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MODUM

Financiado

Cerrado

Models for Optimising Dynamic Urban Mobility

Transport congestion problems contribute ~70% of pollutants to urban environments. The transport sector by itself consumes up to ~30% of the total energy in the EU. These figures suggest that if Europe is to reduce its CO2 emissio... ver más

31/12/2014

TML

3M€

Presupuesto del proyecto: 3M€

Líder del proyecto

TRANSPORT MOBILITY LEUVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 8 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2014-12-31 No tenemos la información de la convocatoria

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8 Participantes

TML

0.00€ | Lider

KU Leuven

N.D. | Participante

AUSTRIAN MOBILITY RESEARCH F...

N.D. | Participante

TECHNOLUTION BV

N.D. | Participante

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Líder del proyecto

TRANSPORT MOBILITY LEUVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 3M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Transport congestion problems contribute ~70% of pollutants to urban environments. The transport sector by itself consumes up to ~30% of the total energy in the EU. These figures suggest that if Europe is to reduce its CO2 emissions by making an efficient use of energy while improving the quality of life in European cities, novel approaches for the optimal management of urban transport complexity must be developed and adopted in the transport sector. MODUM addresses the environmental footprint in the transport sector by aiming to develop a new approach for pro-active demand-responsive management of traffic to enable energy-efficient multi-modal transport choices accommodating dynamic variations, minimising the environmental impact and improving the quality of life in urban environments. Moreover, MODUM will consider commuters, in combinations of both private and public transport, facing dynamic conditions such as unexpected disturbances typical of urban environments. In particular, MODUM focuses on the comparison and then the potential synthesis of two approaches: 1) a traffic flow self-organising mechanism based on ant-like agent technology and 2) a "reverse" route planning based on software agent technology; using real-time data and declared destinations. Both mechanisms have proven successful in other application domains and have the potential of utilising vehicles' computational power and networking capabilities for achieving their active participation in the demand-response management of urban traffic. The metrics for the comparison will be extracted from real needs of traffic control centres and from transport users in our selected cities. Once the metrics are defined, a series of simulation experiments of realistic complexity will be constructed using real-time data feeds available from transport sensing infrastructure. Results from these will profile the two approaches against certain scenarios of traffic disturbances causing rapid changes in conditions. A synthesis of the two approaches will then be developed by the academic partners. Software implementation of the synthesised approach will then be embarked upon, focusing on the telecommunication challenges of a realistic demonstrator. The developed prototype will be validated on the initial scenarios by staging real-life experiments, which the relevant traffic management structures within the traffic control centres will evaluate. Such experiments will include historical data and simulations in combination with real-time data feeds from existing infrastructure and vehicles going through a section of a city in a number of congestion profiles. Analogous experiments will include people moving in a city by different means of transport. The prototype will provide an implementation of an optimisation approach to traffic management capable of dynamically adapting the overall flows of traffic to unexpected disturbances to minimise carbon emissions within an urban complex environment. Fina

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