机构地区:[1]长沙理工大学交通运输工程学院,湖南长沙410114 [2]长沙理工大学智能道路与车路协同湖南省重点实验室,湖南长沙410114
出 处:《中国公路学报》2023年第2期203-215,共13页China Journal of Highway and Transport
基 金:国家自然科学基金项目(52102405);湖南省自然科学基金项目(2019JJ40308);长沙理工大学智能道路与车路协同湖南省重点实验室开放基金项目(kfj210702)。
摘 要:为提升城市公交准点率、减少延误,解决车辆串车问题,研究基于站点群体聚集性客流的公交调度优化方法。以乘客出行意愿、乘车属性、到站规律等标识公交客流变化特征,以车辆载客限制、站点延误、到达率、下车率等描述串车形成场景。考虑准时性、客流需求、调控策略等约束,采用实时混合控制策略,实现车头时距偏差与乘客总行程时间最小的多目标优化。提出的公交串车调度方法,考虑到乘客到达率的不确定性,并通过调控公交车辆站点驻站时间以及路段平均行驶速度,可满足站点时段性群体聚集公交客流出行需求,防范潜在的公交串车。在模型求解上,考虑到双目标优化视角的差异性,运用超车规则对串车场景下的出站车辆重新排序,设计基于NSGA-II的求解算法,以拥挤距离标定序度关系,以精英策略获取新种群,改进交叉算子,并基于TOPSIS法对获取的Pareto解集择优。最后,以实际公交线路为例进行案例分析,结果表明:基于站点群体聚集性客流的公交串车优化调度模型,系统考虑了乘客乘车属性与车辆载客限制,能够输出最优的车辆滞站与车速调整方案,并且能运算得出车辆离站时间、车头时距偏差、准点率、乘客等待时间以及乘客行程时间等多项运营指标。优化前后对比表明:总的车头时距偏差缩短了56%,乘客总出行时间下降了11.7%,乘客平均等待时间减少了12.5%,公交线路站点准点率提高了24.1%,串车次数减少36次。同时随机性验证试验中,两目标函数平均下降比例分别为50.4%与13.7%,下降幅度较大,优化效果较好。模型计算结果可较大程度提升公交运行效率,有效解决公交串车,解的鲁棒性与可靠性较强,方法的实用性与可行性较好。To improve punctuality,reduce delay,and solve bunching of urban buses,an optimization method for bus scheduling was studied based on a group-gathered passenger flow.The travel willingness,ride attributes,and passenger arrival regularity were all identified to feed the model.The bunching scene was described based on the vehicle carrying restrictions,delay at stops,arrival rate,and passenger alighting rate.Constraints such as punctuality,passenger flow demand,and control strategy were all considered in the development of a real-time mixed control strategy,which was adopted for multi-objective optimization of the minimum headway deviation and total passenger travel time.To prevent potential bus bunching,a scheduling method of bus bunching that meets the travel demands of periodic group-gathered passenger flows at bus stops is herein described.The method considers the uncertainty of passenger arrival rate,holding time at bus stops,and average driving speed across different sections.To solve the model,the difference in the view of bi-objective optimization was considered when the overtaking rule was used to reorder outbound vehicles in the bunching scene.To design the NSGA-II algorithm,the order relation was calibrated by the crowding distance,and a new population was obtained by the elite strategy to improve the crossover operator.The resulting Pareto solution set was then optimized based on the TOPSIS method.Finally,an actual bus line was used as an example to experimentally verify the accuracy of the model and its algorithm.The results show that the optimization model of bus bunching based on group-gathered passenger flow at stops systematically predicts the passenger riding attributes and vehicle carrying restrictions.An optimal scheduling scheme for vehicle holding and speed adjustment is obtained by the model,which allows for calculating a series of operational indicators,such as vehicle departure time,headway deviation,punctuality rate,passenger waiting time,and passenger travel time.Comparing the system before
关 键 词:交通工程 调度优化 群体聚集性客流 公交串车 车头时距 最大载客量
分 类 号:U491.17[交通运输工程—交通运输规划与管理]
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