技术领域Technical Field
本发明涉及城市轨道交通技术领域，更具体的说是涉及一种考虑大客流预警信息的轨道交通拥挤传播评估与控制方法。The present invention relates to the technical field of urban rail transit, and more particularly to a rail transit congestion propagation evaluation and control method considering large passenger flow warning information.
背景技术Background Art
城市轨道交通系统作为输送客流的主要方式，随着客运量的增加其客流拥挤现象是影响网络正常运行的问题之一。轨道交通网络中的大客流主要指由于早晚高峰、突发事件或大型活动等影响导致某站点出现较短时间内客流大量聚集的情况，使得既有的设施与客流组织超过了站点最大承载能力，造成了站点拥挤的现象，且由于轨道交通的网络化运营特点使该拥挤现象在网络中进行传播，对网络的安全运行造成了一定的威胁。客流拥挤现象可以分为常发性与偶发性两类。常发性拥挤主要指在一定时段内区间或站台客流拥挤具有周期性与规律性特征，如早晚高峰的通勤客流；偶发性拥挤主要指受天气、突发事件或大型活动等影响导致的客流拥挤。As the main mode of transporting passenger flow, the urban rail transit system is facing the problem of passenger congestion as the passenger volume increases, which is one of the problems that affect the normal operation of the network. Large passenger flow in the rail transit network mainly refers to the situation where a large number of passengers gather at a station in a short period of time due to the influence of morning and evening peaks, emergencies or large-scale activities, which makes the existing facilities and passenger flow organization exceed the maximum carrying capacity of the station, resulting in station congestion. In addition, due to the network operation characteristics of rail transit, the congestion phenomenon spreads in the network, posing a certain threat to the safe operation of the network. Passenger congestion can be divided into two categories: regular and occasional. Regular congestion mainly refers to the periodic and regular characteristics of passenger congestion in the interval or platform within a certain period of time, such as commuter passenger flow during peak hours in the morning and evening; occasional congestion mainly refers to passenger congestion caused by weather, emergencies or large-scale activities.
针对客流拥挤现象，轨道交通系统中的拥挤发生站点、换乘站点和中间站点可采取多种拥挤控制策略对拥挤客流进行疏散，主要包括运力供给管理与客流需求管理两个方面。运力供给管理主要从列车运营优化角度，调整开行方案、增加行车密度，从而提升运能，满足大客流拥挤状态下的客流运输需求。客流需求管理主要从站点客流组织角度通过限流措施以控制或减少进入拥挤站点的客流量，减少站内拥挤客流疏散压力；并结合运力供给管理提升拥挤疏散效率。In response to the phenomenon of passenger congestion, congestion-occurring stations, transfer stations and intermediate stations in the rail transit system can adopt a variety of congestion control strategies to evacuate congested passengers, mainly including capacity supply management and passenger demand management. Capacity supply management mainly adjusts the operation plan and increases the driving density from the perspective of train operation optimization, thereby improving the transportation capacity and meeting the passenger transportation needs under large passenger flow congestion. Passenger demand management mainly controls or reduces the passenger flow entering the congested station through flow limiting measures from the perspective of station passenger flow organization, reducing the pressure of evacuating congested passengers in the station; and combines capacity supply management to improve the efficiency of congestion evacuation.
当前主要采用复杂网络传播动力学理论(如经典SIS与SIR传播模型)，构建拥挤传播与拥挤策略量化评估模型，结合计算机仿真技术，模拟拥挤现象在轨道交通网络中的传播过程，量化拥挤传播的规模，如拥挤传播的影响范围与持续时间等指标；并通过调整仿真参数评估拥挤策略的实施效果。张琦等提出了一种基于元胞自动机的城市轨道交通拥堵状态传播的仿真方法，主要对通勤场景下的车站和区间的拥堵传播过程进行仿真分析。耿丹阳等提出了一种城市轨道交通突发事件下限流措施评估方法及系统，通过构建突发事件下乘客出行选择行为模型、搭建突发事件仿真模拟环境来分析拥挤客流的传播机理，并对不同强度限流措施进行仿真分析。李凌燕构建了基于经典SIRS传播模型的大客流拥挤传播模型，并以客流运输能力为基础对模型的参数进行标定，通过仿真分析了网络中拥挤站点数量随时间的变化趋势。苏怀朗基于复杂网络理论与元胞自动机方法，构建了面向高峰时段大客流的拥挤传播仿真网络，对拥挤传播过程进行仿真。高丽燃利用传播模型和元胞自动机理论，面向突发大客流场景构建拥挤传播仿真网络，量化了拥挤传播影响时间及范围等指标。熊志华等以SIR模型为基础，针对轨道交通拥挤传播模型中的各参数进行了量化分析，构建了拥挤传播速率的量化模型，并分析了拥挤传播特征。At present, the complex network propagation dynamics theory (such as the classic SIS and SIR propagation models) is mainly used to construct a quantitative evaluation model of congestion propagation and congestion strategy. Combined with computer simulation technology, the propagation process of congestion phenomena in rail transit networks is simulated to quantify the scale of congestion propagation, such as the impact range and duration of congestion propagation; and the implementation effect of congestion strategies is evaluated by adjusting simulation parameters. Zhang Qi et al. proposed a simulation method for the propagation of urban rail transit congestion status based on cellular automata, mainly simulating and analyzing the congestion propagation process of stations and sections in commuting scenarios. Geng Danyang et al. proposed an evaluation method and system for flow restriction measures under emergencies in urban rail transit. By constructing a passenger travel choice behavior model under emergencies and setting up an emergency simulation environment, the propagation mechanism of congested passenger flow is analyzed, and flow restriction measures of different intensities are simulated and analyzed. Li Lingyan constructed a large passenger flow congestion propagation model based on the classic SIRS propagation model, calibrated the parameters of the model based on the passenger flow transport capacity, and analyzed the change trend of the number of congested stations in the network over time through simulation. Based on complex network theory and cellular automaton method, Su Huailang constructed a congestion propagation simulation network for large passenger flow during peak hours and simulated the congestion propagation process. Gao Liran used propagation model and cellular automaton theory to construct a congestion propagation simulation network for sudden large passenger flow scenarios and quantified indicators such as the time and scope of congestion propagation impact. Based on the SIR model, Xiong Zhihua et al. conducted a quantitative analysis of the various parameters in the rail transit congestion propagation model, constructed a quantitative model of congestion propagation rate, and analyzed the congestion propagation characteristics.
在实际运营中，当轨道交通网络中发生大客流拥挤事件后，运营管理部门会即时采取控制措施；且未发生拥挤的站点在接收到相邻站点的拥挤预警信息后，可提前采取客流控制措施减小大客流拥挤对网络运营所产生的影响。然而，当前基于经典传播模型的方法主要将轨道交通站点划分为“正常(未发生拥挤)”、“拥挤”、“恢复正常运营”等三类，该划分方法无法刻画运营中站点在接收大客流拥挤预警信息后提前采取相关控制措施对于拥挤传播的影响，因而无法准确描述拥挤现象的实际传播过程，导致不能准确量化拥挤传播规模，进而无法有效的对拥挤控制策略实施前后的拥挤传播过程与疏散效果进行仿真、评估，造成拥挤控制策略实施效果不佳，进而难以对实际运营中的客流运输组织策略的制定提供有效支撑。In actual operation, when a large passenger flow congestion event occurs in the rail transit network, the operation management department will take control measures immediately; and after receiving the congestion warning information from the adjacent station, the station that has not been congested can take passenger flow control measures in advance to reduce the impact of large passenger flow congestion on network operation. However, the current method based on the classical propagation model mainly divides rail transit stations into three categories: "normal (no congestion)", "congestion", and "restoration of normal operation". This classification method cannot describe the impact of the relevant control measures taken in advance by the operating station after receiving the large passenger flow congestion warning information on congestion propagation, and thus cannot accurately describe the actual propagation process of the congestion phenomenon, resulting in the inability to accurately quantify the scale of congestion propagation, and thus cannot effectively simulate and evaluate the congestion propagation process and evacuation effect before and after the implementation of the congestion control strategy, resulting in poor implementation of the congestion control strategy, and thus it is difficult to provide effective support for the formulation of passenger flow transportation organization strategies in actual operation.
因此，如何提供一种考虑大客流预警信息的轨道交通拥挤传播评估与控制方法是本领域技术人员亟需解决的技术问题。Therefore, how to provide a rail transit congestion propagation assessment and control method that takes into account large passenger flow warning information is a technical problem that technical personnel in this field urgently need to solve.
发明内容Summary of the invention
有鉴于此，本发明提供了一种考虑大客流预警信息的轨道交通拥挤传播评估与控制方法，相比于当前基于经典传播模型的方法，本发明在描述站点状态时加入预警状态以描述站点接收拥挤预警信息并采取相关措施的过程，能够准确量化拥挤传播规模，更加符合实际运营场景。In view of this, the present invention provides a rail transit congestion propagation assessment and control method taking into account large passenger flow warning information. Compared with the current method based on the classical propagation model, the present invention adds a warning status when describing the station status to describe the process of the station receiving congestion warning information and taking relevant measures. It can accurately quantify the scale of congestion propagation and is more in line with actual operation scenarios.
为了实现上述目的，本发明采用如下技术方案：In order to achieve the above object, the present invention adopts the following technical solution:
考虑大客流预警信息的轨道交通拥挤传播评估与控制方法，包括：The rail transit congestion propagation assessment and control method considering the large passenger flow warning information includes:
判断客流拥挤现象类型，包括常发性与偶发性；Determine the types of passenger flow congestion, including frequent and occasional;
针对常发性拥挤场景，构建宏观尺度拥挤传播量化模型及设计拥挤传播控制策略仿真方法；具体包括：For frequent crowding scenarios, a macro-scale crowding propagation quantitative model is constructed and a crowding propagation control strategy simulation method is designed; specifically, it includes:
依据轨道交通网络所具有的复杂网络中的无标度特征，构建宏观城市轨道交通拓扑网络；Based on the scale-free characteristics of the complex network of rail transit network, a macro urban rail transit topological network is constructed;
划分宏观城市轨道交通拓扑网络中站点的不同状态，包括正常运营状态S、预警状态A和拥挤状态I；Divide the different states of stations in the macro urban rail transit topology network, including normal operation state S, warning state A and congestion state I;
基于站点的不同状态定义站点的状态转移路径；Define the state transition path of the site based on different states of the site;
根据宏观城市轨道交通拓扑网络和站点的状态转移路径构建宏观尺度拥挤传播量化模型；A macro-scale congestion propagation quantitative model is constructed based on the macro-urban rail transit topology network and the state transition path of the stations;
针对偶发性拥挤场景，构建微观尺度拥挤传播量化模型及设计拥挤传播控制策略仿真方法，具体包括：For occasional crowding scenarios, a micro-scale crowding propagation quantitative model and a crowding propagation control strategy simulation method are constructed, including:
根据复杂网络理论及元胞自动机原理，构建微观城市轨道交通拓扑网络；Based on complex network theory and cellular automaton principles, construct a microscopic urban rail transit topological network;
定义微观城市轨道交通拓扑网络中元胞状态集合，包括正常运营状态S、预警状态A和拥挤状态I；Define the set of cell states in the micro-urban rail transit topology network, including normal operation state S, warning state A and congestion state I;
基于元胞状态定义站点元胞的状态转移路径；Define the state transition path of the site cell based on the cell state;
设置仿真时间步长；Set the simulation time step;
基于元胞自动机原理，并根据站点元胞的状态转移路径以及仿真时间步长将微观城市轨道交通拓扑网络的拥挤传播过程离散化处理，构建微观尺度拥挤传播量化模型。Based on the principle of cellular automata, the congestion propagation process of the micro-urban rail transit topology network is discretized according to the state transition path of the station cells and the simulation time step, and a micro-scale congestion propagation quantitative model is constructed.
优选地，依据轨道交通网络所具有的复杂网络中的无标度特征，构建宏观城市轨道交通拓扑网络，具体为：Preferably, a macroscopic urban rail transit topology network is constructed based on the scale-free characteristics of the complex network of the rail transit network, specifically:
宏观城市轨道交通拓扑网络定义为G＝(V,E)；The macro urban rail transit topological network is defined as G = (V, E);
其中，G表示宏观城市轨道交通拓扑网络；V为网络中的站点集合，即V＝{vs|s＝1,2,...,N}，N为站点总数，E为网络中的边集合，定义为E＝{esl|s,l＝1,2,...,N；s≠l}，每条边表示站点之间的连接关系，即轨道交通网络中的区间。Among them, G represents the macro-urban rail transit topology network; V is the set of sites in the network, that is, V = {vs |s = 1, 2, ..., N}, N is the total number of sites, and E is the set of edges in the network, defined as E = {esl |s,l = 1, 2, ..., N; s ≠ l}, each edge represents the connection relationship between sites, that is, the interval in the rail transit network.
优选地，基于站点不同状态定义站点的状态转移路径，其中，状态转移路径包括以下情况：Preferably, a state transition path of a site is defined based on different states of the site, wherein the state transition path includes the following situations:
正常运营状态S→预警状态A→拥挤状态I→正常运营状态S；Normal operation state S → warning state A → congestion state I → normal operation state S;
正常运营状态S→预警状态A→正常运营状态S；Normal operation state S → warning state A → normal operation state S;
正常运营状态S→拥挤状态I→正常运营状态S。Normal operating state S→congested state I→normal operating state S.
优选地，宏观尺度拥挤传播量化模型的平均场演化方程为：Preferably, the mean field evolution equation of the macro-scale crowded propagation quantization model is:
其中，Sk(t)，Ik(t)与Ak(t)分别为t时刻度为k的站点处于正常状态、预警状态与拥挤状态的相对密度，满足Sk(t)+Ak(t)+Ik(t)＝1，β0为拥挤传播率，δ为拥挤恢复率，βα(ρ,k)为预警状态转变为拥挤状态站点的概率，Θ(t)∈[0,1]为在t时刻，宏观城市轨道交通拓扑网络中的任一条边与拥挤站点连接的平均概率，定义为：Among them,Sk (t),Ik (t) andAk (t) are the relative densities of the station with scale k at time t in normal state, warning state and congested state, respectively, satisfyingSk (t)+Ak (t)+Ik (t)＝1,β0 is the congestion propagation rate, δ is the congestion recovery rate,βα (ρ,k) is the probability of the station in the warning state turning into the congested state, Θ(t)∈[0,1] is the average probability of any edge in the macro urban rail transit topology network connecting to the congested station at time t, defined as:
其中，为宏观城市轨道交通拓扑网络中所有站点的度的平均值，p(k)为节点的度分布，正常站点变为预警状态的概率为：in, is the average degree of all stations in the macro urban rail transit topology network, p(k) is the degree distribution of the node, and the probability of a normal station becoming a warning state is:
其中ρ∈(0,1)；kinf表示与度为k的正常状态站点相连接站点中拥挤站点的数量。Where ρ∈(0,1); kinf represents the number of congested sites among the sites connected to the normal state site with degree k.
优选地，微观城市轨道交通拓扑网络中的站点定义为站点元胞，微观城市轨道交通拓扑网络定义为GM＝(H,IR,M)；其中，GM表示微观城市轨道交通拓扑网络；定义网络中站点集合H＝{1,2,...,Nm}，Nm为网络中的站点数量。当考虑站点所对应的不同线路时，定义站点元胞集合IR＝{ir|i∈H,r∈R}；令R＝{1,2,...,r,...,Nr}为线路集合，Nr为集合R中的线路数量；因此，元素ir表示站点i所对应线路r的站点元胞。M＝{mij|i,j＝1,2,...,Nm；i≠j}，定义为该网络中连接站点的有向边集合，表示为运营中的列车运行和客流拥挤传播的方向。Preferably, the stations in the micro-urban rail transit topology network are defined as station cells, and the micro-urban rail transit topology network is defined as GM = (H, IR , M); wherein GM represents the micro-urban rail transit topology network; the station set H in the network is defined as H = {1, 2, ..., Nm }, and Nm is the number of stations in the network. When considering different lines corresponding to the stations, the station cell set IR = {ir |i∈H, r∈R} is defined; let R = {1, 2, ..., r, ..., Nr } be the line set, and Nr be the number of lines in the set R; therefore, element ir represents the station cell of line r corresponding to station i. M = {mij |i, j = 1, 2, ..., Nm ; i ≠ j}, defined as the set of directed edges connecting stations in the network, representing the direction of train operation and passenger congestion propagation in operation.
优选地，仿真时间步长ΔT计算公式为：Preferably, the simulation time step ΔT is calculated as:
其中，为每个区间的发车时间间隔。in, The departure time interval for each section.
优选地，微观尺度拥挤传播量化模型具体为：Preferably, the micro-scale crowding propagation quantification model is specifically:
1)定义站点元胞的状态演化规则：1) Define the state evolution rules of site cells:
式中，x(jr,t+1)为站点元胞jr(j∈H，r∈R)在t+1时刻的状态，x(i1,t),…,为站点元胞jr的所有邻居元胞在t时刻的状态，为一个仿真时间步长内站点元胞jr的所有Nd个邻居站点元胞i1,，i2，…，对站点元胞jr的传播率，为站点元胞jr在t时刻对于拥挤传播的恢复率；Where x(jr ,t+1) is the state of site cell jr (j∈H, r∈R) at time t+1, x(i1 ,t),…, is the state of all neighbor cells of site cell jr at time t, are all Nd neighboring site cells i1 , i2 , …, of site cell jr within a simulation time step. The propagation rate of the site cell jr , is the recovery rate of site cell jr to congestion propagation at time t;
2)根据站点元胞的状态演化规则以及站点元胞的状态转移路径，对站点元胞下一时刻t+1的状态进行定义：2) According to the state evolution rules of the site cell and the state transition path of the site cell, the state of the site cell at the next time t+1 is defined:
i)当站点元胞jr在t时刻的状态为正常运营状态S时，状态转移函数fa(t)表示为：i) When the state of the site cell jr at time t is the normal operation state S, the state transfer function fa (t) is expressed as:
其中，jr表示站点或节点j所对应线路r的站点元胞，fα表示正常运营状态下站点元胞经过一个时间步长后状态演化结果，θ1，θ2与θ2为参数，可根据实际运营情况进行设定；传播率受列车满载率、换乘客流的影响：Where jr represents the site cell of line r corresponding to site or node j, fα represents the state evolution result of the site cell after one time step under normal operation, θ1 , θ2 and θ2 are parameters, which can be set according to the actual operation situation; the propagation rate Affected by train load factor and transfer passenger flow:
其中，若站点j为换乘站点，对于站点元胞jr，表示由站点j其他线路换乘至站点j中对应线路r的元胞jr的客流量；表示由该站点j中线路r所对应元胞jr换乘至站点j中其他线路的客流量；Vj,r表示对于线路r，列车到达站台jr之前的断面客流量；Oj,r表示节点j对应于线路r的元胞jr的出站客流量；Ij,r表示节点j对应于线路r的元胞jr的进站客流量；Cj,r表示对于线路r，列车的最大载客量，可视为该运行方向上的客流输送能力；ω1为0-1二元变量，当ω1＝1时，表示站点元胞jr为换乘站，当ω1＝0时，站点元胞jr表示单条线路中的站点；If station j is a transfer station, for station cell jr , represents the passenger flow of cell jr that transfers from other routes at station j to the corresponding route r at station j; represents the passenger flow of the cell jr corresponding to line r in the station j transferring to other lines in the station j; Vj,r represents the cross-sectional passenger flow before the train arrives at platform jr for line r; Oj,r represents the outbound passenger flow of the cell jr corresponding to line r at node j; Ij,r represents the inbound passenger flow of the cell jr corresponding to line r at node j; Cj,r represents the maximum passenger capacity of the train for line r, which can be regarded as the passenger flow transport capacity in the running direction; ω1 is a 0-1 binary variable. When ω1 =1, it means that the station cell jr is a transfer station. When ω1 =0, the station cell jr represents a station in a single line.
ii)当站点元胞jr处于预警状态A时，状态转移函数fβ(t)定义为：ii) When the site cell jr is in the warning state A, the state transition function fβ (t) is defined as:
其中，为提前采取相关措施后的恢复能力；为一个时间步长内到达站点元胞jr的下车乘客人数；为一个时间步长内在站点元胞jr的等待乘车乘客人数；表示一个时间步长内的客流输送能力；in, To ensure the ability to recover after taking relevant measures in advance; is the number of passengers getting off at station cell jr within a time step; is the number of passengers waiting for the bus in station cell jr within a time step; It represents the passenger flow transport capacity within a time step;
iii)当站点元胞jr处于拥挤状态I时，其状态转移函数fr(x)定义为：iii) When the site cell jr is in the crowded state I, its state transition function fr (x) is defined as:
上式描述了处于拥挤状态的站点元胞在经过一个时间步长后状态演化结果；令即在拥挤状态下，站点元胞以为概率恢复为正常运营状态、以为概率维持原有拥挤状态。The above formula describes the state evolution of the station cell in a crowded state after one time step; let That is, in a crowded state, the station cell The probability of returning to normal operation is To maintain the original crowded state with probability.
优选地，构建微观尺度拥挤传播量化模型后还包括：Preferably, after constructing the micro-scale crowding propagation quantitative model, the method further includes:
基于微观尺度拥挤传播量化模型，从列车运营优化角度和客流组织优化角度，对不同客流拥挤控制措施下的拥挤传播演化过程进行仿真分析，得到不同控制措施对于轨道交通网络拥挤传播的缓解程度。Based on the micro-scale crowding propagation quantitative model, the crowding propagation evolution process under different passenger flow congestion control measures is simulated and analyzed from the perspective of train operation optimization and passenger flow organization optimization, and the degree to which different control measures alleviate the crowding propagation of the rail transit network is obtained.
优选地，构建宏观尺度拥挤传播量化模型后还包括：Preferably, after constructing the macro-scale crowding propagation quantitative model, the method further includes:
基于宏观尺度拥挤传播量化模型，通过改变宏观尺度拥挤传播量化模型中的控制参数模拟列车运营优化后对于拥挤传播规模的影响程度，评估运输组织优化策略对于拥挤传播规模的影响，通过多次仿真比较不同运输组织优化策略对于拥挤传播的控制效果。Based on the macro-scale congestion propagation quantification model, the influence of train operation optimization on the scale of congestion propagation is simulated by changing the control parameters in the macro-scale congestion propagation quantification model, and the influence of the transport organization optimization strategy on the scale of congestion propagation is evaluated. The control effects of different transport organization optimization strategies on congestion propagation are compared through multiple simulations.
经由上述的技术方案可知，与现有技术相比，本发明公开提供了一种考虑大客流预警信息的轨道交通拥挤传播评估与控制方法，具有以下优点：It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a rail transit congestion propagation evaluation and control method considering large passenger flow warning information, which has the following advantages:
1)相较于传统方法，本发明在描述站点状态时加入“预警状态”，充分考虑了轨道交通中站点提前接收预警信息并及时采取相应控制策略对于拥挤传播的抑制作用，更加符合实际运营场景。1) Compared with the traditional method, the present invention adds "warning status" when describing the station status, which fully considers the inhibitory effect of stations in rail transit receiving warning information in advance and taking corresponding control strategies in time on the spread of congestion, which is more in line with actual operation scenarios.
2)从宏观与微观两个层面构建拥挤传播量化模型，并设计拥挤传播控制策略仿真方法，可适用于实际运营中的不同应用场景。2) Construct a quantitative model of congestion propagation from both macro and micro levels, and design a simulation method for congestion propagation control strategy, which can be applied to different application scenarios in actual operations.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
图1为本发明提供的考虑大客流预警信息的轨道交通拥挤传播评估与控制方法流程图；FIG1 is a flow chart of a rail transit congestion propagation evaluation and control method considering large passenger flow warning information provided by the present invention;
图2为本发明提供的站点状态转移路径示意图；FIG2 is a schematic diagram of a site state transfer path provided by the present invention;
图3为邻居节点示意图；Figure 3 is a schematic diagram of neighbor nodes;
图4为本发明与经典方法拥挤站点平均密度演化趋势对比示意图；FIG4 is a schematic diagram showing a comparison of the average density evolution trend of crowded sites between the present invention and the classical method;
图5为列车运营优化下拥挤站点平均密度演化趋势示意图。Figure 5 is a schematic diagram of the evolution trend of the average density of crowded stations under train operation optimization.
具体实施方式DETAILED DESCRIPTION
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
本发明实施例公开了一种考虑大客流预警信息的轨道交通拥挤传播评估与控制方法，如图1所示，包括：The embodiment of the present invention discloses a rail transit congestion propagation evaluation and control method considering large passenger flow warning information, as shown in FIG1 , comprising:
判断客流拥挤现象类型，包括常发性与偶发性；Determine the types of passenger flow congestion, including frequent and occasional;
针对常发性拥挤场景，构建宏观尺度拥挤传播量化模型及设计拥挤传播控制策略仿真方法；For frequent crowding scenarios, a macro-scale crowding propagation quantitative model is constructed and a simulation method for crowding propagation control strategy is designed;
针对偶发性拥挤场景，构建微观尺度拥挤传播量化模型及设计拥挤传播控制策略仿真方法。For occasional congestion scenarios, a micro-scale crowding propagation quantitative model is constructed and a simulation method for designing crowding propagation control strategies is developed.
具体的，宏观尺度拥挤传播量化评估与控制方法主要利用平均场方法构建可用于仿真拥挤传播过程的量化评估模型，分析拥挤传播在轨道交通全局网络中的传播趋势与特征；并从列车运营组织优化角度，在仿真过程中通过改变模型控制参数评估拥挤控制方法的效果。具体过程为：Specifically, the macro-scale crowding propagation quantitative evaluation and control method mainly uses the mean field method to construct a quantitative evaluation model that can be used to simulate the crowding propagation process, analyze the propagation trend and characteristics of crowding propagation in the global rail transit network; and from the perspective of train operation organization optimization, the effect of the crowding control method is evaluated by changing the model control parameters during the simulation process. The specific process is:
S101：依据轨道交通网络所具有的复杂网络中的无标度特征，构建宏观城市轨道交通拓扑网络。S101: Based on the scale-free characteristics of the complex network of rail transit network, construct a macro urban rail transit topology network.
宏观城市轨道交通拓扑网络包括节点和边，定义为G＝(V,E)；其中G表示宏观城市轨道交通拓扑网络；V为网络中的站点(节点)集合，即V＝{vs|s＝1,2,...,N}，N为站点(节点)总数。E为网络中的边(区间)集合，定义为E＝{esl|s,l＝1,2,...,N；s≠l}，每条边表示站点之间的连接关系(轨道交通网络中的区间)，在网络中连接相邻站点边的长度表示站点间的距离。The macro urban rail transit topology network includes nodes and edges, defined as G = (V, E); where G represents the macro urban rail transit topology network; V is the set of sites (nodes) in the network, that is, V = {vs |s = 1, 2, ..., N}, and N is the total number of sites (nodes). E is the set of edges (intervals) in the network, defined as E = {esl |s, l = 1, 2, ..., N; s ≠ l}, each edge represents the connection relationship between sites (intervals in the rail transit network), and the length of the edge connecting adjacent sites in the network represents the distance between sites.
S102：划分宏观城市轨道交通拓扑网络中站点的不同状态。S102: Divide different states of stations in the macro urban rail transit topology network.
根据大客流影响下轨道交通站点运营状态的变化情况，本发明将站点分为三类：According to the changes in the operating status of rail transit stations under the influence of large passenger flow, the present invention divides the stations into three categories:
第一类为未受到大客流冲击正常运行的站点(用S表示)；由于在实际运营中，由拥挤站点恢复到正常运行状态的站点，恢复到正常状态的站点会有一定的概率重新变为拥挤站点，因而本发明将由拥挤站点恢复到正常运行状态的站点也视为状态S；The first category is a station that is not impacted by a large passenger flow and is operating normally (represented by S). In actual operation, a station that is restored from a congested station to a normal operating state has a certain probability of becoming a congested station again. Therefore, the present invention also regards a station that is restored from a congested station to a normal operating state as being in state S.
第二类为预警状态站点(用A表示)。对于正常运行的站点，若可实时获取上游拥挤车站发生大客流的信息，可以提前采取相关措施或控制策略，提高站点的客流运输组织能力，降低大客流在网络中所产生的影响，控制拥挤状态在网络中的传播规模。本发明在拥挤传播仿真过程中引入“预警状态”站点(A)，即正常运行的站点在接收到网络中拥挤预警信息后会以α的概率转变为预警站点。The second category is the warning state site (indicated by A). For the normally operating sites, if the information of large passenger flow in the upstream crowded station can be obtained in real time, relevant measures or control strategies can be taken in advance to improve the passenger flow transportation organization ability of the site, reduce the impact of large passenger flow in the network, and control the propagation scale of the congestion state in the network. The present invention introduces the "warning state" site (A) in the congestion propagation simulation process, that is, the normally operating site will be transformed into a warning site with a probability of α after receiving the congestion warning information in the network.
第三类为受到大客流冲击而发生拥挤的站点(用I表示)。The third category is stations that are crowded due to large passenger flow (indicated by I).
S103：根据站点的不同状态，定义站点的状态转移路径。S103: Define a state transition path of the site according to different states of the site.
在实际运营中，正常运营和预警状态站点均有转变为拥挤站点的风险。当正常运营站点与拥挤站点相连接时，正常运营站点将以传播率或传播强度β0的概率转变为拥挤状态；或者正常运营站点在获取相邻站点的拥挤预警信息时，该站点会以一定的概率α转化为预警站点。In actual operation, both normal operation and warning status stations have the risk of becoming congested stations. When a normal operation station is connected to a congested station, the normal operation station will become congested with a probability of propagation rate or propagation intensity β0 ; or when a normal operation station obtains congestion warning information from an adjacent station, the station will be transformed into a warning station with a certain probability α.
预警站点变为拥挤站点的概率为βα。在实际运营中，由于预警状态站点会提前采取拥挤控制策略(如运输组织优化、限流措施等)来避免拥挤发生，因此，0＜βα＜β0。随着站点采取控制策略对拥挤客流进行疏散，令拥挤站点转变为正常站点的恢复率或消散率为δ。The probability that a warning station becomes a congested station is βα . In actual operation, since the warning station will take congestion control strategies (such as transportation organization optimization, flow control measures, etc.) in advance to avoid congestion, 0＜βα ＜β0 . As the station adopts control strategies to evacuate the congested passenger flow, the recovery rate or dissipation rate of the congested station turning into a normal station is δ.
根据以上参数的定义，拥挤传播模型中站点的状态转移路径包括以下几种情况，状态转移示意图如图2所示：According to the definition of the above parameters, the state transition path of the site in the crowded propagation model includes the following cases. The state transition diagram is shown in Figure 2:
①正常运营状态S→预警状态A→拥挤状态I→正常运营状态S；① Normal operation state S → warning state A → congestion state I → normal operation state S;
②正常运营状态S→预警状态A→正常运营状态S；② Normal operation state S → warning state A → normal operation state S;
③正常运营状态S→拥挤状态I→正常运营状态S。③Normal operating state S→congested state I→normal operating state S.
S104：构建宏观尺度拥挤传播量化模型。S104: Construct a quantitative model of macro-scale crowded propagation.
令Sk(t)，Ik(t)与Ak(t)分别为t时刻度为k的站点处于正常运营状态、预警状态与拥挤状态的相对密度，满足以下归一化条件：LetSk (t),Ik (t) andAk (t) be the relative densities of the station with scale k at time t in normal operation, warning and congestion, respectively, satisfying the following normalization conditions:
Sk(t)+Ak(t)+Ik(t)＝1Sk (t) + Ak (t) + Ik (t) = 1
令S(t)，A(t)，I(t)分别为t时刻轨道交通网络中全部站点分别处于正常运营状态、预警状态与拥挤状态的平均密度，该密度通过度为k的节点的相对密度来定义：Let S(t), A(t), and I(t) be the average density of all stations in the rail transit network in normal operation, warning, and congestion at time t, respectively. The density is defined by the relative density of nodes with degree k:
其中，p(k)为节点的度分布。节点的度表示与单个节点连接的边数量。Where p(k) is the degree distribution of the node. The degree of a node represents the number of edges connected to a single node.
基于以上定义，利用平均场方法，考虑大客流预警信息的宏观尺度拥挤传播量化模型的平均场演化方程定义如下：Based on the above definition, using the mean field method, the mean field evolution equation of the macro-scale crowding propagation quantitative model considering large passenger flow warning information is defined as follows:
其中，Sk(t)，Ik(t)与Ak(t)分别为t时刻度为k的站点处于正常状态、预警状态与拥挤状态的相对密度，满足Sk(t)+Ak(t)+Ik(t)＝1，β0为拥挤传播率，δ为拥挤恢复率，βα(ρ,k)为预警状态转变为拥挤状态站点的概率，Θ(t)∈[0,1]为在t时刻，网络中的任一条边与拥挤站点连接的平均概率，定义为：Where,Sk (t),Ik (t) andAk (t) are the relative densities of the station with degree k in normal state, warning state and congested state at time t, respectively, satisfyingSk (t)+Ak (t)+Ik (t)＝1,β0 is the congestion propagation rate, δ is the congestion recovery rate,βα (ρ,k) is the probability of the station in warning state turning into congested state, Θ(t)∈[0,1] is the average probability that any edge in the network is connected to a congested station at time t, defined as:
其中，为网络中所有节点的度的平均值。in, is the average degree of all nodes in the network.
目前在部分城市的轨道交通运营中对于大客流风险采取分级制，即对于换乘枢纽站点(即站点的度较大)这类设计客流量较高的站点，属于重点监测与控制的站点。因此，为与轨道交通实际运营中的场景相符合，定义正常站点变为预警状态的概率：At present, in the rail transit operation of some cities, a grading system is adopted for large passenger flow risks, that is, transfer hub stations (i.e. stations with a large degree) with high designed passenger flow are the key monitoring and control stations. Therefore, in order to conform to the scenarios in the actual operation of rail transit, the probability of a normal station turning into a warning state is defined as follows:
其中，ρ∈(0,1)；kinf为与度为k的正常状态站点相连接站点中拥挤站点的数量；上式表示节点的度越大，预警率越大，呈现出正比关系。公式描述了度为k的正常运营状态站点在接收到预警信息后，可以选择提前采取相关控制措施降低变为拥挤状态站点的风险。Among them, ρ∈(0,1); kinf is the number of congested stations among the stations connected to the normal station with degree k; the above formula indicates that the greater the degree of the node, the greater the warning rate, showing a proportional relationship. The formula describes that after receiving the warning information, the normal operation station with degree k can choose to take relevant control measures in advance to reduce the risk of becoming a congested station.
因此，通过以上拥挤传播量化模型可模拟轨道交通网络在受大客流影响下，拥挤站点数量随运营时间的变化趋势，仿真拥挤传播过程。Therefore, the above congestion propagation quantitative model can be used to simulate the changing trend of the number of congested stations with operating hours under the influence of large passenger flow in the rail transit network, and simulate the congestion propagation process.
S105：拥挤传播控制策略仿真方法。S105: Simulation method of congestion propagation control strategy.
基于宏观尺度拥挤传播量化模型，通过改变模型中的控制参数模拟列车运营优化后(如运能改变)对于拥挤传播规模的影响程度，如通过拥挤状态下运输需求、列车运行通过能力以及采取相关控制措施后实际客流运输能力等轨道交通运营指标进行模型参数标定。评估运输组织优化策略对于拥挤传播规模(如拥挤状态站点的数量变化)的影响，通过多次仿真比较不同运输组织优化策略(即不同参数组合)对于拥挤传播的控制效果。主要参数如下。Based on the macro-scale congestion propagation quantitative model, the influence of train operation optimization (such as capacity change) on the scale of congestion propagation is simulated by changing the control parameters in the model, such as the transportation demand under congestion, train operation capacity, and actual passenger flow capacity after taking relevant control measures. Model parameters are calibrated. The impact of the transport organization optimization strategy on the scale of congestion propagation (such as the change in the number of congested stations) is evaluated, and the control effect of different transport organization optimization strategies (i.e. different parameter combinations) on congestion propagation is compared through multiple simulations. The main parameters are as follows.
①拥挤传播率β0① Crowded transmission rate β0
在大客流影响下，拥挤的传播主要与列车运能无法完全满足运输需求有关，如列车停站时间加长导致的运行时间间隔的增加。因此，拥挤传播率可由供需差异定义：Under the influence of large passenger flows, the spread of congestion is mainly related to the inability of train capacity to fully meet transportation demand, such as the increase in running time intervals caused by longer train stops. Therefore, the congestion spread rate can be defined by the difference between supply and demand:
其中，N1表示拥挤状态时所需要的列车运行通过能力；N2表示在拥挤状态下的实际运输能力；N3表示设计运行通过能力。在实际运营中，N2为随运营周期变化的量；例如，在拥挤状态发生时，由于列车的在站延误时间的增加，导致客流的运输能力降低，因而在拥挤状态下运输能力是变化的量；本发明将N2取值为发生延误等事件后的平均运行通过能力。Among them,N1 represents the train running capacity required in a crowded state;N2 represents the actual transport capacity in a crowded state;N3 represents the designed running capacity. In actual operation,N2 is a quantity that changes with the operation cycle; for example, when a crowded state occurs, the passenger transport capacity decreases due to the increase in the train's delay time at the station, so the transport capacity is a variable quantity in a crowded state; the present invention takesN2 as the average running capacity after delays and other events occur.
②拥挤恢复率δ②Crowding recovery rate δ
在实际运营中，δ的取值与拥挤的程度、列车运行通过能力、站台对客流的承载能力与疏散策略等因素相关，可定义为：In actual operation, the value of δ is related to factors such as the degree of congestion, train running capacity, platform carrying capacity for passenger flow and evacuation strategy, and can be defined as:
其中N4表示站点采取控制策略后列车的运输能力，通常N4＞N3。Where N4 represents the transport capacity of the train after the station adopts the control strategy, usually N4 > N3 .
③预警率参数ρ③ Warning rate parameter ρ
根据预警率公式，α的值与站点的空间位置相关(度的值)，其中关键参数ρ可由通过能力的变化率定义：According to the early warning rate formula, the value of α is related to the spatial location of the site (the value of degree), where the key parameter ρ can be defined by the rate of change of the through capacity:
具体的，微观尺度拥挤传播量化评估与控制方法主要针对某特定站点受到突发大客流应影响的情况，不同于宏观尺度场景主要用于量化拥挤传播在全局网络上的平均趋势，微观尺度场景需要考虑拥挤传播过程中站点之间的交互作用，因此本发明将元胞自动机理论与宏观尺度量化模型相结合，构建微观尺度拥挤传播量化评估模型；并从列车运营与客流组织优化角度，在仿真过程中通过改变模型控制参数评估不同拥挤控制方法及运营组织策略的实施效果。具体包括以下步骤：Specifically, the micro-scale crowding propagation quantitative evaluation and control method is mainly aimed at the situation where a specific station is affected by a sudden large passenger flow. Different from the macro-scale scenario, which is mainly used to quantify the average trend of crowding propagation on the global network, the micro-scale scenario needs to consider the interaction between stations during the crowding propagation process. Therefore, the present invention combines the cellular automaton theory with the macro-scale quantitative model to construct a micro-scale crowding propagation quantitative evaluation model; and from the perspective of train operation and passenger flow organization optimization, the implementation effects of different crowding control methods and operation organization strategies are evaluated by changing the model control parameters during the simulation process. Specifically, the following steps are included:
S201：根据复杂网络理论及元胞自动机原理，构建微观城市轨道交通拓扑网络。S201: Based on complex network theory and cellular automaton principles, construct a micro urban rail transit topological network.
该网络包括轨道交通各站点、站台以及列车运行区间；其中将网络中的站点定义为站点元胞；结合宏观尺度模型，定义元胞自动机各元素，包括元胞空间、邻居节点、状态及演化规则。为区分每条线路的上下行方向，每个站点元胞包括上行站台元胞与下行站台元胞。若某站点为换乘站点，则将该站点所包括的各相邻线路的所有站台定义为一个站点元胞；且根据元胞自动机原理，换乘站点的各站台间会相互影响。因此，将网络内的站点分为换乘站与非换乘站点，根据站点所通过线路数量Nc，每个站点元胞包含2Nc个站台元胞；且拥挤客流在元胞上传播方向为单一方向。在实际处理中，为计算方便，以站点元胞为基础进行建模分析。The network includes rail transit stations, platforms and train running sections; the stations in the network are defined as station cells; combined with the macro-scale model, the elements of the cellular automaton are defined, including cell space, neighbor nodes, states and evolution rules. In order to distinguish the up and down directions of each line, each station cell includes an up platform cell and a down platform cell. If a station is a transfer station, all platforms of the adjacent lines included in the station are defined as a station cell; and according to the principle of cellular automaton, the platforms of the transfer station will affect each other. Therefore, the stations in the network are divided into transfer stations and non-transfer stations. According to the number of linesNc passed by the station, each station cell contains2Nc platform cells; and the propagation direction of crowded passenger flow on the cell is a single direction. In actual processing, for the convenience of calculation, modeling and analysis are carried out based on station cells.
通过元胞空间定义上述拓扑网络，将其作为微观尺度轨道交通客流拥挤传播仿真网络。可将该微观城市轨道交通拓扑网络描述为一个有向网络，即GM＝(H,IR,M)；其中GM表示微观城市轨道交通拓扑网络；定义网络中站点集合H＝{1,2,...,Nm}，即站点元胞集合，Nm为网络中的节点或站点数量，即对站点元胞进行编号；当考虑站点所对应的不同线路时，定义站点元胞集合IR＝{ir|i∈H,r∈R}；令R＝{1,2,...,r,...,Nr}为线路集合，Nr为集合R中的线路数量；因此，元素ir表示站点i所对应线路r的站点元胞。M＝{mij|i,j＝1,2,...,Nm；i≠j}定义为该网络中连接站点的有向边集合，可表示为运营中的列车运行和客流拥挤传播的方向。进一步，定义邻居节点或邻居元胞，如图3所示；节点v1为节点v2、v3、v4以及v5的邻居节点，且v1为换乘站(有2条线路通过)。根据定义，v1包括2个上行站台元胞和2个下行站台元胞。The above topological network is defined by cellular space and used as a micro-scale rail transit passenger flow congestion propagation simulation network. The micro-urban rail transit topological network can be described as a directed network, that is, GM = (H, IR , M); where GM represents the micro-urban rail transit topological network; the site set H = {1,2,...,Nm } in the network is defined, that is, the site cell set, Nm is the number of nodes or sites in the network, that is, the site cells are numbered; when considering different lines corresponding to the site, the site cell set IR = {ir |i∈H,r∈R} is defined; let R = {1,2,...,r,...,Nr } be the line set, and Nr is the number of lines in the set R; therefore, element ir represents the site cell of line r corresponding to site i. M = {mij |i,j = 1,2,...,Nm ; i≠j} is defined as the set of directed edges connecting sites in the network, which can be represented as the direction of train operation and passenger flow congestion propagation in operation. Furthermore, neighbor nodes or neighbor cells are defined, as shown in Figure 3; nodev1 is the neighbor node of nodesv2 ,v3 ,v4 , andv5 , andv1 is a transfer station (with 2 lines passing through). According to the definition,v1 includes 2 up-platform cells and 2 down-platform cells.
S202：定义元胞状态集合。S202: Define a cell state set.
在实际应用中，元胞状态集合:可由多个整数组成的离散集合进行描述。根据宏观尺度模型，本发明将站点的状态分为正常运营状态、预警状态与拥挤状态。定义线路r中的站点元胞ir在时刻t的状态向量为x(ir,t)∈U，其中集合U＝{1,2,3}中的元素分别代表{正常，预警，拥挤}三种状态。元胞站点或站台的状态转移路径同宏观尺度模型。In practical applications, the cellular state set: can be described by a discrete set consisting of multiple integers. According to the macro-scale model, the present invention divides the state of the site into normal operation state, warning state and congestion state. Define the state vector of the site cell ir in line r at time t as x(ir ,t)∈U, where the elements in the set U＝{1,2,3} represent the three states of {normal, warning, congestion} respectively. The state transition path of the cellular site or platform is the same as the macro-scale model.
S203：设置仿真时间步长。S203: Set the simulation time step.
在轨道交通网络中，每个站台客流变化受列车到站进行客流交换的影响，且列车在区间运行或拥挤沿线路各站台传播也需要一定的时间。因此，站点元胞状态的更新也需要时间来完成；例如，在一个时间区间内至少有一列列车的到站与出发。In the rail transit network, the passenger flow change at each platform is affected by the passenger flow exchange when the train arrives at the station, and it takes a certain amount of time for the train to run in the interval or the congestion to propagate to each platform along the line. Therefore, the update of the station cell state also takes time to complete; for example, at least one train arrives and departs in a time interval.
设定一个仿真时间步长ΔT作为拥挤传播时间；在一个时间步长内，站点元胞只处于一个状态且保持不变。选取网络中各区间列车平均发车间隔最大值作为元胞状态更新的仿真时间步长ΔT，并按以下公式计算：Set a simulation time step ΔT as the congestion propagation time; within a time step, the station cell is in only one state and remains unchanged. Select the maximum value of the average departure interval of each section of the network as the simulation time step ΔT for cell state update, and calculate it according to the following formula:
其中为每个区间的发车时间间隔。此外，根据元胞自动机原理，令网络中所有元胞的状态同时完成更新。in is the departure time interval for each section. In addition, according to the principle of cellular automation, the states of all cells in the network are updated simultaneously.
S204：构建微观尺度拥挤传播量化模型。S204: Construct a quantitative model of micro-scale crowd propagation.
1)将轨道交通网络的拥挤传播过程离散化。对于单条线路r，令站点元胞jr为邻居站点元胞ir的下游站台，即客流输送或拥挤传播方向由站点元胞ir至站点元胞jr；t+1时刻与t时刻间隔一个时间步长ΔT。站点元胞jr在t+1时刻的状态x(jr,t+1)由其在t时刻的状态x(jr,t)、邻居元胞ir在t时刻的状态x(ir,t)共同决定；此外，根据站点元胞jr在时刻t的状态不同及不同状态转移路径，站点元胞jr在t+1时刻的状态受不同参数影响。若站点元胞jr所对应站点为换乘站，则站点元胞jr状态除受线路r上游邻居站台客流影响，还受通过换乘站的其他线路r’的换乘客流影响。本发明中考虑的参数包括：一个时间步长内站点元胞jr所有邻居站点元胞i1,，i2，…，对站点元胞jr的传播率(或传播强度)站点元胞jr在t时刻对于拥挤传播的恢复率1) Discretize the congestion propagation process of the rail transit network. For a single line r, let the station cell jr be the downstream platform of the neighboring station cell ir , that is, the direction of passenger flow or congestion propagation is from station cell ir to station cell jr ; the interval between time t+1 and time t is a time step ΔT. The state x(jr ,t+1) of station cell jr at time t+1 is jointly determined by its state x(jr ,t) at time t and the state x(ir ,t) of neighboring cell ir at time t; in addition, according to the different states of station cell jr at time t and different state transfer paths, the state of station cell jr at time t+1 is affected by different parameters. If the station corresponding to station cell jr is a transfer station, the state of station cell jr is not only affected by the passenger flow of the upstream neighboring platform of line r, but also by the transfer passenger flow of other lines r' passing through the transfer station. The parameters considered in the present invention include: within a time step, the site cell jr all neighboring site cells i1 ,，i2 ，…， The transmission rate (or transmission intensity) of the site cell jr Recovery rate of site cell jr to congestion propagation at time t
综上，站点元胞jr的状态演化规则可由隐式方程表示：In summary, the state evolution rule of the site cell jr can be expressed by the implicit equation:
式中x(i1,t),…,为站点元胞jr所有邻居元胞在t时刻的状态。其中演化规则是指根据站点元胞jr当前状态及其邻居状态，确定下一时刻该站点元胞状态的动力学函数。where x(i1 ,t),…, is the state of all neighboring cells of site cell jr at time t. The evolution rule refers to the dynamic function that determines the state of the site cell at the next moment based on the current state of site cell jr and the states of its neighbors.
2)根据站点元胞的状态演化规则以及站点元胞的状态转移路径，对站点元胞下一时刻t+1的状态进行定义：2) According to the state evolution rules of the site cell and the state transition path of the site cell, the state of the site cell at the next time t+1 is defined:
①当站点元胞jr的在t时刻的状态为正常状态S时(即x(jr,t)＝1)，其状态由传播强度定义，此时状态转移函数表示为：① When the state of the site cell jr at time t is the normal state S (i.e. x(jr ,t) = 1), its state is defined by the propagation intensity. At this time, the state transfer function is expressed as:
其中，fα表示正常状态站点元胞经过一个时间步长后状态演化结果；公式中传播强度值受列车满载率、换乘客流的影响：Among them, fα represents the state evolution result of the normal state site cell after one time step; the propagation intensity value in the formula Affected by train load factor and transfer passenger flow:
若站点j为换乘站点，对于站点元胞jr，表示由站点j其他线路换乘至站点j中对应线路r的元胞jr的客流量； If station j is a transfer station, for station cell jr , represents the passenger flow of cell jr that transfers from other routes at station j to the corresponding route r at station j;
对于站点j，表示由站点j中线路r所对应元胞jr换乘至站点j中其他线路的客流量； For site j, represents the passenger flow from cell jr corresponding to line r in station j to other lines in station j;
Vj,r：对于线路r，列车到达站台jr之前的断面客流量；Vj,r : For line r, the cross-sectional passenger flow before the train arrives at platform jr ;
Oj,r：节点j对应于线路r的元胞jr的出站客流量；Oj,r : outbound passenger flow of cell jr of node j corresponding to route r;
Ij,r：节点j对应于线路r的元胞jr的进站客流量；Ij,r : the passenger flow of cell jr corresponding to node j on route r;
Cj,r：对于线路r，列车的最大载客量，可视为该运行方向上的客流输送能力。Cj,r : For line r, the maximum passenger capacity of the train, which can be regarded as the passenger transport capacity in the running direction.
此外，θ1，θ2与θ2为参数，可根据实际运营情况进行设定；公式中描述了换乘客流对于站点元胞jr状态的影响；描述了本线路r拥挤客流传播的影响。ω1为0-1二元变量；当ω1＝1时，表示站点元胞jr为换乘站。In addition, θ1 , θ2 and θ2 are parameters that can be set according to actual operating conditions; Describes the impact of transfer passenger flow on the jr state of the station cell; It describes the impact of crowded passenger flow propagation on line r.ω 1 is a 0-1 binary variable; when ω1 = 1, it means that station cell jr is a transfer station.
当ω1＝0时，站点元胞jr表示单条线路r中的站点。此时，站点元胞jr状态仅受线路r中上游邻居站台客流影响，即上述传播强度公式中无换乘客流因素；在该场景下，定义传播强度When ω1 = 0, the station cell jr represents the station in a single line r. At this time, the state of the station cell jr is only affected by the passenger flow of the upstream neighbor platform in line r, that is, there is no transfer passenger flow factor in the above propagation intensity formula; in this scenario, the propagation intensity is defined as
列车到达站点元胞jr之前的断面客流量； The cross-sectional passenger flow before the train arrives at station cell jr ;
节点j对应于线路r的元胞jr的出站客流量； Node j corresponds to the outbound passenger flow of cell jr of route r;
节点j对应于线路r的元胞jr的进站客流量； Node j corresponds to the incoming passenger flow of cell jr of route r;
对于线路r，列车的最大载客量，可视为该运行方向上的客流输送能力。 For line r, the maximum passenger capacity of the train can be regarded as the passenger transport capacity in the running direction.
②当站点元胞jr处于预警状态A时(即x(jr,t)＝2)，地铁运营部门会提前采取相关措施，因此拥挤客流在t+1时刻的状态由站点元胞jr的所有上游邻居的传播作用和站点元胞jr采取相关措施后的恢复能力共同决定，其中恢复能力或恢复率可定义为：② When the station cell jr is in the warning state A (i.e. x(jr ,t) = 2), the subway operation department will take relevant measures in advance. Therefore, the state of the crowded passenger flow at time t+1 is determined by the propagation effect of all upstream neighbors of the station cell jr. and site cell jr recovery capability after taking relevant measures Jointly determine where the recovery capacity or recovery rate can be defined as:
其中，为一个时间步长内到达站点元胞jr的下车乘客人数；为一个时间步长内在站点元胞jr的等待乘车乘客人数。表示一个时间步长内的客流输送能力。上式可描述为当站台采取客流组织措施后使得站台乘车人数减少，提升预警或拥挤状态恢复至正常状态站点的恢复率；该值随时间变化，且一般情况下为非负值；恢复率的值越大，表示客流组织措施越有效。在该场景下对站点元胞jr的拥挤传播作用强度可定义为：in, is the number of passengers getting off at station cell jr within a time step; is the number of passengers waiting to board the bus at station cell jr within one time step. Represents the passenger flow transport capacity within a time step. The above formula can be described as the recovery rate of the station when the platform takes passenger flow organization measures to reduce the number of passengers on the platform, improve the warning or congestion state to restore to normal state; this value changes with time and is generally non-negative; the larger the value of the recovery rate, the more effective the passenger flow organization measures. In this scenario, the intensity of the congestion propagation effect on the station cell jr can be defined as:
因此，当站点元胞jr处于预警状态，其状态转移函数fβ(t)定义为：Therefore, when the site cell jr is in the warning state, its state transfer function fβ (t) is defined as:
③当站点元胞jr处于拥挤状态I时(x(jr,t)＝3)，其状态转移函数定义为：③ When the site cell jr is in the crowded state I (x(jr ,t)＝3), its state transition function is defined as:
上式描述了处于拥挤状态的站点元胞在经过一个时间步长后状态演化结果；令即在拥挤状态下，站点元胞以为概率恢复为正常状态、以为概率维持原有拥挤状态。The above formula describes the state evolution of the station cell in a crowded state after one time step; let That is, in a crowded state, the station cell is the probability of returning to normal state, To maintain the original crowded state with probability.
S205：拥挤传播控制策略仿真方法。S205: Congestion propagation control strategy simulation method.
进一步，基于微观尺度拥挤传播量化模型，从列车运营优化角度(如提升运力)，客流组织优化角度(如采取限流措施等以提升恢复率)，对不同客流拥挤控制措施下的拥挤传播演化过程进行仿真分析(设定不同参数组合进行仿真)，得到不同控制措施对于轨道交通网络拥挤传播的缓解程度，在实际运营中主要考虑两类指标：Furthermore, based on the micro-scale crowding propagation quantitative model, from the perspective of train operation optimization (such as improving transportation capacity) and passenger flow organization optimization (such as taking flow limiting measures to improve recovery rate), the crowding propagation evolution process under different passenger flow congestion control measures is simulated and analyzed (simulation is performed by setting different parameter combinations), and the degree to which different control measures alleviate the crowding propagation of the rail transit network is obtained. In actual operation, two types of indicators are mainly considered:
拥挤传播的影响范围Rp：该指标表示在仿真时间内，轨道交通网络中处于拥挤状态的最大节点数量。The influence range of congestion propagation Rp : This indicator represents the maximum number of nodes in the rail transit network that are in a congested state within the simulation time.
拥挤传播持续时间Tp：该指标表示在城市轨道交通网络受大客流影响后，发生客流拥挤状态到拥挤状态在网络中消散(恢复为正常状态)所持续的时间。Congestion propagation duration Tp : This indicator indicates the duration from the occurrence of passenger congestion to the dissipation of the congestion in the network (return to normal state) after the urban rail transit network is affected by large passenger flow.
具体的，基于微观尺度拥挤传播量化模型，对列车运营与客流组织优化过程的仿真分析步骤如下：Specifically, based on the micro-scale crowd propagation quantitative model, the simulation analysis steps of the train operation and passenger flow organization optimization process are as follows:
第一步，利用微观尺度拥挤传播量化模型，结合轨道交通客流刷卡数据，计算进出站客流量、断面客流量以及换乘客流量，量化微观尺度拥挤传播量化模型中的拥挤传播强度、恢复率等参数。其中，由于恢复率需要对一个时间步长内的上下车乘客数量进行标定，需要通过调研或视频数据获取，因此为计算方便，在实际操作时可由断面客流、换乘客流数据进行估算。The first step is to use the micro-scale crowding propagation quantitative model, combined with the rail transit passenger flow card swiping data, to calculate the passenger flow in and out of the station, the cross-section passenger flow, and the transfer passenger flow, and quantify the crowding propagation intensity, recovery rate and other parameters in the micro-scale crowding propagation quantitative model. Among them, since the recovery rate needs to be calibrated with the number of passengers getting on and off the train within a time step, it needs to be obtained through surveys or video data. Therefore, for the convenience of calculation, it can be estimated by the cross-section passenger flow and transfer passenger flow data in actual operation.
第二步，从列车运营、客流组织优化角度，仿真在不同行车密度、限流措施条件下拥挤传播的过程。针对列车运营优化，当网络中存在预警或拥挤状态站点时，在不改变列车编组、停站方案等调度策略的条件下，通过增加列车行车密度的方式增加运能，如加开备用车、调整发车间隔，重新计算客流输送能力等参数，更新模型中各相关参数。The second step is to simulate the process of congestion propagation under different traffic densities and flow control measures from the perspective of train operation and passenger flow organization optimization. For train operation optimization, when there are warning or congested stations in the network, without changing the dispatching strategies such as train formation and stop plan, the transportation capacity can be increased by increasing the train density, such as adding spare cars, adjusting the departure interval, recalculating parameters such as passenger flow transport capacity, and updating the relevant parameters in the model.
针对客流组织(如限流措施)，对于预警或拥挤状态站点，通过增加节点的恢复率模拟限流过程。根据恢复率公式，对于某站点元胞jr，当进站客流大于出站客流时，恢复率变为负值，因此为提升恢复率，需对一个步长时间内的进站客流量进行控制，避免短时客流的大规模聚集对网络的运行造成影响。在微观尺度模型中，根据进站客流量的变化以计算不同限流措施下的拥挤传播强度参数。For passenger flow organization (such as flow control measures), for warning or congested stations, the flow control process is simulated by increasing the recovery rate of the node. According to the recovery rate formula, for a station cell jr , when the incoming passenger flow is greater than the outgoing passenger flow, the recovery rate becomes negative. Therefore, in order to improve the recovery rate, the incoming passenger flow within a step time needs to be controlled to avoid the large-scale aggregation of short-term passenger flow affecting the operation of the network. In the micro-scale model, the crowding propagation intensity parameters under different flow control measures are calculated according to the changes in the incoming passenger flow.
第三步，根据微观尺度模型，计算不同拥挤控制策略下(即不同拥挤传播与恢复率参数组合)的轨道交通拥挤传播演化过程，量化拥挤传播影响范围与拥挤传播持续时间两类指标。通过多次仿真实验，对不同参数组合下的指标结果进行对比，量化评估列车运营及客流组织优化的实施效果。The third step is to calculate the evolution of rail transit congestion propagation under different congestion control strategies (i.e., different combinations of congestion propagation and recovery rate parameters) based on the micro-scale model, and quantify two types of indicators: congestion propagation impact range and congestion propagation duration. Through multiple simulation experiments, the indicator results under different parameter combinations are compared to quantitatively evaluate the implementation effect of train operation and passenger flow organization optimization.
为了验证本发明的效果，以A市轨道交通网络为案例进行仿真验证，根据2015年A市地铁网络拓扑结构，该网络中节点数量为267，平均度为2.283；最大的度值为5。假设网络中站点受到大客流影响后变为拥挤站点，令模型初始条件S(t)和I(t)分别为S(0)＝0.99；I(0)＝0.01，用于计算拥挤传播的规模。下面对比分析两种场景下控制参数的变化对于拥挤传播规模的影响，其中各参数由运营和疏散周期内的平均运能进行标定。In order to verify the effect of the present invention, the rail transit network of City A is used as an example for simulation verification. According to the topological structure of the subway network of City A in 2015, the number of nodes in the network is 267, the average degree is 2.283, and the maximum degree value is 5. Assuming that the station in the network becomes a crowded station after being affected by a large passenger flow, the initial conditions of the model S(t) and I(t) are respectively S(0) = 0.99; I(0) = 0.01, which are used to calculate the scale of crowded propagation. The following is a comparative analysis of the impact of changes in control parameters on the scale of crowded propagation under the two scenarios, where each parameter is calibrated by the average transport capacity during the operation and evacuation cycle.
1)该场景将本发明所提供方法与基于经典传播模型的方法进行比较。图4为两种方法中拥挤站点的平均密度随时间的演化图。该场景参数设定为：拥挤传播率β0＝0.5，恢复率δ＝0.3；模型中单位时间的定义为：t＝L/V；其中L是相邻站点的距离；V是列车的行驶速度。如图4所示，在相同初始条件下，考虑预警信息的拥挤传播量化模型中的拥挤站点平均密度小于经典传播模型，即本发明所提供方法能够有效控制拥挤传播规模。因此，当模型考虑拥挤预警信息并提前采取相应控制策略时，可有效降低拥挤在网络中的影响程度，更加符合实际运营情况。1) This scenario compares the method provided by the present invention with the method based on the classical propagation model. Figure 4 is a graph showing the evolution of the average density of congested sites over time in the two methods. The parameters of this scenario are set as follows: congestion propagation rate β0 = 0.5, recovery rate δ = 0.3; the unit time in the model is defined as: t = L/V; where L is the distance between adjacent sites; and V is the speed of the train. As shown in Figure 4, under the same initial conditions, the average density of congested sites in the congestion propagation quantification model considering warning information is smaller than that in the classical propagation model, that is, the method provided by the present invention can effectively control the scale of congestion propagation. Therefore, when the model considers congestion warning information and adopts corresponding control strategies in advance, it can effectively reduce the impact of congestion in the network, which is more in line with actual operating conditions.
2)该场景考虑在拥挤传播率(β0＝0.3)不变的情况下，当某站点拥挤发生以及相邻站点接收到拥挤预警信息时，站点采取控制策略，从列车运营优化角度，通过提高N4的值改变运输能力以控制拥挤传播，并提升疏散效率。该场景的参数取值为：ρ＝0.2，δ＝0.3；ρ＝0.3，δ＝0.35；ρ＝0.4，δ＝0.4。如图5所示，由于拥挤传播率为固定值，因此运能的提升没有推迟拥挤发生与高峰时段的时间。但随着站点及区间的客流运输能力提升，有效控制了拥挤传播规模；另一方面随着拥挤恢复率的提升，加快了恢复到正常站点的速率，有效降低了拥挤传播的影响。仿真结果也说明了站点在运营时考虑预警信息并采取有效控制措施的必要性。2) This scenario considersthat when congestion occurs at a station and the adjacent station receives congestion warning information, the station adopts a control strategy. From the perspective of train operation optimization, the transport capacity is changed by increasing the value of N4 to control congestion propagation and improve evacuation efficiency. The parameter values of this scenario are: ρ = 0.2, δ = 0.3; ρ = 0.3, δ = 0.35; ρ = 0.4, δ = 0.4. As shown in Figure 5, since the congestion propagation rate is a fixed value, the increase in transport capacity does not delay the time of congestion and peak hours. However, with the improvement of passenger flow transport capacity of stations and sections, the scale of congestion propagation is effectively controlled; on the other hand, with the improvement of congestion recovery rate, the rate of recovery to normal stations is accelerated, effectively reducing the impact of congestion propagation. The simulation results also illustrate the necessity of considering warning information and taking effective control measures when operating stations.
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的装置而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.
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| CN118095660A (en)* | 2024-04-24 | 2024-05-28 | 深圳市城市交通规划设计研究中心股份有限公司 | BIM-based subway station congestion analysis method, electronic equipment and storage medium |
| CN118195102A (en)* | 2024-05-15 | 2024-06-14 | 北京建筑大学 | A passenger flow congestion event propagation detection method and system based on traffic big data |
| CN119417678A (en)* | 2025-01-08 | 2025-02-11 | 北京国信城研科学技术研究院 | A rail transit passenger flow safety monitoring management method and system |
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| CN118095660B (en)* | 2024-04-24 | 2024-07-19 | 深圳市城市交通规划设计研究中心股份有限公司 | BIM-based subway station congestion analysis method, electronic equipment and storage medium |
| CN118195102A (en)* | 2024-05-15 | 2024-06-14 | 北京建筑大学 | A passenger flow congestion event propagation detection method and system based on traffic big data |
| CN119417678A (en)* | 2025-01-08 | 2025-02-11 | 北京国信城研科学技术研究院 | A rail transit passenger flow safety monitoring management method and system |
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