@inproceedings{10a62885d5cd4d57b416b73c28b6f01d,
title = "Traffic Flow Indicator: Predicting Jams in a City",
abstract = "Road traffic inside cities is responsible for noise and pollution, that causes health problems, fuel consumption and waste of time in jams. Mitigation solutions are usually used to soften the impact of this problem in most cities. In particular, the city of Lisbon has taken measures to reduce pollution by closing areas of the city to the most polluting cars-The zero emission zones. However, the city still lacks visual analytics support for traffic decisions in real-Time. In this paper we present a traffic flow indicator that can indicate the road traffic fluidity inside a region of interest for a given time frame, and integrated it into a interactive dashboard supported by a predictive model. With this solution, decision makers can analyse historical data and predict short-Term traffic behaviour.",
keywords = "machine learning, traffic flow forecasting, traffic flow indicator, visual analytics",
author = "Joao Vaz and Nuno Datia and Matilde Pato and Pires, {Jo{\~a}o Moura}",
note = "Funding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT# info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00408%2F2020/PT# Publisher Copyright: {\textcopyright} 2022 IEEE.; 26th International Conference Information Visualisation, IV 2022 ; Conference date: 19-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IV56949.2022.00056",
language = "English",
isbn = "978-1-6654-9008-5",
series = "Proceedings of the International Conference on Information Visualisation",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "287--292",
booktitle = "Proceedings",
address = "United States",
}
