REDUCTION - Reducing Environmental Footprint based on Multi-Modal Fleet management System for Eco-Routing and Driver Behaviour Adaptation
Overview
Background & policy context:
Reduction of CO2 emissions is the great challenge of the transport sector nowadays. Despite progress in vehicle manufacturing and fuel technology, additional innovative technologies are needed to address this challenge. According to the International Association of Public Transport, a significant fraction of CO2 emissions in EU cities is resulting from public transport and other mass transport means, which are commonly organised into multi-modal transport fleets, because their vehicles have, on average, nearly substantial mileage and fuel consumption.
Objectives:
REDUCTION follows an interdisciplinary approach and brings together expertise from several communities. Its innovative, decentralised architecture allows scalability to large fleets by combining both V2V and V2I approaches. Its planned commercial exploitation, based on its proposed cutting-edge technology, aims at providing a major breakthrough in the fast growing market of services for 'green' fleets in EU and worldwide, and present substantial impact to the challenging environmental goals of EU.
Methodology:
The REDUCTION project focuses on advanced ICT solutions for managing multimodal fleets and reducing their environmental footprint. REDUCTION collects historic and real-time data about driving behaviour, routing information, and emissions measurements, that are processed by advanced predictive analytics to enable fleets enhancing their current services as follows:
Optimizsing driving behaviour: supporting effective decision making for the enhancement of drivers education and the formation of effective policies about optimal traffic operations (speeding, braking, etc.), based on the analytical results over the data that associate driving-behaviour patterns with CO2 emissions
Eco-routing: suggesting environmental-friendly routes and allowing multi-modal fleets to reduce their overall mileage automatically
Support for multi-modality: offering a transparent way to support multiple transportation modes and enabling co-modality
REDUCTION will develop powerful methodologies for providing predictive analytics based on advanced data mining technology, which will reveal patterns and useful information for meeting the objectives of fleet management, such as decision making for driver-adaption, ecorouting, and thus CO2 emissions control and improved fuel economy. Web-based access will be provided to clients of the provided service, in order to provide explanatory reports, based on graphical visualisations, about the discovered knowledge and for providing rapid alerting information that enables monitoring the performance of the fleets. Finally, the REDUCTION platform will provide appropriate interfaces to intelligent mobile devices (e.g., smartphones, netbooks) in order to enable public user - for instance, passenger - applications leverage the information residing in the platform.
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