影响因子：7.6
DOI码：10.1016/j.trc.2025.105124
所属单位：Hefei University of Technology
教研室：School of Automotive and Transportation Engineerin
发表刊物：Transportation Research Part C
刊物所在地：UK
项目来源：National Natural Science Foundation of China
关键字：Intelligent connected environment; Ramp layout; Connected-Automated Vehicle; Macroscopic fundamental diagram (MFD)
摘要：In urban traffic systems, traffic efficiency is often improved through road upgrading, especially freeway renovation in the old urban zone. Meanwhile, Connected-Automated Vehicles (CAV) technology is being developed and will be gradually popularized. The driving behavior and route search algorithms of CAVs are significantly different from those of Human-driving Vehicles (HV), and the traffic flow transmission characteristics of the mixed freeway and arterial road networks will also change. Therefore, in the intelligent CAV environment, how to optimize the ramp layout and improve traffic efficiency under the expanded freeway road network becomes a concerning issue. In this paper, a traffic flow transmission model of mixed urban road networks is first proposed that contains freeways and arterials based on the Fundamental Diagram (FD) and Macroscopic Fundamental Diagram (MFD) theories. Secondly, the sending capacity and receiving capacity of arterial road networks with different CAV penetration rates are analysed, and the influence of traffic flow proportion carried by the freeway on the transmission capacity of mixed road networks is applied. Then, subregion traffic flow transmission efficiency optimization models are established, and a Ramp Location and Size Optimization (RLSO) method for mixed road networks is prompted. Finally, a numerical experiment is conducted with a regional road network in Hefei and compared with the No Ramp Optimization (NRO) and Ramp Size Optimization (RSO) methods. The results show that the RLSO method can effectively improve the trip completion flow and reduce the total time spent on the road network.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：176
页面范围：130641
字数：17500
是否译文：否
发表时间：2025-05-05
收录刊物：SCI、EI
发布期刊链接：https://doi.org/10.1016/j.trc.2025.105124