Application of Mixed Multinomial Logit Models in Urban Traffic Split of Hefei City

Abstract

In this paper, logit models as well as mixed multinomial logit models(MMNL) were established to model traffic split. The properties of Johnson distribution bounded on both sides distribution was analyzed fisrt and it was found that Johnson distribution which are bounded on both sides can describe uniform preference factors better than normal and log- normal distribution. Based the residents' daily travel survey of Hefei city, a logit model and two distribution mixed logit model(MMNLl and MMNL2) with different restriction condition were established to model traffic mode split of a certain area, and the results indicats that MMNL2 is more fitable to explain the original data and more reasonable to describe uniform preference factors such as travel time. The sensitivity of the mixed logit model to transit travel time factor was also discussed in the paper, and the results shows that the implementation of bus priority policy make the passenger flow share of the bus get some increase which mainly comes from the bike users. Furthermore, mixed logit model is more reasonable to model traffic mode split due to its reasonable to describe uniform preference factors and no possession of IIA limitation.