智能网联车环境下高速匝道汇入车流轨迹优化模型

为了解决传统匝道控制车流汇入时车辆需要减速至停止，从而造成延误时间过长的问题，提出了一种智能网联车环境下的高速匝道汇入车辆轨迹优化的两阶段优化模型，其中，第1 阶段优化车辆进入匝道口的时序；第2 阶段基于第1 阶段的最优时序，优化车辆轨迹. 根据所构建的模型设计了一种启发式算法优化车辆通过匝道冲突区域的时序，然后结合 GPOPS工具优化车辆的轨迹.为了验证所提出方法的有效性，将所提出的方法应用到20 min 随机到达的车流，进行仿真实验.实验结果表明，与先进先出的方法相比，本文所提出的方法能够使总延误减少59.7%，总油耗减少10.5%，说明该方法能够实现车辆以较高的速度通过匝道冲突区域，有效地减少了车辆汇入延误，同时也节约了油耗.

Vehicle Trajectory Optimization Model for Ramp Based on Connect Automatically Vehicles

Vehicles from ramp need to reduce the speed and stop at the ramp for merging when signal is red, which causes the long- time delay. A two- level trajectory optimization model based on connected automatically vehicles was proposed. The merging sequence of vehicles is optimized in the first level and the trajectory of vehicles is optimized in the second level. A heuristic model was proposed to solve the first level model and the GPOPS tool was used to solve the second level model. A simulation experiment was carried out to evaluate the proposed approach. 20 min random arrival flow simulation process was used in the simulation experiment. The experiment results show that the proposed method can decrease the total delay and gas consumption by 59.7% and 10.5%, respectively. The vehicle can pass through the conflict zone at high speed, which can reduce the delay and gas cost by using the proposed method.