中文摘要：

一般来说,用于交通需求预测的数学模型往往缺少对出行个体微观水平上的异质性和可变交通情景的考虑.针对这些问题,本文提出了一种基于计算实验的公共交通需求预测方法.该方法主要由交通调查、基于Agent的人工交通系统（Artificial transportation system,ATS）和计算实验3部分组成.在出行个体Agent建模中引入BDI（Belief-desire-intention）模型,来推演各出行个体在出行过程中对各交通选择的决策制定过程.在人工交通系统的基础上,可以设计并执行大量的计算实验来进行交通需求预测.本文通过基于校车系统的一系列交通调查和计算实验验证了该方法的可行性和优越性,并针对各种不同交通情景进行了交通分布预测和交通方式划分预测.

英文摘要：

Mathematical models used in traffic demand forecast usually do not consider individual heterogeneity at the micro level and changeable traffic scenes. To solve these issues, a forecast method based on computational experiment that is composed of traffic survey, agent-based artificial transportation system（ATS）, and computational experiments is proposed. A BDI（belief-desire-intention） modeling method is introduced in individual passenger agent to deduce each passenger s decision-making process of traffic selection. By using a series of computational experiments on ATS, a case study on a school bus system is conducted to validate the feasibility and superiority of our method. Several computational experiments are conducted to predict the traffic distribution and the traffic mode choice under different traffic scenarios.