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Intelligent transportation systems: An economic and environmental policy assessment

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  • Kanninen, Barbara J.

Abstract

This paper discusses the congestion relief and environmental impacts expected of Intelligent Transportation Systems (ITS) based on a qualitative assessment of the incentives generated. It uses theoretical and empirical results from the literature to evaluate the private and externality impacts of Advanced Traveler Information Systems (ATIS), Automated Highway Systems (AHS) and Intelligent Transit Systems and discusses the appropriate role of the public sector in their development based on these impacts. It is argued that although ITS are intended to improve system efficiency, the technologies may, in fact, exacerbate the existing, economic inefficiencies in the surface transportation system and that policies to correct these inefficiencies become all the more crucial as we consider implementation of ITS. Several policies that target environmental externalities are discussed as possible complements to ITS.

Suggested Citation

  • Kanninen, Barbara J., 1996. "Intelligent transportation systems: An economic and environmental policy assessment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(1), pages 1-10, January.
    • Handle: RePEc:eee:transa:v:30:y:1996:i:1:p:1-10
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    References listed on IDEAS

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    1. Washington, Simon P. & Guensler, Randall, 1994. "Carbon Monoxide Impacts of Automatic Vehicle Identification Applied to Electronic Vehicle Tolling," University of California Transportation Center, Working Papers qt92v0436v, University of California Transportation Center.
    2. Baumol,William J. & Oates,Wallace E., 1988. "The Theory of Environmental Policy," Cambridge Books, Cambridge University Press, number 9780521322249.
    3. Kenneth Train, 1980. "A Structured Logit Model of Auto Ownership and Mode Choice," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(2), pages 357-370.
    4. Shladover, Steven E., 1993. "Potential contributions of intelligent vehicle/ highway systems (IVHS) to reducing transportation's greenhouse gas production," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(3), pages 207-216, May.
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    1. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1999. "Information and time-of-usage decisions in the bottleneck model with stochastic capacity and demand," European Economic Review, Elsevier, vol. 43(3), pages 525-548, March.
    2. Thomas J.T. Van der Wardt & Amro M. Farid, 2017. "A Hybrid Dynamic System Assessment Methodology for Multi-Modal Transportation-Electrification," Energies, MDPI, vol. 10(5), pages 1-25, May.
    3. Kawakami, Tetsu & Tiwari, Piyush & Doi, Masayuki, 2004. "Assessing Impact Of Its On Japan'S Economy Using A Computable General Equilibrium Model," Research in Transportation Economics, Elsevier, vol. 8(1), pages 525-547, January.
    4. Kwan, Mei-Po & Golledge, Reginald G. & Speigle, Jon M., 1996. "A Review of Object-Oriented Approaches in Geographical Information Systems for Transportation Modeling," University of California Transportation Center, Working Papers qt2qf6b23x, University of California Transportation Center.
    5. Shaheen, Susan A. & Bejamin-Chung, Jade & Allen, Denise & Howe-Steiger, Linda, 2009. "Achieving California’s Land Use and Transportation Greenhouse Gas Emission Targets Under AB 32: An Exploration of Potential Policy Processes and Mechanisms," Institute of Transportation Studies, Working Paper Series qt8bm4t7w5, Institute of Transportation Studies, UC Davis.

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