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.

Simulating train movement in an urban railway based on an improved car-following model

Based on the optimal velocity car-following model, in this paper, we propose an improved model for simulating train movement in an urban railway in which the regenerative energy of a train is considered. Here a new additional term is introduced into a traditional car-following model. Our aim is to analyze and discuss the dynamic characteristics of the train movement when the regenerative energy is utilized by the electric locomotive. The simulation results indicate that the improved car-following model is suitable for simulating the train movement. Further, some qualitative relationships between regenerative energy and dynamic characteristics of a train are investigated, such as the measurement data of regenerative energy presents a power-law distribution. Our results are useful for optimizing the design and plan of urban railway systems.

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.

CDIO Based Optimization of Urban Planning Personnel Training Courses in Forestry Universities

On the basis of analyzing existing course system of urban planning discipline,this paper came up with a framework for optimization of urban planning personnel training mode and course system,oriented towards training practical engineering personnel and based on CDIO engineering education and teaching platform. Then it made empirical study on setting of the urban planning discipline in Northeast Forestry University. It proposed changing the original "3 + 2" course system,exploring and optimizing the course system,improving teaching effect of urban planning personnel training courses,and raising planning and design ability of students.

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.

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 Training Program for Land Resource Management Major under the Background of Urban and Rural Integration

With constant deepening of development outlook of urban and rural integration,education circle starts to attach great importance to reform of personnel training program.Since scarcity of land resource and key role of land management in urban and rural integration,it is urgent to optimize the personnel training program for land resource management major.From aspects of optimum objectives,principles,program and implementation effect,this paper discusses training program for land resource management major under the background of urban and rural integration.It is expected to provide reference for cultivating land resource management personnel suitable for development of urban and rural integration.

Training Mode of Applied Talents in Urban and Rural Planning Major

Facing the development and change of contemporary urban and rural planning discipline and social talent demand,it is imperative to develop the talent training of urban and rural planning major.Based on the teaching platform,this study constructed a perfect talent training mode,implemented modular teaching,took the occupation as the guidance,carried out the application-oriented talent training,and delivered high-quality professionals to the society.

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。

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。

基于Sarsa算法的城轨列车节能控制策略研究

针对城市轨道交通节能运行问题,提出一种基于Sarsa强化学习算法的城轨列车节能控制策略,实现了城轨列车在自动驾驶状态下,面对不同路况,执行减少能源消耗驾驶策略的同时兼顾准时性和舒适性。根据线路条件将列车状态进行离散化处理,将连续的驾驶过程分为若干个子区间进行分段求解。结合区间限速、初始状态、终末状态等限制条件,基于能耗及运行时间分别构造适当的奖励函数。同时,用当前状态下可达的最大速度与最小速度对可选速度集合进行限制,缩小探索空间,加快算法收敛。最后,通过对北京铁路亦庄线小红门站至肖村站的实例进行仿真。实验结果表明,与传统的动态规划方法相比,Sarsa算法在满足舒适性和准时性要求的情况下节能9.32%。相比于强化学习中的Q学习算法,在速度的选取过程中,超速次数也有明显下降。仿真结果证明Sarsa算法具有更好的节能效果和安全性。在算法参数不变的情况下,调整限速条件,与传统动态规划算法进行二次对比,依旧节能4.21%,验证了算法的鲁棒性。

不同牵引特性城轨列车车轮磨耗与疲劳研究

针对列车频繁牵引起动对车轮磨耗与疲劳损伤产生的影响问题,在恒定牵引力的基础上,探究了不同牵引特性曲线恒功率区转折点速度对车轮磨耗与疲劳的影响规律。采用SIMPACK和MATLAB软件进行联合仿真,建立了不同牵引特性下城轨列车动力学模型和基于Archard的车轮磨耗模型,分析在牵引电机不同起动转矩下牵引特性曲线恒功率区转折点速度对城轨列车轮轨蠕滑率、车轮磨耗深度、磨耗面积以及疲劳指数的影响规律。结果表明:列车牵引起动过程中,电机起动转矩越大,轮轨蠕滑率、车轮磨耗深度和磨耗面积也越大,而疲劳指数则越小;电机起动转矩一定时,随着列车牵引特性曲线恒功率区转折点速度的增大,轮轨蠕滑率、车轮磨耗深度和磨耗面积也增大,疲劳指数则有所减小;当电机起动转矩从800 N·m增大到1400 N·m,车轮磨耗深度和磨耗面积增长率最大为6.9%和12.6%,而疲劳指数下降率最大为7.2%;当恒功率区转折点速度从50 km/h增加到80 km/h,车轮磨耗深度和磨耗面积增长率最大为4.4%和4.3%,疲劳指数下降率最大为1.9%。本文研究了车辆起动转矩与恒功率转折点速度变化对车轮磨耗与疲劳的影响规律,可为车辆牵引特性设计和电机控制以及车轮运维提供参考。

从规律认知到类型建构——城市设计的入门探索

城市空间具有明确的建构规律,它通过建设单元地块划分与类型建筑的组织,展现出“建筑—街坊—街区”的空间逻辑。为帮助首次接受城市设计训练的高年级学生快速认知这一逻辑,本文通过国际现代城市成熟案例的分析,完成空间建构规律总结,将其作为操作工具并完成一个当代城市街区的城市设计。具体操作分两步走:首先通过建筑的组合建构城市街坊;然后通过街坊的组合构成城市街区。不同寻常的是,要求学生在上海城市语境下完成一个抽象基地的设计工作,从而避开复杂现实条件的干扰,专注空间规律,以此揭示这种简单逻辑的深刻性及其跨越文化的属性,从而验证其对城市设计的普适意义。

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。

数字化转型背景下县域城乡教师网络研修:样态、困境与破解

县域城乡师资均衡配置是义务教育优质均衡发展的重点,也是难点。网络研修为突破这一难点提供了数字化战略选择。如何推进县域城乡教师网络研修,是县域义务教育数字化转型不得不面对的问题,必须予以高度重视。县域城乡教师网络研修主要存在区域常规式网络研修、名师引领式网络研修和校际协同式网络研修三种样态。县域城乡教师网络研修“形象”虽已数字化,但“灵魂”依旧停留在工业化阶段,整体上还是依附于原有的教师研修体制机制,沿用了传统教师研修的方式、方法。数字化与工业化的矛盾引发了县域城乡教师网络研修的诸多困境。破解困境的根本出路在于实施供给改革,由原来的供给驱动转变为需求驱动;关键在于统筹规划,以教学实践为核心构建“教—研—训”一体化研修体系;落脚点在于以方法创新推进数字技术与教师研修深度融合。

基于加线技术的轨道交通临客开行计划优化模型

基于运行图加线技术构建适于计划性临客开行的运行线铺画方法.以运力与客流匹配度最大化、对原运行图调整最小化为优化目标,考虑行车安全、临客加线位置、开行时间域等约束,构建临客运行线铺画非线性整数规划模型,并对其线性转换以提高求解效率.以北京地铁8号线某大型活动散场大客流为场景进行案例分析,结果表明:所构建方法能够在既有运行图上有效铺画临客运行线,满足不同行车密度及停站模式下的临客开行需求.建议在较高密度行车条件下不采用“空车直达”停站模式.

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。

城市轨道交通列车故障救援方案的研究

[目的]城市轨道交通一旦发生故障,需要其他列车救援,就会严重影响后续列车运行和乘客的出行安全。若救援的路径选择不合适,还会对已开通的线路造成二次影响。故需基于救援路径和组织,研究列车救援方案。[方法]分析现有的城市轨道交通列车故障救援程序。结合列车故障救援的实际作业时间和流程,分析列车故障救援过程及作业时间。对一条线路不同位置发生列车故障时的救援路径选择进行了探讨,从救援列车的选择、救援方式的选择和故障列车存放位置的选择三方面入手,对其进行组合,得到列车故障救援的不同方案。以上海轨道交通7号线为例,进行列车故障救援方案的比选和汇总。[结果及结论]把列车故障救援过程分为故障处置、救援准备、救援处置和救援结束4个阶段。基于救援列车的选择、救援方式的选择和故障列车存放位置的选择及组合,得到了上海轨道交通7号线全线列车故障救援方案图。经实践验证,该救援方案能有效地避免二次延误,进而提升地铁服务质量。