为研究高速公路特长隧道环境车道行驶速度特性，采用基于雷达组网技术的实时交通参数获取方法，提取典型特长隧道全路段全样本高精度时序速度数据，在划分隧道入口段、行车段、出口段的基础上，对比分析特长隧道不同路段车道速度分布特性，构建基于时序Transformer框架的特长隧道环境车道行驶速度短时预测模型。结果表明： 1）特长隧道入口段速度均值最低，分布最为离散； 2）在同一路段，各车道85%分位车速统计值呈现依次递减趋势； 3）小客车运行速度整体呈现下降规律，出入口段差值大； 4）货车在特长隧道入口段会降低速度，但小客车实际运行速度较主线限速值高； 5）所构建的速度预测模型准确率可达97.82%，平均绝对误差为1.67 km/h。上述结果表明： 1）暗适应对驾驶人车速控制行为的影响较为显著； 2）主线限速标准对于特长隧道环境并不完全适用，应考虑速度顺适过渡关系，针对不同车型、不同车道提前诱导； 3）所建立的时序Transformer模型适应于高速公路特长隧道环境所有车道短时速度预测。
Vehicle driving
speed characteristics in extralong freeway tunnels are analyzed
using radar networking technologybased realtime traffic parameter acquisition method. The highprecision timeseries speed data of all
samples from the entire tunnel are extracted. The tunnel is then divided into
three sections: entrance, driving, and exit, and a shortterm prediction model for detecting vehicle driving speed in extralong freeway tunnels is constructed using a timeseries Transformer framework. The main findings of the study are as
follows: (1) The lowest average vehicle driving speed is discretely distributed
at the entrance section. (2) The statistical value of 85% quantile vehicle
driving speed in each lane shows a decreasing trend along the same road
section. (3) The driving speed of a passenger car at different sections
exhibits a decreasing law, and there is a considerable difference between the
entrance and exit sections. (4) Although the freight cars slow down at the
entrance section, the actual driving speed of passenger cars is higher than the
maximum speed limit of the main lane. (5) The maximum accuracy of the speed
prediction model is 97.82%, and the average absolute error is 1.67 km/h. These
results demonstrate that dark adaptation has a significant impact on the driver′s speed control behavior. The speed limit of the main lane is not
fully applicable on the extralong tunnel environment,
and thus, guidance must be provided for different vehicles and lanes in advance
to avoid accidents. The timeseries Transformer model
established in this paper is suitable for shortterm
speed prediction of all lanes in extralong freeway
tunnels.
