Public transport routing plays a crucial role in transit network design, ensuring a satisfactory level of service for passengers. However, current routing solutions rely on traditional operational research heuristics, which can be time-consuming to implement and lack the ability to provide quick solutions. Here, we propose a novel deep learning-based methodology for a decision support system that enables public transport (PT) planners to identify short-term route improvements rapidly. By seamlessly adjusting specific sections of routes between two stops during specific times of the day, our method effectively reduces times and enhances PT services. Leveraging diverse data sources such as GTFS and smart card data, we extract features and model the transportation network as a directed graph. Using self-supervision, we train a deep learning model for predicting lateness values for road segments. These lateness values are then utilized as edge weights in the transportation graph, enabling efficient path searching. Through evaluating the method on Tel Aviv, we are able to reduce times on more than 9\% of the routes. The improved routes included both intraurban and suburban routes showcasing a fact highlighting the model's versatility. The findings emphasize the potential of our data-driven decision support system to enhance public transport and city logistics, promoting greater efficiency and reliability in PT services.

翻译：公共交通线路规划在公交网络设计中至关重要，它保障了乘客满意的服务水平。然而，当前的线路规划解决方案依赖于传统的运筹学启发式算法，这些方法实施耗时且无法快速提供解决方案。本文提出了一种基于深度学习的新方法，构建了一个决策支持系统，使公共交通规划人员能够快速识别短期路线改进方案。通过无缝调整一天中特定时间段内两个站点之间的特定路段，我们的方法有效减少了行程时间并提升了公交服务质量。利用GTFS和智能卡数据等多源数据，我们提取特征并将交通网络建模为有向图。通过自监督学习，我们训练了一个深度学习模型来预测道路段的延误值。这些延误值随后被用作交通图中的边权重，从而实现高效的路径搜索。在特拉维夫市评估该方法后，我们能够在超过9%的线路上减少行程时间。改进的线路包括城市内和郊区的线路，这突显了模型的多样性。研究结果强调了我们的数据驱动决策支持系统在提升公共交通和城市物流方面的潜力，从而促进公交服务更高的效率和可靠性。