Train operation scheduling real-time adjustment method and system considering skip-stop strategy

Abstract

A train operation scheduling real-time adjustment method and system considering skip-stop strategy, the method comprising: obtaining train operation adjustment data and passenger flow dynamic fluctuation data based on original train operation plan, real-time station passenger flow data and predicted OD data; establishing a mathematical model with urban rail system capacity and service quality as targets, obtaining train scheduling and skip-stop adjustment strategy based on train operation adjustment data and passenger flow dynamic fluctuation data; determining the stop time of skip-stop train at each station and the interval running time based on train scheduling and skip-stop adjustment strategy and combining real-time train scheduling situation, and performing real-time adjustment on train operation. The application studies the comprehensive optimization problem based on skip-stop strategy, and proposes a novel idea for train operation control method in improving the performance of urban rail transit system and improving passenger service quality in real time.

Description

Technical Field

[0001] The present invention relates to the field of train operation optimization and adjustment in urban rail transit systems, and in particular to a method and system for real-time adjustment of train operation scheduling considering a skip stop strategy. Background Art

[0002] Passenger flow and train operation are the core of urban rail transit operations. Formulating operation plans that meet the characteristics of passenger flow is the key to urban rail transit providing high-quality transportation services to the outside world. At present, urban rail transit operations in most cities in my country usually refer to historical passenger flow data and passenger flow forecast results, pre-dividing the operating hours of the day into several time periods based on passenger flow demand, and adopting equal operating standards and departure intervals in different time periods. However, with the evolution of urban passenger flow conditions, this method of formulating transportation plans based on preset passenger flow time periods can no longer well meet the passenger flow demand that changes dynamically over time. The method of pre-dividing time periods and departing at equal intervals has poor flexibility and cannot quickly and effectively adjust to real-time passenger flow fluctuations caused by time disturbances. It is easy to cause large-scale train delays and passenger congestion at transfer stations, resulting in an increased risk of damage to the interests of enterprises and passengers.

[0003] With the continuous construction of urban rail transit networks, the number of super-long urban rail lines continues to increase. Taking Beijing as an example, there are already 12 lines with mainline operating lengths exceeding 35 kilometers, accounting for nearly half of the total road network mileage, and have become an indispensable part of the road network structure. Compared with other lines, super-long urban rail lines with large spatial spans and a large number of transfer stations often need to bear more transportation pressure. The increase in passenger volume and the degree of interference places higher demands on the convenience and scientificity of train organization methods. At the same time, the construction of super-long lines usually needs to be divided into multiple phases. Therefore, the lines completed in each phase have the ability to operate independently, which provides the infrastructure foundation for the development of more diverse operational organization methods.

[0004] The existing urban rail train operation adjustment technology has achieved certain practicality, but there are still some issues that deserve further study, including:

[0005] 1) Most train operation adjustment methods only consider the adjustment of train timetables. Few methods consider the adjustment of route plans or stop schedules based on passenger flow fluctuations.

[0006] 2) Relatively few studies have considered real-time adjustment optimization during the delay adjustment process. There is also little research on the impact of real-time passenger flow fluctuations on technical indicators such as train stop time at stations and running time in sections. This will lead to a decrease in the coupling between train operation and passenger flow after adjustment. Summary of the Invention

[0007] In view of the technical defects and drawbacks in the prior art, the embodiments of the present invention provide a method and system for real-time adjustment of train operation scheduling taking into account a skipping strategy to overcome the above problems or at least partially solve the above problems. The specific solution is as follows:

[0008] As a first aspect of the present invention, a method for real-time adjustment of train operation scheduling considering a skip stop strategy is provided, the method comprising:

[0009] Step 1: Based on the original train operation plan, real-time station passenger flow data, and predicted OD data, obtain train operation adjustment data and passenger flow dynamic fluctuation data;

[0010] Step 2: Build a mathematical model targeting the urban rail system capacity and service quality, and derive train scheduling and skipping adjustment strategies based on train operation adjustment data and passenger flow dynamic fluctuation data.

[0011] Step 3: Based on the train mediation and skip stop adjustment strategies and combined with the real-time train scheduling situation, determine the stop time and interval running time of the skip stop train at each station, and make real-time adjustments to the train operation.

[0012] Furthermore, step 1 includes:

[0013] During the train operation, due to various factors, there will be small delays, causing the train to deviate from the nominal schedule, which can be expressed by the formula:

[0014]

[0015]

[0016] Where: represents the actual departure time of train i at station k, is the actual departure time of train i at station k-1, and are the actual running time of the i-th train from station k-1 to station k and the stop time of the i-th train at station k, is the departure time deviation of the i-th train at station k, is the scheduled departure time of train i at station k;

[0017] The dynamic equation for the change in passengers on the bus from one stop to the next can be expressed as:

[0018]

[0019] In the formula: represents the number of passengers on train i when it leaves station k, represents the number of passengers on train i when it leaves station k-1, represents the number of passengers boarding train i at station k, represents the number of passengers who get off train i at station k;

[0020] Since the number of passengers boarding the train is determined by the number of people waiting and the train's passenger capacity, the following formula can be obtained:

[0021]

[0022] Where: is the maximum passenger capacity of the train, is the number of people waiting at platform k when train i−1 departs, represents the passenger arrival rate at platform k before train i departs and after train i−1 departs, which can be measured in real time using monitoring technology. is a 0-1 variable used to determine whether train i stops at station k;

[0023] When train i skips station k, no passengers are allowed to board the train, i.e. =0, which can be expressed as:

[0024]

[0025] Where, represents the number of passengers arriving at station k during the time interval between the departure of train i-1 and the arrival of train i.

[0026] Furthermore, step 2 includes:

[0027] From the perspective of the urban rail transit system, the model objectives are to minimize the deviation between the actual train departure time and the timetable and to minimize the adjustment cost. A mathematical model based on the urban rail transit system capacity and service quality is established, which can be expressed as follows:

[0028]

[0029] Where J represents the total service quality, which is the objective function of the mathematical model. Indicates the train punctuality service quality, represents the station service quality, α and β are the weight coefficients of train punctuality service quality and station service quality, respectively. By setting the weight coefficients α and β, a trade-off between passenger service quality and system performance can be achieved;

[0030] The station service quality is measured by the weighted number of skipped stations and the weighted number of total waiting passengers. Since the skipping station strategy reduces the station service quality for passengers who want to get off at the skipped station and changes the original transportation plan, it increases the difficulty of urban rail transit operation scheduling. In addition, the increase in the number of passengers waiting at the platform causes platform congestion, creates safety hazards, and reduces passenger satisfaction. Based on this, the mathematical model uses the number of waiting passengers at each platform to measure service quality, which is expressed as:

[0031]

[0032]

[0033] in is the weight coefficient, is the departure time deviation of the i-th train at station k, is the train running time adjustment of train i from station k-1 to station k, in seconds. is the adjustment of train i’s stop time at station k, is a 0-1 variable used to determine whether train i stops at station k. It refers to the number of passengers stranded on the platform after train i leaves station k.

[0034] Through the mathematical model, based on It can be determined whether train i stops at station k. The model can be used to calculate whether train i stops at each station in turn. Based on the adjustment amount of train i's stop time at station k and the stop time of train i at station k in the original train operation plan, the stop time of train i at station k can be calculated to realize the scheduling and skipping adjustment of the train.

[0035] Furthermore, step 3 includes:

[0036] The stop time of train i at station k calculated according to the skip stop adjustment strategy is defined as ,have:

[0037]

[0038] In the formula Indicates train At the station Fixed working hours, For trains At the station Passenger boarding and alighting times, For trains At the station Redundant boarding and alighting time.

[0039] The ATS system automatically adjusts the running trains. The value is consistent with the minimum residence time preset in the ATS system and maximum residence time Compare and finally determine the actual stay time of train i at station k according to the following formula , which is expressed as follows:

[0040]

[0041] Define the running time of train i from station k to station k+1 calculated by ATS as ,but:

[0042]

[0043] Where, is the scheduled running time of train i from station k to station k+1, The additional time that train i spends stopping at station k+1.

[0044] The ATS system will The calculated value and the pre-set minimum running time and maximum run time Compare and finally determine the actual running time according to the following formula , which is expressed as follows:

[0045] .

[0046] Furthermore, the method further includes: maintaining the safe running distance of the train, checking whether the current train and the adjacent trains meet the safety constraints, and if not, further adjusting or , until the safety constraints are met to ensure the normal operation of the train set.

[0047] Furthermore, the method also includes: using an MPC algorithm to perform comprehensive real-time optimization on train scheduling and skip-stop adjustment strategies.

[0048] Furthermore, the use of the MPC algorithm to perform comprehensive real-time optimization of train scheduling and skip adjustment strategies includes:

[0049] Step 101: At each sampling stage h, the prediction time step is , according to the current measured state information, the optimization problem is obtained in the optimization layer The desired forecast status in

[0050] Step 102: Based on the measured state data and the preset optimized time domain (including the number of stations within the optimization range), establish a mixed integer quadratic programming model, and solve the model to obtain a series of optimal control behaviors ;

[0051] In step 103, the first control action is applied, and steps 101 and 102 are repeated until the optimization range is exceeded.

[0052] As a second aspect of the present invention, a real-time adjustment system for train operation scheduling considering a skip stop strategy is provided, the system comprising:

[0053] The train operation dynamic adjustment module is used to obtain train operation adjustment data and passenger flow dynamic fluctuation data based on the original train operation plan, real-time station passenger flow data and predicted OD data;

[0054] The train skipping adjustment module establishes a mathematical model based on the urban rail system capacity and service quality, and obtains train scheduling and skipping adjustment strategies based on train operation adjustment data and passenger flow dynamic fluctuation data;

[0055] The real-time train adjustment module is used to determine the stop time and interval running time of skip-stop trains at each station based on the train mediation and skip-stop adjustment strategies and combined with real-time train scheduling, and make real-time adjustments to train operations.

[0056] Furthermore, the train operation dynamic adjustment module is specifically used to:

[0057] During the train operation, due to various factors, there will be small delays, causing the train to deviate from the nominal schedule, which can be expressed by the formula:

[0058]

[0059]

[0060] Where: represents the actual departure time of train i at station k, is the actual departure time of train i at station k-1, and are the actual running time of the i-th train from station k-1 to station k and the stop time of the i-th train at station k, is the departure time deviation of the i-th train at station k, is the scheduled departure time of train i at station k;

[0061] The dynamic equation for the change in passengers on the bus from one stop to the next can be expressed as:

[0062]

[0063] In the formula: represents the number of passengers on train i when it leaves station k, represents the number of passengers on train i when it leaves station k-1, represents the number of passengers boarding train i at station k, represents the number of passengers who get off train i at station k;

[0064] Since the number of passengers boarding the train is determined by the number of people waiting and the train's passenger capacity, the following formula can be obtained:

[0065]

[0066] Where: is the maximum passenger capacity of the train, is the number of people waiting at platform k when train i-1 departs, represents the passenger arrival rate at platform k before train i departs and after train i-1 departs, which can be measured in real time using monitoring technology. is a 0-1 variable used to determine whether train i stops at station k;

[0067] When train i skips station k, no passengers are allowed to board the train, i.e. =0, which can be expressed as:

[0068]

[0069] Where, represents the number of passengers arriving at station k during the time interval between the departure of train i-1 and the arrival of train i;

[0070] The train skip adjustment module is specifically used for:

[0071] From the perspective of the urban rail transit system, the model objectives are to minimize the deviation between the actual train departure time and the timetable and to minimize the adjustment cost. A mathematical model based on the urban rail transit system capacity and service quality is established, which can be expressed as follows:

[0072]

[0073] Where J represents the total service quality, which is the objective function of the mathematical model. Indicates the train punctuality service quality, represents the station service quality, α and β are the weight coefficients of train punctuality service quality and station service quality, respectively. By setting the weight coefficients α and β, a trade-off between passenger service quality and system performance can be achieved;

[0074] The service quality of the station is measured by the weighted number of skipped stations and the weighted number of waiting passengers. The skip-stop strategy reduces the service quality of the station where passengers want to get off, and changes the original transportation plan, thereby increasing the difficulty of urban rail transit operation scheduling. In addition, the increase in the number of passengers waiting at the platform increases the platform congestion, which poses a safety hazard and reduces passenger satisfaction. Therefore, the mathematical model uses the number of passengers waiting at each platform to measure the service quality, which is expressed as:

[0075]

[0076]

[0077] wherein is a weight coefficient, is an adjustment amount of train running time of train i in the interval from station k-1 to station k, and the unit is second, is an adjustment amount of stop time of train i at station k, is a 0-1 variable used to determine whether train i stops at station k, denotes the number of passengers remaining on the platform after train i leaves station k.

[0078] Further, the train real-time adjustment module is specifically used for:

[0079] The stop time of train i at station k calculated according to the skip-stop adjustment strategy is defined as , and has:

[0080]

[0081] In the formula, denotes the fixed operation time of train i at station k, denotes the passenger boarding and alighting time of train i at station k, denotes the redundant boarding and alighting time of train i at station k. The value of is compared with the minimum stay time and the maximum stay time previously set in the ATS system, and finally the actual stay time of train i at station k is determined according to the following formula , which is expressed as:

[0082]

[0083]

[0084] ​​​​​​​Define the running time of train i from station k to station k+1 calculated by ATS as ,but:

[0085]

[0086] Where, is the scheduled running time of train i from station k to station k+1, The additional time that train i spends stopping at station k+1.

[0087] The ATS system will The calculated value and the pre-set minimum running time and maximum run time Compare and finally determine the actual running time according to the following formula , which is expressed as follows:

[0088] .

[0089] The present invention has the following beneficial effects:

[0090] With the goal of reducing timetable errors in urban rail transit systems and improving passenger satisfaction, this paper studies the comprehensive optimization problem based on a skip-stop strategy while taking into account vehicle constraints and the dynamic evolution of passenger flow. It proposes a novel approach to train operation control methods in terms of improving the performance of urban rail transit systems and enhancing passenger service quality in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A flow chart of a method for real-time adjustment of train operation scheduling considering a skip stop strategy provided by an embodiment of the present invention;

[0092] Figure 2 A diagram of the bidirectional urban rail lines and stations provided in an embodiment of the present invention;

[0093] Figure 3 This is a diagram of the state transfer portion of the algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0094] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0095] See also Figure 1As shown, a method for real-time adjustment of train operation scheduling considering a skipping strategy is provided in an embodiment of the present invention, and the method includes:

[0096] Step 1: Based on the original train operation plan, real-time station passenger flow data, and predicted OD data, obtain train operation adjustment data and passenger flow dynamic fluctuation data;

[0097] Step 2: Build a mathematical model targeting the urban rail system capacity and service quality, and derive train scheduling and skipping adjustment strategies based on train operation adjustment data and passenger flow dynamic fluctuation data.

[0098] Step 3: Based on the train mediation and skip stop adjustment strategies and combined with the real-time train scheduling situation, determine the stop time and interval running time of the skip stop train at each station, and make real-time adjustments to the train operation.

[0099] With the goal of reducing timetable errors in urban rail transit systems and improving passenger satisfaction, this paper studies the comprehensive optimization problem based on a skip-stop strategy while taking into account vehicle constraints and the dynamic evolution of passenger flow. It proposes a novel approach to train operation control methods in terms of improving the performance of urban rail transit systems and enhancing passenger service quality in real time.

[0100] Preferably, step 1 comprises:

[0101] Assume that all trains in the original timetable generated before daily operation are station-stopping trains. Ideally, all trains will run strictly according to the predetermined schedule and perfectly complete their planned transportation tasks. However, in actual train operation, various interferences may prevent perfect execution. Therefore, it is necessary to dynamically adjust trains according to actual conditions. The dynamic nature of this aspect is specifically reflected in the following aspects:

[0102] Get train operation adjustment data, as follows:

[0103] During the operation of a train, due to various factors (such as equipment failure, weather, etc.), there will be small delays, causing the train to deviate from the nominal schedule, which can be expressed by the formula:

[0104]

[0105]

[0106] Where: represent The train is The actual departure time of the station, is the actual departure time of train i at station k-1, and Respectively The train from Arrival at Actual running time of the station and the Station time of the train at Station, Departure time deviation of the train at Station, Station; The planned departure time of the train at station k.

[0107] Obtain passenger flow dynamic fluctuation data, specifically as follows:

[0108] The model clearly describes the dynamic changes of passengers on board and waiting at the station as each train moves from one station to the next. The dynamic equation for the change in passengers on board from one station to the next can be expressed as:

[0109]

[0110] In the formula, represents the number of passengers on board train i when it leaves station k, represents the number of passengers on board train i when it leaves station k-1, represents the number of passengers boarding train i at station k, represents the number of passengers alighting from train i at station k.

[0111] Since boarding passengers are determined by both the number of waiting passengers and the train's passenger capacity, the following formula can be obtained:

[0112]

[0113] In the formula, is the maximum passenger capacity of the train, is the number of waiting passengers at platform k when train i-1 departs, represents the passenger arrival rate at platform k before train i departs and after train i-1 departs, which can be measured in real time through monitoring technology, is a 0-1 variable used to determine whether train i stops at station k.

[0114] When train i skips station k, no passengers are allowed to board the train, i.e. = 0, which can be expressed as:

[0115]

[0116] In the formula, represents the number of passengers arriving at station k during the time interval from when train i-1 departs to when train i arrives.

[0117] Preferably, step 2 comprises: ​

[0118] Urban rail transit operators often struggle to make real-time decisions that simultaneously satisfy all stakeholders. Considering this, this paper proposes a comprehensive optimization method that balances urban rail transit system capacity and passenger service quality. From the perspective of the urban rail transit system, the model objectives are to minimize the deviation between the actual train departure time and the schedule and to minimize the adjustment cost. The method can be expressed as:

[0119]

[0120] Where J represents the total service quality, which is the objective function of the mathematical model. Indicates the train punctuality service quality, represents the station service quality, α and β are the weight coefficients of train punctuality service quality and station service quality, respectively. By setting the weight coefficients α and β, a trade-off between passenger service quality and system performance can be achieved;

[0121] The service quality of a station is measured by the weighted number of skipped stations and the weighted number of the total number of waiting passengers. Since the skipping station strategy reduces the service quality of stations where passengers want to get off, and the skipping stop strategy changes the original transportation plan, thereby increasing the difficulty of urban rail transit operation scheduling, and the increase in the number of passengers waiting at the platform causes platform congestion, creates safety hazards, and thus reduces passenger satisfaction, based on this, the mathematical model uses the number of waiting passengers at each platform to measure service quality, which is expressed as:

[0122]

[0123]

[0124] in is the weight coefficient, is the departure time deviation of the i-th train at station k, is the train running time adjustment of train i from station k-1 to station k, in seconds. is the adjustment of train i’s stop time at station k, is a 0-1 variable used to determine whether train i stops at station k. It refers to the number of passengers stranded on the platform after train i leaves station k.

[0125] Through the mathematical model, based on It is possible to determine whether train i stops at station k. The model is used to sequentially calculate whether train i stops at each station. Based on the adjustment amount of train i's stop time at station k and the stop time of train i at station k in the original train operation plan, the stop time of train i at station k can be calculated. That is, the mathematical model can be used to determine whether the train stops at each station and the corresponding stop time, thereby realizing the scheduling and skipping adjustment of the train.

[0126] Preferably, step 3 includes:

[0127] Combined with real-time train scheduling, under the condition of ensuring no conflicts in train operation, the arrival and departure times of skip-stop trains at each station are determined. The real-time adjustment technology of train operation scheduling for super-long urban rail lines based on the skip-stop strategy is considered as follows:

[0128] The stop time of train i at station k calculated according to the skip stop adjustment strategy is defined as ,have:

[0129]

[0130] In the formula Indicates train At the station Fixed working hours, For trains At the station Passenger boarding and alighting times, For trains At the station Redundant boarding and alighting time.

[0131] The ATS system automatically adjusts the running trains. The value is consistent with the minimum residence time preset in the ATS system and maximum residence time Compare and finally determine the actual stay time of train i at station k according to the following formula , which is expressed as follows:

[0132]

[0133] Define the running time of train i from station k to station k+1 calculated by ATS as ,but:

[0134]

[0135] Where, is the scheduled running time of train i from station k to station k+1, The additional time that train i spends stopping at station k+1.

[0136] The ATS system will The calculated value and the pre-set minimum running time and maximum run time Compare and finally determine the actual running time according to the following formula , which is expressed as follows:

[0137] .

[0138] Maintain safe running distance of trains, check whether the current train and adjacent trains meet safety constraints, and if not, make further adjustments or , until the safety constraints are met to ensure the normal operation of the train set.

[0139] Preferably, the method for real-time adjustment of train operation scheduling considering skip stop strategy provided by the present invention further comprises: using MPC algorithm to comprehensively optimize train scheduling and skip stop adjustment strategy in real time;

[0140] The specific steps are as follows:

[0141] 1a) At each sampling stage, the predicted state required by the optimization problem in the optimization layer is obtained based on the current measured state information.

[0142] 1b) Based on the measured state data and the given optimization horizon (including the number of stations within the optimization range), a mixed integer quadratic programming model is established and solved to obtain a series of optimal control behaviors.

[0143] 1c) Apply the first control action and repeat the above steps until the optimization range is exceeded.

[0144] Reference Figure 2-3 As shown, the present invention:

[0145] At each sampling stage h, the prediction time step is , according to the current measured state information, the optimization problem is obtained in the optimization layer The desired forecast status in

[0146] According to the measured state data and the preset optimized time domain (including the number of stations within the optimization range), establish a mixed integer quadratic programming model, and solve the model to obtain a series of optimal control behaviors ;

[0147] In step 103, the first control action is applied, and steps 101 and 102 are repeated until the optimization range is exceeded.

[0148] In the above embodiment, a model predictive control (MPC) algorithm is specially designed to solve the optimization model using a heuristic method, while meeting the real-time requirements of train operation automatic control for train scheduling.

[0149] As a second aspect of the present invention, a real-time adjustment system for train operation scheduling considering a skip stop strategy is provided, the system comprising:

[0150] The train operation dynamic adjustment module is used to obtain train operation adjustment data and passenger flow dynamic fluctuation data based on the original train operation plan, real-time station passenger flow data and predicted OD data;

[0151] The train skipping adjustment module establishes a mathematical model based on the urban rail system capacity and service quality, and obtains train scheduling and skipping adjustment strategies based on train operation adjustment data and passenger flow dynamic fluctuation data;

[0152] The real-time train adjustment module is used to determine the stop time and interval running time of skip-stop trains at each station based on the train mediation and skip-stop adjustment strategies and combined with real-time train scheduling, and make real-time adjustments to train operations.

[0153] Preferably, the train operation dynamic adjustment module is specifically used to:

[0154] During the train operation, due to various factors, there will be small delays, causing the train to deviate from the nominal schedule, which can be expressed by the formula:

[0155]

[0156]

[0157] Where: represents the actual departure time of train i at station k, is the actual departure time of train i at station k-1, and are the actual running time of the i-th train from station k-1 to station k and the stop time of the i-th train at station k, is the departure time deviation of the i-th train at station k, is the scheduled departure time of train i at station k;

[0158] The dynamic equation for the change in passengers on the bus from one stop to the next can be expressed as:

[0159]

[0160] In the formula: represents the number of passengers on train i when it leaves station k, represents the number of passengers on train i when it leaves station k-1, represents the number of passengers boarding train i at station k, represents the number of passengers who get off train i at station k;

[0161] Since the number of passengers boarding the train is determined by the number of people waiting and the train's passenger capacity, the following formula can be obtained:

[0162]

[0163] Where: is the maximum passenger capacity of the train, is the number of people waiting at platform k when train i-1 departs, represents the passenger arrival rate at platform k before train i departs and after train i-1 departs, which can be measured in real time using monitoring technology. is a 0-1 variable used to determine whether train i stops at station k;

[0164] When train i skips station k, no passengers are allowed to board the train, i.e. =0, which can be expressed as:

[0165]

[0166] Where, represents the number of passengers arriving at station k during the time interval between the departure of train i-1 and the arrival of train i;

[0167] The train skip adjustment module is specifically used for:

[0168] From the perspective of the urban rail transit system, the model objectives are to minimize the deviation between the actual train departure time and the timetable and to minimize the adjustment cost. A mathematical model based on the urban rail transit system capacity and service quality is established, which can be expressed as follows:

[0169]

[0170] Where J represents the total service quality, which is the objective function of the mathematical model. Indicates the train punctuality service quality, represents the station service quality, α and β are the weight coefficients of train punctuality service quality and station service quality, respectively. By setting the weight coefficients α and β, a trade-off between passenger service quality and system performance can be achieved;

[0171] The service quality of a station is measured by the weighted number of skipped stations and the weighted number of the total number of waiting passengers. Since the skipping station strategy reduces the service quality of stations where passengers want to get off, and the skipping stop strategy changes the original transportation plan, thereby increasing the difficulty of urban rail transit operation scheduling, and the increase in the number of passengers waiting at the platform causes platform congestion, creates safety hazards, and thus reduces passenger satisfaction, based on this, the mathematical model uses the number of waiting passengers at each platform to measure service quality, which is expressed as:

[0172]

[0173]

[0174] in is the weight coefficient, is the train running time adjustment of train i from station k-1 to station k, is the adjustment of train i’s stop time at station k, is a 0-1 variable used to determine whether train i stops at station k. It refers to the number of passengers stranded on the platform after train i leaves station k.

[0175] Preferably, the train real-time adjustment module is specifically used to:

[0176] The stop time of train i at station k calculated according to the skip stop adjustment strategy is defined as ,have:

[0177]

[0178] In the formula Indicates train At the station Fixed working hours, For trains At the station Passenger boarding and alighting times, For trains At the station Redundant boarding and alighting time.

[0179] The ATS system automatically adjusts the running trains. The value is consistent with the minimum residence time preset in the ATS system and maximum residence time Compare and finally determine the actual stay time of train i at station k according to the following formula , which is expressed as follows:

[0180]

[0181] Define the running time of train i from station k to station k+1 calculated by ATS as ,but:

[0182]

[0183] Where, is the scheduled running time of train i from station k to station k+1, The additional time that train i spends stopping at station k+1.

[0184] The ATS system will The calculated value and the pre-set minimum running time and maximum run time Compare and finally determine the actual running time according to the following formula , which is expressed as follows:

[0185] .

[0186] The present invention discloses a real-time adjustment technology for train operation scheduling on super-long urban rail lines taking into account a skip-stop strategy. Aiming to reduce timetable errors in urban rail transit systems and improve passenger satisfaction, the comprehensive optimization problem based on the skip-stop strategy is studied while taking into account vehicle constraints and the dynamic evolution of passenger flow.

[0187] Firstly, a collaborative nonlinear programming model is established and then transformed into a mixed integer quadratic programming model which is easy to solve.

[0188] A model predictive control (MPC) algorithm is specially designed to solve the optimization model with a heuristic method, while meeting the real-time requirements of train operation automatic control for train scheduling.

[0189] The proposed model was tested under different adjustment strategies, passenger demands, and model parameters. Compared with the actual urban rail train operation adjustment strategy, the proposed method showed the following characteristics:

[0190] 1) The skip-stop adjustment strategy can effectively reduce train schedule deviations and reduce the number of stranded passengers at stations.

[0191] 2) The proposed skip-stop strategy is also robust under uncertain passenger demand.

[0192] 3) A sensitivity analysis of the weight coefficient of the objective function was performed to provide a reference for adjusting the weight coefficient according to actual needs.

[0193] This work will provide a novel approach to train operation control methods in improving the performance of urban rail transit systems and enhancing passenger service quality in real time.

[0194] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time adjustment method for train operation scheduling considering skip stop strategy, characterized in that: The method comprises: Step 1: Based on the original train operation plan, real-time station passenger flow data, and predicted OD data, obtain train operation adjustment data and passenger flow dynamic fluctuation data; Step 2: Build a mathematical model targeting the urban rail system capacity and service quality, and derive train scheduling and skipping adjustment strategies based on train operation adjustment data and passenger flow dynamic fluctuation data. Step 3: Based on the train mediation and skipping adjustment strategies and combined with the real-time train dispatch situation, the stopping time and interval running time of the skipping train at each station are determined, and the train operation is adjusted in real time; Wherein, step 2 includes: From the perspective of the urban rail transit system, the model objectives are to minimize the deviation between the actual train departure time and the timetable and to minimize the adjustment cost. A mathematical model based on the urban rail transit system capacity and service quality is established, which can be expressed as follows: Where J represents the total service quality, which is the objective function of the mathematical model. Indicates the train punctuality service quality, represents the station service quality, α and β are the weight coefficients of train punctuality service quality and station service quality, respectively. By setting the weight coefficients α and β, a trade-off between passenger service quality and system performance can be achieved; The station service quality is measured by the weighted number of skipped stations and the weighted number of total waiting passengers. Since the skipping station strategy reduces the station service quality for passengers who want to get off at the skipped station and changes the original transportation plan, it increases the difficulty of urban rail transit operation scheduling. In addition, the increase in the number of passengers waiting at the platform causes platform congestion, creates safety hazards, and reduces passenger satisfaction. Based on this, the mathematical model uses the number of waiting passengers at each platform to measure service quality, which is expressed as: in is the weight coefficient, is the departure time deviation of the i-th train at station k, is the train running time adjustment of train i from station k-1 to station k, is the adjustment of train i’s stop time at station k, is a 0-1 variable used to determine whether train i stops at station k. It refers to the number of passengers stranded on the platform after train i leaves station k.

2. The method for real-time adjustment of train operation scheduling considering skipping stop strategy according to claim 1 is characterized in that: Step 1 includes: During the train operation, due to various factors, there will be small delays, causing the train to deviate from the nominal schedule, which can be expressed by the formula: Where: represents the actual departure time of train i at station k, is the actual departure time of train i at station k-1, and are the actual running time of the i-th train from station k−1 to station k and the stop time of the i-th train at station k, is the departure time deviation of the i-th train at station k, is the scheduled departure time of train i at station k; The dynamic equation for the change in passengers on the bus from one stop to the next can be expressed as: In the formula: represents the number of passengers on train i when it leaves station k, represents the number of passengers on train i when it leaves station k-1, represents the number of passengers boarding train i at station k, represents the number of passengers who get off train i at station k; Since the number of passengers boarding the train is determined by the number of people waiting and the train's passenger capacity, the following formula can be obtained: Where: is the maximum passenger capacity of the train, is the number of people waiting at platform k when train i−1 departs, represents the passenger arrival rate at platform k before train i departs and after train i−1 departs, is a 0-1 variable used to determine whether train i stops at station k; When train i skips station k, no passengers are allowed to board the train, i.e. =0, which can be expressed as: Where, represents the number of passengers arriving at station k during the time interval between the departure of train i-1 and the arrival of train i.

3. The method for real-time adjustment of train operation scheduling considering skipping stop strategy according to claim 1 is characterized in that: Step 3 includes: The stop time of train i at station k calculated according to the skip stop adjustment strategy is defined as ,have: In the formula Indicates train At the station Fixed working hours, For trains At the station Passenger boarding and alighting times, For trains At the station Redundant boarding and alighting time; The ATS system automatically adjusts the running trains. The value is consistent with the minimum residence time preset in the ATS system and maximum residence time Compare and finally determine the actual stay time of train i at station k according to the following formula , which is expressed as follows: Define the running time of train i from station k to station k+1 calculated by ATS as ,but: Where, is the scheduled running time of train i from station k to station k+1, is the additional time that train i spends at station k+1, is a 0-1 variable used to determine whether train i stops at station k+1; The ATS system will The calculated value and the pre-set minimum running time and maximum run time Compare and finally determine the actual running time according to the following formula , which is expressed as follows: 。 4. The method for real-time adjustment of train operation scheduling considering skipping stop strategy according to claim 3 is characterized in that: The method further includes: maintaining the safe running distance of the train, checking whether the current train and the adjacent trains meet the safety constraints, and if not, further adjusting or , until the safety constraints are met to ensure the normal operation of the train set.

5. The method for real-time adjustment of train operation scheduling considering skipping stop strategy according to claim 1 is characterized in that: The method further includes: using an MPC algorithm to perform comprehensive real-time optimization on train scheduling and skip-stop adjustment strategies.

6. The method for real-time adjustment of train operation scheduling considering skipping strategy according to claim 5 is characterized in that: The comprehensive real-time optimization of train scheduling and skip adjustment strategies using the MPC algorithm includes: Step 101: In each sampling phase, the predicted state required by the optimization problem in the optimization layer is obtained based on the current measured state information; Step 102: Establish a mixed integer quadratic programming model based on the measured state data and the given optimization horizon, and solve the model to obtain a series of optimal control behaviors; Step 103 , starting from applying the first control action, steps 101 - 102 are repeated until the optimization range is exceeded.

7. A real-time adjustment system for train operation scheduling considering skip stop strategy, characterized in that: The system comprises: The train operation dynamic adjustment module is used to obtain train operation adjustment data and passenger flow dynamic fluctuation data based on the original train operation plan, real-time station passenger flow data and predicted OD data; The train skipping adjustment module establishes a mathematical model based on the urban rail system capacity and service quality, and obtains train scheduling and skipping adjustment strategies based on train operation adjustment data and passenger flow dynamic fluctuation data; The real-time train adjustment module is used to determine the stop time and interval running time of skip-stop trains at each station based on the train mediation and skip-stop adjustment strategies and combined with the real-time train scheduling situation, and make real-time adjustments to train operations; The train skip adjustment module is specifically used for: From the perspective of the urban rail transit system, the model objectives are to minimize the deviation between the actual train departure time and the timetable and to minimize the adjustment cost. A mathematical model based on the urban rail transit system capacity and service quality is established, which can be expressed as follows: Where J represents the total service quality, which is the objective function of the mathematical model. Indicates the train punctuality service quality, represents the station service quality, α and β are the weight coefficients of train punctuality service quality and station service quality respectively. By setting the weight coefficients α and β, a trade-off between passenger service quality and system performance can be achieved. The station service quality is measured by the weighted number of skipped stations and the weighted number of the total number of waiting passengers. Since the skipping station strategy will reduce the station service quality for passengers who want to get off at the skipped station, and the skipping station strategy will reduce the station service quality for passengers who want to get off at the skipped station. Strategy This will change the original transportation plan, thereby increasing the difficulty of urban rail transit operation scheduling. In addition, the increase in the number of passengers waiting on the platform will cause platform congestion, create safety hazards, and thus reduce passenger satisfaction. Based on this, the mathematical model uses the number of people waiting at each platform to measure service quality, which is expressed as: in is the weight coefficient, is the departure time deviation of the i-th train at station k, is the train running time adjustment of train i from station k-1 to station k, is the adjustment of train i’s stop time at station k, is a 0-1 variable used to determine whether train i stops at station k. It refers to the number of passengers stranded on the platform after train i leaves station k.

8. The real-time adjustment system for train operation scheduling considering skip stop strategy according to claim 7 is characterized in that: The train operation dynamic adjustment module is specifically used for: During the train operation, due to various factors, there will be small delays, causing the train to deviate from the nominal schedule, which can be expressed by the formula: Where: represents the actual departure time of train i at station k, is the actual departure time of train i at station k-1, and are the actual running time of the i-th train from station k−1 to station k and the stop time of the i-th train at station k, is the departure time deviation of the i-th train at station k, is the scheduled departure time of train i at station k; The dynamic equation for the change in passengers on the bus from one stop to the next can be expressed as: In the formula: represents the number of passengers on train i when it leaves station k, represents the number of passengers on train i when it leaves station k-1, represents the number of passengers boarding train i at station k, represents the number of passengers who get off train i at station k; Since the number of passengers boarding the train is determined by the number of people waiting and the train's passenger capacity, the following formula can be obtained: Where: is the maximum passenger capacity of the train, is the number of people waiting at platform k when train i−1 departs, represents the passenger arrival rate at platform k before train i departs and after train i−1 departs, is a 0-1 variable used to determine whether train i stops at station k; When train i skips station k, no passengers are allowed to board the train, i.e. =0, which can be expressed as: Where, represents the number of passengers arriving at station k during the time interval between the departure of train i-1 and the arrival of train i.

9. The real-time adjustment system for train operation scheduling considering skipping stop strategy according to claim 7 is characterized in that: The train real-time adjustment module is specifically used for: The stop time of train i at station k calculated according to the skip stop adjustment strategy is defined as ,have: In the formula Indicates train At the station Fixed working hours, For trains At the station Passenger boarding and alighting times, For trains At the station Redundant boarding and alighting time; The ATS system automatically adjusts the running trains. The value is consistent with the minimum residence time preset in the ATS system and maximum residence time Compare and finally determine the actual stay time of train i at station k according to the following formula , which is expressed as follows: Define the running time of train i from station k to station k+1 calculated by ATS as ,but: Where, is the scheduled running time of train i from station k to station k+1, is the additional time that train i spends at station k+1, is a 0-1 variable used to determine whether train i stops at station k+1; The ATS system will The calculated value and the pre-set minimum running time and maximum run time Compare and finally determine the actual running time according to the following formula , which is expressed as follows: 。

Images