Effective and unburdensome forecast of highway traffic flow with adaptive computing

• Published in: Knowledge-based systems Vol. 212; p. 106603
• Main Authors: Alves, Matheus A.C., Cordeiro, Robson L.F.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 05-01-2021; Elsevier Science Ltd
• Subjects: Accident data; Adaptive algorithms; Adaptive computing; Data stream forecast; Digital media; Driver behavior; Highway traffic flow; Highways; Local flow; Roads & highways; Traffic flow; Weather forecasting; United Kingdom > UK; Highway traffic flow; Adaptive computing; Data stream forecast
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2020.106603

Given traffic flow measurements for one highway, how to forecast its flow in future periods? Recent works in traffic forecast propose burdensomeprocedures by depending on additional data that is not always available, like traffic measurements from other roads linked to the one of interest, social media, trajectory and car accident data, geographical and socio-demographic attributes, driver behavior information and weather forecast. The most accurate algorithms force anyone to monitor an entire network of highways, even when there is a single highway of interest. This procedure is commonly unaffordable. How to obtain highly accurate results without using any additional data? We answer the question with AdaptFlow: a novel, adaptive algorithm able to accurately forecast traffic flow by individually monitoring highways that are connected to each other in a complex network using local flow measurements only. We performed experiments on large datasets from highways in UK and USA. Our AdaptFlow notably outperformed well-known related works on many settings. For example, it achieved 95.5% accuracy on average when forecasting the next 15 minutes flow of the UK highways, leading to an error rate that is 36% smaller than the one of the most accurate related work that does not use additional data.