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基于浮动车的城市交通信息采集技术是智能交通系统获取实时交通信息的重要手段之一。针对浮动车的城市交通信息等间距采样的不足,本文设计了一种基于城市道路复杂度的自适应采样算法:①根据道路属性定义道路结点对城市道路网络复杂度的影响因子;②利用四叉树对城市道路网络复杂度进行描述;③根据浮动车的瞬时速度和道路复杂度自适应计算浮动车的采样周期。通过仿真和试验表明,新算法能够在不同复杂程度的道路情况下自适应提供有效、可靠的采样周期。
Urban traffic information collection technology based on floating car is one of the important means of acquiring real-time traffic information in intelligent transportation system. Aiming at the shortage of floating traffic information such as equal distance sampling, this paper designs an adaptive sampling algorithm based on urban road complexity: (1) According to the road attributes, the influence factors of road nodes on the complexity of urban road network; (2) Fork tree to describe the complexity of urban road network; ③ According to the instantaneous speed of floating car and road complexity adaptive calculation floating car sampling period. Simulation and experiment show that the new algorithm can adaptively provide an effective and reliable sampling period under different complexity road conditions.