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

Real-Time Traffic Information Provision Using Individual Probe and Five-Minute Aggregated Data

개별차량 및 5분 집계 프로브 자료를 이용한 실시간 교통정보 제공

  • Jang, Jinhwan (Dept. of Highway Res., Korea Inst. of Civil Eng. and Building Tech.)
  • 장진환 (한국건설기술연구원 도로연구소)
  • Received : 2018.12.01
  • Accepted : 2019.02.22
  • Published : 2019.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Probe-based systems have been gaining popularity in advanced traveler information systems. However, the high possibility of providing inaccurate travel-time information due to the inherent time-lag phenomenon is still an important issue to be resolved. To mitigate the time-lag problem, different prediction techniques have been applied, but the techniques are generally regarded as less effective for travel times with high variability. For this reason, current 5-min aggregated data have been commonly used for real-time travel-time provision on highways with high travel-time fluctuation. However, the 5-min aggregation interval itself can further increase the time-lags in the real-time travel-time information equivalent to 5 minutes. In this study, a new scheme that uses both individual probe and 5-min aggregated travel times is suggested to provide reliable real-time travel-time information. The scheme utilizes individual probe data under congested conditions and 5-min aggregated data under uncongested conditions, respectively. As a result of an evaluation with field data, the proposed scheme showed the best performance, with a maximum reduction in travel-time error of 18%.

무선통신기기 보급 확대로 인해 프로브 기반 교통정보시스템이 확대 구축되고 있다. 프로브 기반 통행시간 정보의 시간 처짐 현상 극복을 위해 다수의 예측 기법들이 적용되고 있지만, 일별 및 요일별 교통패턴이 불규칙한 구간에서는 예측 기법의 효용성이 저하되는 것으로 알려져 있다. 이로 인해 불규칙한 교통패턴을 나타내는 구간에서는 일반적으로 5분 집계단위의 프로브 정보를 사용하는데, 이는 집계 시간간격만큼 시간 처짐 현상을 증대시킨다. 이에 본 연구에서는 통행시간 패턴이 불규칙한 구간에 적용 가능한 교통정보 제공 방법론을 제안하였다. 제안된 방법은 개별차량 단위 프로브 정보와 5분 집계 프로브 정보를 융합 적용하는 것으로써, 제안된 방법론 적용 시 통행시간 정보 오차를 최대 18%까지 감소시킬 수 있는 것으로 분석되었다.

Keywords

References

  1. Bajwa S., Chung E. and Kuwahara M.(2005), "Performance Evaluation of an Adaptive Travel Time Prediction Model," Proc. Intelligent Transportation Systems Conference, IEEE, New York, pp.1000-1005.
  2. Billings D. and Yang J.-S.(2006), "Application of the ARIMA Models to Urban Roadway Travel Time Prediction: A Case Study," Proc. IEEE International Conference on Systems, Man, and Cybernetics, vol. 3.
  3. Boxel D. V., Schneider IV W. H. and Bakula C.(2011), "An Innovative Real-Time Methodology for Detecting Travel Time Outliers on Interstate Highways and Urban Arterials," TRB 2011 Annual Meeting CD-ROM, Washington D.C.
  4. Boxel D. V., Schneider W. H. and Bakula, C.(2011), "An Innovative Real-Time Methodology for Detecting Travel Time Outliers on Interstate Highways and Urban Arterials," TRB 2011 Annual Meeting CD-ROM, Washington D.C.
  5. Chien S. and Kuchipudi C.(2003), "Dynamic Travel Time Prediction with Real-Time and Historic Data," Journal of Transportation Engineering, ASCE, vol. 129, no. 6,
  6. Clark S. D., Grant-Muller S. and Chen H.(2002), "Cleaning of Matched License Plate Data," Transportation Research Record, no. 1804.
  7. Dion F. and Rakha H.(2006), "Estimating Dynamic Roadway Travel Times Using Automatic Vehicle Identification Data," Transportation Research Part B, Elsevier.
  8. Jang J.(2012), "Analysis of Time Headway Distribution on Suburban Arterial," KSCE Journal of Civil Engineering, vol. 16, no. 4.
  9. Jang J.(2013), "Short-Term Travel Time Prediction Using the Kalman Filter Combined with a Variable Aggregation Interval Scheme," Journal of the Eastern Asia Society of Transportation Studies, vol. 10.
  10. Jang J.(2016), "Data-Cleaning Technique for Reliable Real-Life Travel Time Estimation: Use of Dedicated Short-Range Communication Probes on Rural Highways," Transportation Research Record, no. 2593.
  11. Kim P.(2010), Kalman Filter for Beginners: with MATLAB Examples, A-JIN Publishing.
  12. Lim S., Lee H. and Jang J.(2013), Study on Improvement of System of Relay and Provision of Real-Life Traffic Information for Integrated Traffic Management, Korea Institute of Civil Engineering and Building Technology.
  13. Ma X. and Koutsopoulos H.(2010), "Estimation of the Automatic Vehicle Identification Based Spatial Travel Time Information Collected in Stockholm," IET Intelligent Transport Systems, vol. 4, Iss. 4.
  14. Ministry of Land, Infrastructure, and Transport(2010), ITS Performance Evaluation Guidelines.
  15. Moghaddam S. S. and Hellinga B.(2014), "Algorithm for Detecting Outliers in Bluetooth Data in Real Time," Transportation Research Record, no. 2442.
  16. Myung J., Kim D.-K., Kho S.-Y. and Park C.-H.(2011), "Travel Time Prediction Using k-Nearest Neighbor Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System," Transportation Research Record, no. 2256.
  17. National Institute of Standards and Technology(2017), NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/, Accessed October, 2017.
  18. Robinson S. and Polak J. W.(2005), "Modeling Urban Link Travel Time with Inductive Loop Detector Data by Using the k-NN Method," Transportation Research Record, no. 1935.
  19. Southwest Research Institute(1998), Automatic Vehicle Identification Model Deployment Initiative-System Design Document, Texas Department of Transportation.
  20. van Hinsbergen C. P. I., van Lint J. W. C. and van Zuylen H. J.(2009), "Bayesian Training and Committees of State-Space Neural Networks for Online Travel Time Prediction," Transportation Research Record, no. 2105.
  21. van Lint J., Hoogendoorn S. and van Zuylen H. J.(2005), "Accurate Freeway Travel Time Prediction with State-Space Neural Networks under Missing Data," Transportation Research Part C, vol. 13, no. 5/6.
  22. Wismans L., Suijs L., Krol L. and Berkum E.(2015), "In-Car Advice to Reduce Negative Effects of Phantom Traffic Jams," Transportation Research Record, no. 2489.
  23. You J. and Kim T. J.(2000), "Development and Evaluation of a Hybrid Travel Time Forecasting Model," Transportation Research Part C, vol. 8, no. 1-6, pp.231-256. https://doi.org/10.1016/S0968-090X(00)00012-7