城市道路应急车辆借道通行与信号协同优化模型

Cooperative Optimization Model for Emergency Vehicles Using Reverse Left-turn Lanes and Traffic Signals

考虑单一的信号优先策略无法保证应急车辆不停车通过交叉口,因此,本文提出应急车辆借用逆向左转车道与交叉口信号协同优化模型。首先,在路段上通过应急车辆速度引导与逆向左转车道预信号配时的协同控制,优化应急车辆路段行驶速度,实现借用逆向左转车道通行;其次,增设应急相位保证应急车辆优先通过交叉口,以社会车辆出行总时间最小为目标,相位开启时间和绿灯持续时间为决策变量,考虑交叉口相位间的冲突、预信号开启和持续时间等约束条件,建立混合整数线性规划模型;并通过AMPL(A Mathematical Programming Language)编译并调用求解器求解,最后,以主干道和次干道相交的交叉口为例,对所提模型进行验证。结果表明:与绿灯延长模型相比,协同优化模型能显著降低应急车辆在交叉口区域的出行时间,减少社会车辆出行总时间。通过对流量和车道长度等参数的敏感性分析发现,随着流量和车道长度的增加,协同优化模型具有更好的优化效果。

This study presents a cooperative optimization model that enables emergency vehicles to use reverse left-turn lanes along with intersection signals because a single signal priority method cannot ensure the uninterrupted passage of emergency vehicles through junctions.First,a cooperative control method is used on road segments.This strategy involves timing pre-signals on reverse left-turn lanes and synchronizing emergency vehicle speed advice.As a result,emergency vehicles can more efficiently use the reverse left-turn lane for passage on road stretches where their travel speed is optimized.Secondly,an emergency phase is added to ensure the priority passage of emergency vehicles through the intersection.To minimize the total travel time for regular vehicles,the phase start time and green light duration are considered decision variables,taking into account constraints such as phase conflicts,pre-signal start,and duration.A mixed-integer linear programming model is built and solved by CPLEX with the A Mathematical Programming Language(AMPL)coding language.Finally,the proposed model is validated using an intersection example where a main road intersects with a secondary road.The results show that compared to the green light extension model,the coordinated optimization model can significantly reduce the travel time of emergency vehicles in the intersection area and decrease the total travel time for regular vehicles.Sensitivity analysis of parameters such as traffic volume and lane length reveal that the cooperative optimization model exhibits better optimization performance as traffic volume and lane length increase.