Modeling Long-distance Intercity Passenger Travel in the Global Multi-modal System

新冠肺炎强调了出城旅游在我们的生活和经济中的关键作用。尽管长途旅行的模型很重要，但交通工程师对它的关注有限，他们的努力集中在日常的、更常规的本地旅行上，往往是为了满足运力扩张的需求。据估计，飞机、火车、公交车和汽车的长途出行里程占总里程的30%-45%，战略工程工具的这一差距损害了我们优化交通以及将温室气体排放、拥堵和系统成本降至最低的能力。该计划的总体目标是开发所需的数据方法，以促进准确的长途旅行需求预测模型。我们独特的战术战略将从适用于日常旅行模型的数据、概念和方法出发。我们将采用a)半被动移动设备踪迹、b)空间数据库和c)将地面模式与航空旅行相结合的网络可访问性测量的替代方法。在全国范围内用全球目的地模拟年度过夜旅行不可能是基于代理商的，这是当地旅行模型的最新水平。取而代之的是，将使用由跨大空间尺度和网络的可获得性新衡量标准定义的准分解流量。具体地说，我们将：1.设计和验证“大数据出行”的混合方法，其中包括需求建模所必需的基于人员和出行的属性。2.创建数据驱动的长途旅行概念框架以及无障碍和社会网络分布指标，为模型培训提供信息。3.利用新的空间措施开发捆绑目的地/模式选择模型。该计划中开发的方法将允许对由电动汽车、自动车辆和新的移动服务组成的替代长途旅行系统进行环境影响、社会公平和成本评估。加拿大的交通系统太重要了，不能不受限制地发展，这个计划将有助于更好地衡量当前的系统，更好地设计未来的系统。在交通系统背景下培养研究生是一种成熟的优势。首先，大型复杂的关系数据库需要编程技能，并注意准确性、空间相关性和隐私/伦理问题。其次，数据收集需要混合的方法，从移动设备跟踪，到基于互联网的数据和调查/采访。第三，跨学科的主题对工程专业招收不同的人和学科很有吸引力。第四，旅行机会的不平等需要明确地将其纳入模型。最后，可以磨练沟通技能，以便下一代工程师能够建立公共和私营部门所需的合作伙伴关系，以实现更可持续的交通系统。

COVID-19 has highlighted the critical role of out-of-town travel in our lives and our economy. Despite its importance, modeling long-distance travel has received limited attention by transportation engineers whose efforts have instead focused on daily, more routine, local trips often to address the need for capacity expansion. Given that long-distance travel by plane, train, bus, and automobile has been estimated to account for 30-45% of miles traveled, this gap in strategic engineering tools compromises our ability to optimize access as well as to minimize greenhouse gas emissions, congestion and system-wide costs. The overarching goal of this program is to develop the data methods needed to facilitate accurate long-distance travel demand forecasting models. Our unique tactical strategy will depart from the data, concepts and approaches appropriate for use in daily travel models. We will embrace alternative ways of using a) semi-passive mobile device traces, b) spatial databases, and c) network accessibility measures that integrate surface modes with air travel. Simulating annual overnight travel on a nation-wide scale with global destinations cannot be agent-based, the state-of-the art for local travel modeling. Instead, quasi-disaggregate flows defined by new measures of accessibility across large spatial scales and networks will be used. Specifically, we will: 1.Design and validate a hybrid method for "Big Travel Data" that includes the person- and trip-based attributes essential for demand modeling. 2.Create data-driven conceptual frameworks of long-distance travel and indicators of accessibility and social network distribution to inform model training. 3.Develop bundled destination/mode choice models utilizing novel spatial measures. The methods developed in this program will allow alternative systems of long-distance travel, comprised of electric vehicles, automated vehicles, and new mobility services, to be assessed for their environmental impacts, social equity and costs. The Canadian transportation system is too important to evolve unchecked, and this program will facilitate better measurement of the current system, to better design the future system. Training graduate students in the context of transportation systems is ripe with advantage. First, large complex relational databases require programming skills with attention to accuracy, spatial correlation and privacy/ethics concerns. Second, data collection requires mixed methods from mobile device tracking, to internet-based data and surveys/interviews. Third, the transdisciplinary subject matter is attractive to recruiting diverse people and disciplines to engineering. Fourth, inequity in travel opportunity requires explicit coverage of inclusion in models. Finally, communication skills can be honed so that the next generation of engineers can build the collaborative partnerships needed in both public and private sectors to effect more sustainable transportation systems.