使用本体实现上下文感知智能交通管理的物联网方法

Deepti Goel, S. Chaudhury, Hiranmay Ghosh
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引用次数: 17

摘要

本文提出了一种新的基于物联网的智能交通管理服务框架,通过分析实时交通环境,利用本体来调节智慧城市的交通顺畅。提出的方法可以更智能地利用运输网络来实现与运输系统性能相关的目标。这需要有效的交通规划措施,这些措施涉及到通过使用物联网技术在时间和空间上调整网络需求和容量的行动。在智能交通系统中采用传感器和物联网设备有助于捕捉用户的偏好和上下文信息,这些信息可以以旅行时间、天气条件或现实驾驶模式的形式呈现。我们使用多媒体本体从对交通信息的感知观察中获得更高层次的交通状况和车辆描述,这为我们提出的物联网框架提供了重要依据。用多媒体Web本体语言(multimedia Web ontology Language, MOWL)编码的多媒体本体有助于定义可能的交通环境的类、属性和结构,从而提供跨交通网络的洞察。MOWL支持动态贝叶斯网络(DBN)来处理时间序列数据和与上下文观测相关的不确定性,符合智能物联网系统的定义。因此,我们提出的智能交通框架聚合了与交通领域相对应的信息,例如使用闭路电视摄像机捕获的交通视频,并允许自动预测动态变化的情况,这有助于交通管理部门做出更快的反应。我们通过使用上下文信息来说明我们的方法的使用,以评估道路上的实时拥堵情况,从而使规划服务可视化。一旦预测到拥堵情况,就会提出符合期望标准的备用无拥堵路线,并通过短信或电子邮件传播给用户。
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An IoT approach for context-aware smart traffic management using ontology
This paper exhibits a novel context-aware service framework for IoT based Smart Traffic Management using ontology to regulate smooth traffic flow in smart cities by analyzing real-time traffic environment. The proposed approach makes smarter use of transport networks to achieve objectives related to performance of transport system. This requires efficient traffic planning measures which relate to the actions designed to adjust the demand and capacity of the network in time and space by use of IoT technologies. The adoption of sensors and IoT devices in Smart Traffic System helps to capture the user's preferences and context information which can be in the form of travel time, weather conditions or real-life driving patterns. We have employed multimedia ontology to derive higher level descriptions of traffic conditions and vehicles from perceptual observation of traffic information which provides important grounds for our proposed IoT framework. The multimedia ontology encoded in Multimedia Web Ontology Language(MOWL) helps to define classes, properties, and structure of a possible traffic environment to provide insights across the transportation network. MOWL supports Dynamic Bayesian networks (DBN) to deal with time-series data and uncertainties linked with context observations which fits the definition of an intelligent IoT system. Thus, our proposed smart traffic framework aggregates information corresponding to traffic domain such as traffic videos captured using CCTV cameras and allows automatic prediction of dynamically changing situations which helps to make traffic authorities more responsive. We have illustrated use of our approach by utilizing contextual information, to assess real-time congestion situation on roads thus allowing to visualize planning services. Once the congestion situation is predicted, alternate congestion free routes which are in accordance with the coveted criteria are suggested that can be propagated through text-messages or e-mails to the users.
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