基于车速引导的交叉口公交优先自适应控制模型

Adaptive Control Model for Bus Priority at an Intersection Based on Speed Guidance

为进一步满足公交车辆在交叉口不同流量情形下实时优先的需求,本文以交叉口人均平均等待时间最小为目标,提出基于车速引导的交叉口公交优先自适应控制模型。通过考虑交叉口进口道实时流量情况和公交车的实时运行状态,建立公交车车速与信号配时参数之间的联系,实现交叉口信号配时方案和公交车车速引导方案的同步优化求解。用0-1决策变量表示交叉口的相位相序以降低模型的计算复杂度,进而提升模型的求解速度;最后通过案例仿真实验对比,验证了本文模型的有效性。案例分析结果表明,本文模型在3种不同流量情形下与对比方案相比,可至少减少17.86%、12.04%、8.81%的公交车车均延误时间,21.82%、17.86%、17.74%的公交车均停车次数,以及24.56%、8.03%、3.38%的乘客平均等待时间。同时本文模型在不同流量情形的求解时间均不足0.01 s,相比现有模型,计算时间大幅降低,进一步满足了公交实时优先计算效率的要求。由此可见,本文通过建立基于车速引导的交叉口公交优先自适应模型,在不影响交叉口整体和社会车辆通行效率情况下,实现了公交车优先通行并减少了交叉口人均平均等待时间,同时也能满足网联环境中对于模型优化速率的需要。

This paper proposes an adaptive control model for bus priority at an intersection based on speed guidance to minimize the average waiting time per passenger at the intersection,This method further satisfies the demand for real-time priority for buses at the intersection with different volume scenarios.The relationship between bus speed and signal timing parameters is established,and the simultaneous optimization of the intersection signal timing scheme and bus speed guidance scheme is realized by taking into account the real-time volume of the intersection approach and the real-time operation status of buses.To lower the computational complexity of the model and increase its speed of solution,the phase sequence of the intersection is represented by the 0-1 decision variable.The effectiveness of the model is confirmed by simulation experiments.When compared to the comparison schemes for the three different traffic volume scenarios,the proposed model can reduce average bus delays by at least 17.86%,12.04%,and 8.81%,average bus stops by 21.82%,17.86%,and 17.74%,and average passenger waiting times by 24.56%,8.03%,and 3.38%.Furthermore,the model's solution time for different traffic scenarios is less than 0.01 seconds,which is a significant reduction in computing time when compared to the traditional models and further satisfies the real-time bus priority calculation efficiency requirements.The proposed model can meet the requirement for model optimization rate in the networked environment while simultaneously achieving bus priority and lowering the average waiting time per passenger at intersections without compromising the overall intersection and social vehicle efficiency.