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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749390
• 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 interconn

基于活动/旅游的城市交通需求模型越来越多地用于运营规划实践。这些通常是在基于代理的微观仿真（ABM）框架内实现的，在该框架中，为个人旅行者（代理）模拟户外活动参与和相关旅行。ABM为模拟复杂的时空、社会经济行为（如旅行）提供了极其灵活、强大和有效的手段。TASHA是这类车型的一个主要例子，自2016年以来一直在大多伦多地区（GTA）投入使用。来自各种来源的关于出行行为的“大数据”的快速增长，计算能力的持续增长，以及不断变化（和越来越具有挑战性）的政策分析问题（自动驾驶汽车，新的移动服务，促进主动交通，社会公平问题等）。这既创造了机会，也需要继续开发更先进、更强大的出行需求建模方法，以帮助引导全球城市地区沿着更可持续的道路持续爆炸式增长。目前的型号是“第一代”反弹道导弹。然而，存在相当大的需求和机会，以开发更强大的“第二代”ABM，建立在新兴的大数据集（以及其他信息源）和高性能计算。然而，要做到这一点，将涉及开发新的行为表示和计算算法，在更灵活的软件环境中实施，既充分利用现有的计算能力，又能够灵活地试验和扩展新的交通模式和服务以及不断发展的旅行行为的表示。本研究的目标是开发第二代反弹道导弹模型系统：TASHA/2。基于TASHA的成熟基础设计，下一个版本将解决上面确定的许多弱点。该研究将在6个互联网内组织。