Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph
This work addresses the need for rapid route improvements in public transport planning, offering a data-driven tool for planners, though it is incremental as it builds on existing graph-based methods with deep learning enhancements.
The paper tackled the problem of slow public transport route planning by proposing a deep learning-based decision support system that models the network as a weighted graph to predict lateness, resulting in reduced times on over 9% of routes in Tel Aviv.
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.