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Evaluation of travel demand measures and programs: a data envelopment analysis approach

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  • Nozick, Linda K.
  • Borderas, Hector
  • Meyburg, Arnim H.

Abstract

Traffic congestion in urban as well as suburban areas has increased steadily in the United States despite substantial increases in federal, state and local spending. While no adequate responses to control the growth of congestion have been identified, travel demand management (TDM) has become a recognized policy option. Despite the recognition of TDM measures as a potential option, few methods have been suggested that can critically evaluate the improvements achieved through these measures at individual worksites or can isolate the effects of particular measures. This paper presents such a methodology and applies that methodology to 33 worksites that have TDM programs in effect.

Suggested Citation

  • Nozick, Linda K. & Borderas, Hector & Meyburg, Arnim H., 1998. "Evaluation of travel demand measures and programs: a data envelopment analysis approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(5), pages 331-343, September.
  • Handle: RePEc:eee:transa:v:32:y:1998:i:5:p:331-343
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    References listed on IDEAS

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    2. Chu, Xuehao & Fielding, Gordon J. & Lamar, Bruce W., 1992. "Measuring transit performance using data envelopment analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 26(3), pages 223-230, May.
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    Cited by:

    1. D'Acierno, Luca & Cartenì, Armando & Montella, Bruno, 2009. "Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System," European Journal of Operational Research, Elsevier, vol. 196(2), pages 719-736, July.
    2. Van Malderen, Laurent & Jourquin, Bart & Pecheux, Claude & Thomas, Isabelle & Van De Vijver, Elien & Vanoutrive, Thomas & Verhetsel, Ann & Witlox, Frank, 2013. "Exploring the profession of mobility manager in Belgium and their impact on commuting," Transportation Research Part A: Policy and Practice, Elsevier, vol. 55(C), pages 46-55.
    3. George E. Halkos & Nickolaos G. Tzeremes, 2015. "Measuring Seaports' Productivity: A Malmquist Productivity Index Decomposition Approach," Journal of Transport Economics and Policy, University of Bath, vol. 49(2), pages 355-376, April.
    4. Ramanathan, R, 2001. "Comparative Risk Assessment of energy supply technologies: a Data Envelopment Analysis approach," Energy, Elsevier, vol. 26(2), pages 197-203.
    5. Sundo, Marloe B. & Fujii, Satoshi, 2005. "The effects of a compressed working week on commuters' daily activity patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(10), pages 835-848, December.
    6. Cairns, S. & Newson, C. & Davis, A., 2010. "Understanding successful workplace travel initiatives in the UK," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(7), pages 473-494, August.
    7. Laurent Van Malderen & Bart Jourquin & Isabelle Thomas, 2012. "Employers Transport Plans: Do They Change The Commuting Behaviour Of Workers?," ERSA conference papers ersa12p1048, European Regional Science Association.
    8. Nakanishi, Yuko J & Falcocchio, John C, 2004. "Performance Assessment Of Intelligent Transportation Systems Using Data Envelopment Analysis," Research in Transportation Economics, Elsevier, vol. 8(1), pages 181-197, January.
    9. Cullinane, Kevin & Wang, Teng-Fei & Song, Dong-Wook & Ji, Ping, 2006. "The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(4), pages 354-374, May.
    10. Azadeh, A. & Ghaderi, S.F. & Maghsoudi, A., 2008. "Location optimization of solar plants by an integrated hierarchical DEA PCA approach," Energy Policy, Elsevier, vol. 36(10), pages 3993-4004, October.

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