Deep Learning Models for Mobility Data Mining

位置获取和跟踪设备的进展（例如，GPS技术、移动的设备、位置传感器）已经引起了巨大的移动数据的生成，该移动数据由诸如人、车辆、船只、无人机等移动对象的轨迹组成。类似地，大量的移动性数据由IoT设备生成（例如，可穿戴设备）来自传感器、仪表和其他互连设备。移动数据是一种特殊类型的时空数据-包含有关移动对象随时间推移的空间/位置信息的数据。在大型时空数据中提取和发现模式对于几个现实世界的应用至关重要，包括理解人类移动性（例如，行人移动性挖掘），健康护理（例如，流行病传播，步态分析），智能交通和城市规划（例如，交通预测和优化），以及基于位置的服务（例如，兴趣点的建议）。随着时空数据的数量和复杂性迅速增加，使用传统的统计建模和数据挖掘方法将这些数据转化为有意义的行动仍然具有挑战性。近年来，随着深度学习技术的发展，卷积神经网络（CNN）、递归神经网络（RNN）和图神经网络（GNN）等深度学习模型由于其强大的学习能力，在各种机器学习任务中取得了相当大的成功。它们已广泛应用于许多领域，包括计算机视觉，自然语言处理，图形数据挖掘和时间序列数据预测，这激发了我们提出的研究采用深度学习模型进行各种移动数据挖掘任务。我们的研究的预期成果是发展深度学习时空模型的理论和方法，以有效地挖掘移动数据。在这种情况下，这些模型的几个方面将被调查有关其可行性，准确性，鲁棒性，可扩展性和可解释性。还将开发用于培训、测试和验证时空模型的最佳实践，以便安全有效地部署这些模型，以解决从智能交通到基于位置的服务和健康等各种环境和领域中与移动性相关的问题。我们的研究计划符合泛加拿大人工智能（AI）战略。在数据挖掘、图挖掘、大数据分析领域进行世界一流的机器学习研究，有可能吸引来自世界各地最聪明的学生，同时有助于留住国内顶尖的研究人才。同时，它将有助于培养高素质的人才（HQP），具有一流的分析和解决问题的技能，有能力刺激知识型公司，利用人工智能提高生产力和创新，促进经济增长，改善加拿大及其他地区的生活条件。

Advances in location acquisition and tracking devices (e.g., GPS technologies, mobile devices, location sensors) have given rise to the generation of enormous mobility data, consisting of trajectories of moving objects, such as people, vehicles, vessels, drones, and more. Similarly, enormous mobility data is generated by IoT devices (e.g., wearables) coming from sensors, meters, and other interconnected devices. Mobility data is a special type of spatiotemporal data - data that contains information about the space/location of moving objects, over time. Extracting and discovering patterns in large spatiotemporal data is critically important to several real-world applications, including understanding human mobility (e.g., pedestrian mobility mining), health care (e.g., epidemics spreading, gait analysis), smart transportation and urban planning (e.g., traffic forecasting and optimization), and location-based services (e.g., recommendations of points of interest). As the volume and complexity of spatiotemporal data has increased rapidly, turning these data into meaningful actions using conventional statistical modeling and data mining methods remains challenging. Recently, with the advances of deep learning techniques, deep learning models such as convolutional neural network (CNN), recurrent neural network (RNN) and graph neural network (GNN) have enjoyed considerable success in various machine learning tasks because of their powerful learning ability. They have been broadly applied in many areas including computer vision, natural language processing, graph data mining, and time series data prediction, which inspires our proposed research to adopt deep learning models for various mobility data mining tasks. The anticipated outcome of our research is the development of the theory and methods of deep learning spatiotemporal models for effectively mining mobility data. Within this context, several aspects of these models will be investigated related to their feasibility, accuracy, robustness, scalability, and interpretability. Best practices will also be developed for training, testing, and validating spatiotemporal models so they can be safely and effectively deployed to address mobility related problems in diverse settings and domains, ranging from intelligent transportation to location-based services and health. Our research program aligns with the Pan-Canadian Artificial Intelligence (AI) Strategy. Conducting world-class machine learning research in data mining, graph mining, big data analytics has the potential to attract the brightest students from around the world, while helping to retain domestic top research talent. Meanwhile, it will contribute to the training of high-quality personnel (HQP), with first-rate analytical and problem-solving skills that have the capacity to stimulate knowledge-based companies, leveraging AI to boost productivity and innovation, foster economic growth and improve the life conditions in Canada and beyond.