Addressing the Local-Road VMT Estimation Problem Using Spatial Interpolation Techniques
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 143, Issue 8
Abstract
Vehicle miles of travel (VMT) data have a wide range of applications in highway agency business processes. However, at all administrative levels, highway agencies continue to be stymied by the poor reliability of local-road VMT estimates resulting from the inadequacy of local-road traffic counts. This paper presents a methodology to address this problem. The methodology first clusters local roads and then imputes traffic-volume data for segments within each cluster by applying spatial interpolation techniques and sparse traffic-volume data. The proposed methodology uses geographic information system (GIS)-enabled spatial interpolation algorithms, including Kriging, inverse distance weighting (IDW), natural neighbor (NN), and trend techniques. The accuracy, in terms of prediction error, of each technique was validated using actual traffic counts. Spatial interpolation techniques can yield efficient imputations of absent traffic data and therefore can produce reliable estimates of local-road VMT. The results in this paper suggest that the use of spatial interpolation for local-road VMT estimation is cost-effective because it makes use of the available traffic-count data from existing road segments and therefore does not require additional data collection efforts. Also, a comparison was made of the relative efficacies of the alternative spatial interpolation techniques for purposes of imputing missing traffic data at certain links and ultimately for VMT estimation or prediction. The methodology can be updated easily with new traffic-count data and can be used by any highway agency for local-road VMT estimation. An essential prerequisite is a comprehensive inventory of the local roads for which the VMT is sought.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research was funded by the Joint Transportation Research Program of the Indiana Department of Transportation and Purdue University, Project No. SPR-3829. The research would not have been possible without the valuable support of the Study Advisory Committee, particularly, Mark Ratliff, Bill Weinmann, Roy Nunnally, Samy Noureldin, and Guy Boruff. Also, Dr. Bismark Agbelie is acknowledged for his contributions. The contents of this paper reflect the views of the authors. The contents do not necessarily reflect the official views or policies of any of the agencies or organizations mentioned here.
References
AASHTO. (2009). “AASHTO guidelines for traffic data programs.” Washington, DC.
Abedini, A., Ebrahimkhani, H., and Abedini, B. (2016). “Mixed method approach to delineation of functional urban regions: Shiraz Metropolitan Region.” J. Urban Plann. Dev., 04016001.
Agbelie, B., Volovski, M., Bardaka, E., Zhang, Z., Labi, S., and Sinha, K. (2016). “Updating state and local highway cost allocation and revenue attribution: A case study for Indiana.” Transp. Res. Rec. J. Transp. Res. Board, 2597, 1–10.
ArcGIS 10.2 [Computer software]. ESRI, Redlands, CA.
Balducci, P., and Stowers, J. R. (2008). “State highway cost allocation studies.”, Transportation Research Board, Washington, DC.
Cervero, R., and Murakami, J. (2010). “Effects of built environments on vehicle miles traveled: Evidence from 370 U.S. metropolitan areas.” Environ. Plann. A, 42(2), 400–418.
Davis, G., and Yang, S. (2001). “Accounting for uncertainty in estimates of total traffic volume: An empirical Bayes approach.” J. Transp. Stat., 4(1), 27–38.
Delfiner, P. (1976). “Linear estimation of non-stationary spatial phenomena.” Advanced geostatistics in the mining industry, M. Guarascio, M. David, and C. Huijbrechts, eds., Reidel, Dordrecht, Netherlands, 221–236.
Duddu, V., and Pulugurtha, S. (2013). “Principle of demographic gravitation to estimate annual average daily traffic: Comparison of statistical and neural network models.” J. Transp. Eng., 585–595.
Eom, J. K., Park, M. S., Heo, T. Y., and Huntsinger, L. F. (2006). “Improving the prediction of annual average daily traffic for non-freeway facilities by applying a spatial statistical method.” Transp. Res. Rec., 1968, 20–29.
ESRI Corporation. (2016). “Geographically weighted regression.” ⟨desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/geographically-weighted-regression.htm⟩ (Oct. 2016).
FHWA (Federal Highway Administration). (2005). “MOBILE6 Implementation Experience.” ⟨http://www.fhwa.dot.gov/environment/air_quality/conformity/research/mobile6_implementation_2005/⟩ (Mar. 15, 2015).
FHWA (Federal Highway Administration). (2013). Traffic monitoring guide, Federal Highway Administration of the U.S. Dept. of Transportation, Washington, DC.
Fricker, J. D., and Kumapley, R. K. (2002). “Updating procedures to estimate and forecast vehicle-miles traveled.”, Joint Transportation Research Program, Indiana Dept. of Transportation and Purdue Univ., West Lafayette, IN.
Fricker, J. D., Xu, C., and Jin, L. (2008). “Comparison of annual average daily traffic estimates: Traditional factor, statistical, artificial neural network, and fuzzy basis neural network approach.” 87th Annual Meeting Compendium of Papers, Transportation Research Board, Washington, DC.
Garrett, M. (2014). Encyclopedia of transportation social science and policy, Sage, Los Angeles.
Goovaerts, P. (2000). “Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall.” J. Hydrol., 228(1–2), 113–129.
INDOT (Indiana Department of Transportation). (2015). “Traffic count database system (TCDS) and historical traffic statistics.” ⟨http://indot.ms2soft.com/tcds/tsearch.asp?loc=Indot&mod=⟩ (Apr. 20, 2015).
INDOT (Indiana Department of Transportation). (2015). “Traffic statistics.” ⟨www.in.gov/indot/2 720.htm⟩ (Apr. 20, 2015).
Jiang, Z. (2005). “Incorporating image-based data in AADT estimation—Methodology and numerical investigation of increased accuracy.” Ph.D. dissertation, Ohio State Univ., Columbus, OH.
Kelsall, J., and Wakefield, J. (2002). “Modeling spatial variation in disease risk: A geostatistical approach.” J. Am. Stat. Assoc., 97(459), 692–701.
Kim, S., Park, D., Heo, T.-Y., Kim, H., and Hong, D. (2016). “Estimating vehicle miles traveled (VMT) in urban areas using regression kriging.” J. Adv. Transp., 50(5), 769–785.
Kumapley, R. K., and Fricker, J. D. (1996). “A review of methods for estimating vehicle-miles traveled.” Transp. Res. Rec., 1551, 59–66.
Kusam, P. R., and Pulugurtha, S. S. (2015). “Spatial proximity and dependency to model urban travel demand.” J. Urban Plann. Dev., 04015014.
Lam, W. H. K., and Xu, J. (2000). “Estimation of AADT from short period counts in Hong Kong: A comparison between neural network method and regression analysis.” J. Adv. Transp., 34(2), 249–268.
Lethanh, N., Richmond, C., and Adey, B. T. (2016). “Investigation of the ability to estimate values of road section condition indicators based on their spatial correlation.” J. Infrastruct. Syst., 04016006.
Li, J., and Heap, A. D. (2011). “Review of spatial interpolation methods in environmental sciences: Performance and impacts.” Ecol. Inf., 6(3–4), 228–241.
Mahendra, A., Grant, M., Higgin, T., and Bhatt, K. (2011). “Road prisoning: public perceptions and program development.”, Transportation Research Board, Washington, DC.
McCord, M., Yang, Y., Jiang, Z., Coifman, B., and Goel, P. (2003). “Estimating AADT from satellite imagery and air photos: Empirical results.” Transp. Res. Rec., 1855, 136–142.
Mitas, L., and Mitasova, H. (1999). “Spatial interpolation.” Geographical information systems: Principles, techniques, management and applications, P. Longley, M. Good-child, D. Maguire, and D. Rhind, eds., Wiley, London, 481–492.
Mohamad, D., Sinha, K. C., Kuczek, T., and Scholer, C. F. (1998). “Annual average daily traffic prediction model for county roads.” Transp. Res. Rec., 1617, 69–77.
Morey, J., Niemeier, D., and Limanond, T. (2004). “Statistical framework using GIS to estimate unpaved road VMT for PM10 emission inventories.” J. Urban Plann. Dev., 83–93.
Myers, D. (1994). “Spatial interpolation: An overview.” Geoderma, 62(1-3), 17–28.
Nasri, A., and Zhang, L. (2012). “Impact of metropolitan-level built environment on travel behavior.” Transp. Res. Rec., 2323(1), 75–79.
Nasri, A., and Zhang, L. (2014). “Assessing the impact of metropolitan-level, county-level, and local-level built environment on travel behavior: Evidence from 19 US urban areas.” J. Urban Plann. Dev., 04014031.
Oh, J., Labi, S., and Sinha, K. C. (2007). “Implementation and evaluation of self-financing highway pricing schemes: A case study.” Transp. Res. Rec., 1996, 25–33.
Rossi, R., Gastaldi, M., and Gecchele, G. (2014). “Comparison of clustering methods for road group identification in FHWA traffic monitoring approach: Effects on AADT estimates.” J. Transp. Eng., 04014025.
Seaver, W. L., Chatterjee, A., and Seaver, M. L. (2000). “Estimation of traffic volume on rural local roads.” Transp. Res. Rec., 1719, 121–128.
Shamo, B., Asa, E., and Membah, J. (2012). “Linear spatial interpolation and analysis of annual average daily traffic data.” J. Comput. Civ. Eng., 04014022.
Sharma, S. C., Gulati, B. M., and Rizak, S. (1996). “Statewide traffic volume studies and precision of AADT estimates.” J. Transp. Eng., 430–439.
Sharma, S. C., Lingras, P., Xu, F., and Liu, G. X. (1999). “Neural networks as an alternative to the traditional factor approach of annual average daily traffic estimation from traffic counts.” Transp. Res. Rec., 1660, 24–31.
Shen, L. D., Zhao, F., and Ospina, D. (1999). “Estimation of annual average daily traffic for off-system roads in Florida.”, Florida Dept. of Transportation, Tallahassee, FL.
Soltani, A., and Allan, A. (2006). “Analyzing the impacts of microscale urban attributes on travel: Evidence from suburban Adelaide, Australia.” J. Urban Plann. Dev., 132–137.
Tang, Y. F., Lam, W. H. K., and Ng, L. P. (2003). “Comparison of four modeling techniques for short-term AADT forecasting in Hong Kong.” J. Transp. Eng., 271–277.
USEPA (U.S. Environmental Protection Agency). (1999). “Guidance for the development of facility type VMT and speed distributions.”, U.S. Environmental Protection Agency, Washington, DC.
Varma, A., Sinha, K. C, and Spalding, J. L. (1991). “The development of a highway revenue forecasting model for Indiana.”, Joint Transportation Research Program, Indiana Dept. of Transportation and Purdue Univ., West Lafayette, IN.
Washington, L. J. (2008). “Highway safety: Foresight issues challenge DOT’s efforts to assess and respond to new technology-based trends.”, U.S. Governmental Accounting Office, Washington, DC.
Whitman, M. B., Larsen, L., and Whitty, J. M. (2012). “Equity evaluation of vehicle miles traveled fees in Texas.” Texas Transportation Institute, Texas A&M Univ. System, College Station, TX.
Wohl, M., and Hendrickson, C. (1984). “Transportation investment and pricing principles.” An introduction for engineers, planners, and economists, Wiley, New York, 1–102.
Xia, Q., Zhao, F., Chen, Z., Shen, L. D., and Ospina, D. (1999). “Estimation of annual average daily traffic for nonstate roads in a Florida county.” Transp. Res. Rec., 1660, 32–40.
Yin, C., Shi, Y., Wang, H., and Wu, J. (2014). “Disaggregation of an urban population with M_IDW interpolation and building information.” J. Urban Plann. Dev., 04014012.
Zhao, F., and Chung, S. (2001). “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.
Zhao, F., and Park, N. (2004). “Using geographically weighted regression models to estimate annual average daily traffic.” Transp. Res. Rec., 1879, 99–107.
Zhong, M., Lingras, P., and Sharma, S. (2004). “Estimation of missing traffic counts using factor, genetic, neural, and regression techniques.” Transp. Res. Part C, 12(2), 139–166.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 143 • Issue 8 • August 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: May 7, 2016
Accepted: Feb 10, 2017
Published online: May 10, 2017
Published in print: Aug 1, 2017
Discussion open until: Oct 10, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.