传统感应线圈的交通状态估计方法已无法满足准确性和实时性的状态估计需要，为此提出了基于联网公交车辆实时速度的交通状态估计模型。所提模型借助实时信息采集系统的高效性和准确性的优势，对道路交通运行状态进行估计，同时利用卡尔曼滤波算法对交通状态变量进行更新。基于历史观测数据对更新后的交通状态变量进行修正，进而得到交通状态的估计值。通过采集数据并进行大量的实验，研究结果表明：基于联网公交实时速度的状态估计模型，在各种交通环境条件和占有率下，估计值误差指数（变异系数） 均小于15%，最大仅为13.15%；状态估计修正模型与状态估计模型相比，估计值误差指数下降了2%，总体误差优化性能提升了11.87%。在确保实时性和高效性的同时，基于联网公交车辆实时速度的交通状态估计模型解决了传统道路交通状态估计方法准确性低的问题。
Abstract
The traffic state estimation method based on the traditional induction loop was unable to meet the accurate and real-time needs. In order to solve this problem, a traffic state estimation model based on real-time speed of connected buses was proposed. The high-efficiency and accuracy advantages of the real-time information acquisition system was used to estimate road traffic operation state in the model, and the Kalman filter algorithm was used to update the traffic state variables at the same time. Then, the updated traffic state variables were corrected using the historical observation data, and finally the estimated value of traffic state was obtained. Based on data collection and a large number of experiments, the results showed that the error index (coefficient of variation) of the estimated value of the state estimation model based on real-time bus speed was less than 15% and the maximum was only 13.15% under various traffic environmental conditions and occupancy rates. Compared with the state estimation model, the error index of the estimated value of the state estimation correction model was decreased by an average of 2%, and the overall error optimization performance was increased by 11.87%. The traffic state estimation model based on real-time speed of connected buses can handle the problem of low accuracy of the traditional road traffic state estimation method with the guarantee of real-time and high efficiency.
关键词
城市交通 /
公交车速 /
状态估计模型 /
卡尔曼滤波 /
估计修正模型
Key words
urban traffic /
bus speed /
state estimation model /
Kalman filter /
estimation correction model
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