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Dynamic Forecasting Of Travel Demand, Residential Location And Land Development

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  • Alex Anas
  • Liang Shyong Duann

Abstract

ABSTRACT The Chicago Area Transportation/Land‐Use Analysis System (CATLAS) is a large scale urban simulation model which synthesizes “location rent analysis” from urban economics with “travel demand analysis” from transportation planning. This paper describes the theoretical formulation, empirical estimation and policy application of CATLAS to the evaluation of CBD‐oriented rapid transit projects in Chicago.

Suggested Citation

  • Alex Anas & Liang Shyong Duann, 1985. "Dynamic Forecasting Of Travel Demand, Residential Location And Land Development," Papers in Regional Science, Wiley Blackwell, vol. 56(1), pages 37-58, January.
    • Handle: RePEc:bla:presci:v:56:y:1985:i:1:p:37-58
    DOI: 10.1111/j.1435-5597.1985.tb00837.x
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    Cited by:

    1. Beaudoin, Justin & Tyndall, Justin, 2023. "The effect of bus rapid transit on local home prices," Research in Transportation Economics, Elsevier, vol. 102(C).
    2. Alex Anas & Richard Arnott, 1989. "Dynamic Housing Market Equilibrium with Taste Heterogeneity," Discussion Papers 834, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    3. McDonald, John F. & Osuji, Clifford I., 1995. "The effect of anticipated transportation improvement on residential land values," Regional Science and Urban Economics, Elsevier, vol. 25(3), pages 261-278, June.
    4. Voith, Richard, 1997. "Fares, Service Levels, and Demographics: What Determines Commuter Rail Ridership in the Long Run?," Journal of Urban Economics, Elsevier, vol. 41(2), pages 176-197, March.
    5. Möller, J. & Zierer, M., 2018. "Autobahns and jobs: A regional study using historical instrumental variables," Journal of Urban Economics, Elsevier, vol. 103(C), pages 18-33.
    6. Russo, Francesco & Musolino, Giuseppe, 2012. "A unifying modelling framework to simulate the Spatial Economic Transport Interaction process at urban and national scales," Journal of Transport Geography, Elsevier, vol. 24(C), pages 189-197.
    7. J. Elhorst & Jan Oosterhaven, 2006. "Forecasting the impact of transport improvements on commuting and residential choice," Journal of Geographical Systems, Springer, vol. 8(1), pages 39-59, March.
    8. Pinjari, Abdul Rawoof & Bhat, Chandra R. & Hensher, David A., 2009. "Residential self-selection effects in an activity time-use behavior model," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 729-748, August.
    9. Yen-Jong Chen & Cheng-Kai Hsu, 2020. "Comparison of Housing Price Elasticities Resulting from Different Types of Multimodal Rail Stations in Kaohsiung, Taiwan," International Real Estate Review, Global Social Science Institute, vol. 23(3), pages 1043-1058.
    10. Yen-Jong Chen & Cheng-Kai Hsu, 2020. "Comparison of Housing Price Elasticities Resulting from Different Types of Multimodal Rail Stations in Kaohsiung, Taiwan," International Real Estate Review, Global Social Science Institute, vol. 23(3), pages 417-432.
    11. Abdul Pinjari & Ram Pendyala & Chandra Bhat & Paul Waddell, 2011. "Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions," Transportation, Springer, vol. 38(6), pages 933-958, November.
    12. Sophie Masson, 1998. "Interactions between transport system and land use system. From traditionals models heritage to the contribution of integrated land use and transport models [Interactions entre système de transport," Post-Print halshs-01359553, HAL.

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