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用于运营计划实践中。这些通常是在基于代理的微观仿真（ABM）框架中实施的，其中为单个旅行者（代理商）对室外活动参与和相关旅行进行了建模。 ABM提供了一种非常灵活，强大和有效的手段，用于建模复杂的时空，社会经济行为，例如旅行。此类模型的一个主要例子是Tasha，自2016年以来一直在大多伦多地区（GTA）的运营使用。 “大数据”迅速增长，这些有关从多种来源的旅行行为，持续增长，计算能力的持续增长以及不断变化（越来越受挑战的）政策分析问题（自动驾驶汽车，新的行动能力服务，积极的运输，社会公平的关注，社会公平的关注等），同时创造更多的机会，并能够继续开发更高级的旅行型号，并需要开发更高级的旅行模型。路径。 当前的模型代表“第一代” ABM。但是，存在着相当大的需求和机遇，它是建立在新兴的大数据集（以及其他信息源）和高性能计算的基于更强大的“第二代” ABM的开发中。但是，为此，将涉及新的行为表示和计算算法的开发，这些算法在更灵活的软件环境中实现，既可以充分利用可用的计算能力，又可以通过新的运输方式和服务的表示以及扩展的灵活实验以及不断发展的旅行行为。 拟议的研究的目的是开发这样的第二代ABM模型系统：Tasha/2。在塔莎（Tasha）的经过验证的基础设计的基础上，下一个版本将解决上述许多弱点。该研究将在6个互连主题中进行： 改进了旅行决策的代表。 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，这些服务在代理商的旅行选择和通过物理运输网络对车辆和人员的运动模拟之间进行“调解”。 “流动性工具”选择的明确建模（汽车所有权常规和自动，共享汽车会员资格，自行车共享会员等）。 停车供应和选择的明确表示。 使用高级计算方法来确保tasha/2可以在实际可接受的运行时间内建模复杂的传输过程。