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

交通系统正在迅速发展，以响应新的移动趋势和技术的发展。这些技术包括自动驾驶汽车（AVs）、叫车服务和拼车服务，以及共享单车和电动滑板车等微出行选择。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（如公共交通）整合到一个单一的技术支持生态系统中。这种集成，通常被称为移动即服务（MaaS），属于NSERCs战略兴趣领域的广泛的信息和通信技术保护伞。拟议的研究项目将侧重于开发预测模型，用于评估新的移动技术将如何改变交通系统。提出的模型将侧重于预测城市居民的旅行行为和旅行需求的变化。这些模型可以应用在一个模拟框架中，以主动测试政策的影响，这些政策鼓励每个生活在城市地区的人选择安全、可持续和具有成本效益的出行方式。要开发的模型使用最佳实践和新颖的方法。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；长时间使用全球定位系统（GPS）和智能手机跟踪应用程序；2.一项虚拟现实研究，让受访者体验乘坐自动驾驶汽车和其他交通方式；3.一个群体决策实验，在这个实验中，家庭协商在移动出行上花多少钱。这些数据集将导致行为模型预测：出行模式的习惯性，目的是了解预先安排乘车服务的可行性。2.乘坐自动驾驶汽车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，家庭将如何协商出行支出？参加该项目的HQP将在交通规划和需求预测行业中需要的关键领域获得培训，包括交通调查设计和部署、数据处理、交通模型开发和政策评估。这些技能在研究和实际环境中都广泛适用于交通工程师。此外，开发的模型将提供对政策开发的见解，允许决策者在实现之前测试模型中特定政策的有效性。这些模型将理想地帮助决策者了解如何最好地使用自动驾驶汽车和MaaS。各行各业的加拿大人都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.