基于统计学模型的CBTC系统ATO停车精度自适应优化算法

Adaptive Optimization Algorithm for ATO Stopping Accuracy of CBTC System Based on Statistical Model

城轨CBTC系统运营对列车停车精度具有较高要求,其中ATO精确停车是系统的重要功能。而外部干扰造成的不确定性和系统延时是影响停车精度的重要因素。提出一种基于统计学模型的自适应优化算法,对停车精度的概率分布曲线进行拟合,而后采用二元马尔可夫模型优化ATO精确停车模型。通过3种不同类型情景的优化模型获得相应的停车精度数据,以期望和方差作为对比指标,结果表明优化模型对ATO停车精度有一定改善,获得较为理想的停车精度。

The operation of urban rail CBTC system has high requirements for accurate train stopping,ATO accurate stopping is an important function of the system.The uncertainty caused by external interference and system delay is important factors affecting stopping accuracy.This paper proposes an adaptive optimization algorithm based on statistical model,to fit the probability distribution curve of stopping accuracy,and then use the binary Markov model to optimize ATO accurate stopping model.The corresponding stopping accuracy data is obtained through optimization models of three different types of scenarios,and expectation and variance are taken as a comparison indicator.The results show that the optimization models have some improvement for ATO stopping accuracy,and obtain ideal stopping accuracy.