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  • 1.
    Krüger, Niclas
    et al.
    Centre of Transport Studies, Stockholm, Sweden; Swedish National Road and Transport Research Institute, Linköping, Sweden; TRENoP, Royal Institute of Technology, Stockholm, Sweden .
    Haglund, Alexander
    Department of Economics, Karlstad University, Karlstad, Sweden.
    Consumer value of fuel choice flexibility: a case study of the flex-fuel car in Sweden2013In: European Transport Research Review, ISSN 1867-0717, E-ISSN 1866-8887, Vol. 5, no 4, p. 207-215Article in journal (Refereed)
    Abstract [en]

    Purpose: This paper examines the value of fuel choice flexibility derived from a flex-fuel engine.

    Method: Based on the stochastic properties of fuel prices, we use Monte-Carlo simulation in order to value the option to switch fuel.

    Results: Our findings indicate a considerable value of fuel choice flexibility, ranging between 7,500 and 37,800 SEK, depending on the underlying stochastic process we assume that fuel prices follow. This can be compared to the state subsidy of 10,000 SEK provided until recently for buying a flex-fuel car.

    Conclusion: Compared to an environmentally friendly pure ethanol strategy, the switching strategy is considerably less costly, about 2,000-19,000 SEK depending on the assumed underlying stochastic process, a fact that is important to take into consideration with environmental policy.

  • 2.
    Krüger, Niclas
    et al.
    Örebro University, Örebro University School of Business. Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Vierth, Inge
    School of Business, Örebro University, Örebro, Sweden; Swedish National Road and Transport Research Institute, Linköping, Sweden.
    Precautionary and operational costs of freight train delays: a case study of a Swedish grocery company2015In: European Transport Research Review, ISSN 1867-0717, E-ISSN 1866-8887, Vol. 7, no 1, p. 1-10Article in journal (Refereed)
    Abstract [en]

    Methods: We develop on a conceptual level an estimation approach for the value of reduced transportation time variability (VTTV) related to the cargo component based on precautionary and operative delay costs. This approach is inspired by the safety stock approach but includes more general precautionary measures that firms take to avoid stock-out costs. This paper presents the analysis of a Swedish grocery company’s transports by shuttle train as a case study. First, the distribution of the arrival times of the shuttle train is analyzed in order to estimate the transportation time variability for the firm. Second, precautionary costs for measures undertaken to manage and mitigate the transportation time variability are estimated and the additional operational costs that occur in case of major delays are calculated.

    Results: It is found that the 10 % worst delays contribute to more than half of the total train delays, showing that actual transportation times exhibit a heavily skewed distribution with fat tails, indicating that the standard deviation might not be a sufficient measure of transport time variability. The calculated VTTV related to the cargo component based on the precautionary costs is around €4 per delay-tonne-hour and around €2.2 per standard deviation of transportation time.

    Conclusions: We show that by conducting a case study it is possible to get VTTV estimates for the cargo component valid for a specific company. In conclusion, assuming a high degree of transport market concentration with regard to shippers, a limited number of case studies for key companies in the market might be sufficient to get a representative VTTV measure. We therefore advocate further case studies and research aimed at getting more inputs from firms that send and receive goods. More research should also be done on how to incorporate risks for delays and the extremeness of empirical delays in transport models and VTTV definitions.

    Purpose: There is limited up-to-date knowledge about the monetary valuation of improved reliability for freight transports. This means that the benefits of reduced variability in transport time are not properly taken into account in cost-benefit analysis relating to, for example, infrastructure investments.

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