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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=691134
• 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资源提供功能，以及不断变化的（越来越具有挑战性的）政策分析ISSU（自动驾驶汽车，新移动服务，促进积极的运输，社会公平问题等）创造了机会，也需要脚趾发展高级，强大的旅行需求建模方法，以指导全球范围内的城市区域的持续膨胀，更多地更具可持续性的道路。新兴的数据集（除其他信息源）和高性能计算。不断发展的行为。 6个国际主题：***通过数据融合和机器学习方法改善了旅行的趋势，以新的方式建立新的模型。模拟志趣相投的人的运动和ph ysical运输网络。显式代表停车场和选择。