Geographically-aware Transformer-based Traffic Forecasting for Urban Motorway Digital Twins

• URL: http://arxiv.org/abs/2602.05983v1
• Date: Thu, 05 Feb 2026 18:33:03 GMT
• Title: Geographically-aware Transformer-based Traffic Forecasting for Urban Motorway Digital Twins
• Authors: Krešimir Kušić, Vinny Cahill, Ivana Dusparic,
• Abstract summary: This paper introduces a Geographically-aware Transformer-based Traffic Forecasting GATTF model.<n>The model has been evaluated using real-time data from the Geneva motorway network in Switzerland.<n>Results confirm that incorporating geographical awareness through MI enhances the accuracy of GATTF forecasting compared to a standard model.
• Score: 2.0569660530137583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The operational effectiveness of digital-twin technology in motorway traffic management depends on the availability of a continuous flow of high-resolution real-time traffic data. To function as a proactive decision-making support layer within traffic management, a digital twin must also incorporate predicted traffic conditions in addition to real-time observations. Due to the spatio-temporal complexity and the time-variant, non-linear nature of traffic dynamics, predicting motorway traffic remains a difficult problem. Sequence-based deep-learning models offer clear advantages over classical machine learning and statistical models in capturing long-range, temporal dependencies in time-series traffic data, yet limitations in forecasting accuracy and model complexity point to the need for further improvements. To improve motorway traffic forecasting, this paper introduces a Geographically-aware Transformer-based Traffic Forecasting GATTF model, which exploits the geographical relationships between distributed sensors using their mutual information (MI). The model has been evaluated using real-time data from the Geneva motorway network in Switzerland and results confirm that incorporating geographical awareness through MI enhances the accuracy of GATTF forecasting compared to a standard Transformer, without increasing model complexity.

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