The COVID-19 pandemic has resulted in significant disruptions in mobility patterns, leading to changes in user travel behavior. Understanding users’ travel demand, travel behaviors, and changes in the structure of the travel network becomes the basis for governments and operators to provide improved service quality. Public transportation in a city provides essential mobility, accessibility, and connectivity for residents. The burgeoning shared mobility sector utilizes the Internet to establish a management platform that leverages digital technology to offer convenient travel services. Bike sharing presents a new transport mode for short-distance trips strengthening connectivity with public travel modes such as buses and subways, while online taxi services take on long-distance trips within cities. This paper proposes a network-based deep learning method to address the COVID-induced changes in spatiotemporal travel mobility and community structure detection, which integrates graph learning and optimization in an end-to-end training approach. The approach involves constructing a dynamic travel network and adopting complex network theory to develop metrics that uncover the changes in user mobility patterns and explore the correlation between different travel modes. Our results show that the pandemic reduces overall trip volume and network structure changes, suggesting that productive and residential activities have partially recovered but remain far from pre-pandemic levels, especially for taxi and subway trips. These findings provide valuable insights for transportation planners and policymakers to explore strategies that promote more sustainable and resilient mobility patterns in the post-pandemic era.

COVID-19 大流行对出行模式造成了重大破坏，导致用户出行行为发生变化。了解用户的出行需求、出行行为以及出行网络结构的变化成为政府和运营商提供提升服务质量的基础。城市的公共交通为居民提供了必要的流动性、可达性和连通性。新兴的共享出行行业利用互联网建立管理平台，利用数字技术提供便捷的出行服务。共享单车为短途出行提供了一种新的交通方式，加强了与公交、地铁等公共出行方式的连接，而网约车服务则承担了城市内的长途出行。本文提出了一种基于网络的深度学习方法，以解决新冠肺炎引起的时空旅行流动性和社区结构检测的变化，该方法将图学习和优化集成到端到端训练方法中。该方法涉及构建动态出行网络并采用复杂网络理论来开发指标，以揭示用户移动模式的变化并探索不同出行模式之间的相关性。我们的研究结果表明，疫情减少了总体出行量和网络结构变化，这表明生产和居住活动已部分恢复，但仍远低于大流行前的水平，尤其是出租车和地铁出行。这些发现为交通规划者和政策制定者探索在大流行后时代促进更可持续和更有弹性的出行模式的策略提供了宝贵的见解。