Methods, systems, and equipment for adjusting train schedules during peak and off-peak transition periods in urban rail transit

Abstract

This invention discloses a method, system, and equipment for adjusting train schedules during peak and off-peak transition periods in urban rail transit, relating to the field of urban rail train operation control technology. The method includes: constructing a set of train operation constraints for the peak and off-peak transition period based on train operation data and passenger data for the target area. These constraints include train departure time constraints, train stop time constraints, train interval running time constraints, train departure interval constraints, train turnaround constraints, station service frequency constraints, rolling stock turnover constraints, and passenger boarding and alighting constraints. Then, with the objectives of minimizing the number of passengers stranded on platforms, the number of train cancellations, and the train schedule deviation, a train operation adjustment model for the peak and off-peak transition period is established. A two-stage algorithm is used to solve this model to obtain the dataset of trains to be adjusted and the dataset of additional trains, thereby constructing the adjusted train schedule. This invention reduces passenger dwell time on platforms.

Description

Technical Field

[0001] This invention relates to the field of urban rail train operation control technology, and in particular to a method, system and equipment for adjusting train timetables during the transition between peak and off-peak hours in urban rail transit. Background Technology

[0002] Currently, urban rail transit (hereinafter referred to as LT) is developing rapidly, and due to its safety, reliability, speed, and comfortable riding environment, it has become one of the main modes of transportation chosen by citizens. LT passenger flow distribution exhibits significant temporal unevenness. To save operating costs, LT operating companies typically divide the day's operating hours into peak and off-peak periods based on passenger volume. During peak hours, more trains operate on the line, with shorter intervals and sufficient capacity. Conversely, during off-peak hours, fewer trains operate, with longer intervals and lower capacity.

[0003] Urban rail transit is a complex mega-system, and during operation, it is inevitable that interference from complex factors such as door malfunctions, line congestion, and broadcast malfunctions will occur. Operational interference usually causes delays to one or more trains, rendering the original schedule unfeasible. To reduce the negative impact of delays on operations and passengers, train timetables need to be adjusted. Urban rail dispatchers are the core of urban rail transit operation control. In the event of malfunctions and emergencies, dispatchers need to react quickly based on the magnitude of the operational interference and the operating status of trains, making full use of urban rail resources such as spare trains to adjust the timetable and restore the line's operational order as quickly as possible. If trains are delayed during the transition from peak to off-peak hours, it will cause passengers to be stranded on platforms during peak hours. The current strategy of dispatchers to adjust train timetables mainly considers the perspective of the operating company, ensuring that trains on the line run according to the planned timetable as quickly as possible, while reducing train delays and improving the timetable fulfillment rate. However, after delays occur, as the departure intervals of peak-hour trains gradually increase, the previously stranded passengers cannot be better served, reducing the quality of passenger service. Therefore, the current methods for adjusting train schedules are often not applicable to scenarios where delays occur during the transition between peak and off-peak periods. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system and equipment for adjusting train schedules during the peak and off-peak transition phases of urban rail transit based on a two-stage method, thereby improving the train schedule fulfillment rate and reducing passenger dwell time on the platform.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] In a first aspect, the present invention provides a method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit, comprising:

[0007] Acquire train operation data and passenger data for the target area; the train operation data includes data on trains to be adjusted, data on additional trains, station data, and train data; the data on trains to be adjusted includes the departure and arrival times of the trains to be adjusted at different stations; the data on additional trains includes the departure and arrival times of the additional trains at different stations; the station data includes the train's dwell time at the station and the train's travel time between two adjacent stations or line junctions; the train data includes the departure interval between two consecutive trains, the tracking interval between two consecutive trains, the train turnaround time, and the number of rolling stock stored in the depot; the passenger data includes the number of passengers waiting to board the trains to be adjusted at different stations, the number of passengers actually boarding the trains to be adjusted at different stations, the passenger arrival rate between the departure times of adjacent trains to be adjusted, the number of passengers actually boarding the additional trains at different stations, and the maximum number of passengers boarding at each station.

[0008] Based on the train operation data and passenger data of the target area, a set of train operation constraints for the high-peak and off-peak transition phase is constructed. The set of train operation constraints for the high-peak and off-peak transition phase includes a train departure time constraint model, a train stop time constraint model, a train section running time constraint model, a train departure interval constraint model, a train tracking interval constraint model, a train turnaround constraint model, a station service frequency constraint model, a rolling stock turnover constraint model, and a passenger boarding and alighting constraint model.

[0009] Based on the set of train operation constraints during the peak-off-peak transition period, a train operation adjustment model for the peak-off-peak transition period is established with the objectives of minimizing the number of passengers stranded on the platform, the number of train cancellations, and the train timetable offset. The train timetable offset includes the offset between the actual arrival time and the planned arrival time of each train at the terminal station and the offset between the actual departure time and the planned departure time of each train at the originating station.

[0010] A two-stage algorithm is used to solve the train operation adjustment model during the high-off-peak transition phase to obtain the operation dataset of trains to be adjusted and the operation dataset of additional trains.

[0011] Based on the train operation dataset to be adjusted and the additional train operation dataset, an adjusted train operation schedule is constructed.

[0012] Optionally, the objective function in the train operation adjustment model for the peak-off-peak transition phase is:

[0013] minω1·Sum1+ω2·Sum2+ω3·Sum3;

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] Wherein, ω1 is the first weight parameter, ω2 is the second weight parameter, and ω3 is the third weight parameter; Sum1 is the sum of the number of passengers stranded at all stations, Sum2 is the sum of the deviations of the departure time and arrival time at the destination station of all trains from the planned timetable, and Sum3 is the sum of the number of canceled trains. This indicates the number of people stranded at station s due to capacity constraints and unable to board the train to be rescheduled, f. This indicates the number of passengers waiting at station s to board the adjusted train f; This indicates the actual number of passengers traveling on the train number to be adjusted (f). This represents the number of people stranded at station s due to capacity constraints and unable to board additional train q. This represents the number of passengers waiting at station s for the additional train q. U represents the actual number of passengers taking the additional train q; f This indicates the deviation of the departure time and arrival time at the destination station of train number f to be adjusted from the planned train schedule; This indicates the actual arrival time of train number f to be adjusted at the terminal station; This indicates the planned arrival time of train number f at the terminal station to be adjusted; d f,γ(f) This indicates the actual departure time of train number f to be adjusted at the originating station; This indicates the planned departure time of train number f to be adjusted at the originating station; e f Indicates whether train number f to be adjusted is cancelled, e f The variables are 0-1; F represents the set of trains to be adjusted, Q represents the set of trains to be added, and S represents the set of stations.

[0021] Secondly, the present invention provides a train schedule adjustment system for the transition phase between peak and off-peak hours in urban rail transit, comprising:

[0022] The urban rail information acquisition module is used to acquire train operation data and passenger data for the target area. The train operation data includes data on trains to be adjusted, data on additional trains, station data, and train data. The data on trains to be adjusted includes the departure and arrival times of the trains to be adjusted at different stations. The data on additional trains includes the departure and arrival times of the additional trains at different stations. The station data includes the train's dwell time at the station and the train's travel time between two adjacent stations or line junctions. The train data includes the departure interval between two consecutive trains, the tracking interval between two consecutive trains, the train turnaround time, and the number of rolling stock stored in the depot. The passenger data includes the number of passengers waiting to board the trains to be adjusted at different stations, the number of passengers actually boarding the trains to be adjusted at different stations, the passenger arrival rate between the departure times of adjacent trains to be adjusted, the number of passengers actually boarding the additional trains at different stations, and the maximum number of passengers boarding at each station.

[0023] The operation constraint module is used to construct a set of train operation constraints for the high-peak-off-peak transition phase based on the train operation data and passenger data of the target area. The set of train operation constraints for the high-peak-off-peak transition phase includes a train departure time constraint model, a train stop time constraint model, a train section running time constraint model, a train departure interval constraint model, a train tracking interval constraint model, a train turnaround constraint model, a station service frequency constraint model, a rolling stock turnover constraint model, and a passenger boarding and alighting constraint model.

[0024] The operation adjustment model construction module is used to establish a train operation adjustment model for the high-off-peak transition phase based on the set of train operation constraints during the high-off-peak transition phase, with the objectives of minimizing the number of passengers stranded on the platform, the number of train cancellations, and the train timetable offset. Among them, the train timetable offset includes the offset between the actual arrival time and the planned arrival time of each train at the terminal station and the offset between the actual departure time and the planned departure time of each train at the originating station.

[0025] The model solving module is used to solve the train operation adjustment model during the high-peak transition phase using a two-stage algorithm to obtain the operation dataset of the trains to be adjusted and the operation dataset of the additional trains.

[0026] The train timetable adjustment module is used to construct an adjusted train timetable based on the train operation dataset to be adjusted and the additional train operation dataset.

[0027] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute a method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit.

[0028] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] This invention discloses a method, system, and equipment for adjusting train schedules during the peak-off-peak transition phase of urban rail transit. Based on train operation data and passenger data of the target area, it establishes constraint models for train departure time, train stop time, train interval operation time, train departure interval, train turnaround, station service frequency, rolling stock turnover, and passenger boarding and alighting. Then, with the goal of minimizing the number of passengers stranded on the platform, the number of train cancellations, and the train schedule deviation, it establishes a train operation adjustment model for the peak-off-peak transition phase. After solving the model using a two-stage algorithm, it obtains the operation datasets of trains to be adjusted and the operation datasets of additional trains. This invention takes into account multiple factors affecting train delays, including train stop time, train running time between sections, train departure intervals, train following intervals, train turnarounds, and rolling stock turnover. It fully utilizes resources such as spare trains and combines this with strategies to increase train frequency, thereby reducing passenger dwell time on platforms and improving passenger service quality. Simultaneously, by constructing and solving a train operation adjustment model for the peak-off-peak transition phase, the invention improves the timetable fulfillment rate, enabling the train timetable to be restored to the planned schedule as quickly as possible. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the method for adjusting train schedules during the off-peak transition phase of urban rail transit according to the present invention.

[0032] Figure 2 This is a topology diagram of an urban rail line in an example of the present invention;

[0033] Figure 3 This is a flowchart of the method for adjusting the train timetable during the peak-hour transition phase of urban rail transit in an example of the present invention;

[0034] Figure 4 This is a schematic diagram of the urban rail transit train schedule adjustment system for peak and off-peak transition phases according to the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] During the transition from peak to off-peak hours, train delays can cause passengers to be stranded on platforms. Currently, dispatchers adjust train schedules primarily from the perspective of the operating company, aiming to ensure trains run according to the planned schedule as quickly as possible, minimizing delays and improving schedule fulfillment. However, after delays occur, the intervals between returning peak-hour trains and those returning to their depots gradually increase, leaving stranded passengers unserved and reducing passenger service quality. Therefore, this invention provides a method, system, and equipment for adjusting train schedules during the transition from peak to off-peak hours in urban rail transit. Considering the coupling of passenger and vehicle flow, it automatically adjusts urban rail train schedules in delay scenarios during the transition from peak to off-peak hours, improving the level of urban rail transit operation and management.

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] Example 1

[0039] like Figure 1 As shown, the present invention provides a method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit, comprising:

[0040] Step 100: Obtain train operation data and passenger data for the target area. The train operation data includes data on trains to be adjusted, data on additional trains, station data, and train data. The data on trains to be adjusted includes the departure and arrival times of the trains to be adjusted at different stations. The data on additional trains includes the departure and arrival times of the additional trains at different stations. The station data includes the train's dwell time at the station and the train's travel time between two adjacent stations or line junctions. The train data includes the departure interval between two consecutive trains, the tracking interval between two consecutive trains, the train turnaround time, and the number of rolling stock stored in the depot. The passenger data includes the number of passengers waiting to board the trains to be adjusted at different stations, the number of passengers actually boarding the trains to be adjusted at different stations, the passenger arrival rate between the departure times of adjacent trains to be adjusted, the number of passengers actually boarding the additional trains at different stations, and the maximum number of passengers boarding at each station.

[0041] Among them, the train's dwell time at the station includes the minimum dwell time; the train's running time between two adjacent stations or line junctions includes the minimum running time and the maximum running time; the following interval between two consecutive trains includes the minimum following interval; and the departure interval between two consecutive trains includes the minimum departure interval.

[0042] In a specific example, a route topology diagram of the urban rail train can be constructed first. This topology diagram includes, in addition to the train operation data and passenger data mentioned above, the locations of each station, the location of the depot, the direction of travel of each train, delayed trains, the location of the station where the delayed train is located, and the delay time of the delayed train. For example... Figure 2 As shown, there are 2s platforms on the line, denoted as S = {1, 2, 3...s, s+1, s+2... 2s}. Let the platforms in the up-direction be numbered from 1 to s, and the platforms in the down-direction be numbered from s+1 to 2s. For simplicity, the platforms are simplified to stations, and the station number is denoted by s. The planned train schedule has f trains ordered by departure time in ascending order, which can be represented as F = {1, 2, 3..., f-1, f}, with the train number denoted by f. In set F, the set of trains running in the opposite direction to train f is denoted by σ(f), where... The number of additional train services is q, which can be represented as Q = {1, 2, 3...q-1, q}, and the number of the additional train services is represented by q.

[0043] Step 200: Based on the train operation data and passenger data of the target area, construct a set of train operation constraints for the high-peak-off-peak transition phase; the set of train operation constraints for the high-peak-off-peak transition phase includes a train departure time constraint model, a train stop time constraint model, a train interval operation time constraint model, a train departure interval constraint model, a train turnaround constraint model, a station service frequency constraint model, a rolling stock turnover constraint model, and a passenger boarding and alighting constraint model.

[0044] The train departure time constraint model is used to ensure that if the service of the train to be adjusted, f, is canceled at station s, then the departure time of the train at station s is 0. The corresponding train departure time constraint model is:

[0045] d f,s ≤e f ·M,f∈F,s∈S;

[0046] d q,s ≤e q ·M,q∈Q,s∈S;

[0047] Where, d f,s This indicates the departure time of train number f to be adjusted at station s, d q,sThis indicates the departure time of the additional train q at station s; both of these are actual departure times. f Indicates whether train number f to be adjusted is cancelled, e f For 0-1 variables, when e f =1, indicating that the train number f to be adjusted has not been cancelled; e q Indicates whether to add extra trains (q) or (e). q For 0-1 variables, when e q =1, indicating that train number q was added; M represents a sufficiently large positive number; F represents the set of train numbers to be adjusted, Q represents the set of added train numbers, S is the set of stations, f is the train number to be adjusted, q is the added train number, and s is the station number. When e f =1, since M is sufficiently large, the train departure time constraint naturally holds. Conversely, when e = 1... f When d = 0, f,s =0; d q,s Similarly.

[0048] To ensure normal passenger boarding and alighting operations, the train's dwell time at the station must be greater than the minimum value Ts. min The train stopping time constraint model is then:

[0049] d f,s -a f,s ≥Ts min -M·(1-e f ), f∈F, s∈S;

[0050] d q,s -a q,s ≥Ts min -M·(1-e q ),q∈Q,s∈S;

[0051] Among them, a f,s For the arrival time of train f to be adjusted at station s, a q,s To add the arrival time of train q at station s, both of which are actual arrival times, Ts min This is the minimum stopping time for the train at the station. When e f When d = 0, f,s =0, a sufficiently large M can guarantee the feasibility of constraints when the train number to be adjusted f is cancelled.

[0052] The minimum travel time of a train within a section is determined by the train's traction characteristics, track conditions, and operational constraints. Furthermore, excessively long travel times within a section can negatively impact passengers. Therefore, a train section travel time constraint model is established to ensure that the train's travel time within a section remains within a certain range. This train section travel time constraint model is as follows:

[0053] Trs min -M(1-e f )≤a f,s+1 -d f,s ≤Tr s max +M(1-e f ), f∈F, s∈S;

[0054] Tr s min -M(1-e q )≤a q,s+1 -d q,s ≤Tr s max +M(1-e q ),q∈Q,s∈S;

[0055] Among them, Tr s min Tr represents the minimum running time of a train within a given section. s max a represents the maximum running time of the train within the section. f,s+1 This indicates the arrival time of train number f to be adjusted at station s+1, a q,s+1 This indicates the arrival time of the additional train q at station s+1.

[0056] The train departure interval constraint model is used to indicate that the departure interval between two consecutive trains in the same direction at a station must be greater than a minimum value, as follows:

[0057] d f,s -d f-1,s ≥Td min -M(1-e f ), f∈F, s∈S;

[0058] d q,s -d q-1,s ≥Td min -M(1-e q ),q∈Q,s∈S;

[0059] Among them, Td min d represents the minimum departure interval between two consecutive train services. f-1,s This indicates the arrival time of train f-1 to be adjusted at station s, d q-1,s This indicates the arrival time of the additional train q-1 at station s.

[0060] The train tracking interval constraint model is designed to ensure that the departure and arrival times of two consecutive trains traveling in the same direction at the same station must be greater than the minimum value Th. minThese two consecutive train services include two consecutive train services to be adjusted, or adjacent train services to be adjusted and additional train services. The train tracking interval constraint model is as follows:

[0061] a f,s -d f-1,s ≥Th min -M(1-e f ), f∈F, s∈S;

[0062] a q,s -d q-1,s ≥Th min -M(1-e q ),q∈Q,s∈S;

[0063] a q,p -d f,s ≥Th min -M(1-x q,f ), f∈F, q∈Q, s∈S;

[0064] a f+1,s -d q,s ≥Th min -M(1-x q,f ), f∈F, q∈Q, s∈S;

[0065] Among them, Th min x is the minimum tracking interval between two consecutive trains. q,f This indicates whether the additional train q departs after the train to be adjusted f, and is a 0-1 variable; if the additional train q departs after the train to be adjusted f, then x... q,f =1.

[0066] The train turnaround constraint model ensures that the train turnaround time is within a certain range, namely:

[0067]

[0068] Among them, Tz min Tz represents the minimum turnaround time for the train. max Indicates the maximum turnaround time of the train; γ(g) represents the originating station of the train number g to be adjusted. Indicates the terminal station of train number f to be adjusted; This indicates that the train number f to be adjusted is at the corresponding terminal station. departure time, a g,γ(g) This represents the arrival time of the train number g to be adjusted at the corresponding originating station γ(g); y f,g Indicates whether the train number f to be adjusted is at the corresponding terminal station. The train number g after the turnaround is a 0-1 variable; σ(f) represents the set of trains running in the opposite direction to the train number f to be adjusted.

[0069] To ensure passenger service quality, the interval between train arrivals at stations should not be too large. Therefore, a station service frequency constraint model is set to ensure that two consecutive train services cannot be cancelled, thus guaranteeing the service frequency of each station. The station service frequency constraint model is as follows:

[0070]

[0071] Among them, e f+1 This indicates whether train number f+1 to be adjusted should be cancelled.

[0072] The undercarriage turnover constraint model is as follows:

[0073]

[0074]

[0075] The two function formulas above indicate that a train can only connect to one train in the opposite direction after it turns around, and each train can only be connected to another train in the opposite direction.

[0076]

[0077] The above formula indicates that if a train service is not available, the service will be cancelled. Whether a train service is available at a station depends on two factors: either a train traveling in the opposite direction must turn back at the terminal station to run the train service, or the train service must be run directly from a train in the depot; otherwise, the service of the train service at that station will be cancelled.

[0078]

[0079] The above function formula also ensures that the number of cars dispatched from the depot should be less than or equal to the initial number of cars stored in the depot.

[0080] Among them, y g,f This indicates whether the train number to be adjusted, g, will turn back at the corresponding terminal station before departing the train number to be adjusted, f; it is a 0-1 variable; RN represents the number of rolling stock stored in the depot; x f This indicates whether the train number f to be adjusted departs from the depot; it is a 0-1 variable. If the train number f to be adjusted departs from the depot, then x... f =1.

[0081] The passenger boarding and alighting constraint model is as follows:

[0082]

[0083]

[0084]

[0085] in, This indicates the number of passengers waiting at station s for the rescheduled train f; e f,s M indicates that if the train number to be adjusted, f, is cancelled, the number of people waiting to board will be 0. This indicates the number of passengers waiting at station s for the rescheduled train f-1. This indicates the actual number of passengers traveling on the train to be adjusted, f-1. and As a supplementary variable for train departure time, This means that if the train to be adjusted, f, is cancelled at station s, then... The departure time is the first non-cancelled train before train number f to be adjusted; otherwise... The departure time of train number f to be adjusted at station s; This means that if the train to be adjusted, f-1, is cancelled at station s, then... The departure time is the first train that has not been cancelled before train number f-1 to be adjusted; otherwise... The departure time of train number f-1 to be adjusted at station s; AR f,s This represents the passenger arrival rate from the departure time of train number f-1 to the departure time of train number f; x q,f This indicates whether the additional train q is issued after the train to be adjusted f, and is a 0-1 variable; d f,s This indicates the departure time of train number f to be adjusted at station s, d q,s This indicates the departure time of the additional train q at station s; This represents the actual number of passengers who took the additional train q. Indicates the actual number of passengers traveling on the train to be adjusted (f); NA max This indicates the maximum number of passengers that can board at each station.

[0086] The above The function formula calculates the number of passengers waiting on the platform. It is an expression that takes the minimum value, with the left-hand side being e. f,s • M indicates that if train f is cancelled, the number of passengers waiting to board is 0; the right-hand side indicates that the number of passengers waiting for train f at station s is equal to the number of passengers waiting for train f-1 at station s minus the number of passengers who actually boarded train f-1. Add the number of passengers arriving between the departure time of the train before the adjusted train f and the departure time of the adjusted train f, and finally subtract the number of passengers taking the additional train q. The above. The formula for calculating the number of passengers waiting for train f at station s is NA, which represents the sum of the waiting number and the maximum number of passengers allowed to board. max The minimum value.

[0087] Step 300: Based on the set of train operation constraints for the high-peak transition phase, establish a train operation adjustment model for the high-peak transition phase with the goal of minimizing the number of passengers stranded on the platform, the number of train cancellations, and the train timetable offset; wherein, the train timetable offset includes the offset between the actual arrival time and the planned arrival time of each train at the terminal station and the offset between the actual departure time and the planned departure time of each train at the originating station.

[0088] The objective function in the train operation adjustment model for the peak-off-peak transition phase is:

[0089] minω1·Sum1+ω2·Sum2+ω3·Sum3. This formula calculates the combined value of platform congestion, number of canceled trains, and timetable offset.

[0090]

[0091]

[0092]

[0093] The above three formulas are the specific calculation process for the number of passengers stranded at the station, Sum1.

[0094]

[0095]

[0096] The above two formulas are the specific calculation process for the offset Sum2 between the departure time of all trains at the originating station and the arrival time at the destination station and the planned train schedule.

[0097]

[0098] The above formula is the specific calculation process for the sum of all canceled train trips, Sum3.

[0099] Wherein, ω1 is the first weight parameter, ω2 is the second weight parameter, and ω3 is the third weight parameter; Sum1 is the sum of the number of passengers stranded at all stations, Sum2 is the sum of the deviations of the departure time and arrival time at the destination station of all trains from the planned timetable, and Sum3 is the sum of the number of canceled trains. This indicates the number of people stranded at station s due to capacity constraints and unable to board the train to be rescheduled, f. This indicates the number of passengers waiting at station s to board the adjusted train f; This indicates the actual number of passengers traveling on the train number to be adjusted (f). This represents the number of people stranded at station s due to capacity constraints and unable to board additional train q. This represents the number of passengers waiting at station s for the additional train q. U represents the actual number of passengers taking the additional train q; f This indicates the deviation of the departure time and arrival time at the destination station of train number f to be adjusted from the planned train schedule; This indicates the actual arrival time of train number f to be adjusted at the terminal station; This indicates the planned arrival time of train number f at the terminal station to be adjusted; d f,γ(f) This indicates the actual departure time of train number f to be adjusted at the originating station; This indicates the planned departure time of train number f at the originating station.

[0100] Step 400: Solve the high-peak-off-peak transition phase train operation adjustment model using a two-stage algorithm to obtain the train operation dataset to be adjusted and the additional train operation dataset. The train operation dataset to be adjusted includes the actual departure time of each train at each station, the actual arrival time of each train at each station, the rolling stock turnover relationship between each train, and whether each train departs from the depot. The additional train operation dataset includes the order relationship between each additional train and the trains to be adjusted, the actual departure time of each additional train at each station, and the actual arrival time of each additional train at each station.

[0101] Step 400 specifically includes:

[0102] (1) Linearize the train operation adjustment model during the high-peak transition phase; specifically, linearize the multiple constraint models and objective functions in the train operation adjustment model during the high-peak transition phase.

[0103] Among them, 11) the passenger boarding and alighting constraint model in the train operation adjustment model during the peak-off-peak transition phase. The function formula is linearized by performing a min function transformation; specifically, it is rewritten as:

[0104]

[0105] 12) Passenger boarding and alighting constraint model in the train operation adjustment model during the peak-off-peak transition period. The function formula is linearized using the max function.

[0106] Specifically, f∈F and f>1, s∈S can be rewritten as:

[0107]

[0108] 13) Passenger boarding and alighting constraint model in the train operation adjustment model during the peak-off-peak transition period. The function formula is linearized by performing a min function.

[0109] Specifically, f∈F, s∈S can be rewritten as:

[0110]

[0111] 14) Linearize the objective function in the train operation adjustment model for the peak-off-peak transition phase; linearize the U-axis of the objective function in the train operation adjustment model for the peak-off-peak transition phase. f The function formula is linearized, that is, firstly...

[0112] f∈F can be rewritten as the following function:

[0113] U f =(u f +v f )·e f ,f∈F; then, the above formula is rewritten with the following constraints:

[0114] U f ≤M·e f ,f∈F;

[0115] U f ≤u f +v f ,f∈F;

[0116] U f ≥u f +v f -M(1-e f ),f∈F;

[0117] Among them, u f ,v f For custom auxiliary variables, and U f The function formula uses u f ,v f Represented as:

[0118] U f =(u f +v f )·e f ,f∈F;

[0119] Among them, u f ,v f The constraints are:

[0120]

[0121] (2) Construct a set of faulty stations; the set of faulty stations includes the originating station of the faulty train in both directions of operation, the destination station of the faulty train, the station where the train is faulty, the station before the station where the train is faulty, and the station after the station where the train is faulty.

[0122] Specifically, the stations in the high-peak-off-peak transition phase train operation adjustment model are simplified, retaining only the originating station of the faulty train, the destination station of the faulty train, the station where the train failed, the station preceding the station where the train failed, and the station following the station where the train failed, as well as the five stations in the opposite direction, to construct a new station set S. p ={1,s r-1 ,s r ,s r+1 ,s,s+1,2s-s r ,2s+1-s r ,2s+2-s r ,2s};where, s r The faulty station number is assigned.

[0123] (3) Based on the set of faulty stations and the train interval running time constraint model in the high-off-peak transition phase train operation adjustment model after linearization, construct the interval running time constraint corresponding to the faulty train; that is, transform the train interval running time constraint model in the high-off-peak transition phase train operation adjustment model to obtain the following function formula:

[0124]

[0125]

[0126]

[0127] in, The new maximum travel time between sections for trains, The new minimum travel time between sections for trains, s p For the simplified station numbering, s l For station s p The previous station number is used as a constraint, which indicates that the minimum interval travel time of a train under the two-stage algorithm should be the sum of all ignored minimum interval travel times in that section plus the sum of all minimum stop times, and the maximum interval travel time of a train should be the sum of all ignored maximum interval travel times in that section plus the sum of all maximum stop times.

[0128] (4) Based on the set of faulty stations and the passenger boarding and alighting constraint model in the high-peak transition phase train operation adjustment model after linearization, construct the passenger boarding and alighting constraints corresponding to the faulty trains; since passengers at intermediate stations can also be served when the train arrives at the next station, the passenger arrival rate of intermediate stations can be accumulated to the next reserved station, that is, the passenger boarding and alighting constraints in the high-peak transition phase train operation adjustment model are transformed to obtain the following function formula:

[0129]

[0130]

[0131] f∈F, q∈Q, s p ∈S p

[0132]

[0133]

[0134] in, To simplify passenger arrival rates at subsequent stations, To simplify the process for waiting passengers at the station and passengers actually boarding the train.

[0135] (5) Based on the passenger boarding and alighting constraints and section running time constraints corresponding to the faulty train, the train operation adjustment model for the high-peak transition phase, which has been linearized, is updated.

[0136] (6) Solve the updated train operation adjustment model for the peak-off-peak transition phase to obtain the rolling stock turnover relationship for each train. Specifically, solve the model using solvers such as cplex and gurobi. Thus, based on the simplified station, the solution to the updated train operation adjustment model for the peak-off-peak transition phase is completed.

[0137] (7) Input the rolling stock turnover relationship of each train into the linearized high-peak transition phase train operation adjustment model and solve the model to obtain the arrival and departure times of each train at the station; wherein, the arrival and departure times of the train to be adjusted at the station constitute the train operation dataset of the train to be adjusted; the arrival and departure times of the additional trains at the station constitute the additional train operation dataset. At this time, the high-peak transition phase train operation adjustment model after linearization is the unupdated model in step (5). Input the configuration parameters and rolling stock turnover relationship, use solvers such as cplex and gurobi to solve the model, and output the arrival and departure times of each train at the station and the rolling stock turnover relationship. Thus, based on the rolling stock turnover relationship and the linearized high-peak transition phase train operation adjustment model, the second stage of solving the model is completed. The present invention obtains the turnover relationship of the train undercarriage through the first update solution in steps (5)-(6) above, and then inputs the turnover relationship of the train undercarriage as a known quantity into the high-peak transition stage train operation adjustment model before the update and performs model solution, which can more quickly obtain the required arrival and departure times of each train at the station.

[0138] Step 500: Based on the train operation dataset to be adjusted and the additional train operation dataset, construct the adjusted train operation schedule.

[0139] like Figure 3 The diagram shows a flowchart of the train timetable adjustment method for the high-speed and off-peak transition phase of urban rail transit in an example of this invention. The steps are as follows: First, input train operation information and passenger information; second, define decision variables such as train arrival and departure times and rolling stock turnover; third, establish a mixed-integer linear programming model, i.e., an automatic train timetable adjustment model based on mixed-integer programming, including establishing constraints on train departure time, train stop time, train interval running time, train departure interval, train tracking interval, train turnaround, station service frequency, rolling stock turnover, passenger boarding and alighting, and an objective function; fourth, simplify the stations; fifth, linearize the nonlinear constraints; sixth, solve the model in the first stage to obtain the rolling stock turnover relationship; seventh, solve the model in the second stage to obtain the arrival and departure times of each train at the station; eighth, output the adjusted train timetable, and finally, the process ends.

[0140] Based on the flowchart of the above adjustment method, this invention also provides an application example, selecting the Yizhuang Line of the Beijing Subway in China. This line has a total of 28 stations in both directions, with 25 planned train services in each direction. The train indices for the upward direction are [1,25], and the train indices for the downward direction are [26,50]. The total number of train sets is 18. The depot is connected to the station with index 0. During peak hours, the train interval for both directions is 3 minutes, and during off-peak hours, the train interval for both directions is 6 minutes.

[0141] The train timetable adjustment for the peak-off-peak transition phase of urban rail transit, implemented according to the method of this invention and oriented towards passenger flow and vehicle flow coupling, has the basic parameter configuration shown in Table 1. In this case, a delay occurs during the peak-off-peak period, specifically, train number 10 is set to experience a 300-second delay at station 9. Both the train and station indices are calculated starting from 1.

[0142] Table 1

[0143]

[0144] If the train timetable is adjusted to minimize timetable deviation and the number of canceled trains, a two-stage algorithm calculates that there are 14,394 passengers stranded on platforms, a deviation of 1,040 from the planned timetable, and 0 canceled trains, with a calculation time of 26.32 seconds. If the train timetable is adjusted to minimize the number of passengers stranded on platforms, timetable deviation, and the number of canceled trains, a two-stage algorithm calculates that there are 8,511 passengers stranded on platforms, a deviation of 2,285 from the planned timetable, and 0 canceled trains, with a calculation time of 23.56 seconds. Therefore, considering both passenger service quality and the urban rail transit company's operational indicators, the train timetable can be restored to the planned operation, effectively reducing the number of passengers stranded on platforms.

[0145] In summary, the present invention provides a method for adjusting train schedules during peak-off-peak transition periods in urban rail transit. Applied to delay scenarios during these transition periods, the method establishes a mixed-integer linear programming model for automatic train schedule adjustment based on pre-defined basic parameters and solves it using a two-stage algorithm. By comprehensively considering passenger service quality and the operational indicators of the urban rail transit company, the method automatically adjusts the train schedule to meet operational needs during peak-off-peak delays. In particular, it incorporates scheduling strategies such as adding extra trains, fully utilizing resources like spare trains to match capacity with passenger volume, improving passenger service quality, effectively reducing platform congestion, and increasing schedule fulfillment rate, enabling the train schedule to quickly return to the planned schedule.

[0146] Example 2

[0147] like Figure 4 As shown, in order to implement the technical solution in Embodiment 1 and achieve the corresponding functions and technical effects, this embodiment provides a train timetable adjustment system for the transition phase between peak and off-peak hours in urban rail transit, including:

[0148] The urban rail information acquisition module 101 is used to acquire train operation data and passenger data for the target area. The train operation data includes data on trains to be adjusted, data on additional trains, station data, and train data. The data on trains to be adjusted includes the departure and arrival times of the trains to be adjusted at different stations. The data on additional trains includes the departure and arrival times of the additional trains at different stations. The station data includes the train's dwell time at the station and the train's travel time between two adjacent stations or line junctions. The train data includes the departure interval between two consecutive trains, the tracking interval between two consecutive trains, the train turnaround time, and the number of rolling stock stored in the depot. The passenger data includes the number of passengers waiting to board the trains to be adjusted at different stations, the number of passengers actually boarding the trains to be adjusted at different stations, the passenger arrival rate between the departure times of adjacent trains to be adjusted, the number of passengers actually boarding the additional trains at different stations, and the maximum number of passengers boarding at each station.

[0149] The operation constraint module 201 is used to construct a set of train operation constraints for the high-peak transition phase based on the train operation data and passenger data of the target area; the set of train operation constraints for the high-peak transition phase includes a train departure time constraint model, a train stop time constraint model, a train section running time constraint model, a train departure interval constraint model, a train tracking interval constraint model, a train turnaround constraint model, a station service frequency constraint model, a rolling stock turnover constraint model, and a passenger boarding and alighting constraint model;

[0150] The operation adjustment model construction module 301 is used to establish a train operation adjustment model for the high-peak transition phase based on the set of train operation constraints for the high-peak transition phase, with the goal of minimizing the number of passengers stranded on the platform, the number of train cancellations, and the train timetable offset; wherein, the train timetable offset includes the offset between the actual arrival time and the planned arrival time of each train at the terminal station and the offset between the actual departure time and the planned departure time of each train at the originating station.

[0151] The model solving module 401 is used to solve the train operation adjustment model during the high-peak transition phase using a two-stage algorithm to obtain the operation dataset of the trains to be adjusted and the operation dataset of the additional trains.

[0152] The train timetable adjustment module 501 is used to construct an adjusted train timetable based on the train operation dataset to be adjusted and the additional train operation dataset.

[0153] Example 3

[0154] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to execute the urban rail transit peak-hour transition method of Embodiment 1.

[0155] Alternatively, the aforementioned electronic device may be a server.

[0156] In addition, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for adjusting train schedules during the transition phase between peak and off-peak hours in urban rail transit as described in Embodiment 1.

[0157] Compared with the prior art, the present invention also has the following advantages:

[0158] (1) The train timetable automatically adjusted by the present invention can meet the constraints of tracking interval, turnaround time, and rolling stock turnover in the train delay scenario during the transition period between high and off-peak hours, ensuring the feasibility of the adjusted timetable and improving the fulfillment rate of the urban rail train timetable.

[0159] (2) The present invention constructs a two-stage algorithm to solve the train operation adjustment model during the high-peak transition period, which can obtain a high-quality feasible solution in a short time and meet the real-time requirements of train timetable adjustment.

[0160] (3) The present invention comprehensively considers passenger service quality and operational indicators to adjust the train timetable, makes full use of resources such as spare trains, and adopts a scheduling strategy of adding trains, which can improve passenger service quality and effectively reduce passengers stranded on the platform.

[0161] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0162] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit, characterized in that, The methods include: Acquire train operation data and passenger data for the target area; the train operation data includes data on train numbers to be adjusted, data on additional train numbers, station data, and train data. The data on train services to be adjusted includes the departure and arrival times of the train services to be adjusted at different stations; the data on additional train services includes the departure and arrival times of the additional train services at different stations; the station data includes the train's dwell time at the station and the train's travel time between two adjacent stations or line junctions; the train data includes the departure interval between two consecutive train services, the tracking interval between two consecutive trains, the train turnaround time, and the number of rolling stock stored in the depot; the passenger data includes the number of passengers waiting to board the train services to be adjusted at different stations, the number of passengers actually boarding the train services to be adjusted at different stations, the passenger arrival rate between the departure times of adjacent train services to be adjusted, the number of passengers actually boarding the additional train services at different stations, and the maximum number of passengers boarding at each station. Based on the train operation data and passenger data of the target area, a set of train operation constraints for the high-peak and off-peak transition phase is constructed. The set of train operation constraints for the high-peak and off-peak transition phase includes a train departure time constraint model, a train stop time constraint model, a train section running time constraint model, a train departure interval constraint model, a train tracking interval constraint model, a train turnaround constraint model, a station service frequency constraint model, a rolling stock turnover constraint model, and a passenger boarding and alighting constraint model. Based on the set of train operation constraints during the peak-off-peak transition period, a train operation adjustment model for the peak-off-peak transition period is established with the objectives of minimizing the number of passengers stranded on the platform, the number of train cancellations, and the train timetable offset. The train timetable offset includes the offset between the actual arrival time and the planned arrival time of each train at the terminal station and the offset between the actual departure time and the planned departure time of each train at the originating station. A two-stage algorithm is used to solve the train operation adjustment model for the peak-off-peak transition phase to obtain the dataset of train services to be adjusted and the dataset of additional train services; specifically including: The high-peak transition phase train operation adjustment model is linearized; a set of faulty stations is constructed, including the originating station, the terminating station, the station where the train failed, the station preceding the station where the train failed, and the station following the station where the train failed, in both directions of travel; based on the set of faulty stations and the train interval running time constraint model in the linearized high-peak transition phase train operation adjustment model, the interval running time constraint corresponding to the faulty train is constructed; based on the set of faulty stations and the passenger boarding and alighting constraint model in the linearized high-peak transition phase train operation adjustment model, the passenger boarding and alighting constraint corresponding to the faulty train is constructed. Passenger boarding and alighting constraints; based on the passenger boarding and alighting constraints and section running time constraints corresponding to the faulty train, the linearized high-peak and off-peak transition phase train operation adjustment model is updated; the updated high-peak and off-peak transition phase train operation adjustment model is solved to obtain the rolling stock turnover relationship of each train; the rolling stock turnover relationship of each train is input into the linearized high-peak and off-peak transition phase train operation adjustment model and the model is solved to obtain the arrival time and departure time of each train at the station; wherein, the arrival time and departure time of the train to be adjusted at the station constitute the train operation dataset of the train to be adjusted; the arrival time and departure time of the additional train at the station constitute the additional train operation dataset; Based on the train operation dataset to be adjusted and the additional train operation dataset, an adjusted train operation schedule is constructed.

2. The method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit according to claim 1, characterized in that, The train departure time constraint model is as follows: d f,s ≤e f ·M,f∈F,s∈S; d q,s ≤e q ·M,q∈Q,s∈S; Where, d f,s This indicates the departure time of train number f to be adjusted at station s, d q,s This indicates the departure time of train number q at station s; e f Indicates whether train number f to be adjusted is cancelled, e f e is a 0-1 variable; q Indicates whether to add extra trains (q) or (e). q Let M be a 0-1 variable; F represents a sufficiently large positive number; F represents the set of trains to be adjusted; Q represents the set of trains to be added; and S is the set of stations. The train stopping time constraint model is as follows: d f,s -a f,s ≥Ts min -M·(1-e f ),f∈F,s∈S; d q,s -a q,s ≥Ts min -M·(1-e q ),q∈Q,s∈S; Among them, a f,s For the arrival time of train f to be adjusted at station s, a q,s To add the arrival time of train q at station s, Ts min This is the minimum stopping time for a train at a station; The train interval running time constraint model is as follows: in, This indicates the minimum running time of the train within the section. a represents the maximum running time of the train within the section. f,s+1 This indicates the arrival time of train number f to be adjusted at station s+1, a q,s+1 This indicates the arrival time of the additional train q at station s+1; The train departure interval constraint model is as follows: d f,s -d f-1,s ≥Td min -M(1-e f ),f∈F,s∈S; d q,s -d q-1,s ≥Td min -M(1-e q ),q∈Q,s∈S; Among them, Td min d represents the minimum departure interval between two consecutive train services. f-1,s This indicates the arrival time of train f-1 to be adjusted at station s, d q-1,s This indicates the arrival time of the additional train q-1 at station s; The train tracking interval constraint model is as follows: a f,s -d f-1,s ≥Th min -M(1-e f ),f∈F,s∈S; a q,s -d q-1,s ≥Th min -M(1-e q ),q∈Q,s∈S; a q,p -d f,s ≥Th min -M(1-x q,f ),f∈F,q∈Q,s∈S; a f+1,s -d q,s ≥Th min -M(1-x q,f ),f∈F,q∈Q,s∈S; Among them, Th min x is the minimum tracking interval between two consecutive trains. q,f This indicates whether the additional train q is issued after the train f to be adjusted, and is a 0-1 variable; The train turnaround constraint model is as follows: Among them, Tz min Tz represents the minimum turnaround time for the train. max Indicates the maximum turnaround time of the train; γ(g) represents the originating station of the train number g to be adjusted. Indicates the terminal station of train number f to be adjusted; This indicates that the train number f to be adjusted is at the corresponding terminal station. departure time, a g,γ(g) This represents the arrival time of the train number g to be adjusted at the corresponding originating station γ(g); y f,g Indicates whether the train number f to be adjusted is at the corresponding terminal station. The train number g after the turnaround is a 0-1 variable; σ(f) represents the set of trains running in the opposite direction to the train number f to be adjusted; The station service frequency constraint model is as follows: Among them, e f+1 Indicate whether the train number f+1 to be adjusted should be cancelled; The undercarriage turnover constraint model is as follows: Among them, y g,f This indicates whether the train number to be adjusted, g, will turn back at the corresponding terminal station before departing the train number to be adjusted, f; it is a 0-1 variable; RN represents the number of rolling stock stored in the depot; x f This indicates whether the train number f to be adjusted originates from the depot; it is a 0-1 variable.

3. The method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit according to claim 1, characterized in that, The passenger boarding and alighting constraint model is as follows: in, This indicates the number of passengers waiting at station s for the rescheduled train f; e f,s M indicates that if the train number to be adjusted, f, is cancelled, the number of people waiting to board will be 0. This indicates the number of passengers waiting at station s for the rescheduled train f-1. This indicates the actual number of passengers traveling on the adjusted train f-1; and As a supplementary variable for train departure time, This means that if the train to be adjusted, f, is cancelled at station s, then... The departure time is the first non-cancelled train before train number f to be adjusted; otherwise... The departure time of train number f to be adjusted at station s; This means that if the train to be adjusted, f-1, is cancelled at station s, then... The departure time is the first train that has not been cancelled before train number f-1 to be adjusted; otherwise... The departure time of train number f-1 to be adjusted at station s; AR f,s This represents the passenger arrival rate from the departure time of train number f-1 to the departure time of train number f; x q,f This indicates whether the additional train q is issued after the train to be adjusted f, and is a 0-1 variable; d f,s This indicates the departure time of train number f to be adjusted at station s, d q,s This indicates the departure time of the additional train q at station s; This represents the actual number of passengers who took the additional train q. Indicates the actual number of passengers traveling on the train to be adjusted (f); NA max This indicates the maximum number of passengers that can board at each station.

4. The method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit according to claim 3, characterized in that, The objective function in the train operation adjustment model for the peak-off-peak transition phase is: minω1·Sum1+ω2·Sum2+ω3·Sum3; Wherein, ω1 is the first weight parameter, ω2 is the second weight parameter, and ω3 is the third weight parameter; Sum1 is the sum of the number of passengers stranded at all stations, Sum2 is the sum of the deviations of the departure time and arrival time at the destination station of all trains from the planned timetable, and Sum3 is the sum of the number of canceled trains. This indicates the number of people stranded at station s due to capacity constraints and unable to board the train to be rescheduled, f. This indicates the number of passengers waiting at station s to board the adjusted train f; This indicates the actual number of passengers traveling on the train to be adjusted (f). This represents the number of people stranded at station s due to capacity constraints and unable to board additional train q. This represents the number of passengers waiting at station s for the additional train q. U represents the actual number of passengers taking the additional train q; f This indicates the deviation of the departure time and arrival time at the destination station of train number f to be adjusted from the planned train schedule; This indicates the actual arrival time of train number f to be adjusted at the terminal station; This indicates the planned arrival time of train number f at the terminal station to be adjusted; d f,γ(f) This indicates the actual departure time of train number f to be adjusted at the originating station; This indicates the planned departure time of train number f to be adjusted at the originating station; e f Indicates whether train number f to be adjusted is cancelled, e f The variables are 0-1; F represents the set of trains to be adjusted, Q represents the set of trains to be added, and S represents the set of stations.

5. The method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit according to claim 4, characterized in that, The dataset of train schedules to be adjusted includes the actual departure time of each train to be adjusted at each station, the actual arrival time of each train to be adjusted at each station, the turnover relationship between the trains to be adjusted, and whether each train to be adjusted departs from the depot; the dataset of additional train schedules includes the order relationship between each additional train and the trains to be adjusted, the actual departure time of each additional train at each station, and the actual arrival time of each additional train at each station.

6. The method for adjusting train schedules during the transition period between peak and off-peak hours in urban rail transit according to claim 5, characterized in that, The linearization process is performed on the train operation adjustment model during the peak-off-peak transition period, including: The objective function in the train operation adjustment model during the peak-off-peak transition phase is linearized. For the objective function U in the train operation adjustment model during the peak-off-peak transition phase, f After linearizing the function formula, the following constraints are obtained: The f ≤M·e f ,f∈F; U f ≤u f +v f ,f∈F; U f ≥u f +v f -M(1-e f ),f∈F; Among them, u f ,v f U is a user-defined auxiliary variable. f The function formula uses u f ,v f Represented as: U f =(u f +v f )·e f ,f∈F; Among them, u f ,v f The constraints are:

7. A train timetable adjustment system for the transition phase between peak and off-peak hours in urban rail transit, characterized in that, The system includes: The urban rail information acquisition module is used to acquire train operation data and passenger data for the target area. The train operation data includes data on trains to be adjusted, data on additional trains, station data, and train data. The data on trains to be adjusted includes the departure and arrival times of the trains to be adjusted at different stations. The data on additional trains includes the departure and arrival times of the additional trains at different stations. The station data includes the train's dwell time at the station and the train's travel time between two adjacent stations or line junctions. The train data includes the departure interval between two consecutive trains, the tracking interval between two consecutive trains, the train turnaround time, and the number of rolling stock stored in the depot. The passenger data includes the number of passengers waiting to board the trains to be adjusted at different stations, the number of passengers actually boarding the trains to be adjusted at different stations, the passenger arrival rate between the departure times of adjacent trains to be adjusted, the number of passengers actually boarding the additional trains at different stations, and the maximum number of passengers boarding at each station. The operation constraint module is used to construct a set of train operation constraints for the high-peak-off-peak transition phase based on the train operation data and passenger data of the target area. The set of train operation constraints for the high-peak-off-peak transition phase includes a train departure time constraint model, a train stop time constraint model, a train section running time constraint model, a train departure interval constraint model, a train tracking interval constraint model, a train turnaround constraint model, a station service frequency constraint model, a rolling stock turnover constraint model, and a passenger boarding and alighting constraint model. The operation adjustment model construction module is used to establish a train operation adjustment model for the high-off-peak transition phase based on the set of train operation constraints during the high-off-peak transition phase, with the objectives of minimizing the number of passengers stranded on the platform, the number of train cancellations, and the train timetable offset. Among them, the train timetable offset includes the offset between the actual arrival time and the planned arrival time of each train at the terminal station and the offset between the actual departure time and the planned departure time of each train at the originating station. The model solving module is used to solve the train operation adjustment model for the peak-off-peak transition phase using a two-stage algorithm, to obtain the operation datasets of trains to be adjusted and the operation datasets of additional trains; specifically, it includes: The high-peak transition phase train operation adjustment model is linearized; a set of faulty stations is constructed, including the originating station, the terminating station, the station where the train failed, the station preceding the station where the train failed, and the station following the station where the train failed, in both directions of travel; based on the set of faulty stations and the train interval running time constraint model in the linearized high-peak transition phase train operation adjustment model, the interval running time constraint corresponding to the faulty train is constructed; based on the set of faulty stations and the passenger boarding and alighting constraint model in the linearized high-peak transition phase train operation adjustment model, the passenger boarding and alighting constraint corresponding to the faulty train is constructed. Passenger boarding and alighting constraints; based on the passenger boarding and alighting constraints and section running time constraints corresponding to the faulty train, the linearized high-peak and off-peak transition phase train operation adjustment model is updated; the updated high-peak and off-peak transition phase train operation adjustment model is solved to obtain the rolling stock turnover relationship of each train; the rolling stock turnover relationship of each train is input into the linearized high-peak and off-peak transition phase train operation adjustment model and the model is solved to obtain the arrival time and departure time of each train at the station; wherein, the arrival time and departure time of the train to be adjusted at the station constitute the train operation dataset of the train to be adjusted; the arrival time and departure time of the additional train at the station constitute the additional train operation dataset; The train timetable adjustment module is used to construct an adjusted train timetable based on the train operation dataset to be adjusted and the additional train operation dataset.

8. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the urban rail transit peak-hour transition phase train timetable adjustment method according to any one of claims 1 to 6.

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