Monte Carlo(MCMC) Simulation," Transportation Research Part B, vol. 81, pp.1-17. https://doi.org/10.1016/j.trb.2015.08.008
  9. Lee S. H., Kim J. S., Kim M. S. and Woo Y. H.(2013), "A Study on Users' Travel Behavior Analysis of Transit Transfer," The Journal of The Korea Institute of Intelligent Transport Systems, vol. 12, no. 12, pp.147-157. https://doi.org/10.12815/kits.2013.12.1.147
  10. Li D., Dong Y.(2014), "Deep Learning Methods and Applications" Foundations And Trends In Signal Processing, vol. 7 no. 3-4, pp.197-387. https://doi.org/10.1561/2000000039
  11. Lim S. Y.(2017), Exploring the Relationship between Transfer Trips and Land Use, Masters dissertation, Hongik University, Korea.
  12. Lim S. Y., Lee H. S. and Choo S. H.(2016), "Analysis of Influencing Factors on Transfer Time of Public Transportation in Seoul-Focusing on Transfer Users from Subway to Bus," The Journal of The Korea Institute of Intelligent Transport Systems, vol. 15, no. 2, pp.1-12. https://doi.org/10.12815/kits.2016.15.2.001
  13. Lv Y., Duan Y., Kang W., Li Z. and Wang F.(2015), "Traffic Flow Prediction With Big Data: A Deep Learning Approach," IEEE Transactions on Intelligent Transportation System, vol. 16, no. 2, pp.865-873. https://doi.org/10.1109/TITS.2014.2345663
  14. Ma X., Tao Z., Wang Y., Yu H. and Wang Y.(2015), "Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data," Transportation Research Part C, vol. 54, pp.187-197. https://doi.org/10.1016/j.trc.2015.03.014
  15. Ma X., Yu H., Wang Y. and Wang Y.(2015), "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLoS One, vol. 10, no. 3, pp.1-17.
  16. Ministry of Land, Infrastructure and Transport(2018), Public Transportation Investigation 2018, p.14.
  17. Polson N. G. and Sokolov V. O.(2017) "Deep learning for short-term traffic flow prediction," Transportation Research Part C, vol. 79, pp.1-17. https://doi.org/10.1016/j.trc.2017.02.024
  18. Ryu J. D. and Yoon I. S.(2018), Development of Expressway Traffic Accident Prediction Model Using Deep Learning, Doctoral dissertation, Ajou University, Korea.
  19. Seoul Solution, https://www.seoulsolution.kr, 2018.4.18.
  20. Xiong G., Kang W., Wang F., Zhu F., Lv Y., Dong X., Riekki J. and Pirttikangas S.(2015), "Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network," IEEE 18th International Conference, pp.524-528.