Highway Improvement Project Rankings due to Uncertain Model Inputs: Application of Traditional Transportation and Land Use Models
Publication: Journal of Urban Planning and Development
Volume 136, Issue 4
Abstract
While much research has been devoted to analyzing the variation in transportation and land use model outputs due to uncertainty, little has been done to quantitatively answer the more important question of how decision making will change based on recognition of this uncertainty. This paper aims to begin to fill this gap by evaluating how roadway investment decisions will differ depending on whether or not uncertainty is recognized. Population and employment control totals, as well as trip generation and trip distribution parameters, are found via antithetic sampling, and a full feedback integrated gravity-based land use and four-step travel model is used. It is found that the ranking of improvement projects may indeed be different if uncertainty is considered relative to treating all parameters and data as deterministic. The experimental analysis conducted in this paper found this percent difference to be between 4 and 25% depending on the performance metric used: total system travel time, vehicle miles traveled, total delay, average network speed, and standard deviation of network speed were all examined.
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Acknowledgments
The research contained in this paper was funded by Texas Department of Transportation Project No. UNSPECIFIED0-5667, “Analysis and Guidelines for Establishing Unified Urban Land-Use and Transportation System Planning.” The writers would also like to acknowledge Avinash Unnikrishnan for his assistance in developing the antithetic sampling code, Brenda Zhou and Varun Valsaraj for their assistance in developing the land use model code, and several anonymous reviewers for their suggestions.
References
Armoogum, J., Madre, J. L., and Bussière, Y. (2002). “Uncertainty in long term forecasting of travel demand from demographic modeling.” 13th Mini EURO Conf.
Beckmann, M., McGuire, C., and Winston, C. (1956). Studies in the economics of transportation, Yale University Press, New Haven, Conn.
Bell, M. G. H. (2004). “Games, heuristics, and risk averseness in vehicle routing problems.” J. Urban Plann. Dev., 130(1), 37–41.
Cheng, R. C. H. (1984). “The use of anti-thetic variates in computer simulations.” J. Oper. Res. Soc., 33, 229–237.
Dijkstra, E. W. (1959). “A note on two problems in connexion with graphs.” Numer. Math., 1, 269–271.
Duthie, J., Unnikrishnan, A., and Waller, S. T. (2006). “Network evaluation with uncertain and correlated long-term demand.” 85th Transportation Research Board Meeting, Transportation Research Board of the National Academies, Washington, D.C.
Gifford, J. L. (1994). “Adaptability and flexibility in urban transportation policy and planning.” Technol. Forecast. Soc. Change, 45, 111–117.
Gregor, B. (2007). “(LUSDR): A practical land use model using a stochastic microsimulation framework.” Transportation Research Record 2003, Transportation Research Board of the National Academies, Washington, D.C., 93–102.
Harvey, G., and Deakin, E. (1995). Description of the step analysis package.
Khan, A. M. (1989). “Realistic planning for transportation—A flexible approach.” Long Range Plann., 22, 128–136.
Krishnamurthy, S., and Kockelman, K. (2003). “An investigation of DRAM-EMPAL and UTPP predictions in Austin, Texas.” Transportation Research Record 1831, Transportation Research Board of the National Academies, Washington, D.C., 219–229.
Lam, W. H. K., and Tam, M. L. (1998). “Risk analysis of traffic and revenue forecasts for road investment projects.” J. Infrastruct. Syst., 4(1), 19–27.
Law, A. M., and Kelton, W. D. (1992). Simulation modeling and analysis, McGraw-Hill Higher Education Press, Boston.
Lemp, J. (2007). “Travel demand forecasting models: Development, application, and comparison of aggregate and activity-based approaches for the Austin, Texas region.” MS thesis, Dept. of Civil, Architectural and Environmental Engineering, Univ. of Texas at Austin, Austin, Tex.
Mahmassani, H. S. (1984). “Uncertainty in transportation systems evaluation: Issues and approaches.” Transp. Plan. Technol., 9, 1–12.
Martin, W. (1998). “Travel estimation techniques for urban planning.” National Cooperative Highway Research Program Rep. No. 365, Transportation Research Board, Washington, D.C.
Miller, J. S., Hoel, L. A., Goswami, A. K., and Ulmer, J. M. (2006). “Borrowing residential trip generation parameters.” J. Transp. Eng., 132(2), 105–113.
Niles, J. S., and Nelson, D. (2001). “Identifying uncertainties in forecasts of travel demand.” Preprint for the Transportation Research Board’s 80th Annual Meeting
Pradhan, A., and Kockelman, K. (2002). “Uncertainty propagation in an integrated land use-transportation modeling framework: Output variation via UrbanSim.” Transportation Research Record 1805, Transportation Research Board of the National Academies, Washington, D.C., 128–235.
Putman, S. (1983). Integrated urban models: Policy analysis of transportation and land use, Pion, London.
Rodier, C. J., and Johnston, R. A. (2002). “Uncertain socioeconomic projections used in travel demand and emissions models: Could plausible errors result in air quality nonconformity?” Transp. Res., Part A: Policy Pract., 36(7), 613–631.
Sevcikova, H., Raftery, A. E., and Waddell, P. A. (2007). “Assessing uncertainty in urban simulations using Bayesian melding.” Transp. Res., Part B: Methodol., 41, 652–669.
Sheffi, Y. (1985). Urban transportation networks: Equilibrium analysis with mathematical programming methods, Prentice-Hall, Englewood Cliffs, N.J.
Smith, S. K., and Shahidullah, M. (1995). “An evaluation of population projection errors for census tracts.” J. Am. Stat. Assoc., 90(429), 64–71.
Sussman, J., Sgouridis, S. P., and Ward, J. L. (2005). “21st century: Regional strategic transportation planning as a complex large-scale integrated open system.” Transportation Research Record 1931, Transportation Research Board of the National Academies, Washington, D.C., 89–98.
Tarko, A. P., and Rouphail, N. M. (1997). “Intelligent traffic data processing for ITS applications.” J. Transp. Eng., 123(4), 298–307.
Waller, S. T., Schofer, J. L., and Ziliaskopoulos, A. K. (2001). “Evaluation with traffic assignment under demand uncertainty.” Transportation Research Record 1771, Transportation Research Board of the National Academies, Washington, D.C., 69–75.
Wardrop, J. G. (1952). “Some theoretical aspects of road traffic research.” Proc.-Inst. Civ. Eng., 1, 325–378.
Zhao, Y., and Kockelman, K. (2002). “The propagation of uncertainty through travel demand models: An exploratory analysis.” Ann. Reg. Sci., 36(1), 145–163.
Zhou, B., Kockelman, K. M., and Lemp, J. (2009). “Applications of integrated transport and gravity-based land use for policy analysis.” Transportation Research Record 2133, Transportation Research Board of the National Academies, Washington, D.C., 123–132.
Information & Authors
Information
Published In
Journal of Urban Planning and Development
Volume 136 • Issue 4 • December 2010
Pages: 294 - 302
Copyright
© 2010 ASCE.
History
Received: Nov 30, 2008
Accepted: Dec 18, 2009
Published online: Dec 21, 2009
Published in print: Dec 2010
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