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首页> 外文会议>International forum on advanced microsystems for automotive applications;AMAA >Traffic Jam Warning Messages from Measured Vehicle Data with the Use of Three-Phase Traffic Theory
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Traffic Jam Warning Messages from Measured Vehicle Data with the Use of Three-Phase Traffic Theory

机译：三相交通理论从实测车辆数据中获取交通拥堵警告消息

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摘要

Based on Kerner's three-phase theory, we study an algorithm for the generation of traffic jam warming messages from measured GPS and GSM probe vehicle data that have been collected in TomTom's HD-traffic service both from nomadic devices and vehicle's embedded systems. We find that the data allows us to reconstruct the structure of congested traffic patterns with a much greater quality of spatiotemporal resolution than has been possible before. It occurs that congested traffic in measured traffic patterns consists of the two traffic phases of Kerner's three-phase theory, synchronized flow and wide moving jams. The application method distinguishes between the fronts of the congested traffic phases, wide moving jam and synchronized flow. It will be shown that a penetration of about 2% of the total traffic flow is enough to implement a precise traffic jam warning message for navigation systems.
机译：基于Kerner的三相理论，我们研究了一种算法，该算法用于从测量的GPS和GSM探测车辆数据生成交通拥堵预热消息，该数据已从TomTom的高清交通服务中从游牧设备和车辆嵌入式系统中收集。我们发现，这些数据使我们能够以比时空分辨率更高的时空分辨率质量来重建拥塞交通模式的结构。可能发生的情况是，在已测交通模式中拥堵的交通包括克纳（Kerner）三相理论的两个交通阶段，同步流动和大范围堵塞。该应用方法区分拥挤的交通阶段，广泛的交通拥堵和同步流量。将会显示，大约2％的总交通流量的渗透足以为导航系统实施精确的交通拥堵警告消息。

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