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

Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph