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

TL;DR

This paper introduces a deep learning-based decision support system for rapid short-term public transport route improvements, utilizing diverse data sources and graph modeling to enhance service efficiency and reliability.

Contribution

It presents a novel deep learning approach that predicts lateness to optimize transit routes quickly, outperforming traditional heuristics in speed and adaptability.

Findings

Reduced route times by over 9% on evaluated routes

Effective for both intraurban and suburban routes

Demonstrated versatility across different transit scenarios

Abstract

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.