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Making an Informed Vehicle Scrappage Decision

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  • Cynthia Chen
  • Jie (Jane) Lin

Abstract

According to the US Federal Highway Administration (FHWA), the number of publicly owned vehicles in the USA reached 3 913 999 in 2003. In order to maintain a stable vehicle fleet, government agencies must repeatedly make vehicle scrappage decisions because older vehicles must first retire to make room for newer vehicles. Typically, these decisions are made based on a deterministic ranking evaluation model to select candidate vehicles for replacement. The paper applied an objective and probabilistic method to a vehicle dataset collected by the DuPage County Forest Preserve District ((DCFPD), in the state of Illinois). A Weibull‐form survival model with time‐varying covariate and unobserved heterogeneity was estimated on the dataset. The results suggest that in addition to the fact that vehicle age is negatively related to the vehicle’s survival probability, there are other variables that also appear influential. The survival probabilities of alternative fuel vehicles are similar to those of reformulated unleaded gasoline vehicles. The results suggest that a probabilistic and objective model can benefit government agencies in their vehicle scrappage decisions.

Suggested Citation

  • Cynthia Chen & Jie (Jane) Lin, 2006. "Making an Informed Vehicle Scrappage Decision," Transport Reviews, Taylor & Francis Journals, vol. 26(6), pages 731-748, April.
    • Handle: RePEc:taf:transr:v:26:y:2006:i:6:p:731-748
    DOI: 10.1080/01441640600752545
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    References listed on IDEAS

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    1. Alan Greenspan & Darrel Cohen, 1999. "Motor Vehicle Stocks, Scrappage, And Sales," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 369-383, August.
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    Cited by:

    1. Julie Fisher & Rudy Hirschheim & Robert Jacobs, 2008. "Understanding the outsourcing learning curve: A longitudinal analysis of a large Australian company," Information Systems Frontiers, Springer, vol. 10(2), pages 165-178, April.
    2. Guerrero, Sebastian E. & Madanat, Samer M. & Leachman, Robert C., 2013. "The Trucking Sector Optimization Model: A tool for predicting carrier and shipper responses to policies aiming to reduce GHG emissions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 59(C), pages 85-107.
    3. Cynthia Chen & Jason Chen, 2009. "What is responsible for the response lag of a significant change in discretionary time use: the built environment, family and social obligations, temporal constraints, or a psychological delay factor?," Transportation, Springer, vol. 36(1), pages 27-46, January.
    4. Lee, J.F. Jennifer & Kwok, Peter K. & Williams, Jeffrey, 2014. "Heterogeneity among motorists in traffic-congested areas in southern California," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 281-293.
    5. Jie Lin & Cynthia Chen & Debbie Niemeier, 2008. "An analysis on long term emission benefits of a government vehicle fleet replacement plan in northern illinois," Transportation, Springer, vol. 35(2), pages 219-235, March.

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