
科学技术与工程

中国技术经济学会
基于分合流区事故风险变化预测的地下互通立交合理间距
Determining Reasonable Spacing of Underground Interchanges Based on Crash Risk Variation Prediction for the Diverging and Merging Areas
为了确定城市地下道路的互通立交合理间距设置,以武汉市两湖隧道文荟街地下互通立交为实际案例,开展了不同间距情况下的VISSIM微观交通仿真实验,分析了不同间距、主线及匝道交通量和设计速度条件,对地下互通立交分合流区事故风险的影响,并进一步利用极端梯度提升模型(extreme gradient boosting, XGBoost)、支持向量机(support vector machine, SVM)、随机森林(random forest, RF)、多层感知机(multilayer-perceptron, MLP),开展了地下互通立交分合流区事故风险变化预测。研究结果显示:当间距由1.5 km增加至2.5 km时,行程时间、平均延误、平均排队长度、交通冲突率有显著降低,碰撞风险指标[碰撞时间(time-to-collision, TTC)]有显著的增大;当间距在2.5 km以上时,行程时间、平均延误、平均排队长度、交通冲突率降低趋势缓慢,TTC增大趋势较小;间距在2.5 km及以上时,地下互通立交整体交通运行效率显著提高,在不影响服务沿线重点片区功能定位的前提下;通过过采样技术均衡化数据样本后,XGBoost模型对于事故风险变化预测精确率达到88.3%。研究结论可为地下互通立交间距设置提供理论支撑和应用借鉴。
In order to determine the reasonable spacing of underground interchanges, a series of traffic simulation experiments with varying spacing cases was conducted in VISSIM using the Wenhui Street underground interchange of the Two Lakes Tunnel in Wuhan City as a practical case. The effects of interchange spacing, mainline and ramp traffic volumes and design speeds on crash risk at the diverging and merging areas of the underground interchanges were analyzed. Then, the crash risk variation at the diverging and merging areas of the underground interchanges was predicted using the four typical machine learning algorithms, i. e., extreme gradient boosting(XGBoost), support vector machine (SVM), random forest (RF), and multilayer-perceptron (MLP). Results show that when the spacing increases from 1.5 km to 2.5 km, travel time, average delay, average queue length, and traffic conflict rate decrease significantly, and the collision risk index of time-to-collision(TTC) increase significantly. When the spacing is above 2.5 km, the decreases in travel time, average delay, average queue length, and traffic conflict rate start to slow down, and so does the increase in TTC. When the spacing is 2.5 km and above, the overall traffic operation efficiency and safety of the underground interchange increase significantly. The XGBoost model can predict the crash risk variation reaching a precision of 88.3%. This study can be a theoretical support and practical case for the setting of underground interchange spacing.
交通工程 / 地下互通立交 / 合理间距 / 事故风险 / VISSIM仿真 / 机器学习
traffic engineering / underground interchange / reasonable spacing / traffic operation efficiency / VISSIM simulation / machine learning
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