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

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

How to Set an Appropriate Scale of Traffic Analysis Zone for Estimating Travel Patterns of E-Scooter in Transporation Planning?

전동킥보드 통행분포모형 추정을 위한 적정 존단위 선정 연구

  • Received : 2023.05.08
  • Accepted : 2023.06.22
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Travel demand estimation of E-Scooter is the start point of solving the regional demand-supply imbalance problem and plays pivotal role in a linked transportation system such as Mobility-as-a-Service (a.k.a. MaaS). Most focuses on developing trip generation model of shared E-Scooter but it is no study on selection of an appropriate zone scale when it comes to estimating travel demand of E-Scooter. This paper aimed for selecting an optimal TAZ scale for developing trip distribution model for shared E-Scooter. The TAZ scale candidates were selected in 250m, 500m, 750m, 1,000m square grid. The shared E-Scooter usage historical data were utilized for calculating trip distance and time, and then applying to developing gravity model. Mean Squared Error (MSE) is applied for the verification step to select the best suitable gravity model by TAZ scale. As a result, 250m of TAZ scale is the best for describing practical trip distribution of shared E-Scooter among the candidates.

정확한 전동킥보드 중장기수요예측은 지역별 수요공급 불균형 문제해결 및 MaaS 등 연계교통체계 마련을 위해 필요하다. 공유 전동킥보드의 지역별 발생-유입량을 예측하는 연구는 많지만, 공유 전동킥보드의 존간 통행분포를 예측하는 연구는 전무한 실정이다. 본 연구에서는 공유 전동킥보드의 통행분포모형 추정을 위한 적정 존단위를 선정하고자 하였다. 분석 대상 존단위는 250m, 500m, 750m, 1,000m 정사각형 그리드로 설정하였다. 공유 전동킥보드 이용 이력 데이터는 각 공간 단위별 통행거리, 통행시간 계산 및 중력모형 도출을 위해 활용되었다. 평균제곱오차는 각 중력모형의 적정성을 검증하는데 활용되었다. 분석 결과, 250m 그리드가 실제 공유킥보드 통행분포를 가장 잘 묘사하는 것으로 나타났다.

Keywords

Acknowledgement

본 논문은 한국ITS학회 2023년도 춘계학술대회(2023.04.20.~2023.04.21.)에서 발표된 내용을 수정·보완하여 작성하였습니다. This research was supported by Basic Science Reasearch Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1C1C1012456).

References

  1. Abdel-Aal, M. M. M.(2014), "Calibrating a trip distribution gravity model stratified by the trip purposes for the city of Alexandria", Alexandria Engineering Journal, vol. 53, no. 3, pp.677-689. https://doi.org/10.1016/j.aej.2014.04.006
  2. Clifton, K. J., Singleton, P. A., Muhs, C. D. and Schneider, R. J.(2016), "Development of destination choice models for pedestrian travel", Transportation Research Part A: Policy and Practice, vol. 94, pp.255-265. https://doi.org/10.1016/j.tra.2016.09.017
  3. De Dios Ortuzar, J. and Willumsen, L. G.(1994), Modelling Transport, Wiley, Chichester.
  4. Ham, S. W., Cho, J. H., Park, S. and Kim, D. K.(2021), "Spatiotemporal demand prediction model for e-scooter sharing services with latent feature and deep learning", Transportation Research Record, vol. 2675, no. 11, pp.34-43. https://doi.org/10.1177/03611981211003896
  5. James, O., Swiderski, J. I., Hicks, J., Teoman, D. and Buehler, R.(2019), "Pedestrians and E-scooters: An Initial Look at E-scooter Parking and Perceptions by Riders and Non-riders", Sustainability, vol. 11, p.5591.
  6. Jeong, C. Y., Son, U. Y. and Kim, D. G.(2009), "A Study on Forecasting Trip Distribution of Land Development Project Using Middle Zone Size And Gravity Model", Journal of Korean Society of Transportation, vol. 27, no. 6, pp.19-28.
  7. Kim, K. and Lee, C. H.(2021), "A K-Means-Based Clustering Algorithm for Traffic Prediction in a Bike-Sharing System", KIPS Transactions on Software and Data Engineering, vol. 10, no. 5, pp.169-178. https://doi.org/10.3745/KTSDE.2021.10.5.169
  8. Kim, S., Choo, S., Lee, G. and Kim, S.(2022), "Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method", Sustainability, vol. 14, no. 5, p.2564.
  9. Korea Consumer Agency(2021), Safety Survey on Electric Kickboard Sharing Service, pp.1-51.
  10. Kwon, S. I.(2022), Trip boundary identification of Personal Mobility and Applications based on Trip Patterns, Master's Thesis, Chungbuk National University.
  11. Lenormand, M., Bassolas, A. and Ramasco, J. J.(2016), "Systematic comparison of trip distribution laws and models", Journal of Transport Geography, vol. 51, pp.158-169. https://doi.org/10.1016/j.jtrangeo.2015.12.008
  12. Lim, H. and Chung, K.(2019), "Development of demand forecasting model for Seoul shared bicycle", The Journal of the Korea Contents Association, vol. 19, no. 1, pp.132-140. https://doi.org/10.5392/JKCA.2019.19.01.132
  13. Mozolin, M., Thill, J. C. and Usery, E. L.(2000), "Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation", Transportation Research Part B: Methodological, vol. 34, no. 1, pp.53-73. https://doi.org/10.1016/S0191-2615(99)00014-4
  14. The Korea Transport Institute(2012), National Passenger OD Transmission and Forecast of Future Demand II, pp.1-396.
  15. Yim, S.(2018), "An Analysis of Daegu Metropolitan Area Focusing on Functional Relations in City-region", Journal of the Korean Geographical Society, vol. 53 no. 1, pp.19-35.