KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Estimating Route Travel Time Using Collected Data of Bus Information System

버스정보시스템(BIS) 수집자료를 이용한 경로통행시간 추정

  • 이영우 (대구대학교 토목공학과)
  • Received : 2012.08.01
  • Accepted : 2013.02.19
  • Published : 2013.05.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently the demands for traffic information tend to increase, and travel time might one of the most important traffic information. To effectively estimate exact travel time, highly reliable traffic data collection is required. BIS(Bus Information System) data would be useful for the estimation of the route travel time because BIS is collecting data for the bus travel time on the main road of the city on real-time basis. Traditionally use of BIS data has been limited to the realm of bus operating but it has not been used for a variety of traffic categories. Therefore, this study estimates a route travel time on road networks in urban areas on the basis of real-time data of BIS and then eventually constructs regression models. These models use an explanatory variable that corresponds to bus travel time excluding service time at the bus stop. The results show that the coefficient of determination for the constructed regression model is more than 0.950. As a result of T-test performance with assistance from collected data and estimated model values, it is likely that the model is statistically significant with a confidence level of 95%. It is generally found that the estimation for the exact travel time on real-time basis is plausible if the BIS data is used.

각종 교통정보에 대한 요구수준이 높아지고 있으며 그 중에서도 도시 교통관리나 이용자 측면에서 통행시간 정보는 매우 유용한 것이다. 정확성 높은 통행시간의 추정을 위해서는 신뢰성 높은 교통데이터의 수집이 필수적으로 요구된다. 버스정보시스템(BIS)은 도시 주요도로를 운행하는 시내버스를 대상으로 통행시간 정보를 실시간으로 수집 관리하고 있어 경로통행시간 추정에 매우 유용한 데이터라 할 수 있다. 그러나 기존 BIS수집데이터는 시내버스의 운행과 관련된 정보를 생성하고 안내하는 기능에만 제한적으로 사용되고 있고 다양한 분야에 활용되지 못하고 있는 실정이다. 따라서 본 연구에서는 BIS를 통해 실시간으로 수집되고 있는 데이터를 이용하여 경로통행시간을 추정하기 위한 연구를 수행하였다. 시내버스의 총 통행시간에서 버스정류장서비스시간을 제외한 통행시간을 설명변수로 경로통행시간 추정모형을 구축한 결과 결정계수($R^2$)가 모두 0.950이상이었으며 T-test를 통한 검정결과 통계적으로 유의한 것으로 분석되었다. 따라서 각 가로별로 BIS를 통해 수집되고 있는 시내버스의 통행시간데이터를 설명변수로 이용하면 실시간 경로통행시간 추정이 가능할 것으로 판단된다.

Keywords

References

  1. Choi, K., Hong, W.-P., Choi, Y.-H. (2006). "A travel time estimation algorithm using transit gps data." Journal of The Korean Society of Civil Engineering, Vol. 26, No. 5D, pp.739-746 (in Korean).
  2. Jang, J. H., Baik, N., Kim, S.-H., Byun, Sang Cheol. (2004). "Dynamic travel time prediction using avi data." Journal of Korean Society of Transportation, Vol. 22, No. 7, pp. 169-175 (in Korean).
  3. Jung, Y. J., Kim, Y., Baek, H. S. (2005). "Development of the signal control algorithm using travel time informations of sectional detection systems." Journal of Korean Society of Transportation, Vol. 23, No. 8, pp. 181-191 (in Korean).
  4. Kim, J. J., Rho, J. H., Park, D. (2006). "On-line departure time based link travel ime estimation using spatial detection system." Journal of Korean Society of Transportation, Vol. 24, No. 2, pp. 157-168 (in Korean).
  5. Kim, Y., Lee, E. M. (2007). "The estimation of link travel time for oversaturated intersections from cosmos detector data." Journal of The Korean Society of Civil Engineering, Vol. 27, No. 2D, pp. 189-198 (in Korean).
  6. Lee, C., Park, H. Y., Kho, S. Y. (2002). "Prediction of path travel time using kalman filter." Journal of The Korean Society of Civil Engineering, Vol. 22, No. 5D, pp. 871-880 (in Korean).
  7. Lee, H. S., Chon, K. S. (2009). "A path travel time esimation study on expressways using tcs link travel time." Journal of Korean Society of Transportation, Vol. 27, No. 5, pp. 209-221 (in Korean).
  8. Lee, Y.-I., Lee, J.-H. (2001). "A study on link travel time estimating methodology for traffic information service (Determination of an adequate sample size)." Journal of Korean Society of Transportation, Vol. 20, No. 3, pp. 55-67 (in Korean).
  9. Park, S. H., Jeong, Y. J., Kim, Tschangho John. (2006). "Development and evaluation of real-time travel time forecasting model : Nonparametric Regression Analysis for the Seoul Transit System." Journal of Korean Society of Transportation, Vol. 24, No. 1, pp. 109-120 (in Korean).
  10. Yoo, B. S., Kang, S. P., Park, C. H. (2005). "Travel time estimation based on mobile information." Journal of The Korean Society of Civil Engineering, Vol. 25, No. 1D, pp. 23-30 (in Korean).
  11. Dion, F. and Rakha, H. (2006). Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates, Transportation Research Part B, Vol. 40, No. 9.
  12. Giovanni Huisken. and Eric C. van Berkum. (2003). A comparative analysis of short-range travel time prediction methods, TRB 82nd Annual Meeting.
  13. Hellinga, B. and Gudapati, R. (2000). "Estimating travel times from different data sources for use in ATMS and ATIS." Proceedings of the ITE District 1 & 7 Joint Annual Conference held in Niagara Falls, Ontario, May 6.
  14. Steven I. Chien, Xiaobo Liu. and kaan Ozbay. (2003). Predicting travel times for the south jersey real-time motorist information system, TRB 82nd Annual Meeting.
  15. Steven I-Jy Chien and Chandra Mouly Kuchipudi. (2003). Dynamic travel time prediction with Real-Time and historic data, Journal of Transportation Engineering.
  16. Z. Wall, D.J. Dailey. (1999). An algorithm for predicting the arrival time of mass transit vehicles using automatic vehicle location data, In Presentation and Review 78th Annual Meeting of the Transportation Research Board.

Cited by

  1. A Study on Traffic Analysis Using Bus Information System vol.5, pp.9, 2016, https://doi.org/10.3745/KTCCS.2016.5.9.261
  2. Formulation and Evaluation of Railway Optimal Alignment Design Model vol.34, pp.6, 2014, https://doi.org/10.12652/Ksce.2014.34.6.1851