Spatiotemporal learning for urban mobility and traffic data

随着信息和通信技术的最新进展，通过各种感测技术在城市中生成连续的时空移动性和交通数据流，所述感测技术包括环路检测器、相机、RFID、手机、浮动汽车和众包平台（例如，Google Waze）。呈指数级增长的城市大数据为我们提供了前所未有的机会，以数据驱动的方式了解城市移动和交通系统。这些时空数据集的有效和可靠的建模可以使广泛的智能交通系统（ITS）和城市规划应用受益，例如出行需求预测，出行规划，出行时间估计，路线规划，乘车共享，公交服务调度，信号控制和拥堵/中断管理。时空移动性/交通数据建模的关键是表征数据中的高阶相关性/依赖性。然而，由于新兴的时空移动/交通数据的大规模，高维，不完整，非线性，非平稳和异构的性质，传统的模型变得不足以服务于这一角色。该领域正在呼吁基于人工智能和机器学习的新概念和工具。 Discovery计划的长期目标是建立新颖的统计学习和创新的计算方法，从城市时空大数据中学习，并为未来智能和弹性城市的运营和规划提供智能交通应用。更具体地说，该计划包括四个主要的研究目标，基于我最近在时空数据分析方面的进展，实现高效可靠的时空学习：（1）为时空移动性/交通数据开发先进的张量学习和深度学习模型，（2）为大规模和实时问题开发可扩展/高效的在线学习模型，（3）开发新的预测方案，捕捉长期时空依赖性，（4）调整所提出的学习框架，以适应数据的异质性，并确保模型的可靠性。 该发现计划将创建最先进的工具和知识，以模拟从城市系统生成的高维时空数据集，并为未来的智能交通提供决策工具和ITS应用。从广义上讲，通过该计划开发的方法管道不仅在交通工程中具有变革性，而且对与时空建模相关的其他智慧城市应用也很有价值，例如天气，空气质量和流行病预测。此外，该计划还将通过将人工智能知识与交通工程领域的专业知识相结合，促进基础跨学科的进步，培养跨学科的HQP，为加拿大人工智能和智慧城市的快速发展做出贡献。

With recent advances in information and communications technology, continuous streams of spatiotemporal mobility and traffic data are generated in cities through various sensing technologies, including loop detectors, cameras, RFID, cellphones, floating cars, and crowdsourcing platforms (e.g., Google Waze). The exponentially growing urban big data provides us with unprecedented opportunities to understand urban mobility and transportation systems in a data-driven way. Efficient and reliable modeling of these spatiotemporal data sets can benefit a wide range of intelligent transportation systems (ITS) and urban planning applications, such as travel demand prediction, trip planning, travel time estimation, route planning, ride sharing, transit service scheduling, signal control, and congestion/disruption management. The key to modeling spatiotemporal mobility/traffic data is to characterize the higher-order correlations/dependencies within the data. However, due to the large-scale, high-dimensional, incomplete, nonlinear, non-stationary and heterogeneous nature of emerging spatiotemporal mobility/traffic data, traditional models become insufficient to serve this role. The field is calling for new concepts and tools based on artificial intelligence and machine learning. The long-term goal of this Discovery program is to establish novel statistical learning and innovative computational methods to learn from urban spatiotemporal big data and provide smart transportation applications for the operation and planning of future smart and resilient cities. More specifically, this program consists of four major research objectives for efficient and reliable spatiotemporal learning based on my recent progress on spatiotemporal data analytics: (1) develop advanced tensor learning and deep learning models for spatiotemporal mobility/traffic data, (2) develop scalable/efficient online learning models for large-scale and real-time problems, (3) develop new prediction schemes capturing long-range spatiotemporal dependencies, and (4) adapt the proposed learning frameworks for data heterogeneity and ensure model reliability. This Discovery program will create state-of-the-art tools and knowledge to model high-dimensional spatiotemporal data sets generated from urban systems, and also provide decision-making tools and ITS applications for smart transportation of the future. In a board sense, the methodological pipeline developed through this program is not only transformative in transportation engineering but also valuable to other smart cities applications related to spatiotemporal modeling, such as weather, air quality and epidemic predictions. In addition, this program will promote fundamental interdisciplinary advances and train interdisciplinary HQP through integrating artificial intelligence knowledge with domain expertise in transportation engineering, contributing to the rapid development of artificial intelligence and smart cities in Canada.