TASHA/2: Next Generation Agent-Based Microsimulation

TASHA/2：下一代基于代理的微观模拟

• 批准号: RGPIN-2019-06519
• 负责人: Miller, Eric
• 金额: $ 4.52万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716184
• 关键词: TASHA; Next; Generation; Agent; Based

Activity/tour-based models of urban travel demand are increasingly used in operational planning practice. These are generally implemented within an agent-based microsimulation (ABM) framework, in which out-of-home activity participation and associated travel are modelled for individual trip-makers (agents). ABM provides an extremely flexible, powerful and efficient means for modelling complex spatial-temporal, socio-economic behaviour such as travel. A leading example of such models is TASHA, which has been in operational use in the Greater Toronto Area (GTA) since 2016. The rapidly growing availability of “big data” concerning travel behaviour from a variety of sources, continuing growth in computing capabilities, and ever-changing (and increasingly challenging) policy analysis issues (autonomous vehicles, new mobility services, promoting active transportation, social equity concerns, etc.) create both the opportunity and the need to continue to develop more advanced, robust travel demand modelling methods to help guide the continuing explosive growth of urban regions worldwide along more sustainable paths. Current models represent “first-generation” ABMs. Considerable need and opportunity, however, exists for the development of significantly more powerful “second-generation” ABMs that build upon emerging big datasets (among other information sources) and High Performance Computing. To do so, however, will involve the development of new behavioural representations and computational algorithms, implemented within much more flexible software environments that both fully exploit available computing power and enable flexible experimentation with and extension of representations of new transportation modes and services and evolving travel behaviour. The objective of the proposed research is to develop such a second-generation ABM model system: TASHA/2. Building upon the proven base design of TASHA, the next version will address many of the weaknesses identified above. The research will be organized within 6 interconnecting themes: Improved representation of travel decision-making. Exploiting new data sources in combination with data fusion and machine learning methods to build new models in new ways. Developing explicit modelling of a range of mobility services which “mediate” between agents' travel choices and simulation of the movement of vehicles and people through the physical transportation network. Explicit modelling of “mobility tool” choices (car ownership conventional and autonomous, car-sharing membership, bike-sharing membership, etc.). Explicit representation of parking supply and choice. Use of advanced computing methods to ensure that TASHA/2 can model complex transportation processes within practically acceptable run times.

基于活动/旅游的城市旅行需求模型越来越多地计划实践。塔莎（Tasha）是诸如旅行的主要例子，自2016年以来一直在大多伦多地区（GTA）的运营用途。 迅速增长的“有关旅行行为的可用性，越来越多的（越来越具有挑战性的）政策分析问题（自动驾驶汽车，新移动服务，SOG CIAL CIAL CEAL CORTISE CORTISS等）都创造了机会，也需要开发更高级，更强大的，更强大，强大的旅行需求建模方法可帮助指导全球城市地区的爆炸性增长，而更多的可持续性道路。 当前的模型代表了“第一代” ABM。但是，在更灵活的Oftware环境中实现了新的行为核心的开发，这些环境既可以使用可用的计算，又可以通过W传输模式和服务以及进化旅行行为进行灵活的实验和扩展。 拟议的研究的目的是开发这样的第二代ABM模型系统：tasha/2。 改善了旅行决策的共态。 利用新的数据源与数据融合和机器学习方法结合使用，以新的方式构建新模型。 开发一系列移动服务的明确建模，这些服务“代理商的旅行选择与通过物理运输网络对“ CLES和人员”的模拟进行了模拟。 “移动性工具”选择的明确建模（汽车所有权Antomosus 明确表示停车供应和选择。 使用高级计算方法来确保tasha/2可以在实际上可接受的符文中建模comportatissssses。