Development of transport mode choice model by using adaptive neuro-fuzzy inference system

Developing precise travel behavior models is important for estimating traffic demand and, consequently, for planning transportation systems. A study is presented that suggests a hybrid model that combines a stochastic model with a neuro-fuzzy inference system. The model is applied for estimating traveler behavior in the context of the problem of transport mode choice. Particularly, the multinomial logit model with neuro-fuzzy utility functions is developed to investigate shopping traveler preferences regarding the modes of bus, subway, and automobile. The model is evaluated by comparing its results with the results of a multinomial logit model. Moreover, the probabilities of selecting a transport mode obtained by applying the two models are compared with the actual transport mode choices, which show better performance of the proposed model. In addition, the model demonstrates good performance by estimating a large number of right choices during the validation process. A sensitivity analysis demonstrates the influence of time variations of mode subway on the probabilities of selecting a transport mode. The analysis highlights different behaviors of the models caused by the different utility functions. The results confirm that the proposed model can describe uncertainties regarding traveler decisions on the time of transport mode choice.