Original scientific paper
Risk Route Choice Analysis and the Equilibrium Model under Anticipated Regret Theory
Pengcheng Yuan
; JiaoTong University
zhicai juan
; JiaoTong University
Abstract
The assumption about travellers’ route choice behaviour has major influence on the traffic flow equilibrium analysis. Previous studies about the travellers’ route choice were mainly based on the expected utility maximization theory. However, with the gradually increasing knowledge about the uncertainty of the transportation system, the researchers have realized that there is much constraint in expected utility maximization theory, because expected utility maximization requires travellers to be ‘absolutely rational’; but in fact, travellers are not truly ‘absolutely rational’. The anticipated regret theory proposes an alternative framework to the traditional risk-taking in route choice behaviour which might be more scientific and reasonable. We have applied the anticipated regret theory to the analysis of the risk route choosing process, and constructed an anticipated regret utility function. By a simple case which includes two parallel routes, the route choosing results influenced by the risk aversion degree, regret degree and the environment risk degree have been analyzed. Moreover, the user equilibrium model based on the anticipated regret theory has been established. The equivalence and the uniqueness of the model are proved; an efficacious algorithm is also proposed to solve the model. Both the model and the algorithm are demonstrated in a real network. By an experiment, the model results and the real data have been compared. It was found that the model results can be similar to the real data if a proper regret degree parameter is selected. This illustrates that the model can better explain the risk route choosing behaviour. Moreover, it was also found that the traveller’ regret degree increases when the environment becomes more and more risky.
Keywords
anticipated regret; expected utility; risk decision; route choice; traffic flow
Hrčak ID:
124147
URI
Publication date:
28.2.2014.
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