Automatic Driving Operation Strategy of Urban Rail Train Based on Improved DQN Algorithm

To realize a better automatic train driving operation control strategy for urban rail trains,an automatic train driving method with improved DQN algorithm(classical deep reinforcement learning algorithm)is proposed as a research object.Firstly,the train control model is established by considering the train operation requirements.Secondly,the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem.Finally,the priority experience playback and“restricted speed arrival time”are used to reduce the useless experience utilization.The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions.From the experimental results,the train operation meets the ATO requirements,the energy consumption is 15.75%more energy-efficient than the actual operation,and the algorithm convergence speed is improved by about 37%.The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation,thereby contributing meaningfully to the advancement of automatic train operation intelligence.

Predictive Control Algorithm for Urban Rail Train Brake Control System Based on T-S Fuzzy Model

Urban rail transit has the advantages of large traffic capacity,high punctuality and zero congestion,and it plays an increasingly important role in modern urban life.Braking system is an important system of urban rail train,which directly affects the performance and safety of train operation and impacts passenger comfort.The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity.Also,urban rail transit has the characteristics of high speed,short station distance,frequent starting,and frequent braking.This makes the braking control system constitute a time-varying,time-delaying and nonlinear control system,especially the braking force changes directly disturb the parking accuracy and comfort.To solve these issues,a predictive control algorithm based on T-S fuzzy model was proposed and applied to the train braking control system.Compared with the traditional PID control algorithm and self-adaptive fuzzy PID control algorithm,the braking capacity of urban rail train was improved by 8%.The algorithm can achieve fast and accurate synchronous braking,thereby overcoming the dynamic influence of the uncertainty,hysteresis and time-varying factors of the controlled object.Finally,the desired control objectives can be achieved,the system will have superior robustness,stability and comfort.

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation.In order to improve the oper-ating energy utilization rate of trains,a multi-objective particle swarm optimiza-tion(MPSO)algorithm with energy consumption,punctuality and parking accuracy as the objective and safety as the constraint is built.To accelerate its the convergence process,the train operation progression is divided into several modes according to the train speed-distance curve.A human-computer interactive particle swarm optimization algorithm is proposed,which presents the optimized results after a certain number of iterations to the decision maker,and the satisfac-tory outcomes can be obtained after a limited number of adjustments.The multi-objective particle swarm optimization(MPSO)algorithm is used to optimize the train operation process.An algorithm based on the important relationship between the objective and the preference information of the given reference points is sug-gested to overcome the shortcomings of the existing algorithms.These methods significantly increase the computational complexity and convergence of the algo-rithm.An adaptive fuzzy logic system that can simultaneously utilize experience information andfield data information is proposed to adjust the consequences of off-line optimization in real time,thereby eliminating the influence of uncertainty on train operation.After optimization and adjustment,the whole running time has been increased by 0.5 s,the energy consumption has been reduced by 12%,the parking accuracy has been increased by 8%,and the comprehensive performance has been enhanced.

Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.

Optimization of train plan for urban rail transit in the multi-routing mode

The train plan of urban rail transit under multi-routing mode can be divided into three parts： train formation, train operation periods and corresponding train counts of each routing in each period. Based on the analysis of passen- ger＇s general travel expenses and operator＇s benefits, the constraints and objective functions are defined and the multiobjective optimization model for the train plan of urban rail transit is presented. Factors considered in the multi- objective optimization model include transport capacity, the requirements of traffic organization, corporation benefits, passenger demands, and passenger choice behavior under multi-train-routing mode. According to the characteristics of this model and practical planning experience, a three-phase solution was designed to gradually optimize the train formarion, train counts as well as operation periods. The instance of Changsha Metro Line 2 validates the feasibility and efficiency of this approach.

Urban rail departure capacity analysis based on a cellular automaton model

As an important traffic mode, urban rail transit is constantly developing toward improvement in service capacity and quality. When an urban rail transit system is evaluated in terms of its service capacity, the train departure capacity is an important index that can objectively reflect the service level of an urban rail transit facility. In light of the existing cellular automaton models, this paper proposes a suitable cellular automaton model to analyze the train departure capacity of urban rail transit under different variable factors and conditions. The established model can demonstrate the train operating processes by implementing the proposed sound rules, including the rules of train departure at the origin and intermediate stations, and the velocity and position updating rules. The properties of train traffic are analyzed via numerical experiments. The numerical results show that the departure capacity is negatively affected by the train departure control manner. In addition, （i） the real-time signal control can offer a higher train service frequency; （ii） the departure capacity gradually rises with the decrease in the line design speed to a limited extent; （iii） the departure capacity decreases with extension in the train length; （iv） the number of departed trains decreases as the train stop time increases; （v） the departure capacity is not affected by the section length. However, the longer the length, the worse the service quality of the urban rail transit line. The experiments show that the proposed cellular automaton model can be used to analyze the train service capacity of an urban rail transit system by performing quantitative analysis under various considered factors, conditions, and management modes.

Preliminary Study on Selling Tickets in Reason for Last Trains on Beijing Rail Transit Network

With the increase of Beijing urban rail transport network, the structure of the road network is becoming more complex, and passengers have more travel options. Together with the complex paths and different timetables, taking the last train is becoming much more difficult and unsuccessful. To avoid losses, we propose feasible suggestions to the last train with reasonable selling tickets system.

面向虚拟连挂的城轨列车群组追踪运行仿真研究

城市轨道交通是解决城市交通拥堵问题最强有效的绿色交通方式,其高峰期客流激增且客流时空分布不均,而面向虚拟连挂的城轨列车动态灵活编组和群组追踪运行能有效满足复杂多变的城轨运营需求。本文剖析面向虚拟连挂的城轨列车群组追踪运行过程,给出虚拟连挂列车群组最小安全追踪距离计算公式;以单列标准车长的线路路段为一个元胞单元,设计速度及位移更新规则,构建基于元胞自动机的城轨列车群组追踪运行仿真模型;以某市地铁2号线为背景,多角度仿真刻画面向虚拟连挂的城轨列车群组追踪运行性能。仿真结果表明:面向虚拟连挂的城轨列车群组追踪运行模型能有效缩短列车最小安全追踪间隔时间,城轨线路通过能力较移动闭塞制式提高78.4%;头车延迟引起的后续列车晚点总数量及总晚点时间更小,呈现出更佳的抗干扰性与恢复性能,且宜采用小编组列车实现虚拟连挂;动态混合编组的城轨列车群组追踪运行性能最佳,单一小编组的次之,大编组列车群宜在小编组列车群前面运行;城轨列车群组旅行速度总体上与平均站间距呈正相关;当列车发车间隔大于延迟时间时,平均站间距对列车群组的延迟影响不敏感,相反,则影响较为显著。研究结论能够为面向虚拟连挂的城轨列车群组追踪运行决策提供技术支撑。

城市轨道交通正交异性钢桥面板疲劳损伤系数研究

基于城市轨道交通正交异性钢桥面板疲劳性能的重要性,对国内多个城市的轨道交通的交通荷载进行了调研,发现各线路各时段的客流量差异较大,各线路的载客平均轴重与空车平均轴重的比值不同,比值为1.02~1.51。轨道交通因乘客流量带来的轴重比例变化相对较小,因此每一列车都对钢桥结构的疲劳有一定影响。研究了疲劳损伤系数的组成,基于不同的应力循环次数,提出了基于列车荷载作用频次的城市轨道交通桥梁的3个疲劳损伤系数。研究结果可为城市轨道交通钢桥的疲劳设计提供参考,并为规范的修订提供技术依据,对完善城市轨道交通钢桥疲劳设计具有理论意义。

基于车车通信列控系统的城市轨道交通列车运行效率分析

[目的]城市轨道交通T2T(车车通信)列控系统是一种新型的信号系统,其通过资源化管理方式对轨旁设备进行管控。为了验证该系统的优越性,需对该系统下的列车运行效率进行分析。[方法]针对T2T列控系统,基于资源管理层面和安全防护原理的特点,在折返工况下,将原本由联锁控制的双动道岔转变为两个单动道岔,并基于列车精确自定位实现ATP(列车自动防护),提高了岔区线路资源的使用能力;在区间和车站追踪工况下,通过前后车对于车速、加速度、所处线路位置等信息的实时互传,利用相对速度安全防护原理进一步缩小了列车的追踪间隔。通过上述两方面的技术革新,结合城市轨道交通线路的实际数据,对站后交叉渡线折返、站前单渡线折返、区间与车站追踪等运营场景进行了模拟仿真计算,确定了T2T列控系统的折返能力和区间追踪能力。[结果及结论]相较传统的CBTC(基于通信的列车控制系统),T2T列控系统在折返能力、追踪能力等方面均有着显著提升,更加适应于超大运量的城市轨道交通线路;T2T列控系统架构简单,轨旁设备少,尤其适合城市轨道交通的大修改造项目。

基于乘客服务水平和安全的城市轨道交通扣车方案评价

[目的]扣车是城市轨道交通发生长时间初始延误条件下经常采用的列车运行方案。为了评价该方案的合理性,需构建基于乘客服务和安全的扣车方案评价模型,并对扣车方案进行评价。[方法]考虑到乘客出行规律的复杂性和特殊性,制定了城市轨道交通扣车方案。构建了基于最大发车间隔、列车总延误时间、列车均衡性、终到延误列车数、滞留乘客数及乘客总旅行时间的扣车方案评价模型。选取上海某条轨道交通线路的4个区间,以早高峰小时的实际OD(起讫点)客流量为例进行计算。[结果及结论]计算表明,方案6下的综合评价指标最小,为最优扣车调整方案。该方案可以有效提高列车运行图的均衡性,减少滞留乘客的数量,降低车站的安全隐患。

考虑动态随机客流的城市轨道交通列车时刻表仿真优化

为更加精准刻画动态随机客流与列车时刻表编制间的复杂匹配过程,综合考虑乘客动态随机到达过程、列车容量限制、车内拥挤度对乘客上下车速率影响、停站时间动态调整和列车运行安全防护等重要运营特征,构建城市轨道交通列车运行离散仿真模型。在此基础上,建立以最小化乘客平均候车时间为目标的随机非线性优化模型,并设计一种基于仿真的有限差分随机逼近算法,用于优化城市轨道交通发车方案。以国内某条线路的实际运营数据为例验证仿真模型及优化方法,结果表明所构建模型及算法具有较好的优化效果和计算效率,优化后的列车实绩运行时刻能更好地适应客流需求的动态随机性,且在不增加发车频次的前提下有效降低乘客平均候车时间,提升城市轨道交通运营管理水平。

利用A2C-ac的城轨车车通信资源分配算法

在城市轨道交通列车控制系统中,车车(T2T)通信作为新一代列车通信模式,利用列车间直接通信来降低通信时延,提高列车运行效率。在T2T通信与车地(T2G)通信并存场景下,针对复用T2G链路产生的干扰问题,在保证用户通信质量的前提下,该文提出一种基于多智能体深度强化学习(MADRL)的改进优势演员-评论家(A2C-ac)资源分配算法。首先以系统吞吐量为优化目标,以T2T通信发送端为智能体,策略网络采用分层输出结构指导智能体选择需复用的频谱资源和功率水平,然后智能体做出相应动作并与T2T通信环境交互,得到该时隙下T2G用户和T2T用户吞吐量,价值网络对两者分别评价,利用权重因子β为每个智能体定制化加权时序差分(TD)误差,以此来灵活优化神经网络参数。最后,智能体根据训练好的模型联合选出最佳的频谱资源和功率水平。仿真结果表明,该算法相较于A2C算法和深度Q网络(DQN)算法,在收敛速度、T2T成功接入率、吞吐量等方面均有明显提升。

城市轨道交通实训中心智能运维管理系统设计

设计了一种基于云-边协同架构、多专业协同融合的城市轨道交通实训中心智能运维管理系统。首先,分析智能化时代实训中心多专业的融合协同、集成化的实时监测、智能化的故障预防、信息化的资源管理、数字化的场景输出和多方面的反哺机制等需求;其次,基于云-边协同架构,对系统逻辑架构和物理架构进行设计;最后,应用智能感知、数据库、云边协同、故障预测与健康管理等技术,实现智能运维管理系统实时监控、数据管理、健康管理、应急管理、物资管理和教学管理等功能。系统测试结果表明,该系统能有效实现上述功能,显著提高实训中心的智能运维管理水平。

基于改进ADE的城轨列车运行节能优化方法

城轨列车运行环境复杂,模式多变。为准确描述列车的实际运行状态,首先基于实际线路条件建立城轨列车动力学模型和多目标优化模型。然后,基于反三角函数logistic映射的初始化改进策略和基于logistic模型的控制参数自适应策略,提出一种改进自适应差分进化(adaptive differential evolution,ADE)算法求解优化模型,可实现列车安全、准点、精确停车,节能、平稳运行。与传统高斯模型相比,通过采用钟形模型构建新的舒适度指标,能更好地改善舒适度。还针对多站间运行普遍采用固定运行策略的问题,结合专家经验和实际线路条件来自动选择站间运行策略,可缩小算法搜索范围并提高算法求解效率。最后,基于实际线路数据和车辆数据的仿真实验结果表明,所提方法有效降低了运行能耗,改善了乘客舒适性。

监测数据驱动的城轨列车轴箱轴承剩余寿命预测

城轨列车轴箱轴承的运行工况复杂多变、外部随机干扰频繁,导致其监测数据中包含大量测量噪声乃至“脏”数据,进而制约了剩余寿命预测模型的精度.为解决上述问题,提出了一种监测数据驱动的动态多重聚合剩余寿命预测方法.首先,通过度量短时数据的幅值分布相似性自动识别并清洗“脏”数据;然后,将健康指标按不同时间尺度进行动态聚合,预测出各类潜在的未来退化轨迹,进而获得轴箱轴承的剩余寿命预测均值与方差;并使用现场实测数据与加速寿命实验数据对提出方法进行验证.结果表明:所提方法能有效剔除监测数据中的空采数据和强干扰数据;剩余寿命预测均值随累计行驶里程的增加逐渐收敛到真实值,且95%置信区间越来越窄;相比于单指数预测模型和混合预测模型,提出方法的累计相对精度平均值分别提高了29.78%和27.63%,预测收敛速度平均值分别增加了10.56%和10.20%.

城市轨道交通列车轮轨垂向力测量方法

[目的]城市轨道交通运营中通常会出现大量的隧道病害,列车轮轨力是各种病害的主要成因,因此,需在不影响城市轨道交通列车正常运营的条件下,对列车轮轨垂向力的测量方法进行研究。[方法]以成都地铁7号线茶店子站—一品天下站下行区间道床剥离病害为研究对象,基于弯矩差法理论,采用ANSYS有限元软件建立了静荷载及动荷载作用下的钢轨有限元模型,并对其进行了数值模拟分析。在室内对1根长2 m、3跨布置的钢轨进行了标定试验。[结果及结论]静荷载数值模拟分析表明,钢轨测点可选择在钢轨跨中轨底上表面边缘两侧位置;当钢轨跨中位置受到垂向力作用时,该点沿钢轨方向的应变最大,且应变随垂向力线性增加。通过标定试验测得的标定系数为0.6260×10^(6)kN,而数值模拟测得的标定系数为0.6441×10^(6)kN,两者偏差仅2.8%,可通过静荷载标定试验确定标定系数。轮轨垂向力的测量方法可以更好地评估轨道结构和列车运行的安全性,及时发现安全隐患,并采取相应措施进行修复和改进。

基于再生能利用的地铁多车协同折返优化研究

折返站作为影响线路发车上限的重要因素,影响着整条线路的运营效率,也是能量浪费较为严重的地方。因此,有必要在兼顾线路运营效率的前提下减少列车折返运行的能耗。为此,通过提出基于再生制动能量利用的列车折返问题对列车的节能运营进行研究。通过建立底层网络图,构建以列车折返为基础考虑再生制动能量利用的能耗优化模型。为提高模型的求解能力,引入辅助变量将其转变为线性化模型。以上海地铁某折返站作为实例对象,借助Gurobi进行求解并对能耗等数据进行分析,证实模型有效。由该模型得到的折返时刻图在保证折返能力的同时,列车能耗与原始折返方式相比降低了5.74%,为考虑再生制动能量利用的列车折返运营方式提供一定的理论支撑与依据。

面向灵活编组的城轨列车开行频率与时刻表综合优化

在城市轨道交通中,列车开行频率与时刻表在很大程度上决定着其运输服务水平。针对城市轨道交通客流时空分布不均衡的问题,本文采用灵活编组运营模式综合优化列车开行频率和时刻表,以达到运输能力供给与客流需求的精准匹配,保证乘客服务水平的同时降低企业运营成本。首先,通过列车运行线候选集的建模方法,考虑列车开行频率、安全间隔等约束,以乘客总等待时间和企业总运营成本最小化为目标,构建灵活编组模式下列车开行频率与时刻表综合优化混合整数非线性数学模型,对列车开行频率、列车编组方案和列车时刻表进行协同决策优化。其次,针对所构建模型非线性的特点,基于变邻域搜索算法框架构造3种邻域结构并结合动态客流加载仿真过程,设计了高效求解的启发式算法。最后,以某城轨线路的实际数据进行案例分析,验证所提模型和算法的有效性。算例结果表明:相较于固定编组运营模式,本文方法可在乘客平均等待时间仅增加0.26 min的情况下,降低约7.05%的运营成本;此外,相较于单一运营方案优化,在灵活编组模式下综合优化列车开行频率和时刻表能够进一步实现列车运输能力与乘客需求精准匹配,在保证乘客服务水平的同时降低运营成本;并且在实际应用中,运营企业还可根据运营目标合理设置权重系数,进一步平衡乘客服务水平与企业运营成本。

城市轨道交通列车碰撞动力学参数对爬车行为的影响

[目的]为了加强城市轨道交通列车在运行过程中的被动安全性设计能力,需要研究列车碰撞动力学参数对爬车行为的影响。[方法]基于车辆-轨道耦合碰撞动力学模型、Hertz接触理论及刚体非线性连接模型,搭建了列车碰撞三维动力学仿真平台。选取速度、加速度和轮对最大抬升量作为对比参数,采用列车碰撞有限元模型来验证列车碰撞三维动力学模型的精度。基于所建立的仿真平台,选取列车结构中的部分重要参数,研究其对轮对抬升量的影响。筛选出吸能防爬装置,头车和中间车的车钩等的安装点(以下简称“安装点”)至车体质心垂向距离、车体质量、车体质心高度、二系悬挂装置垂向刚度及一系悬挂装置垂向刚度共5个敏感度较强的参数,并对其进行列车碰撞动力学敏感度分析。[结果及结论]通过列车碰撞有限元模型,验证了列车碰撞三维动力学模型的精度满足要求。列车碰撞动力学敏感度分析表明:当参数比率在0.8~1.0范围内变化时,5个敏感性参数的排序依次为二系悬挂装置垂向刚度、车体质量、车体质心高度、安装点至车体质心垂向距离、一系悬挂装置垂向刚度;当参数比率在1.0~1.2范围内变化时,上述参数的排序依次为一系悬挂装置垂向刚度、安装点至车体质心垂向距离、车体质心高度、车体质量及二系悬挂装置垂向刚度。