KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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A Study on the Development of a Technique to Predict Missing Travel Speed Collected by Taxi Probe

결측 택시 Probe 통행속도 예측기법 개발에 관한 연구

  • 윤병조 (인천대학교 도시과학대학 도시환경공학부)
  • Received : 2010.11.12
  • Accepted : 2010.11.23
  • Published : 2011.02.28
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Abstract

The monitoring system for link travel speed using taxi probe is one of key sub-systems of ITS. Link travel speed collected by taxi probe has been widely employed for both monitoring the traffic states of urban road network and providing real-time travel time information. When sample size of taxi probe is small and link travel time is longer than a length of time interval to collect travel speed data, and in turn the missing state is inevitable. Under this missing state, link travel speed data is real-timely not collected. This missing state changes from single to multiple time intervals. Existing single interval prediction techniques can not generate multiple future states. For this reason, it is necessary to replace multiple missing states with the estimations generated by multi-interval prediction method. In this study, a multi-interval prediction method to generate the speed estimations of single and multiple future time step is introduced overcoming the shortcomings of short-term techniques. The model is developed based on Non-Parametric Regression (NPR), and outperformed single-interval prediction methods in terms of prediction accuracy in spite of multi-interval prediction scheme.

택시 프로브(Probe)를 이용한 구간통행속도 모니터링체계는 지능형교통체계(ITS)의 핵심적인 하부시스템 중 하나이다. 택시 프로브기법을 통해 수집되는 구간통행속도는 도시가로망의 교통상태 모니터링과 통행시간 정보제공에 널리 활용되고 있다. 그러나 택시 Probe기법은 표본수가 적고 교통혼잡으로 인하여 구간통행시간이 자료수집 주기보다 큰 경우, 실시간으로 자료가 수집되지 않는 누락상태가 발생하게 된다. 이러한 누락상태는 단일시간대에서 다중시간대에 걸쳐 발생하게 되며, 기존의 단일시간대 예측기법으로는 다중시간대의 상태를 예측하지 못하는 단점이 있다. 따라서 다중시간대 누락상태에서 실시간 구간통행속도를 예측하기위한 기법이 요구된다. 본 연구에서는 기존의 단일시간대 예측기법의 한계를 극복하면서 단일 및 다중시간대 통행속도를 예측하기위한 기법을 개발하였다. 개발된 모형은 비모수회귀(NPR)을 기반으로 개발되었으며, 다중시간대 예측에도 불구하고 기존의 단일시간대 예측기법보다 우수한 정확도를 보였다.

Keywords

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