基于随机系数Logit模型的地铁拥挤度影响参数研究

Evaluation of the Metro’s In-vehicle Congestion Parameters Based on Random Parameters Logit Model

出行者对车内拥挤度的评价往往不一致,文中利用随机系数Logit模型对拥挤度影响参数进行研究.考虑到心理因素对出行者出行选择行为具有影响,文中研究加入了出行者对出行舒适性要求这一潜在心理因素,并通过问卷调查的方式进行分析.研究表明:随机系数Logit模型比传统的离散选择模型具有更高的拟合度;随机系数Logit模型的效用函数中,车票价格和车内时间的系数为非随机变量,但车内拥挤度的系数为随机变量,且受到出行者对出行舒适性要求和车内时间的影响,出行者对出行舒适性的要求越高,车内拥挤度对效用的影响越大,随着车内时间的延长,车内拥挤度对效用的影响减小.

There is significant heterogeneity among travelers evaluation on in-vehicle congestion. The impact of in-vehicle congestion was studied with the random parameters Logit model. In view of the impact of the latent psychological factor on travelers mode choice, travelers demand of comfort was taken into account . The results show that the random parameters Logit model fit the data much better than the traditional discrete choice model. From the result, it can be concluded that, the parameters of price and in-vehicle time are non-random. However, the parameter of in-vehicle congestion is random, and it is affected by travelers demand of comfort when traveling and in-vehicle time. The impact of in-vehicle congestion increases as travelers demand of comfort increases, and decreases when in-vehicle time increases.