自动化学报2017，Vol.43Issue(1)：60-71,12.DOI:10.16383/j.aas.2017.c150723
基于计算实验的公共交通需求预测方法
A Computational Experiment Approach to Public Traffic Demand Forecast
摘要
Abstract
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 passenger0s 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.关键词
计算实验/交通需求预测/基于Agent的建模与仿真/BDI (Belief-desire-intention)模型Key words
Computational experiments/traffic demand forecast/agent-based modeling and simulation/BDI (Belief-desire-intention) model引用本文复制引用
陈曦,彭蕾,李炜..基于计算实验的公共交通需求预测方法[J].自动化学报,2017,43(1):60-71,12.基金项目
国家自然科学基金(71571081,91324203)资助Supported by National Natural Science Foundation of China (71571081,91324203) （71571081,91324203）
