Oregon's transportation and land use model integration program: A retrospective

Rick Donnelly

Abstract


An ambitious and innovative integrated land use-transport modeling system has been developed in Oregon over the past two decades. This work, completed under the Transportation and Land Use Model Integration Program (TLUMIP), included the development of two generations of models and the data required to build and use them and spawned the development of two others that have continued independently. An outreach program and collaborative development of freight data and forecasts were also included, as well as system testing and applications. A brief description of the motivation behind TLUMIP and the resulting modeling systems are presented. Perhaps more interesting is the story behind the models, describing several major model design, institutional, and methodology issues that were overcome. Using an integrated model in practice also entailed addressing a wider range of analytical requirements and stakeholder expectations about usability, accuracy, and extensibility than typically considered in academic pursuits. The key lessons learned through development and use of the models are discussed, with the hope that they will inform the development of similar large-scale modeling systems.

Keywords


land use; transport; dynamic; activity-based approach; microsimulation; validation; uncertainty

Full Text:

PDF

References


Acheampong, R. A. and E. A. Silva. 2015. Land use-transport interaction modeling: A review of the literature and future research directions. Journal of Transport and Land Use, 8(3):11–38.

doi:http://dx.doi.org/10.5198/jtlu.2015.806.

Brown, D. G., S. Page, R. Riolo, M. Zellner, and W. Rand. 2005. Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Informa- tion Science, 19(2):153–174. doi:http://dx.doi.org/10.1080/13658810410001713399.

de la Barra, T. 2005. Integrated land use and transport modelling: decision chains and hierarchies. Cambridge University Press.

Donnelly, R., ed. 2017. SWIM v2.5 Model Development Report. Oregon Department of Transportation. URL https://github.com/tlumip/model-dev-report/swim25mdr.pdf.

Donnelly, R., G. Erhardt, R. Moeckel, and W. A. Davidson. 2010. Advanced practices in travel forecasting. NCHRP Synthesis 406, National Cooperative Highway Research Program, Trans- portation Research Board, Washington, D.C.

Echenique, M., A. Flowerdew, J. D. Hunt, I. Skidmore, and D. C. Simmonds. 2007. The MEPLAN models of Bilbao, Leeds and Dortmund. Transport Reviews, 10:309–332.

Hess, S., A. Daly, C. Rohr, and G. Hyman. 2007. On the development of time period and mode choice models for use in large scale modelling forecasting systems. Transportation Research Part A, 41(9):802–826. doi:10.1016/j.tra.2007.04.001.

Hunt, J. D. and J. E. Abraham. 2015. Design and implementation of PECAS: a generalized system for the allocation of economic production, exchange and consumption quantities. In M. Lee- Gosselin and S. Doherty, eds., Integrated land-use and transportation models: behavioural foun- dations, pp. 253–274. Amsterdam: Elsevier.

Knaap, G. and A. C. Nelson. 1992. The regulated landscape: lessons on state land use planning from Oregon. Cambridge, MA: Lincoln Institute of Land Policy.

Kok, K., A. Fallow, A. Veldkamp, and P. H. Verburg. 2001. A method and application of multi-scale validation in spatial land use models. Agriculture, Ecosystems & Environment, 85(1–3):223–238. doi:10.1016/S0167-8809(01)00186-4.

Litman, T. 1997. Full cost accounting of urban transportation: implications and tools. Cities, 14(3):169–174.

Martin, R. 2003. Agile software development: principles, patterns, and practices. Upper Saddle River, NJ: Prentice-Hall.

Miller, E., B. Farooq, F. Chingcuanco, and D. Wang. 2011. Historical validation of integrated transport-land use model system. Transportation Research Record, 2255:91–99. doi: 10.3141/2255-10.

Næss, P., M. S. Nicolaisen, J. Andersen, and A. Strand. 2015. Forecasting inaccuracies: A result of unexpected events, optimism bias, technical problems, or strategic misrepresentation? Journal of Transport and Land Use, 8(3):39–55. doi:http://dx.doi.org/10.5198/jtlu.2015.719.

Oregon DOT. 2017. Rough roads ahead: the cost of poor highway conditions to oregon’s economy. URL https://www.oregon.gov/ODOT/COMM/Documents/RoughRoads2014.pdf.

Waddell, P. 2002. UrbanSim: modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68:297–314.

Waddell, P. 2011. Integrated land use and transportation planning and modelling: Addressing challenges in research and practice. Transport Reviews, 31(2):209–229. doi: 10.1080/01441647.2010.525671.

Walker, P. and P. Hurley. 2011. Planning paradise: politics and visioning of land use in Oregon. Tucson, AZ: The University of Arizona Press.

Zhao, Y. and K. M. Kockelman. 2002. The propagation of uncertainty through travel demand models: an exploratory analysis. Annals of Regional Science, 36(1):145–163.




DOI: http://dx.doi.org/10.5198/jtlu.2018.1210