ABSTRACT The primary objective of this study is to evaluate how intelligent vehicles equipped with cooperative adaptive cruise control (CACC) improve freeway efficiency and safety at an off-ramp bottleneck. Applying randomized forest and back-propagation neural network (BPNN) algorithms, lane-changing characteristics are obtained based on ground-truth vehicle trajectory data extracted from the NGSIM dataset. The results show that both CACC penetration rate and length of diverge influence areas exert considerable influence on road capacity and traffic safety. Overall, the capacity will peak after an initial decrease as the CACC penetration rate increases. The maximum capacity obtained in 100% of CACC vehicle scenarios improved by over 60%, compared with 50% CACC penetration rate scenario. The proposed integration system with 100% CACC penetration rate significantly reduced the rear-end collision risks, decreasing time exposed time-to-collision and time integrated time-to-collision by 70.8%–97.5%.

中文翻译：

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机器学习算法在智能车辆进出匝道换道模型中的应用

摘要 本研究的主要目的是评估配备协同自适应巡航控制 (CACC) 的智能车辆如何在出口匝道瓶颈处提高高速公路效率和安全性。应用随机森林和反向传播神经网络 (BPNN) 算法，基于从 NGSIM 数据集中提取的地面实况车辆轨迹数据获得换道特征。结果表明，CACC渗透率和分流影响区长度对道路通行能力和交通安全都有相当大的影响。总体而言，随着 CACC 渗透率的增加，容量将在最初下降后达到峰值。100%的CACC车辆场景下获得的最大容量比50%的CACC普及率场景提高了60%以上。