  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.
