Spatiotemporal learning for urban mobility and traffic data

***随着信息和通信技术的最新发展，城市通过各种传感技术，包括环路探测器、摄像头、RFID、手机、浮动汽车和众包平台（例如谷歌Waze），产生连续的时空移动和交通数据流。指数级增长的城市大数据为我们以数据驱动的方式了解城市交通和运输系统提供了前所未有的机会。对这些时空数据集进行高效、可靠的建模，有助于广泛的智能交通系统（ITS）和城市规划应用，如出行需求预测、出行规划、出行时间估计、路线规划、拼车、公交服务调度、信号控制和拥堵/中断管理。建模时空移动/交通数据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/交通数据具有大规模、高维、不完整、非线性、非平稳和异质性等特点，传统的模型已不足以满足这一需求。该领域需要基于人工智能和机器学习的新概念和新工具。******该探索项目的长期目标是建立新的统计学习和创新计算方法，从城市时空大数据中学习，为未来智慧和弹性城市的运营和规划提供智能交通应用。更具体地说，根据我最近在时空数据分析方面的进展，这个项目包括四个主要的研究目标，以实现高效可靠的时空学习：(1)为时空移动/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)开发捕获远程时空依赖关系的新预测方案；(4)使所提出的学习框架适应数据异质性并确保模型可靠性。******该探索项目将创造最先进的工具和知识，为城市系统生成的高维时空数据集建模，并为未来的智能交通提供决策工具和ITS应用。从董事会的角度来看，通过该计划开发的方法管道不仅在交通工程方面具有变革性，而且对与时空建模相关的其他智能城市应用（如天气、空气质量和流行病预测）也很有价值。此外，该项目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，促进跨学科的基础进步，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.**