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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Effective and unburdensome forecast of highway traffic flow with adaptive computing

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Author(s):
Alves, Matheus A. C. [1] ; Cordeiro, Robson L. F. [1]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo, Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: KNOWLEDGE-BASED SYSTEMS; v. 212, JAN 5 2021.
Web of Science Citations: 0
Abstract

Given traffic flow measurements for one highway, how to forecast its flow in future periods? Recent works in traffic forecast propose burdensome procedures 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. (C) 2020 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis
Grantee:Agma Juci Machado Traina
Support Opportunities: Regular Research Grants
FAPESP's process: 18/05714-5 - Mining Frequent Data Streams of High Dimensionality with a Case Study in Digital Games
Grantee:Robson Leonardo Ferreira Cordeiro
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FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
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Support Opportunities: Research Projects - Thematic Grants