Smart Transport Networks
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Smart Transport Networks

Market Structure, Sustainability and Decision Making

Edited by Thomas Vanoutrive and Ann Verhetsel

Transport is debated by many, and liberalization processes, transport policy, transport and climate change and increased competition between transport modes are the subject of heated discussion. Smart Transport Networks illustrates that whether concerning road, water, rail or air, knowledge on the structure of transport markets is crucial in order to tackle transport issues. The book therefore explores key factors concerning the structure of transport markets, their environmental impact, and questions why decision makers often fail to tackle transport-related problems.
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Chapter 10: Mixture–amount experiments for measuring consumer preferences of energy-saving adaptation strategies: principles and illustration

Market Structure, Sustainability and Decision Making

Dujuan Yang, Gamze Dane and Harry J.P. Timmermans


Predicting the likely impacts of new policies, plans or projects has been the core of transportation research for decades. In general, two different modeling approaches can be distinguished. Foremost, transportation researchers have collected cross-sectional data about the dependent variable (preferences, choices, and so on) and about the set of person, household, spatial, and transport variables that are assumed to influence the dependent variable of interest. The quintessence of this revealed preference approach is then choosing the right statistical or econometric approach, dependent on: (i) the nature of the dependent variable, (ii) the assumed functional relationship between the dependent variable and set of independent variables, and (iii) assumptions with regard to the un observed variables, reflected in the variance–covariance matrix of the error terms. Rather than collecting cross-sectional data, panel data have been collected in a more limited number of cases, and dynamic models have been formulated. By assuming that the estimated parameters are invariant over time, prediction implies translating policies into the independent variables of the model, keeping the parameters fixed and then applying the model equation to calculate new values for the dependent variable.

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