Spatial Proximity and Dependency to Model Urban Travel Demand
Publication: Journal of Urban Planning and Development
Volume 142, Issue 2
Abstract
Link level annual average daily traffic (AADT) or travel demand is used in several urban planning, roadway design, operational, and safety analyses by transportation planners and engineers. Existing AADT estimation methods do not adequately account for spatial proximity, variations, and dependency to address modeling needs. The primary focus of this paper, therefore, is to incorporate these aspects and develop a method to estimate link level AADT by the urban road functional class. Geospatial analytical techniques were explored to capture spatial data within proximal areas of selected roadway links and develop statistical models to estimate link level AADT. Polygon-based network buffers were generated within the proximal roadway distance of each study link to account for actual connectivity and capture off-network data instead of Euclidean distance-based buffers. On-network characteristics of the study links and upstream, downstream, and cross-street network links were considered to account for the spatial dependency of on-network characteristics. The applicability of the method and predictive capability of the models to estimate link level AADT, considering all of the selected study links and by each road functional class, was researched. The working of the method and development of the models is illustrated using data for the city of Charlotte in the state of North Carolina. The generalized estimating equation (GEE) models developed indicate that a negative binomial distribution fits better than a Poisson distribution for the data considered in this research. The ideal proximal distance to capture spatial data and accurately estimate AADT was observed to vary when all study links and different road functional classes were modeled separately. Overall, the results obtained indicate that spatial proximity and dependency play a vital role in accurately estimating travel demand on various urban road functional classes.
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Acknowledgments
The authors thank the staff of the city of Charlotte Department of Transportation (CDOT) for their help in providing data used in this research.
Disclaimer
The contents of this paper reflect the views of the author(s) and not necessarily the views of the University of North Carolina at Charlotte or CDOT. The author(s) are responsible for the facts and the accuracy of the data presented herein.
References
Boyce, D. (2002). “Is the sequential travel forecasting paradigm counter–productive?” J. Urban Plann. Dev., 169–183.
Davis, S. C., Diegel, S. W., and Boundy, R. G. (2014). Transportation energy data book, 33rd Ed., Vehicle Technologies Office, Office of Energy Efficiency and Renewable Energy, United States Dept. of Energy, Washington, DC.
Ding, C., Mishra, S., Lin, Y., and Xie, B. (2014). “Cross-nested joint model of travel mode and departure time choice for urban commuting trips: Case study in Maryland-Washington, DC region.” J. Urban Plann. Dev., 04014036.
Duddu, V. R., and Pulugurtha, S. 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.”, Transportation Research Board, Washington, DC, 20–29.
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.” TRB 87th Annual Meeting Compendium of Papers DVD, Transportation Research Board, Washington, DC.
Gadda, S. C., Magoon, A., and Kockelman, K. M. (2007). “Quantifying the uncertainty in annual average daily traffic (AADT) count estimates.” 11th World Conf. on Transportation Research, Transport Research Society, Lyon, France.
Gecchele, G., Rossi, R., Gastaldi, M., and Caprini, A. (2011). “Data mining methods for traffic monitoring data analysis: A case study.” Proc. Procedia-Social Behav. Sci., 20, 455–464.
Gecchele, G., Rossi, R., Gastaldi, M., and Kikuchi, S. (2012). “Advances in uncertainty treatment in FHWA procedure for estimating annual average daily traffic volume.”, Transportation Research Board, Washington, DC, 148–156.
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.
Jiang, Z., McCord, M. R., and Goel, P. K. (2006). “Improved AADT estimation by combining information in image and ground-based traffic data.” J. Transp. Eng., 523–530.
Kusam, P. R. (2011). “Methods to estimate link level travel based on spatial effects.” Ph.D. dissertation, Univ. of North Carolina at Charlotte, Charlotte, NC.
Lee, J., Lee, S. K., and Jun, J. (2014). “Connections between land use and driving distance: Causal investigation using acyclic graphs.” J. Urban Plann. Dev., 06014001.
McCord, M., Yang, Y., Jiang, Z., Coifman, B., and Goel, P. (2003). “Estimating AADT from satellite imagery and air photos: Empirical results.”, 136–142.
McCullagh, P., and Nelder, J. A. (1989). Generalized linear models, 2nd Ed., Chapman & Hall, London.
Memmott, J. (2007). “Trends in personal income and passenger vehicle miles.”, Bureau of Transportation Statistics, United States Dept. of Transportation, Washington, DC.
Miller, H. J., and Shaw, S. (2001). Geographic information systems for transportation: Principles and applications, Oxford University Press, New York.
Mohamad, D., Sinha, K. C., Kuczek, T., and Scholer, C. F. (1998). “Annual average daily traffic prediction model for county roads.”, Transportation Research Board, Washington, DC, 69–77.
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 U.S. urban areas.” J. Urban Plann. Dev., 04014031.
Neto, M. C., Jeong, Y., Jeong, M. K., and Han, L. D. (2009). “AADT prediction using support vector regression with data-dependent parameters.” Exp. Syst. Appl., 36(2), 2979–2986.
Neveu, A. J. (1983). “Quick-response procedures to forecast rural traffic.”, Transportation Research Board, Washington, DC, 47–53.
Oppenheim, N. (1995). Urban travel demand modeling: From individual choice to general equilibrium, Wiley, New York.
Ortuzar, J. D., and Willumsen, L. G. (2001). Modelling transport, Wiley, New York.
Pulugurtha, S. S., and Agurla, M. (2012). “Assessment of models to estimate bus-stop level transit ridership using spatial modeling methods.” J. Public Transp., 15(1), 33–52.
Pulugurtha, S. S., and Repaka, S. R. (2009). “An assessment of models to measure pedestrian activity at signalized intersections.”, Transportation Research Board, Washington, DC, 39–48.
Pulugurtha, S. S., and Sambhara, V. R. (2011). “Pedestrian crash estimation models for signalized intersections.” Accid. Anal. Prev. J., 43(1), 439–446.
Roess, R. P., Prassas, E. S., and McShane, W. R. (2004). Traffic engineering, 3rd Ed., Pearson Education, Upper Saddle River, NJ.
Seaver, W. L., Chatterjee, A., and Seaver, M. L. (2000). “Estimation of traffic volume on rural local roads.”, Transportation Research Board, Washington, DC, 121–128.
Selby, B., and Kockelman, K. M. (2011). “Spatial prediction of AADT in unmeasured locations by universal kriging.” TRB 90th Annual Meeting Compedium of Papers DVD, Transportation Research Board, Washington, DC.
Sharma, S., Lingras, P., Xu, F., and Kilburn, P. (2001). “Application of neural networks to estimate AADT on low-volume roads.” J. Transp. Eng., 426–432.
Sharma, S. C., Lingras, P., Liu, G. X., and Xu, F. (2000). “Estimation of annual average daily traffic on low-volume roads factor approach versus neural networks.”, Transportation Research Board, Washington, DC, 103–111.
Sharma, S. C., Lingras, P., Xu, F., and Liu, G. X. (1999). “Neural networks as alternative to traditional factor approach of annual average daily traffic estimation from traffic counts.”, Transportation Research Board, Washington, DC, 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.
Souleyrette, R. R., and Anderson, M. D. (1998). “Developing small area networking planning models using desktop GIS.” J. Urban Plann. Dev., 55–71.
SPSS 16.0 [Computer software]. Chicago, Command Syntax Reference.
Stopher, P. R., and Meyburg, A. H. (1975). Urban transportation modeling and planning, Lexington Books, Lexington, MA.
Tang, Y. F., Lam, W. H. K., and Pan, L. P. (2003). “Comparison of four modeling techniques for short-term AADT forecasting in Hong Kong.” J. Transp. Eng., 271–277.
Traffic Monitoring Guide. (2001). “Office of highway policy information.” United States Dept. of Transportation, Federal Highway Administration, Washington, DC.
Wang, X., and Kockelman, K. M. (2009). “Forecasting network data: Spatial interpolation of traffic counts using Texas data.”, Transportation Research Board, Washington, DC,100–108.
Xia, Q., Zhao, F., Chen, Z., Shen, L. D., and Ospina, D. (1999). “Development of a regression model for estimating AADT in a Florida County.”, Transportation Research Board, Washington, DC, 32–40.
Yang, B., Wang, S., and Bao, Y. (2011). “Efficient local AADT estimation via SCAD variable selection based on regression models.” Proc., Control and Decision Conf. (CCDC), IEEE, New York, 1898–1902.
Zhao, F., and Chung, S. (2001). “Contributing factors of annual average daily traffic in a Florida County.”, Transportation Research Board, Washington, DC, 113–122.
Zhao, F., and Park, N. (2004). “Using geographically weighted regression models to estimate annual average daily traffic.”, Transportation Research Board, Washington, DC, 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.
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Published In
Journal of Urban Planning and Development
Volume 142 • Issue 2 • June 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Apr 28, 2014
Accepted: Feb 20, 2015
Published online: Sep 21, 2015
Discussion open until: Feb 21, 2016
Published in print: Jun 1, 2016
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