| 基于站点群体聚集性客流的公交串车调度优化 |
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| 引用本文： | 李利华,曹慧琪,邓亚军,邢璐,靳竹喧.基于站点群体聚集性客流的公交串车调度优化[J].中国公路学报,2023,36(2):203-215. |
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| 作者姓名： | 李利华 曹慧琪 邓亚军 邢璐 靳竹喧 |
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| 作者单位： | 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 |
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| 基金项目： | 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）* |
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| 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...
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| 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 |
| 收稿时间： | 2022-03-07 |
Optimization of Bus Bunching Scheduling Based on Group-gathered Passenger Flow at Bus Stops |
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| LI Li-hua,CAO Hui-qi,DENG Ya-jun,XING Lu,JIN Zhu-xuan.Optimization of Bus Bunching Scheduling Based on Group-gathered Passenger Flow at Bus Stops[J].China Journal of Highway and Transport,2023,36(2):203-215. |
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| Authors: | LI Li-hua CAO Hui-qi DENG Ya-jun XING Lu JIN Zhu-xuan |
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| Affiliation: | 1. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China;2. Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, Hunan, China |
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| Abstract: | 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 and after the model optimization, it is found that total headway deviation is shortened by 56%, reducing the total travel time of passengers by 11.7%, passenger average waiting time by 12.5%, and increasing the punctuality rate of bus stops by 24.1%. A reduction of 36 in the number of bunches is also observed. An experiment to verify randomness found that the average declined ratio of the two objective functions is 50.4% and 13.7%, which is a large reduction range and a good optimization effect. The results show that implementation of this model in real-life can greatly improve bus operational efficiency and effectively solve the problem of bus bunching; the solution is robust and reliable, and this method is both practical and feasible to implement. |
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| Keywords: | traffic engineering scheduling optimization group-gathered passenger flow bus bunching headway maximum passenger capacity |
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