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• W4300981720 abstract "Eco-routing distributes traffic in cities to improve mobility sustainability. The implementation of eco-routing in real-life requires a diverse set of information, including different kinds of sensors. These sensors are often already integrated in city infrastructure, some are technologically outdated, and are often operated by multiple entities. In this work, we provide a use case-oriented system design for an eco-routing service leveraging Internet-of-Things (IoT) technologies. The methodology involves six phases: (1) defining an eco-routing use case for a vehicle fleet; (2) formulating a routing problem as a multi-objective optimisation to divert traffic at a relevant hub facility; (3) identifying data sources and processing required information; (4) proposing a microservice-based architecture leveraging IoT technologies adequate to a multi-stakeholder scenario; (5) applying a microscopic traffic simulator as a digital twin to deal with data sparsity; and (6) visually illustrating eco-routing trade-offs to support decision making. We built a proof-of-concept for a mid-sized European city. Using real data and a calibrated digital twin, we would achieve hourly total emissions reductions up to 2.1%, when applied in a car fleet composed of 5% of eco-routing vehicles. This traffic diversion would allow annual carbon dioxide and nitrogen oxides savings of 400 tons and 1.2 tons, respectively." @default.
• W4300981720 created "2022-10-04" @default.
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• W4300981720 date "2022-12-01" @default.
• W4300981720 modified "2023-10-06" @default.
• W4300981720 title "MobiWise: Eco-routing decision support leveraging the Internet of Things" @default.
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• W4300981720 doi "https://doi.org/10.1016/j.scs.2022.104180" @default.
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