Forecasting the effects of road user charge by stochastic agent-based modelling

Abstract

This paper develops a new agent-based simulation model to improve discrete choice analysis as well as to analyse the effects of a road user charging scheme for the Upper Derwent Valley in the Peak District National Park. The advantages of discrete choice analysis are well known. However, results with these conventional methods can be biased if interaction effects are significant. The combined approach of the Minority Game, in which agents try to choose the option of the minority side, and discrete choice analysis is appropriate to deal with the problem. The main data was collected by stated preference survey. The agent-based model has four sub-modules: (1) multinomial mixed logit model for mode choice, (2) binary logit model for parking location choice, (3) Markov queue model for parking network, and (4) the Minority Game for parking congestion and learning. The results show that the road user charging scheme reduces car demand in the Upper Derwent Valley. The model also shows that an exemption will increase the utility of elderly visitors. In conclusion, the simulation model demonstrated that oversimplification in conventional discrete choice analysis gave significant biases when real world problems were analysed.