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Transportation

, Volume 46, Issue 3, pp 1011–1032 | Cite as

The potential use of big vehicle GPS data for estimations of annual average daily traffic for unmeasured road segments

  • Hyun-ho Chang
  • Seung-hoon CheonEmail author
Article
  • 244 Downloads

Abstract

A promising methodology is proposed to estimate reliable annual average daily traffic (AADT) volumes for no-surveyed road sections using probe volumes collected by a vehicle global positioning system (GPS). This research was inspired by the obvious concept that probe counts are a direct portion of AADT from the viewpoint of vehicle trip behavior. The method converts the probe volume of target road section to AADT using the nonlinear relationship between geographical neighborhoods composed of observed AADT volumes and annual average daily probe volumes. The relationship is determined with a locally weighted power-curve model. A feasibility of the proposed method was demonstrated through a case study using real-world data. Analysis results show that the proposed method is a practical and cost-effective way to estimate reliable AADT for unmeasured road segments. This indicates that there exists a strong relationship between AADT values and vehicle-GPS probe values from the trip characteristics of a road network.

Keywords

Unmeasured road section Direct traffic demand estimation Large-scale vehicle-GPS data Direct expansion method Weighted power curve 

Notes

Acknowledgements

We are very grateful to the three anonymous reviewers for their constructive comments and suggestions. We owe many parts of this paper to them. Two reviewers provided helpful suggestions and offered useful guidelines and comments on the modeling and its contributions. The third reviewer offered sound ideas for academic and practical contributions. The reviewers are gratefully acknowledged.

References

  1. Anderson, M., Sharfi, K., Gholston, S.: Direct demand forecasting model for small urban communities using multiple linear regression. Transp. Res. Rec. 1981, 114–117 (2006)CrossRefGoogle Scholar
  2. Caceres, N., Romero, L.M., Morales, F.J., Reyes, A., Benitez, F.G.: Estimating traffic volumes on intercity road locations using roadway attributes, socioeconomic features and other work-related activity characteristics. J. Transp (2017).  https://doi.org/10.1007/s11116-017-9771-5 Google Scholar
  3. Cressie, N.: Statistics for spatial data. Wiley, New York (1993)CrossRefGoogle Scholar
  4. Eom, J.K., Park, M.S., Heo, T.Y., Huntsinger, L.F.: Improving the prediction of annual average daily traffic for nonfreeway facilities by applying a spatial statistical method. Transp. Res. Rec. 1968, 20–29 (2006)CrossRefGoogle Scholar
  5. Federal Highway Administration (FHWA): Traffic monitoring guide, FHWA-PL-13-015, US, Department of Transportation, FHWA, Washington, D.C. (2013)Google Scholar
  6. Fotheringham, S., Brunsdon, C., Charlton, M.: Geographically weighted regression: the analysis of spatially varying relationships. Wiley, Chichester (2003)Google Scholar
  7. Lowry, M.: Spatial interpolation of traffic counts based on origin-destination centrality. J. Transp. Geogr. 36, 98–105 (2014)CrossRefGoogle Scholar
  8. Mohamad, D., Sinha, K., Kuczek, T., Scholer, C.: Annual average daily traffic prediction model for county roads. Transp. Res. Rec. 1617, 69–77 (1998)CrossRefGoogle Scholar
  9. Rezaee, H., Asghari, O., Yamamoto, J.K.: On the reduction of the ordinary kriging smoothing effect. J. Mining Environ. 2(2), 102–117 (2011)Google Scholar
  10. Roess, P.R., Prassas, E.S., McShane, W.R.: Traffic engineering. Pearson Prentice Hall, Upper Saddle River (2004)Google Scholar
  11. Selby, B., Kockelman, K.M.: Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regression. J. Transp. Geogr. 29, 24–32 (2013)CrossRefGoogle Scholar
  12. Shamo, B., Aza, E., Membah, J.: Linear spatial interpolation and analysis of annual average daily traffic data. J. Comput. Civ. Eng. (2014).  https://doi.org/10.1061/(ASCE)CP.1943-5487.0000281 Google Scholar
  13. Wang, X., Kockelman, K.: Forecasting network data: spatial interpolation of traffic counts from Texas data. Transp. Res. Rec. 2105, 100–108 (2009)CrossRefGoogle Scholar
  14. Xia, Q., Zhao, F., Chen, Z., Shen, L.D., Ospina, D.: Estimation of annual average daily traffic for nonstate roads in a Florida county. Transp. Res. Rec. 1660, 32–40 (1999)CrossRefGoogle Scholar
  15. Zhao, F., Chung, S.: Contributing factors of annual average daily traffic in a Florida county: exploration with geographic information system and regression models. Transp. Res. Rec. 1769, 113–122 (2001)CrossRefGoogle Scholar
  16. Zhao, F., Park, N.: Using geographically weighted regression models to estimate annual average daily traffic. Transp. Res. Rec. 1879, 99–107 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of Environmental StudiesSeoul National UniversitySeoulRepublic of Korea
  2. 2.Korea Transport Database CenterKorea Transport InstituteSejong-siRepublic of Korea

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