A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application

Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction.

鉴于地铁线网扩张催生了更多可选出行路径，将路径集合的影响以及可选路径间的相互依存性对路径选择概率的作用纳入路径选择建模框架，具备重要的研究价值。因此，本文针对地铁线网构建了约束多项Probit（constrained multinomial probit, CMNP）路径选择模型，并对其建模、参数估计与应用展开研究。该模型的效用函数由三部分构成：补偿性分量为影响因素的函数；非补偿性分量用于衡量路径集合对效用的影响；误差分量的协方差服从多元正态分布，并被拆解为三部分，分别对应路径间的相关性、单路径的换乘方差以及未观测方差。考虑到多元正态概率密度函数的多维积分特性，本文将CMNP模型改写为分层贝叶斯形式，并构建基于Metropolis-Hastings（M-H）采样算法的马尔可夫链蒙特卡洛（Monte Carlo Markov Chain, MCMC）方法以估计所有模型参数。基于广州地铁的实际运营数据，本研究获得了可靠的参数估计结果。此外，所提出的CMNP模型在路径选择概率计算方面展现出优异的预测性能，同时在换乘客流量预测任务中也具备良好的应用效果。