All Issue

2017 Vol.35, Issue 3 Preview Page
The Study for Estimating Traffic Volumes on Urban Roads Using Spatial Statistic and Navigation Data

공간통계기법과 내비게이션 자료를 활용한 도시부 도로 교통량 추정연구

Dahee HONG1*
,
Jinho KIM2
,
Doogik JANG3
,
Taewoo LEE4
홍 대희1*
,
김 진호2
,
장 동익3
,
이 태우4
1Dept. of Road Transport Research, The Korea Transport Institute
2Dept. of National Infra. Policy Research, Korea Development Institute
3Dept. of Transport Big-Data Research, The Korea Transport Institute
4Transportation Pollution Reserch Center, National Institute of Environmental Research
1한국교통연구원 도로교통본부
2한국개발연구원 국토 · 인프라정책연구부
3한국교통연구원 교통빅데이터연구소
4국립환경과학원 교통환경연구소
*교신저자. *Corresponding Author. 
June 2017. pp. 220-233
PDF XML Citation
Abstract

Traffic volumes are fundamental data widely used in various traffic analysis, such as originand- destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.

Keywords
navigation DB
regression kriging
spatial statistic
unobserved traffic volumes
urban roads

교통량은 주말 및 첨두시 O/D 구축, 차량주행거리 산정, 혼잡도로개선 대책 등에 활용되는 중요한 기초자료이다. 그럼에도 불구하고 국내 도시부 도로의 교통량 링크 커버리지는 매우 낮아, 현재 수집 교통량으로는 교통정책 및 분석에 제약이 따를 수밖에 없다. 이에 본 연구에서는 특 ․ 광역시 중 수집교통량 및 속도의 링크 커버리지가 가장 낮은 서울시를 대상으로, 수집 교통량과 속도를 활용하여 교통량 결측링크의 교통량을 추정하는 방안을 제안하였다. 여기서, 교통량 추 정 방법으로 공간적 통계기법을 활용하였다. 교통량 추정모형 구축시, 서울시의 도시고속도로와도시부 도로는 교통류 및 통행패턴은 상이하므로 이를 분류하여 도시고속도로에는 구간별 상수함수, 도시부 도로에 는 회귀크리깅을 적용하였다. 이용 데이터로는 서울시 TOPIS, 국교부 국가교통정보센터 등에서 수집한 공공부문 교 통량, 속도와 민간 내비게이션 DB를 활용하였다. 내비게이션 DB는 대부분의 도로링크에서 수집되므로 교통량 추정 에 매우 용이하다는 강점을 가지고 있다. 단, 내비게이션 DB는 수집 교통데이터의 샘플데이터이므로, 모집단인 교통 량, 속도와 비교․검증하여 적용하였다. 뿐만 아니라 내비게이션 DB도 결측링크가 존재하고, 차종이 승용차로만 구 성되어 있으므로 이를 보정하여 적용하였다. 공간적 통계기법을 통해 추정한 교통량은 MAPE, RMSE를 활용하여 실제 교통량과 비교·검증하였다. 검증결과 model error가 MAPE 6.26%, RMSE 5,410로 모델의 추정력이 높고, prediction error는 MAPE 20.3% 로 교통량 추정에 대한 추정력도 높은 것으로 분석되었다. 본 연구에서 제시한 교통 량 결측링크의 교통량 추정모형은 차량주행거리와 온실가스 배출량 산정 등에 다양하게 활용될 수 있을 것으로 판단 된다.

키워드
내비게이션 DB
회귀크리깅
공간적 통계기법
교통량 결측링크
내비게이션 DB 보정
도시부 도로
References
1
Ahmed S., De Marsily G. (1987), Comparison of Geostatistical Methods for Estimating Transmissivity Using Data on Transmissivity and Specific Capacity, Water Resources Research, 23(9), 1717-1737.
2
Busan Metropolitan City (2014), Traffic survey-vehicle traffic volumes survey results.
3
Cressie N. (1993), Statistics for Spatial Data, Wiley Interscience, New York.
4
Daegu Metropolitan City (2014), Traffic related basic survey.
5
Daejeon Metropolitan City (2014), Traffic survey and analysis reports.
6
Draper N. R., Cox D. R. (1996), On Distributions and Their Transformation to Normality, Journal of the Royal Statistical Society B, 31, 472-476.
7
Eom J. K., Park M. S., Heo T. Y., Huntsinger L. F. (2006), Improving the Prediction of Annual Average Daily Traffic for Nonfreeway Facilities by Applying a Spatial Statistical Method, Transportation Research Record, Journal of the Transportation Research Board, 1968(1), 20-29.
8
Federal Highway Administration (FHWA) (2013), Highway Performance Monitoring System Field Manual, 6-1-6-13.
9
Gadda S., Kockelman K., Maggon A. (2007), Estimates of AADT: Quantifying the Uncertainty, Presented at the World Conference on Transportation Research.
10
Gwangju Metropolitan City (2014), Traffic related basic survey result reports.
11
Hengl T., Bajat B., Blagojević D., Reuter H. I. (2008), Geostatistical Modeling of Topography using Auxiliary Maps, Computers & Geosciences, 34(12), 1886-1899.
12
Hengl T., Heuvelink G. B., Stein A. (2003), Comparison of Kriging With External Drift and Regression-Kriging, Technica note, ITC, 51.
13
Heo T.-Y., Park M. S., Eom J.k., Oh J. S. (2007) A Study on the Prediction of Traffic Counts Based on Shortest Travel Path, Journal of Applied Statistics, 20(3), 459-473.
14
Incheon Metropolitan City (2014), Urban traffic basic survey.
15
Kumapley R. K., Fricker J. D. (1996), Review of Methods for Estimating Vehicle Miles Traveled, TRR, 1551(1), TRB, 59-66.
16
Lim S. H., Oh J. S. (2004) A Study on Deriving of Adjustment Factor to Estimate AADT, Journal of Korea Society of Civil Engineers, 24(1D), 19-29.
17
Matheron G. (1963), Principles of Geostatistics, Economic Geology, 58, 1246-1266.
18
Miller E. J., Ibrahim A. (1988), Urban Form and Vehicular Travel: Some Empirical Findings, Transportation Research Record, Journal of the Transportation Research Board, 1617(1), 18-27.
19
Odeh I.O.A., McBratney A.B., Chittleborough D.J. (1995), Further Results on Prediction of Soil Properties From Terrain Attributes: Heterotopic Cokriging and Regression-kriging, Geoderma, 67(3-4), 215-226.
20
Pu W., Meese A. J. (2013), Using New Data Sources to Meet MAP-21 Requirements for Performance-Based Planning: National Capital Region’s Experience in Monitoring Congestion and Reliability, In Transportation Research Board 92nd Annual Meeting (No.13-0183).
21
Schabenberger O., Gotway C. (2005), Statistical Methods for Spatial Data Analysis, Chapman & Hall/CRC, New York.
22
Selby B., Kockelman K. (2011), Spatial Prediction Of AADT In Unmeasured Locations By Universal Kriging, Transportation Research Board 90th Annual Meeting, No.11-1665.
23
Selby B., Kockelman K. M. (2013), Spatial Prediction of Traffic Levels in Unmeasured Locations: Applications of Universal Kriging and Geographically Weighted Regression, Journal of Transport Geography, 29, 24-32.
24
Seoul Metropolitan City (2014), Seoul city road statistics.
25
Ulsan Metropolitan City (2014), Regular traffic volumes and speed survey results.
26
Wang X., Kockelman K. (2009), Forecasting Network Data: Spatial Interpolation of Traffic counts from Texas data.
27
Wang X., Kockelman K. M. (2009), Forecasting Network Data, Transportation Research Record, Journal of the Transportation Research Board, 2105(1), 100-108.
28
Wegmann F., Everett J. (2008), Minimum Travel Demand Model Calibration and Validation Guidelines for the State of Tennessee, Center for Transportation Research, The University of Tennessee.
29
Woong D.T. (1997), Traffic Engineering Theory(1), ChungMunGak.
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 35
  • No :3
  • Pages :220-233
  • Received Date : 2017-01-25
  • Revised Date : 2017-03-04
  • Accepted Date : 2017-06-14
  • DOI :https://doi.org/10.7470/jkst.2017.35.3.220
Journal Informaiton Journal of Korean Society of Transportation Journal of Korean Society of Transportation
Archives
: 2015 Vol.34~
  • scopus
  • NRF
  • KOFST
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close