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Selective data collection in vehicular networks for traffic control applications

机译:车辆网络中针对交通控制应用的选择性数据收集

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摘要

Vehicular sensor network (VSN) is an emerging technology, which combines wireless communication offered by vehicular ad hoc networks (VANETs) with sensing devices installed in vehicles. VSN creates a huge opportunity to extend the road-side sensor infrastructure of existing traffic control systems. The efficient use of the wireless communication medium is one of the basic issues in VSN applications development. This paper introduces a method of selective data collection for traffic control applications, which provides a significant reduction in data amounts transmitted through VSN. The underlying idea is to detect the necessity of data transfers on the basis of uncertainty determination of the traffic control decisions. According to the proposed approach, sensor data are transmitted from vehicles to the control node only at selected time moments. Data collected in VSN are processed using on-line traffic simulation technique, which enables traffic flow prediction, performance evaluation of control actions and uncertainty estimation. If precision of the resulting information is insufficient, the optimal control action cannot be derived without ambiguity. As a result the control decision becomes uncertain and it is a signal informing that new traffic data from VSN are necessary to provide more precise prediction and to reduce the uncertainty of decision. The proposed method can be applied in traffic control systems of different types e.g. traffic signals, variable speed limits, and dynamic route guidance. The effectiveness of this method is illustrated in an experimental study on traffic control at signalised intersection.
机译:车载传感器网络(VSN)是一项新兴技术,将车载自组织网络(VANET)提供的无线通信与安装在车辆中的传感设备结合在一起。 VSN为扩展现有交通控制系统的路边传感器基础设施提供了巨大的机会。无线通信介质的有效使用是VSN应用程序开发中的基本问题之一。本文介绍了一种用于流量控制应用程序的选择性数据收集方法,该方法可显着减少通过VSN传输的数据量。基本思想是根据业务控制决策的不确定性确定来检测数据传输的必要性。根据所提出的方法,仅在选定的时刻将传感器数据从车辆传输到控制节点。 VSN中收集的数据使用在线交通模拟技术进行处理,该技术可以进行交通流量预测,控制行为的性能评估和不确定性估计。如果所得到的信息的精度不足,则不能毫无歧义地得出最佳控制作用。结果,控制决策变得不确定,并且这是一个信号,通知有来自VSN的新交通数据对于提供更精确的预测并减少决策的不确定性是必要的。所提出的方法可以应用于不同类型的交通控制系统中,例如交通控制系统。交通信号,变速限制和动态路线指引。在信号交叉口的交通控制实验研究中证明了该方法的有效性。

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