高速公路上坡路段6轴铰接列车运行速度预测模型

为确保车辆在上坡路段的行驶安全，针对高速公路6轴铰接列车在上坡路段运行速度预测误差大、安全运营管理难的问题，提出了面向上坡路段6轴铰接列车的运行速度预测模型。采用雷达测速仪和AxleLight路侧激光仪采集西南某山区高速公路5处连续上坡路段的6轴铰接列车的交通流数据，并对实际运行速度与现有规范预测模型进行对比分析。以纵坡坡度、纵坡长度、车辆比功率、初始运行速度4个参数为变量，构建上坡路段运行速度预测模型。提出了预测模型误差修正方法，并分析了模型的有效性。结果表明:现有规范运行速度模型对6轴铰接列车运行速度的预测平均误差率达到了25.37%，模型误差较为显著；上坡路段6轴铰接列车的运行速度与坡度、坡长呈负相关，与车辆比功率呈正相关；构建的多元线性回归模型拟合优度R2为0.978，且满足相关检验指标；模型预测速度与实际速度差在2~4 km/h之间、相对误差平均值为8.86%，其结果较规范模型降低了16.51%；考虑交通密度因素修正后，模型预测速度与实际速度差在1 km/h以内、相对误差平均值为1.08%，其结果较未经修正的预测模型降低了7.78%，较规范模型降低了24.29%。由此可见，该速度预测模型对长上坡路段6轴铰接列车运行速度预测的准确性提升明显。 

A Prediction Model for Operation Speed of Six-axis Articulated Trains in Uphill Sections of Expressways

The prediction error of operation speed of 6-axis articulated trains in uphill sections of expressways is large, and its safe operation management is challenging. An Operation Speed Prediction Model (OSPM) is proposed to address this issue. Radars and AxleLight side lasers are used to collect the traffic flow data of six-axis articulated trains at five continuous uphill sections of an expressway from Southwest China. Moreover, the actual operation speed is compared with the results from an existing standard prediction model. Then the OSPM for six-axis articulated trains in uphill sections of expressways is developed by taking the gradient and the length of the uphill, the specific power, and the initial speed of the trains as variables. Lastly, an error correction method is developed, and effectiveness of the proposed method is analyzed. The main results are shown as follows: the average rate of prediction error of the standard model reaches 25.37%, and the prediction error is relatively significant. The operation speed is negatively correlated to the gradient and the length of uphill, while it is positively correlated to specific power of the train. The goodness of fit of multiple linear regression model R2 is 0.978, meeting the test requirement. The difference between the predicted speed and the actual speed is in the range of 2-4 km/h, and the average relative error is 8.86%, which is 16.51% less than the standard model. Considering the influences of traffic density, the revised model can reduce the error within 1 km/h; the average relative error is 1.08%, which is 7.78% less than the original model and 24.29% less than the standard model. These results reveal that the proposed OSPM can considerably improve the accuracy of the operation speed prediction.