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Inter-temporal variation in the travel time and travel cost parameters of transport models

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Abstract

The parameters for travel time and travel cost are central in travel demand forecasting models. Since valuation of infrastructure investments requires prediction of travel demand for future evaluation years, inter-temporal variation of the travel time and travel cost parameters is a key issue in forecasting. Using two identical stated choice experiments conducted among Swedish drivers with an interval of 13 years, 1994 and 2007, this paper estimates the inter-temporal variation in travel time and cost parameters (under the assumption that the variance of the error components of the indirect utility function is equal across the two datasets). It is found that the travel time parameter has remained constant over time but that the travel cost parameter has declined in real terms. The trend decline in the cost parameter can be entirely explained by higher average income level in the 2007 sample compared to the 1994 sample. The results support the recommendation to keep the travel time parameter constant over time in forecast models, but to deflate the travel cost parameter with the forecasted income increase among travellers and the relevant income elasticity of the cost parameter. Evidence from this study further suggests that the inter-temporal and the cross-sectional income elasticities of the cost parameter are equal. The average elasticity is found to be −0.8 to −0.9 in the present sample of drivers, and the elasticity is found to increase with the real income level, both in the cross-section and over time.

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Notes

  1. Variations in marginal utility of money and time in the population may be relevant in appraisal in the specific decision context of prioritizing infrastructure investment projects financed with tax money, see Mackie et al. (2001).

  2. 1{x > y} is defined to take the value 1 if x > y and 0 otherwise.

  3. In both survey waves a predetermined share of company owned cars were recruited. In 1994, 20 % (more than the actual share on the road) of the recruited cars was owned by companies and 90 % of these was business trips and is thus not included in the analysis in this paper. In 2007, 17 % of the recruited cars were company-owned cars; this share was taken from the national travel survey and reflects the accrual share on the road.

  4. This is partly because the numbers to mobile phones are not available to the same extent as the numbers for fixed telephones in Sweden. The use mobile phones have increased and while fixed telephone subscriptions have decreased substantially between the survey years. Other surveys, for instance the national travel survey experience the same problem.

  5. These observations would not have had to be discarded if applying an ordered logit model in the estimation. However, it is not possible to have panel effects in an ordered logit model in Biogeme (Bierlaire 2003), and for this reason the observations are removed.

  6. The maximum of the log-likelihood function is not affected whether the serial correlation between observations from the same individuals is taken into account or not. However, when the serial correlation is not taken into account, the efficiency of the estimation will decrease. This means essentially, that the estimates get more imprecise when not taking account of the serial correlation, given the number of observations. Hence, for small sample sizes, this may give rise to errors, because the accuracy declines as the estimation become less efficient.

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Acknowledgments

I am indebted to Staffan Algers for help with data and to Jonas Eliasson and Staffan Algers for fruitful discussions. Financial support from the Swedish Road and Rail Administrations and VINNOVA and Centre for Transport studies, KTH, is also gratefully acknowledged.

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  1. Centre for Transport Studies, Royal Institute of Technology, 100 44, Stockholm, Sweden

    Maria Börjesson

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Correspondence to Maria Börjesson.

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Börjesson, M. Inter-temporal variation in the travel time and travel cost parameters of transport models. Transportation 41, 377–396 (2014). https://doi.org/10.1007/s11116-013-9493-2

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