DATA AUGMENTATION FOR AN INTEGRATED ACITIVITY-TRAVEL DEMAND AND MULTIMODAL TRAFFIC MICROSIMULATION MODEL

综合活动-旅行需求和多模式交通微观模拟模型的数据增强

• 批准号: RGPIN-2014-05190
• 负责人: NurulHabib, Khandker
• 金额: $ 2.04万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636255
• 关键词: DATA; AUGMENTATION; INTEGRATED; ACITIVITY; TRAVEL

The proposed research will develop an integrated microsimulation model of activity-travel demand and multimodal traffic flow that can allow the minute-by-minute simulation of an urban transportation system at the individual parcel level (postal code) of spatial disaggregation. Such models have extensive data requirements, and this issue will be addressed by developing data augmentation methods that will collect small-scale but detailed and accurate activity-travel data using smartphone-based applications and fusing it with regional household travel survey data. The study area will be the Greater Toronto and Hamilton Area (GTHA) as the region already has an established household travel survey data collection program and a full prototype multimodal traffic microsimulation model. The major objectives of the research are to: 1)develop new methods for data augmentation by fusing multiple sources of data, 2)estimate and calibrate a consistent, coordinated set of probabilistic behavioural models based on the fused data, 3)develop an integrated multimodal travel demand simulation system, and 4)apply the integrated simulation system to the analysis of transportation policy. The first major objective can be further elaborated as follows: (a)Developing a smartphone-based automated activity-travel data collection tool that can accurately collect detailed spatio-temporal movement information. This will reduce the survey response burden and create a truly pervasive travel data collection method.(b)Developing a data augmentation process to supplement regional household travel survey data with small-scale detailed data. This will enrich the large-sample household travel survey by filling in missing and more disaggregate information.The second and third long-term objectives can be further elaborated as follows:(c)Developing a microsimulation framework that seamlessly integrates peoples' demand for activity-travel within a multimodal (road and public transport) transportation network considering temporal variations of traffic flow in the network within a day.(d)Estimating and validating econometric models for all components of this modelling framework by using augmented activity-travel datasets. Component econometric choice models include fully comprehensive activity generation and scheduling models containing dynamic parcel-based activity location choice models and tour-based mode choice models. Models will be household-based and will consider intra-household resource and task allocations.All of these objectives have both short- and long-term implications. In the short-term, a microsimulation framework will be developed and component models will be estimated for the GTHA. In the long-term, the modelling framework will evolve into a standard transportation demand modelling system for regional-scale planning and policy analysis. The regional-scale dynamic passenger travel demand model will be unique in Canada and can be used as a template to develop similar models for other regions in Canada. The model will be useful in evaluating a variety of transportation policies and infrastructure improvement options that are under consideration by various levels of government in Canada. These include road pricing, alternative network infrasture renewal options (e.g., building subway/metro relief lines, replacing old elevated expressways with new expressways or combinations of highways and transit elements) and alternative land use bylaws in urban areas to balance urban systems and minimize social exclusion. The model will also be able to accurately evaluate various travel demand management policies, e.g. carpooling incentives, flexible office hour option, telecommuting options, etc.

拟议的研究将开发一个综合微观模拟模型的活动，旅行需求和多式联运交通流，可以允许一分钟一分钟的模拟城市交通系统在单个包裹水平（邮政编码）的空间分解。这种模式需要大量数据，解决这一问题的办法是开发数据扩充方法，利用智能手机应用程序收集小规模但详细和准确的活动-旅行数据，并将其与区域家庭旅行调查数据融合。研究区域将是大多伦多和汉密尔顿地区（GTHA），因为该地区已经建立了家庭出行调查数据收集程序和完整的原型多式联运交通微观模拟模型。研究的主要目标是：1）通过融合多个数据源开发新的数据增强方法，2）基于融合数据估计和校准一组一致的、协调的概率行为模型，3）开发一个集成的多式联运出行需求仿真系统，以及4）将集成仿真系统应用于交通政策分析。第一个主要目标可进一步阐述如下：（a）开发基于智能手机的自动化活动-旅行数据收集工具，该工具可准确收集详细的时空移动信息。这将减少调查答复的负担，并建立一个真正普遍的旅行数据收集方法。（B）制定数据扩充程序，以小规模详细数据补充区域住户旅行调查数据。第二和第三个长期目标可进一步阐述如下：（c）开发一个微观模拟框架，将人们对活动旅行的需求无缝地纳入一个多式联运（公路和公共交通）运输网络，同时考虑到该网络一天内交通流量的时间变化。（d）利用扩大的活动-旅行数据集，估计和验证这一建模框架所有组成部分的计量经济学模型。组件计量经济学选择模型包括完全综合的活动生成和调度模型，包含基于动态地块的活动位置选择模型和基于旅游的模式选择模型。这些模式将以家庭为基础，并将考虑家庭内部的资源和任务分配，所有这些目标都有短期和长期的影响。在短期内，将开发一个微观模拟框架，并将为GTHA估计组件模型。从长远来看，模型框架将发展成为一个标准的运输需求模型系统，用于区域规模的规划和政策分析。区域规模的动态乘客出行需求模型在加拿大是独一无二的，可以作为模板，为加拿大其他地区开发类似的模型。该模型将有助于评估加拿大各级政府正在考虑的各种交通政策和基础设施改善方案。这些措施包括道路定价、替代网络基础设施更新选项（例如，建设地铁/地铁缓解线，用新的高速公路或高速公路和交通要素的组合取代旧的高架高速公路）和城市地区的替代土地使用细则，以平衡城市系统，最大限度地减少社会排斥。该模型还将能够准确评估各种出行需求管理政策，例如拼车激励措施、灵活办公时间选项、远程办公选项等。