摘要
在四阶段模型中引入部分非集计的活动模型和出行链模型的建模思路,进而设计和开发混合交通需求模型,可以增强对出行行为的可解释性,同时避免完全采用非集计的活动模型所面临的复杂性和计算效率问题。回顾美国三角区域模型的发展历程,简要介绍第二代三角区域模型(TRMG2)的基本情况与特点。结合对TRMG2的结构设计解读,梳理TRMG2对基于出行的模型理论和建模方法的重要改进。重点介绍了基于人口合成(仿真)的人口重构模型、基于出行链的出行生成模型、分层目的地选择模型和考虑可达性的出行行为选择模型等高级出行需求建模方法。最后,结合当前中国城市交通需求模型的现状和面临问题,总结提出工作机制制度化、模型开发精细化、模型理论实用化和模型成果公开化的发展策略。
Introducing partially disaggregated trip-based models and tour-based models into the four-step modeling framework,and further designing and developing hybrid travel demand models,can enhance the interpretability of travel behavior while avoiding the complexity and computational efficiency problems associated with disaggregate activity models.Reviewing the development of the U.S.triangle regional travel demand model,this paper summarizes the basic information and characteristics of the second generation of the triangle regional model(TRMG2).Combined with the interpretation of the structural design of TRMG2,the paper discusses important improvements of TRMG2 in the modeling method based on tripbased theory,focusing on advanced travel demand modeling methods such as the population reconstruction model with population synthesis,the trip generation model based on trip chains,nested destination choice models,and travel behavior selection models considering accessibility.Finally,to address the current status and problems in urban travel demand models development in China,the paper proposes development strategies in four aspects:institutionalizing working mechanism,refining model development,transitioning model theory to practices,and publicizing model outcomes.
作者
张天然
陈先龙
朱春节
王波
ZHANG Tianran;CHEN Xianlong;ZHU Chunjie;WANG Bo(Shanghai Urban Planning&Design Research Institute,Shanghai 200040,China;Guangzhou Transport Planning Research Institute Co.,Ltd.,Guangzhou Guangdong 510030,China;Guangdong Sustainable Transportation Engineering and Technology Research Center,Guangzhou Guangdong 510030,China)
出处
《城市交通》
2024年第4期41-49,119,共10页
Urban Transport of China
基金
上海市城市规划设计研究院重点项目“上海市新一轮交通模型建设”(2020160-0)
广州市交通规划研究院有限公司科研项目“数据驱动的时空推演城市活动模型研究”(KYHT-2023-01)。