Latest revision as of 14:54, 12 February 2021

Abstract

Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city.

Document type: Article

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The different versions of the original document can be found in:

• http://dx.doi.org/10.1155/2020/8894705 under the license http://creativecommons.org/licenses/by/4.0/

• https://doi.org/10.1155/2020/8894705

• http://downloads.hindawi.com/journals/jat/2020/8894705.pdf,

http://downloads.hindawi.com/journals/jat/2020/8894705.xml,

http://dx.doi.org/10.1155/2020/8894705 under the license cc-by

• http://downloads.hindawi.com/journals/jat/2020/8894705.pdf,

https://www.hindawi.com/journals/jat/2020/8894705,

https://academic.microsoft.com/#/detail/3087166688 under the license http://creativecommons.org/licenses/by/4.0/

• http://dx.doi.org/10.1155/2020/8894705,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195