面向实际道路网络的浮动车采样间隔优化方法

目前基于浮动车的城市交通信息采集通常采用等间距进行采样,无法根据道路网络几何条件和状态的差异进行合理的采样间隔优化.针对现有采样算法的不足,本文提出了一种面向实际道路网络的浮动车采样间隔优化方法.首先通过构建四叉树模型对城市道路网络进行划分,确定空间采样分辨率,然后利用历史轨迹对浮动车的速度进行短时预测,最后在不影响空间采样分辨率的基础上实时动态优化采样间隔,在交通信息的精度与信息的采集成本之间取得平衡.通过仿真试验的定性定量分析,新算法能够在不同复杂程度的道路网络情况下动态调整采样间隔,不仅确保了采样数据的精度,而且降低了采样数据容量.

Real Road Network Oriented Optimization Method of Floating Car Sampling Interval

The technologies of traffic information collection using floating car equipped GPS have been become one of the main important means for real-time collecting traffic information in intelligent transportation system. The intervals of traditional traffic information collection technologies using floating car equipped GPS are simplex and equivalent at present. The sampling interval cannot be obtained according to geometric condition of load network and diversity of traffic status. Aiming at the ineffectiveness of the existing sampling interval algorithms, a real road network oriented optimization method of floating car sampling interval is proposed. Firstly, the urban road network is divided via quad-tree model. Thereby, the spatial sampling resolution can be acquired; Secondly, the short-term speeds of floating car are predicted according to the history track; Finally, the optimal sampling intervals are obtained, simultaneously, the spatial sampling resolution cannot be influenced. The results of simulation and experiment show that the sampling interval can be dynamically determined under the circumstances of different complexities of road network. The sampling result can notonly ensure sampling data precision, but also reduce data capacity.