Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/119108
Authors: 
Kickhöfer, Benjamin
Grether, Dominik
Nagel, Kai
Year of Publication: 
2010
Series/Report no.: 
50th Congress of the European Regional Science Association: "Sustainable Regional Growth and Development in the Creative Knowledge Economy", 19-23 August 2010, Jönköping, Sweden
Abstract: 
Public acceptance has often been named as a key factor for the successful realization of transport projects and policies. One reason, why even economically efficient projects might not be accepted by the major part of the population, could be the unequal distribution of benefits. For instance, individuals with higher Values of Time are expected to benefit more from user-financed improvements in the quality of service (e.g. speed) of any transportation mode. Beyond that, the implementation of road pricing schemes is actually discussed to have a regressive effect on the welfare distribution under certain conditions. In order to address these issues, microscopic multi-agent simulation presented in this paper can be used. Policy makers are directly able to compare the impacts of different policy schemes on the welfare distribution and can thus identify alternatives with higher public acceptance. Generally, by using the multi-agent approach, any segregation of individuals among any socio-demographic attribute is possible what allows a more detailed view on the effects of a policy measure. Furthermore, in contrast to applied economic policy analysis, this framework allows choice modeling and economic evaluation to be realised in a consistent way. This paper shows that (i) the inclusion of individual income in the users' preferences leads to a better understanding of problems that are linked to acceptability, (ii) benefits of transport projects are likely to rise disproportionally with increasing income - both, in terms of utility change and in terms of money -, and (iii) the simulation is already feasible for a real-world large-scale scenario with almost two million individuals.
Document Type: 
Conference Paper

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