Spatiotemporal learning for urban mobility and traffic data

城市交通和交通数据的时空学习

• 批准号: RGPIN-2019-05950
• 负责人: Sun, Lijun
• 金额: $ 2.62万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715860
• 关键词: Spatiotemporal; learning; urban; mobility; traffic

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.

随着信息和通信技术的最新进步，城市中通过各种传感技术(包括环路探测器、摄像头、RFID、手机、浮动汽车和众包平台(例如Google Waze))生成连续的时空流动性和交通数据流。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，让我们以数据驱动的方式了解城市交通和交通系统。这些时空数据集的高效和可靠的建模可以为智能交通系统(ITS)和城市规划的广泛应用带来好处，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。对时空机动性/交通数据进行建模的关键是表征数据中的高阶相关性/相关性。然而，由于新兴的时空移动/交通数据具有大规模、高维、不完全、非线性、非平稳和异构性的特点，传统的模型已经不能满足这一角色。该领域正在呼唤基于人工智能和机器学习的新概念和工具。 该发现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，以学习城市时空大数据，并为未来智慧和弹性城市的运营和规划提供智慧交通应用。更具体地说，基于我在时空数据分析方面的最新进展，该计划包括四个主要研究目标：(1)开发用于时空移动/交通数据的高级张量学习和深度学习模型；(2)开发用于大规模和实时问题的可扩展/高效的在线学习模型；(3)开发新的捕获远程时空依赖关系的预测方案；(4)针对数据异构性和确保模型的可靠性，调整所提出的学习框架。 这一发现计划将创造最先进的工具和知识来对城市系统生成的高维时空数据集进行建模，并为未来的智能交通提供决策工具及其应用。通过该程序开发的方法论流水线不仅在交通工程中具有变革性，而且对其他与时空建模相关的智能城市应用，如天气、空气质量和疫情预测，也具有参考价值。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，推动跨学科的根本进步，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。