基于Pareto多目标遗传算法的高峰时段多地铁列车节能优化

城市轨道交通列车在运行过程中会频繁地启动和制动,如何提高列车运行中电能的利用率、降低牵引能耗在城市轨道交通领域有着重要的意义。在高峰时间段运行时,由于客流量较大,单位时间发车数量较多,所以同一供电区间的相邻列车间重叠运行的时间较长,再生制动可利用能量很大。针对高峰时段多列车运行的特点,采用Pareto多目标遗传算法对高峰时段列车运行进行节能优化。通过合理优化列车在各站的停站时间来优化列车时刻表,最大效率地利用列车运行中再生制动能量。     

基于运行图的快速公交系统运营组织研究

借鉴列车运行图的理念,对快速公交系统基于运行图的运营组织进行了研究。首先分析了快速公交系统基于运行图运营组织的系统构成和系统条件。然后设计了快速公交运行图的图解表示和编制流程,确定了运行图的主--素。最后,以济南BRT1号线为例,根据客流特征进行了高峰小时运行图的编制。结果表明,如果快速公交能够按图运营,能提高旅行速度、节约运用车辆,这为BRT的运营组织管理提出了新的思路,具有一定的指导意义。     

考虑运行图约束的有轨电车干线信号协调优化模型

为了保证有轨电车在交叉口享有信号优先的同时,使其按照运行图行车,在有轨电车干线信号协调模型BAM-TRAMBAND的基础上,增加运行图对行车的约束,包括发车间隔、停站时分、站间运行时分和干线旅行时间,提出2类不同约束的有轨电车干线信号协调优化模型,严格运行图约束的优化模型(HT-TRAM)和宽松运行图约束的优化模型(ST-TRAM). 经算例验证和仿真结果表明,改进的模型能够得到在运行图约束下的有轨电车上下行绿波带,且在算例条件下,两者的路段平均绿波带宽与没有运行图约束的带宽结果相比,相差在1s以内.     

基于 Dueling DQN 算法的列车运行图节能优化研究

通过优化地铁时刻表可有效降低地铁牵引能耗。为解决客流波动和车辆延误对实际节能率影响的问题,提出列车牵引和供电系统实时潮流计算分析模型和基于 Dueling Deep Q Network(Dueling DQN)深度强化学习算法相结合的运行图节能优化方法,建立基于区间动态客流概率统计的时刻表迭代优化模型,降低动态客流变化对节能率的影响。对预测 Q 网络和目标 Q 网络分别选取自适应时刻估计和均方根反向传播方法,提高模型收敛快速性,同时以时刻表优化前、后总运行时间不变、乘客换乘时间和等待时间最小为优化目标,实现节能时刻表无感切换。以苏州轨道交通 4 号线为例验证方法的有效性,节能对比试验结果表明：在到达换乘站时刻偏差不超过 2 s和列车全周转运行时间不变的前提下,列车牵引节能率达 5.27%,车公里能耗下降 4.99%。     

考虑容量限制的多公交车型运行计划优化模型

车容量限制是公交运行计划编制的重要约束.以单条公交线为研究对象,综合考虑了不同公交车型的技术经济性能、车容量大小、车辆数限制和客流需求的时变特征等因素,建立同步优化公交车型和时刻表的规划模型,以确定线路的发车时刻表和选择车型的最优组合.建立了以公交企业运营成本和公交乘客出行成本最小为目标,带有0-1决策变量的非线性整数规划模型,针对该模型多目标函数的求解特点,采用枚举法求解每辆车的发车时刻,应用遗传算法求解车型选择的序列.最后以北京市某公交线为案例进行分析,优化后的发车时刻表和车型配置方案具有较好的运营效果.算例结果表明,采用多车型方案较传统的单一车型方案更具经济性,乘客的出行成本可减少13.9%,企业的运营成本可减少3.5%.     

基于换乘站停站时间延长的城市轨道交通末班车时刻表优化

在城市轨道交通网络中,提高末班车乘客换乘成功性对其夜间出行有重要意义.通过压缩末班车区间运行时间与非换乘站停站时间,延长换乘站停站时间,建立以换乘成功客流量最大为目标的时刻表优化模型,实现末班车双向衔接成功.将模型刻画为混合整数线性规划问题,通过对模型简化,利用CPLEX分支切割算法进行求解.最后,以武汉地铁网络为案例,验证模型的有效性.结果表明,模型可利用CPLEX快速求得精确解.末班车停站时间延长可有效提高末班车间换乘衔接成功性;在末班车收车时间可推迟情形下,推迟收车时间可进一步提升末班车换乘效果.     

基于站外限流与时刻表调整的地铁换乘站大客流协同控制

针对高峰期地铁换乘站站台容易发生拥挤问题,在分析乘客进站及换乘走行过程的基础上,构建了站外限流与列车时刻表协同优化模型。该模型可在满足站台安全容量约束下尽可能降低站外限流率,实现站外站内乘客加权人均等待时间最小。采用混沌遗传算法求解模型,并以惠新西街南口站为例,对本文模型、优化前方案和既有只限流方法进行对比分析。结果表明:与优化前方案相比,模型所得方案可满足站台安全容量约束,加权人均等待时分减少了21.2%;与只限流方法相比,模型可使站外平均限流率从36%下降到7.3%,加权人均等待时分减少了43.8%,且模型方案的各项指标在乘客走行时分发生波动的情形下表现更稳定。     

The multi-objective railway timetable rescheduling problem

Unexpected disruptions occur for many reasons in railway networks and cause delays, cancelations, and, eventually, passenger inconvenience. This research focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of large disruptions. The originality of our approach is to integrate three objectives to generate a disposition timetable: the passenger satisfaction, the operational costs and the deviation from the undisrupted timetable. We formulate the problem as an Integer Linear Program that optimizes the first objective and includes $\epsilon$-constraints for the two other ones. By solving the problem for different values of $\epsilon$, the three-dimensional Pareto frontier can be explored to understand the trade-offs among the three objectives. The model includes measures such as canceling, delaying or rerouting the trains of the undisrupted timetable, as well as scheduling emergency trains. Furthermore, passenger flows are adapted dynamically to the new timetable. Computational experiments are performed on a realistic case study based on a heavily used part of the Dutch railway network. The model is able to find optimal solutions in reasonable computational times. The results provide evidence that adopting a demand-oriented approach for the management of disruptions not only is possible, but may lead to significant improvement in passenger satisfaction, associated with a low operational cost of the disposition timetable.     

干扰条件下高速铁路列车运行实时调整问题研究

高速铁路列车在日常运行过程中不可避免地会受到各种干扰的影响,导致列车偏离计划运行图,需要及时调整列车运行图.本文主要针对由干扰导致的区间能力全失效情况下的列车运行图调整问题进行研究,构建列车运行调整混合整数线性规划模型.该模型在满足车站和线路能力约束的情况下,尽量减少取消列车的数量和列车晚点时间加权求和量.模型还阐述了提高列车终到正点率对列车运行调整结果的影响.最后以京沪高铁为例验证了模型的有效性,结果表明,在列车总晚点时间不明显增加的情况下可以提高列车终到正点率.     

Robust stop-skipping patterns in urban railway operations under traffic alteration situation

In this paper an operation mode which is based on the stop-skipping approach is studied in urban railway lines under uncertainty. In this mode, each train follows a specific stop schedule. Trains are allowed to skip any intermediate stations to increase the commercial speed and to save energy consumption. As the commercial speed increases, the number of required trains in operation reduces and results eliminating unnecessary costs. To that end, a new mathematical model is proposed to reach the optimum stop schedule patterns. In the planning step, based on the traffic studies, the headway distributions are computed for different weekdays, and holidays. However, in practice, because of many unexpected events, the traffic may alter from what is planned. Therefore, in this condition, a robust plan is required that is optimized and immunized from uncertainty. In this paper, a new robust mathematical model, as well as two heuristic algorithms including (1) a decomposition-based algorithm and (2) a Simulated Annealing (SA) based algorithm is proposed. Finally, an Iranian metro line is studied and the optimum patterns are presented and analyzed.