柔性间隔发车下城轨网络换乘协同优化

为提高城市轨道交通网络时刻表和客流需求的匹配性,采用柔性间隔发车策略以提高协同性能.以等间隔发车模式下列车发车相位确定发车基准点,并容许列车发车时刻在基准点附近可柔性调整一定范围,以最大化网络总换乘协同乘客数为目标构建了基于柔性发车间隔的时刻表协同优化模型;考虑优化模型中存在大量0、1变量,模型求解困难,以各线路的列车发车相位以及各列车发车时刻的柔性偏移量为染色体设计了遗传算法来生成优化解.案例研究结果表明:遗传算法求解性能好,在等间隔发车时引入10%柔性,实现换乘协同乘客数提高11.85%.该方法可快速生成网络换乘协同时刻表方案,提高换乘节点效率,能满足大规模网络的求解需求.

Transfer synchronization of urban rail transit network with flexible departure headways

To ensure that the urban rail transit network timetables serve the passenger demand better, a flexible departing scheme was developed to improve the synchronization performance. Departure times with even headways were taken as the benchmark points, and the actual departure times were allowed to vary around the benchmark points within a certain range. Then, a synchronization optimization model with flexible departure headways was proposed to maximize the number of transfer-synchronized passengers in a network. Considering that the model includes an enormous amount of binary variables, an efficient genetic algorithm was designed to solve the model within limited time. The departure phase of each line with even headways and the flexibility of the departure time of each train were chosen as genes to form the chromosome. Finally, experiments were carried out and results showed that the genetic algorithm performed well and the method could improve the number of synchronized passengers significantly compared with the even-headway timetable that a flexibility of 10% in the even headway corresponded to an 11.85% increment in the number of synchronized passengers. The synchronized timetable with high transfer efficiency of a large-scale network could be quickly by the proposed method.