Smart cities shall provide an integrated and green mobility. Green mobility implies to use green, public or shared transports. A lot of development has been spent in Transport, Traffic management, and Vehicle systems. By contrast few public projects address user systems, and they are rather limited, since they focus car mobility and not a multimodal green transport. Our project addresses multimodal public transport, specifically three main services, namely Integrated Real-time Mobility Assistant, City Analyzer & Forecaster, City Feed. IRMA is a cloud service that enables the user to plan (define), execute/buy, reschedule whatever end to end multimodal itinerary. It can be run from smart phone, web and (in future) any Android device. City Analyzer & Forecaster It is a set of business intelligence applications that enable municipalities to assess geo-referenced mobility on multiple analysis keys, and, by a simulation / sensitivity analysis, to forecast the load of public transports. It is based on big data technologies. City Feed It covers the complete life cycle of the cooperation between citizen and municipality. The citizen photographs and comments issues (e.g. a broken traffic light), that are filtered and validated by an appropriate authority, forwarded to appropriate contractors, and monitored until solution. All these three services are now on a proof-of-concept stage, with pilot projects to be started in two university cities, namelyPavia(Italy) and Weihai (China); most of the project is carried out by our Double Master students form HIT, Tongji, UESTC andPavia. Our project on smart citizen adds to the smart city research the new dimension of multimodal transportation and of a systems that is primarily target to the citizen rather than to the government. Early proofs of concept are encouraging, and demos have been welcome by some major IT industries. The system is a cloud service, based on big data technologies (e.g. Dynamo, HANA etc.).

Towards the smart citizen

MOTTA, GIANMARIO PIERO ANTONIO
2013-01-01

Abstract

Smart cities shall provide an integrated and green mobility. Green mobility implies to use green, public or shared transports. A lot of development has been spent in Transport, Traffic management, and Vehicle systems. By contrast few public projects address user systems, and they are rather limited, since they focus car mobility and not a multimodal green transport. Our project addresses multimodal public transport, specifically three main services, namely Integrated Real-time Mobility Assistant, City Analyzer & Forecaster, City Feed. IRMA is a cloud service that enables the user to plan (define), execute/buy, reschedule whatever end to end multimodal itinerary. It can be run from smart phone, web and (in future) any Android device. City Analyzer & Forecaster It is a set of business intelligence applications that enable municipalities to assess geo-referenced mobility on multiple analysis keys, and, by a simulation / sensitivity analysis, to forecast the load of public transports. It is based on big data technologies. City Feed It covers the complete life cycle of the cooperation between citizen and municipality. The citizen photographs and comments issues (e.g. a broken traffic light), that are filtered and validated by an appropriate authority, forwarded to appropriate contractors, and monitored until solution. All these three services are now on a proof-of-concept stage, with pilot projects to be started in two university cities, namelyPavia(Italy) and Weihai (China); most of the project is carried out by our Double Master students form HIT, Tongji, UESTC andPavia. Our project on smart citizen adds to the smart city research the new dimension of multimodal transportation and of a systems that is primarily target to the citizen rather than to the government. Early proofs of concept are encouraging, and demos have been welcome by some major IT industries. The system is a cloud service, based on big data technologies (e.g. Dynamo, HANA etc.).
2013
NICST
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/805658
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