@misc{10498/29818,
year = {2022},
url = {http://hdl.handle.net/10498/29818},
abstract = {A way to reduce carbon emissions in cities is through movement by bicycle or on foot. However, it sometimes means to pass through high-pollution zones and consequently breath low quality air. We then propose a green Intelligent Transportation System (ITS) for zero-emission mobility users, providing users with low-pollution routes to avoid the high-pollution zones. This proposal uses ITS to promote the use of alternative transportation to classical motor vehicles to reduce carbon emissions. This is based on Complex Event Processing (CEP) technology to gather and process real-time data, a Decision Support System designed as a Fuzzy Inference System (FIS) to make decisions about recommended transit zones, taking also into account the user experience level and specific weather data, and Colored Petri Nets (CPN) as a tool to compute the routes. This is therefore an all-in-one solution to provide green routes, with the benefits of each one of the technologies used. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the Conference Program Chairs.},
publisher = {Elsevier},
keywords = {Colored Petri Nets},
keywords = {Complex Event Processing},
keywords = {Fuzzy Inference System},
keywords = {Intelligent Transportation System},
keywords = {Pollution},
title = {greenITS: a proposal to compute low-pollution routes},
doi = {10.1016/j.procs.2022.07.042},
author = {Macià, Hermenegilda and Díaz, Gregorio and Valero, Valentín and Valero, Edelmira and Brazález, Enrique and Boubeta Puig, Juan},
}