移动式道路交通状态模糊评价方法研究

针对道路交通状态评价问题提出了一种基于移动交通流检测信息和多因素模糊模式识别模型的道路交通状态评价方法,利用移动交通流检测技术获取的探测车平均速度、拥堵系数、停车时间比例、加速度噪声和平均速度梯度作为交通拥堵表征量,采用VISSIM仿真方法确定出交通拥堵表征量的阈值,并应用多级模糊模式识别方法实现道路交通状态的评价过程。仿真结果表明:移动式道路交通状态模糊判别方法可以准确方便地实现道路交通拥堵状态的评价,并且能够反映出评估时段内道路交通状态的变化。

Traffic State Fuzzy Evaluation Based on Probe Vehicle Data

A new traffic state evaluation approach based on probe vehicle and multi-classification fuzzy pattern recognition was discussed. This approach has two key points. For the first one, probe vehicle used for achieving five congestion indexes, i.e. average speed of probe vehicle, congestion index, proportion stop time, acceleration noise and mean velocity gradient, and the value of these indexes was decided using VISSIM. For the second point, multi-classification fuzzy pattern recognition was applied in traffic state evaluation processing. The VISSIM simulation results show that this new approach can evaluate the traffic state efficiently and easily, and can also effectively reflect the punctuation of traffic state in the evaluation interval.