鉴于 SML- GHK 方法估计 MNP 模型参数具有初值敏感性，提出了一种初值点确定与参数估计的实用方法。首先应用贝叶斯方法估计 MNP 模型参数的初值，再结合基于GHK 的模拟极大似然方法估计 MNP 模型参数，并通过实际算例对该方法进行了验证。结果表明: 文中方法实用有效，特别是在小样本情况下效果明显; 将基于该方法得到的参数值用于预测，能够较好地再现实际交通分担率; 文中方法能够帮助规划师探讨某些交通政策变化时全体出行者对各种出行方式的选择概率的变化．

As estimating MNP model parameters using the SML- GHK method is sensitive to the initial values,apractical method for initial value determination and parameter estimation is proposed.It uses the Bayesian approachto define the initial values of MNP model parameters,and adopts the simulated maximum likelihood based on GHKframework to estimate the MNP model parameters.Then,a case study is performed to verify the effectiveness of theproposed method.The results show that (1) the proposed method is applicable and effective,especially for small-scale samples; (2) using parameters defined by the proposed approach,better trip mode share forecast results canbe achieved ; and (3) it is a useful tool for transport planners to exam the probability variations of trip mode choiceresulted by transport policy changes.