TASHA/2: Next Generation Agent-Based Microsimulation

基于活动/旅游的城市旅行需求模型越来越多地用于业务规划实践。这些通常是在基于代理的微模拟（ABM）框架中实现的，在这个框架中，外出活动的参与和相关的旅行是为个人旅行者（代理）建模的。ABM为模拟复杂的时空、社会经济行为（如旅行）提供了一种极其灵活、强大和有效的手段。这类模型的一个典型例子是TASHA，它自2016年以来一直在大多伦多地区（GTA）投入使用。***各种来源的关于出行行为的“大数据”的可用性迅速增长，计算能力的持续增长，以及不断变化（也越来越具有挑战性）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），为继续开发更先进的、稳健的旅行需求建模方法，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。***当前型号代表“第一代”反弹道导弹。然而，对于建立在新兴的大数据集（以及其他信息源）和高性能计算基础上的更强大的“第二代”ABMs的发展，存在着相当大的需求和机会。然而，要做到这一点，就需要开发新的行为表征和计算算法，在更灵活的软件环境中实现，既能充分利用现有的计算能力，又能灵活地试验和扩展新的交通模式和服务的表征，以及不断发展的出行行为。***提出的研究目标是开发这样的第二代反弹道导弹模型系统：TASHA/2。基于TASHA经过验证的基础设计，下一个版本将解决上面确定的许多弱点。研究将在6个相互关联的主题下组织：***提高旅行决策的代表性。***利用新的数据源，结合数据融合和机器学习方法，以新的方式构建新的模型。***开发一系列流动服务的明确模型，在代理人的出行选择和模拟车辆和人员通过实体交通网络的移动之间进行“调解”。***“出行工具”选择的明确建模（汽车保有量传统和自动、汽车共享会员、自行车共享会员等）。***停车位供应和选择的明确表示。***使用先进的计算方法，确保TASHA/2可以在实际可接受的运行时间内模拟复杂的运输过程

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.**