Modelling the Impact of New Mobility Trends and Technologies on Travel Behaviour and Demand

随着新的机动性趋势和技术的发展，运输系统正在迅速演变。这些技术包括自动驾驶汽车(AV)、叫车和拼车服务，以及共享自行车和电动滑板车等微型移动选择。还有一种趋势是将这些移动技术以及现有的共享移动工具(如交通工具)整合到一个单一的技术支持的生态系统中。这种整合通常被称为移动性即服务(MAAS)，属于NSERCs感兴趣的战略领域的广泛信息和通信技术保护伞。拟议的研究计划将专注于开发预测模型，这些模型可以用来评估新的移动技术将如何改变交通系统。拟议的模型将侧重于预测城市居民出行行为和出行需求的变化。这些模型可以应用在模拟框架中，以主动测试政策的引入的影响，这些政策鼓励为每个生活在城市地区的人提供安全、可持续和具有成本效益的旅行选择。将要开发的模型使用最佳做法和新的、新颖的方法。为了开发这项研究的工具，将建立一个数据收集系统。数据收集将使用不同的方法来捕捉旅行行为的不同方面。方法将包括：1.在更长的时间范围内使用全球定位系统(GPS)和智能手机跟踪应用程序；2.虚拟现实研究，受访者体验乘坐AV和其他交通工具；3.集体决策实验，家庭协商在移动性上花费多少。这些数据集将导致行为模型来预测：1.出行模式的习惯性，目的是了解提前安排叫车服务的可行性。2.乘坐AV的体验是否会改变用户对这项技术可行性的看法？3.如果有叫车服务，家庭将如何协商移动支出？该项目的HQP将获得关键领域的培训，包括交通规划和需求预测行业中需求很高的旅行调查设计和部署、数据处理、旅行模型开发和政策评估。这些技能在研究和实践中都广泛适用于运输工程师。此外，开发的模型将提供对政策制定的见解，使决策者能够在实施之前测试特定政策在模型中的有效性。理想情况下，这些模型将帮助决策者了解如何最好地使用AVS和MAAS。加拿大各行各业的人将在这一新制度下受益，从而产生广泛的积极的社会、经济和环境成果。

The transportation system is rapidly evolving in response to the development of new mobility trends and technologies. These technologies include autonomous vehicles (AVs), ride hailing and ride sharing services and micro mobility options like shared biked and e-scooters. There is also a movement towards integrating these mobility technologies as well as existing shared mobility tools like transit under a single technology-enabled ecosystem. This integration, commonly referred to as mobility as a service (MaaS) falls under the broad information and communications technology umbrella of NSERCs strategic areas of interest. The proposed research program will focus on developing predictive models that can be used to assess how new mobility technologies will change the transportation system. The models that are proposed will focus on predicting changes to travel behaviour and travel demand for urban residents. These models can be applied in a simulation framework to proactively test the impact of the introduction of policies that encourage safe, sustainable and cost-effective travel options for everyone living in an urban area. The models to be developed use best practice and new and novel methods. To develop the tools for this research, a data collection system will be established. The data collection will use different methods to capture different aspects of travel behaviour. Methods will include: 1.Global positioning system (GPS) and smartphone tracking apps used over an extended time frame; 2.A virtual reality study where respondents experience riding in an AV and other modes of transport; 3.A group decision making experiment where households negotiate how much to spend on mobility. These datasets will lead to behavioural models to predict: 1.The habitualness of travel patterns for the purposes of understanding the feasibility of prescheduling ride hailing services. 2.Does experiencing riding in an AV change users' perception of the viability of the technology? 3.How will households negotiate mobility expenditure if there are ride hailing services? HQP in this program will gain training in key areas including travel survey design and deployment, data processing, travel model development and policy assessment that are in high demand in the transportation planning and demand forecasting industry. These skills are widely applicable for transportation engineers in both research and practical contexts. Furthermore, the developed models will provide insights into policy development, allowing decision makers to test how effective a particular policy is within the model before implementation. These models will ideally help decision makers to understand how best to use AVs and MaaS. Canadians from all walks of life will benefit under this new system leading to broad positive social, economic and environmental outcomes.