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

2019冠状病毒病凸显了出城旅行在我们的生活和经济中的关键作用。尽管长途旅行的建模很重要，但运输工程师对其关注有限，他们的努力主要集中在日常、更常规的本地旅行上，通常是为了满足运力扩张的需求。考虑到乘飞机、火车、公共汽车和汽车的长途旅行估计占旅行里程的30-45%，战略工程工具的这种差距影响了我们优化访问以及减少温室气体排放、拥堵和全系统成本的能力。该项目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。我们独特的战术策略将不同于适用于日常旅行模型的数据、概念和方法。我们将采用其他方式使用a)半被动移动设备跟踪，b)空间数据库，以及c)将地面模式与航空旅行相结合的网络可访问性措施。在全国范围内模拟具有全球目的地的年度过夜旅行不能是基于代理的，这是本地旅行建模的最新技术。相反，将使用由跨大空间尺度和网络的可达性的新措施定义的准分解流。具体来说，我们将：1。为“大旅行数据”设计并验证一种混合方法，该方法包括需求建模所必需的基于人和基于行程的属性。2.创建数据驱动的长途旅行概念框架，以及可访问性和社会网络分布指标，为模型培训提供信息。3.利用新颖的空间测量方法开发捆绑的目的地/模式选择模型。在这个项目中开发的方法将允许长途旅行的替代系统，包括电动汽车、自动驾驶汽车和新的移动服务，对其环境影响、社会公平和成本进行评估。加拿大的交通系统太重要了，不可能不受限制地发展，这个项目将有助于更好地衡量当前的系统，更好地设计未来的系统。在运输系统的背景下培养研究生是成熟的优势。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/道德问题。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/访谈。第三，跨学科的学科对招聘不同的人和学科的工程师是有吸引力的。第四，旅行机会的不平等要求在模型中明确涵盖包容性。最后，沟通技巧可以得到磨练，以便下一代工程师能够在公共和私营部门建立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.