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.

中文翻译：

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可扩展的智能城市交通管理系统

为了对智能交通系统进行有效管理，需要集成各种传感技术以及对大量异构数据的快速处理，以便实时执行城市网络的智能分析。但是，由于传输的传感器数据流的增加，依赖智能需求侧传输管理的动态响应尤其具有挑战性。在这项工作中，提出了一种新颖的，由智能服务驱动的，适应性强的中间件体系结构，用于从异构数据源获取，存储，操纵和集成信息，以便提供旨在支持战略决策的智能分析。该架构提供了自适应和可扩展的数据集成服务，用于获取和处理动态数据，从而缩短响应时间，并结合先进的可视化技术，提供用于实时预测的数据挖掘和机器学习模型。所提出的解决方案已得到实施和验证，证明了它能够在欧洲首都的现有，运营和大规模公交网络上提供实时性能。