TASHA/2: Next Generation Agent-Based Microsimulation

点击翻译按钮获取中文摘要

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.**