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Real-time spatio-temporal forecasting with dynamic urban event and vehicle-level flow information
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Conlan, Chris, Oakley, Joe, Demirci, Gunduz Vehbi, Sfyridis, Alexandros and Ferhatosmanoglu, Hakan (2023) Real-time spatio-temporal forecasting with dynamic urban event and vehicle-level flow information. In: 5th International Workshop on Big Mobility Data Analytics (BMDA). Proceedings of the Workshops of the EDBT/ICDT 2023 Joint Conference, Ioannina, Greece, 28-31 Mar 2023. Published in: CEUR Workshop Proceedings, 3379 doi:urn:nbn:de:0074-3379-5 ISSN 1613-0073.
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Official URL: https://ceur-ws.org/Vol-3379/
Abstract
Building a real-time spatio-temporal forecasting system is a challenging problem which has many practical applications such as traffic and road network management. Most forecasting research typically focuses on the average quality of predictive models, with much less attention paid to building a practical pipeline and achieving timely and accurate forecasts when the network is under heavy load. Additionally, transport authorities face the issue of how to effectively leverage various dynamic data sources, such as urban events (e.g., scheduled roadworks on the road network, cultural events) and vehicle-level flow data. In this paper, we investigate the practical challenges of real-time forecasting, and present Foresight, a cloud-based system for spatio-temporal forecasting developed in collaboration with Transport for the West Midlands (TfWM). Foresight can ingest, aggregate and process streamed traffic data to produce road network forecasts continuously. We adapt spatio-temporal machine learning methods to incorporate dynamic urban events and vehicle-level flow data, and experimentally evaluate a variety of predictive models in our setting. We employ a data-driven approach to identify peak times in the network, and provide insights on how the performance of forecasting solutions varies for these times when accurate forecasts are most important. We observe that incorporating roadworks into a Graph Neural Network (GNN) model can provide up to a 29.1% performance improvement (MAPE) at a 60-minute forecasting horizon. Further, modelling traffic propagation using vehicle-level flow data in order to support graph-based learning can yield performance gains of 8.8% (MAE) at peak times.
Item Type: | Conference Item (Paper) | |||||||||
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Subjects: | H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Traffic estimation, Traffic engineering, Deep learning (Machine learning), Neural networks (Computer science), Roads -- Maintenance and repair -- Data processing, Streaming technology (Telecommunications) | |||||||||
Journal or Publication Title: | CEUR Workshop Proceedings | |||||||||
Publisher: | RWTH Aachen University | |||||||||
ISSN: | 1613-0073 | |||||||||
Official Date: | 26 April 2023 | |||||||||
Dates: |
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Volume: | 3379 | |||||||||
DOI: | urn:nbn:de:0074-3379-5 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Copyright Holders: | The Authors | |||||||||
Date of first compliant deposit: | 3 May 2023 | |||||||||
Date of first compliant Open Access: | 10 May 2023 | |||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||
Title of Event: | 5th International Workshop on Big Mobility Data Analytics (BMDA). Proceedings of the Workshops of the EDBT/ICDT 2023 Joint Conference | |||||||||
Type of Event: | Workshop | |||||||||
Location of Event: | Ioannina, Greece | |||||||||
Date(s) of Event: | 28-31 Mar 2023 | |||||||||
Related URLs: |
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