A method for constructing an urban rail transit train diagram based on skip-stop measures

Abstract

The application discloses a kind of urban rail transit train diagram construction methods based on skip stop measure, comprising the following steps: S1, based on skip stop index, according to the complete running process of train from starting station to terminal station, build traffic optimization model;S2, based on skip stop index in step S1, according to the number of passenger boarding and alighting process, build passenger flow optimization model;S3, according to traffic optimization model in step S1 and passenger flow optimization model in step S2, the optimal solution of traffic optimization model and passenger flow optimization model is solved using iterative algorithm, output optimal train diagram;The application is solved by respectively constructing traffic optimization model and passenger flow optimization model and using iterative algorithm, and the optimal skip stop strategy and train diagram are obtained, effectively reduce the number of passengers stranded after failure, reduce the operating cost of operating company, improve the passenger load factor at the same time, and effectively improve train operation efficiency and the satisfaction of passenger boarding urban rail transit train.

Description

Technical Field

[0001] This invention relates to the field of urban rail transit operation management technology, specifically to a method for constructing urban rail transit train timetables based on skip-stop measures. Background Technology

[0002] Urban rail transit, such as subways and light rail, is gaining popularity due to its advantages of large capacity, punctuality, and speed. Taking subways as an example, their headway is short, only about two minutes on busy lines. A malfunction in the system can lead to train delays, or even a temporary paralysis of the entire system, drastically reducing capacity. The main causes of prolonged train delays due to system malfunctions are signaling system failures, such as those in the Automatic Train Monitoring System (ATMS), trackside equipment (interlocking system and trackside control computer), vehicle-to-ground communication equipment, and onboard equipment. Even though current equipment is more mature and sophisticated than before, and fault detection and monitoring are often performed by equipment or personnel, malfunctions can still occur occasionally. Furthermore, another factor contributing to system malfunctions is human error, such as delays caused by passengers. These human-caused delays are random and sudden. Based on these factors, implementing scheduling measures to control train operations and adjusting train timetables to address delays caused by system malfunctions can effectively improve train capacity.

[0003] When a fault occurs in an urban rail transit system, the headway will increase to protect train safety. In severe cases, trains may be impounded. Prolonged delays caused by the fault can drastically reduce the overall capacity of the train system, leading to congestion and large numbers of passengers stranded at stations. After the fault is resolved, train scheduling strategies can effectively reduce the number of passengers stranded at stations by increasing the line's capacity. These strategies include adding trains, shortening headways, rerouting trains mid-journey, or increasing train speeds in sections. Adding trains requires preparing backup trains in advance, which is costly and therefore not suitable for all lines. Rerouting trains mid-journey requires available tracks for the train to turn back. Shortening headways and increasing train speeds in sections are cheaper than the previous two strategies and have some effect, but their effectiveness is limited. If the fault lasts for a long time, it takes a considerable amount of time to restore train schedules to the original timetable. Summary of the Invention

[0004] To address the aforementioned shortcomings in the existing technology, this invention provides a method for constructing urban rail transit train timetables based on skip-stop measures, designing train timetables to control trains and solve the problem of passenger congestion and stagnation in stations after long delays.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0006] A method for constructing urban rail transit train timetables based on skip-stop measures includes the following steps:

[0007] S1. Based on the skip-stop index, construct a traffic flow optimization model according to the complete operation process of the train from the starting station to the terminal station;

[0008] S2. Based on the stop index in step S1, construct a passenger flow optimization model according to the change process of passenger boarding and alighting numbers;

[0009] S3. Based on the traffic flow optimization model in step S1 and the passenger flow optimization model in step S2, use an iterative algorithm to solve for the optimal solutions of the traffic flow optimization model and the passenger flow optimization model, and output the optimal train operation schedule.

[0010] The present invention has the following beneficial effects:

[0011] The present invention proposes a method for constructing urban rail transit train timetables based on skip-stop measures. By constructing a traffic flow optimization model and a passenger flow optimization model and solving them using an iterative algorithm, the optimal train timetable is obtained. This effectively reduces the number of stranded passengers, lowers the operating costs of the operating company, increases the passenger load factor, and effectively improves train operation efficiency and passenger satisfaction when riding urban rail transit trains. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating a method for constructing urban rail transit train timetables based on skip-stop measures proposed in this invention.

[0013] Figure 2 A schematic diagram of the passenger disembarkation process;

[0014] Figure 3 A schematic diagram of the passenger boarding process;

[0015] Figure 4 This is a schematic diagram of the iterative algorithm structure;

[0016] Figure 5 This is a schematic diagram of the Beijing Subway Yizhuang Line in the example;

[0017] Figure 6 This is a schematic diagram of the Beijing Subway Yizhuang Line in the example;

[0018] Figure 7 This is a schematic diagram of the train operation in the standard mode of the embodiment;

[0019] Figure 8 This is a schematic diagram of the train operation diagram based on the iterative algorithm in the skip-stop mode of the embodiment;

[0020] Figure 9This is a schematic diagram of passenger flow under the standard mode in the embodiment;

[0021] Figure 10 This is a schematic diagram of passenger flow based on an iterative algorithm in the skip-stop mode of the example;

[0022] Figure 11 This is a schematic diagram illustrating the number of passengers stranded in the example. Detailed Implementation

[0023] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0024] like Figure 1 As shown, a method for constructing urban rail transit train timetables based on skip-stop measures is characterized by the following steps:

[0025] S1. Based on the stop index, construct a traffic flow optimization model according to the complete operation process of the train from the starting station to the terminal station.

[0026] In an optional embodiment of the present invention, the present invention generates a traffic flow optimization model to address the measures to be taken after a train malfunction by constructing and optimizing a traffic flow dynamics model. This embodiment employs a skip-stop index, controlling the train to stop normally at some stations, while bypassing stations with few or no passengers and proceeding directly through. The skip-stop strategy requires no preconditions and does not alter existing equipment on the track. By skipping some stations, it saves corresponding stopping time and additional acceleration / deceleration time, significantly reducing the train's overall travel time on the line.

[0027] Specifically, step S1 includes S11-S18:

[0028] S11. Construct a train skip-stop index to limit the total number of platforms a train skips and the range of consecutive skip-stops; and assume that when three consecutive trains pass through the same platform, at least two trains must stop at the same platform, i.e.:

[0029]

[0030] ∑ j∈J y i,j ≥JS max

[0031] y i,j +y i+1,j +yi+2,j ≥2

[0032] Among them, y i,j This indicates whether train i stops at platform j, where J represents the number of platforms on the track, and S... max This represents the upper limit of platform data that a train can skip on the track, y i+1,j Indicates whether train i+1 stops at platform j, y i+2,j This indicates whether train i+2 stops at platform j.

[0033] In this embodiment, a binary variable y is constructed. i,x The skipping indicator is used to indicate whether train i stops at platform j. As a platform is skipped, passengers on that platform have to wait longer to board the next train. In order to improve passenger satisfaction, this implementation imposes two restrictions on the skipping indicator to reduce passenger waiting time and ensure that passengers always have the opportunity to board the next train to reach their destination. For passengers who need to transfer, this restriction can minimize their waiting time.

[0034] In this embodiment, trains operating after a fault are divided into four categories: trains that have already departed on the up line, trains that have not yet departed on the up line, trains that have already departed on the down line, and trains that have not yet departed on the down line. Trains that have already departed refer to train services that departed from the originating station (platform 1 or platform 1) and are running on the line before the fault occurred; trains that have not yet departed refer to train services that have not yet departed from the originating station before the fault occurred. Since trains circulate on the track, when an up line train service turns back, it becomes a down line train that has not yet departed, and the train service number remains unchanged; when a down line train service turns back, it becomes an up line train that has not yet departed, and the train service number is increased by the total number of trains on the line to become the new train service number, but the train itself remains unchanged. This embodiment constructs a traffic flow dynamics model based on six assumptions: Assumption 1: The number of trains on the line is fixed; no train additions or subtractions are performed during the entire process from the occurrence of a fault causing train delays to the implementation of stop-and-go measures to reduce delays; Assumption 2: The train operation process is simply divided into three stages: acceleration, deceleration, and constant speed, and different trains travel at a constant speed for the same period of time in the same section, regardless of the stopping mode (standard or stop-and-go mode); Assumption 3: The effect of stop-and-go measures in adjusting the train timetable is reflected in both the up and down directions, that is, trains in both directions simultaneously... Control is achieved using a stop-and-go mechanism. Assumption 4: At the onset of a fault, trains stopped at the platform temporarily stop in place, while trains traveling on the track temporarily stop at the nearest platform until the fault ends. After the fault ends, all trains on the track simultaneously depart from the platform at the fault end time. Assumption 5: In standard mode, the train interval is fixed. Assumption 6: When the termination condition of the stop-and-go mechanism is met, the service for undeparted trains needs to immediately switch to standard mode. The first undeparted train using standard mode needs to depart from the turnaround section as quickly as possible, and subsequent trains maintain a certain frequency operating in standard mode. In actual operation, the train's speed curve is very close to the permissible speed curve, indicating a very short period of uniform speed. Therefore, to simplify the train operation process, this embodiment proposes Assumption 2: If a stop-and-go mechanism is used to control a train in one direction of travel, then a stop-and-go mechanism must also be used in the other direction of travel to make the train travel times in the two directions mutually exclusive. Assumption 3 explains this. Assumption 6 indicates the process of converting the stop-and-go mode to the standard mode.

[0035] Therefore, in this embodiment, the complete operation process of the train from the starting station to the terminal station is mainly divided into: the train stopping at the station, the train running in the section, and the train turning back.

[0036] S12. Construct the train's stopping time at the station and calculate the train's departure time at the platform, i.e.:

[0037]

[0038]

[0039] Among them, D i,j This indicates the dwell time of train i at platform j, whether it skips or stops there. This indicates the minimum stopping time for the train at platform j. This indicates the departure time of train i at platform j. This indicates the arrival time of train i at platform j.

[0040] In this embodiment, if train i skips platform j by taking a skip-stop measure, the train will pass directly through platform j without stopping, so the stopping time of train i at platform j is 0. If train i stops normally at platform j, the stopping time of the train must be greater than the minimum stopping time. Moreover, the minimum stopping time depends on the shortest time required for passengers to get on and off the train, because the train must first meet the passengers' travel needs while ensuring its own safety.

[0041] S13. Based on the train skip-stop index in step S11 and the train arrival time, departure time, and dwell time at the platform in step S12, construct the relationship between the train departure time and the train skip-stop index, i.e.:

[0042]

[0043] S14. Based on the train's tripping index in step S11, calculate the train's travel time within the section, i.e.:

[0044]

[0045] Where, r i,j This represents the travel time of train i in section j. τ represents the average travel time of the train in section j. a τ represents the train acceleration time. d Indicates the time it takes for the train to decelerate.

[0046] In this embodiment, the train's running time in the section can be divided into three stages: acceleration stage, constant speed stage, and deceleration stage. Compared with the standard mode, the skip-stop mode can save corresponding acceleration and deceleration time. For example, when train i is running in the section from platform j to platform j+1, if the train does not stop at platform j, the train will not have an acceleration stage. If the train does not stop at the next platform after platform j, the train will not have a deceleration stage.

[0047] S15. Based on the train's travel time within the section as calculated in step S14, calculate the train's arrival time at the platform, i.e.:

[0048]

[0049] in, This indicates the arrival time of train i at platform j+1.

[0050] S16. Based on the train's arrival time at the platform in step S15 and the train's departure time at the platform in step S13, construct the minimum turnaround time for the train to return to the down direction and the minimum turnaround time for the train to return to the up direction, and construct the minimum running interval between trains, i.e.:

[0051]

[0052]

[0053] in, This indicates the arrival time of train i at platform J+1. Bt1 represents the departure time of train i at platform J, and Bt1 represents the minimum turnaround time for the train to return to the down direction. This indicates the arrival time of train i at platform 1. This represents the departure time of train i+M at platform 2J, where M represents the number of trains on the track, and Bt2 represents the minimum turnaround time for the train to return to the up direction. h represents the departure time of train i+1 at platform j. min This indicates the minimum operating interval between trains. The minimum operating interval must be set to effectively ensure a safe distance between trains and ensure that there is no possibility of a collision under any circumstances.

[0054] In this embodiment, the train turnaround time refers to the time required for the train to change direction, that is, the time required for the train to travel from platform J to platform J+1 and turn to the down direction, or the time required for the train to travel from platform 2J to platform 1 and turn to the up direction. Furthermore, train i+M represents train i returning from the down direction to the up direction; the train itself remains the same, only the train number changes. Simultaneously, to ensure train operation safety, in addition to the train's own operation, the interval between trains must also meet minimum requirements.

[0055] S17. Based on the four categories of trains that have departed (upbound), have not yet departed (upbound), have departed (downbound), and have not yet departed (downbound) after the fault recovery period, calculate the total running time of all trains after the fault recovery period. Then, based on the total running time of all trains after the fault recovery period, calculate the total running time of all trains.

[0056] In step S17, the total running time of train i after the fault recovery period. The calculation formula is:

[0057]

[0058]

[0059] in, f represents the total running time of train i after the fault recovery period. T t0 represents the initial departure time of the first train. Trains that have already departed refer to train services that have departed from the originating station and are running on the track before the malfunction occurred. Trains that have not yet departed refer to train services that have not yet departed from the originating station before the malfunction occurred. This indicates the departure time of train iM at platform 2J. This indicates the departure time of train i at platform 2J, f T I represents the total travel time of all trains, and I represents the number of trains served.

[0060] In this embodiment, the objective function of the traffic flow model is the total train travel time, which is composed of the travel time of each individual train. When the train is an up-line train that has already departed, the train starts running after the fault recovery period and continues until it leaves platform J. The total train travel time is... When the train is an up-line train that has not yet departed, the total travel time from the time the train leaves platform 2J and begins its turnaround to the time the train leaves platform J is: If the train is a southbound train that has already departed, the total travel time from the time the fault is resolved until the train leaves platform 2J is: When the train is a southbound train that has not yet departed, from the time it leaves platform J and begins its turnaround to the time it leaves platform 2J, the total travel time is:

[0061] S18. Based on the total travel time f of all trains in step S17... T Establish a traffic flow optimization model, namely:

[0062]

[0063] S2. Based on the stop index in step S1, construct a passenger flow optimization model according to the change process of passenger boarding and alighting.

[0064] In an optional embodiment of the present invention, the present invention generates a passenger flow optimization model to address the measures to be taken by the train after a malfunction by constructing and optimizing a passenger flow dynamics model. Specifically, the passenger flow optimization model is constructed by the changes in the number of passengers getting off the train and the changes in the number of passengers getting on the train.

[0065] In this embodiment, the passenger flow change process is modeled based on the skip-stop mode. To accurately establish the passenger flow model, four assumptions are made: Assumption 1: Passenger flow is always within the train line's transport capacity; Assumption 2: If a passenger has already boarded a departing train at the time of fault recovery, and their destination platform will be skipped, the passenger will disembark at the platform closest to their destination platform and wait for the next train that can take them directly to their destination platform; Assumption 3: Passengers who have not yet boarded at the time of fault recovery are not considered for transfers, i.e., passengers will only take the train that can take them directly to their destination platform; Assumption 4: The number of passengers arriving at the platform during the train's stop time is not considered, because the train's stop time is much shorter than the train's travel time between stations, so these passengers can be ignored.

[0066] In this embodiment, the passenger flow changes are mainly caused by the train arriving at the station and stopping, and passengers getting on and off the train. In order to analyze the passenger flow changes more clearly, this embodiment divides the construction of the passenger flow model into a dynamic model of the number of passengers getting off the train and a dynamic model of the number of passengers getting on the train, according to the order of passenger boarding and alighting times.

[0067] Specifically, step S2 includes S21-S23:

[0068] S21. Based on the stop index, construct a dynamic model of the change in the number of passengers during the disembarkation process according to the order of passenger disembarkation time.

[0069] like Figure 2 As shown, the process of passenger disembarking is the change in the number of passengers from the moment train i-1 leaves platform j until train i arrives at platform j and waits for all passengers whose destination is platform j to disembark.

[0070] Specifically, step S21 includes S211-S216:

[0071] S211. Calculate the number of passengers waiting on the platform to take the train to their destination platform, i.e.:

[0072]

[0073] in, P represents the number of passengers waiting at platform j to board train i to reach platform m. j,m λ represents the number of passengers waiting at platform j when the fault is resolved and whose destination is platform m. j,m t0 represents the passenger arrival rate at platform j who are waiting and whose destination is platform m, and t0 represents the initial departure time of the first train during the fault recovery period. This indicates the departure time of train i arriving at platform m at platform j. This represents the number of passengers waiting at platform j to board train i-1 to reach platform m, but who are stranded due to train capacity limitations and excessive passenger flow. This indicates the departure time of train i-1, which arrives at platform m, at platform j.

[0074] In this embodiment, when a passenger arrives at platform j, it means the passenger will travel from platform j to another platform m, satisfying m ≥ J + 1. Typically, passengers always choose the shortest path, therefore m ≤ J. It can be reasonably assumed that the number of passengers waiting on the platform between the departure of one train and the arrival of the next is positively correlated with time; that is, the number of passengers waiting on the platform to reach their destination platform in step S211 can be calculated.

[0075] S212. Based on the number of passengers waiting on the platform to board the train to their destination platform as stated in step S211, calculate the total number of passengers waiting on the platform to board the train, i.e.:

[0076]

[0077] in, This represents the number of passengers waiting to board train i at platform j, where J represents the number of platforms on the track.

[0078] In this embodiment, the first train after a fault occurs refers to a specific platform. The first train is the first train to arrive at the platform after the fault, and it carries all passengers who were stranded on the platform during the fault period. Therefore, all departing trains are considered the first trains. However, for trains that have not yet departed, only the first train that has not yet departed after a fault is the first train. Thus, the number of passengers waiting to board a train on the platform in step S212 can be calculated.

[0079] S213. Based on the skip-stop index, construct the relationship between the number of passengers on the platform who want to take a train to their destination platform and the number of passengers waiting on the platform to take a train to their destination platform, that is:

[0080]

[0081] in, y represents the number of passengers at platform j who want to take train i to their destination at platform m. i,j Indicates whether train i stops at platform j, y i,m This indicates whether train i stops at platform m.

[0082] In this embodiment, among the passengers waiting on the platform, some passengers' destination platforms will be skipped by the train due to the upcoming train's skip-stop mechanism. As can be seen from the above passenger assumptions, these passengers will not take the train, but will continue to wait for the next train that can directly reach their destination platform. Therefore, the relationship between the number of passengers on the platform who want to take the train to their destination platform and the number of passengers waiting on the platform to take the train to their destination platform in step S13 can be constructed.

[0083] S214. Based on the relationship established in step S213 between the number of passengers on the platform who want to take a train to their destination platform and the number of passengers waiting on the platform to take a train to their destination platform, calculate the number of passengers on the platform who want to take a train, i.e.:

[0084]

[0085] in, This represents the number of passengers at platform j who want to take train i.

[0086] The number of passengers waiting on the platform in this embodiment Subtract the number of passengers who want to board. The remaining number of passengers is Figure 2 The passengers shown in the image have no intention of boarding.

[0087] S215. If the train has not yet departed, calculate the number of passengers disembarking at the platform, i.e.:

[0088]

[0089] in, This represents the number of passengers disembarking from train i at platform j. This represents the number of passengers who boarded train i at platform m and whose destination is platform j.

[0090] In this embodiment, due to the skip-stop measure, the number of passengers disembarking needs to be analyzed separately for departing and non-departing trains. The number of passengers disembarking from non-departing trains is only related to the number of passengers boarding at the following platform. That is, the number of passengers disembarking at platform j is the sum of the number of passengers who boarded at all platforms before platform j and whose destination is platform j. Therefore, the number of passengers disembarking at the platform in step S215 can be calculated.

[0091] S216. If the train has already departed, calculate the number of passengers disembarking at the platform, i.e.:

[0092]

[0093] Among them, U i,j U represents the number of passengers who boarded train i and whose destination was platform j before the fault was resolved.i,m This represents the number of passengers who boarded train i and whose destination was platform m before the fault was resolved. i,j,m This indicates whether passengers will need to disembark at platform j earlier because their destination platform has been skipped, and z i,j,m =y i,j (1-y i,j+1 (1-y) i,j+2 )…(1-y i,m ).

[0094] In this embodiment, the number of passengers disembarking from the departing train is related not only to the number of passengers boarding at the subsequent platform, but also to the passengers on the train. That is, some of the passengers on the departing train boarded before the fault recovery period. The destination platform of these passengers may be skipped by the train, so they will get off at the platform before their destination platform and continue to wait for the next train to take them to their destination. Therefore, the number of passengers disembarking at the platform in step S216 can be calculated.

[0095] S22. Based on the stop-stop index, construct a dynamic model of the change in the number of passengers during the boarding process according to the order of passenger boarding time.

[0096] like Figure 3 As shown, the process of passenger boarding is the change in the number of passengers from the moment train i stops waiting for all passengers whose destination is platform j to disembark until all trains leave platform j.

[0097] Specifically, step S22 includes S221-S227:

[0098] S221. Calculate the number of passengers on the train when it departs from the platform, i.e.:

[0099]

[0100] Where, n i,j-1 This represents the number of passengers on train i when it departs from platform j-1, and n i,j-2 This represents the number of passengers on train i when it departs from platform j-2. This represents the number of passengers disembarking from train i at platform j-1. This represents the number of passengers who can board train i at platform j-1.

[0101] S222. Calculate the remaining capacity inside the train, i.e.:

[0102]

[0103] in, C represents the remaining capacity of train i on platform j, and C represents the train capacity.

[0104] S223. If the train capacity is sufficient, the number of passengers who can board the train on the platform depends on the number of passengers who want to board the train on the platform; if the train capacity is insufficient, the number of passengers who can board the train on the platform depends on the remaining capacity inside the train, that is:

[0105]

[0106] in, Let represent the number of passengers who can board train i at platform j, and min(·) represents taking the minimum value.

[0107] In this embodiment, after train i has finished waiting for passengers to disembark, the number of passengers wanting to board train i at platform j is: Due to train capacity limitations, when passenger flow is high, some passengers hoping to board will be delayed. Therefore, the number of passengers wanting to board train i at platform j will be determined based on the number of passengers at platform j. It can calculate the number of passengers who can board the train at the platform in step S223.

[0108] S224. If the ratio of the number of passengers who board the train at the platform and arrive at the destination platform to the number of passengers who can board the train at the platform is equal to the ratio of the number of passengers who want to board the train at the platform and arrive at the destination platform to the number of passengers who want to board the train at the platform, then the number of passengers who board the train at the platform and arrive at the destination platform can be calculated, that is:

[0109]

[0110]

[0111] in, This represents the number of passengers who boarded train i at platform j and arrived at platform m.

[0112] In this embodiment, the number of passengers who board train i at platform j and arrive at platform m is... In this regard, two scenarios need to be discussed: if the train capacity is sufficient, then... If the train capacity is insufficient, then At this point, we can make a reasonable assumption that passengers from different destinations arrive randomly and have a good combination at each station. That is, the ratio of the number of passengers who board the train at the platform and arrive at the destination platform to the number of passengers who can take the train at the platform is equal to the ratio of the number of passengers who want to take the train at the platform and arrive at the destination platform to the number of passengers who want to take the train at the platform. Thus, we can calculate the number of passengers who board the train at the platform and arrive at the destination platform in step S224.

[0113] S225. If the capacity of the undeparted trains is insufficient, then based on the number of passengers who boarded the train on the platform and arrived at the destination platform in step S224 and the number of passengers waiting on the platform to arrive at the destination platform in step S211, calculate the number of passengers who are unable to board the train, i.e.:

[0114]

[0115] in, This represents the number of passengers waiting at platform j to board train i to reach platform m, but who are stranded due to train capacity limitations and excessive passenger flow.

[0116] In this embodiment, the number of passengers stranded on trains also needs to be calculated in two categories: trains that have not yet departed and trains that have already departed.

[0117] S226. Based on the number of passengers waiting to board the train on the platform as stated in step S212 and the number of passengers who can board the train on the platform as stated in step S223, calculate the total number of passengers stranded on the train, i.e.:

[0118]

[0119] in, This represents the total number of passengers waiting to board train i at platform j, but who are stranded due to train capacity limitations and excessive passenger flow.

[0120] S227. If the capacity of the departing trains is insufficient, calculate the number of passengers stranded on the platform based on the number of passengers who boarded the train at the platform and arrived at the destination platform in step S224 and the number of passengers waiting on the platform to board the train to the destination platform in step S211.

[0121]

[0122] In this embodiment, after the train departs from the station, some passengers are left behind. These passengers, along with those who have no intention of boarding, will continue to wait for the next train to arrive.

[0123] S23. Based on the dynamic model of passenger disembarkation in step S21 and the model of passenger boarding in step S22, construct a passenger flow optimization model.

[0124] Specifically, step S23 includes S231-S232:

[0125] S231. Calculate the train's passenger load factor based on the number of passengers on the train and the train's capacity when the train departs from the platform, i.e.:

[0126]

[0127] Among them, li,j Let C represent the passenger load factor of train i at platform j, and let n represent the train capacity. i,j This represents the number of passengers on train i when it departs from platform j.

[0128] S232. Based on the number of stranded passengers on the train in step S226 and the train occupancy rate in step S231, establish a passenger flow optimization model, namely:

[0129]

[0130] Among them, f P This represents the weighted sum of the passenger flow optimization target dwell rate and passenger load factor, where I represents the number of trains served, J represents the number of platforms, and α represents the proportional weight of the train dwell rate. This refers to the total number of passengers waiting to board train i at platform j but being stranded due to train capacity limitations and excessive passenger flow. The nominal value, L i,j This indicates the passenger load factor l of train i at platform j. i,j The nominal value of β represents the proportional weight of the train's passenger load factor.

[0131] The passenger flow optimization model constructed in this embodiment not only reduces the number of stranded passengers, but also increases the passenger load factor, effectively improving the train's operating efficiency and reducing the operating costs of the operating company.

[0132] S3. Based on the traffic flow optimization model in step S1 and the passenger flow optimization model in step S2, use an iterative algorithm to solve for the optimal solutions of the traffic flow optimization model and the passenger flow optimization model, and output the optimal train operation schedule.

[0133] In an optional embodiment of the present invention, the objective of the constructed traffic flow optimization model is to reduce the total train travel time, while the objective of the passenger flow optimization model is to increase the train occupancy rate while reducing stranded passengers. This embodiment solves both the constructed traffic flow optimization model and passenger flow optimization model simultaneously using an iterative algorithm to output the optimal train schedule.

[0134] like Figure 4As shown, the decision variable of the traffic flow optimization model is the skip-stop index, which determines which stations a train skips and stops at. The output of the traffic flow optimization model is a complete optimized train timetable, which includes the train's departure time and arrival time at the platform. The decision variable of the passenger flow optimization model is the train interval. The passenger flow optimization model directly transmits the optimized train interval to the traffic flow optimization model for further optimization, while simultaneously calculating the number of stranded passengers in this round of optimization, preparing for the next round of optimization in the passenger flow optimization model, until the traffic flow optimization model outputs the optimal train timetable. In the traffic flow optimization model, the train interval is a known quantity transmitted from the passenger flow optimization model and is a constant; while in the passenger flow optimization model, the train interval is the variable to be optimized. Therefore, the iterative algorithm does not simply convert the two optimization models into one, but solves the traffic flow optimization model and the passenger flow optimization model separately, using the train interval to make the two optimization models interact and influence each other.

[0135] Specifically, step S3 includes S31-S38:

[0136] S31. Initialization: Input the following parameters: number of trains, number of trains serving, number of platforms, fault recovery time, location of the dispatched train at the platform, number of passengers waiting at the platform and arriving at the destination platform when the fault is recovered, passenger arrival rate waiting at the platform and arriving at the destination platform, train interval set before the fault, minimum train dwell time at the platform, average train running time in the section, train acceleration time, train deceleration time, minimum turnaround time for the train to return to the down direction, minimum turnaround time for the train to return to the up direction, train capacity, weighted average of train delay rate, weighted average of train passenger load factor, maximum number of iterations, and maximum number of delayed passengers.

[0137] S32. Set the iteration index to 1 and assign the train running interval set before the fault to the train running interval.

[0138] S33. Set the initial skip-stop index to stop at all stations, and input it into the traffic flow optimization model constructed in step S1 to calculate the train departure time and arrival time at the platform, thus obtaining the standard train operation diagram.

[0139] S34. Based on the standard train operation diagram obtained in step S33 and the passenger flow optimization model in step S2, calculate the nominal value of the total number of passengers stranded on the train and the nominal value of the train occupancy rate required by the passenger flow optimization model.

[0140] S35. Determine if the iteration index is greater than the maximum number of iterations. If it is, output the current optimal train schedule; otherwise, proceed to step S36 to continue iterating.

[0141] S36. Based on the standard train timetable in step S33, calculate the total number of passengers stranded on all platforms for all trains.

[0142] S37. Determine if there exists a platform where the total number of passengers stranded on trains is greater than the maximum number of stranded passengers. If so, solve the passenger flow optimization model in step S2 and calculate the optimized train interval. Increment the iteration index by one, and use the optimized train interval obtained in step S37 to calculate the traffic flow optimization model in step S1 to obtain a new train schedule. Otherwise, output the current optimal train schedule.

[0143] S38. Return to step S35 until the iteration index is greater than the maximum number of iterations, then the iteration ends and the current optimal train schedule is output.

[0144] In this embodiment, the Beijing Subway Yizhuang Line is used as a case study for simulation experiments. By comparing it with the train timetable in the standard mode, the advantages of the skip-stop mode are highlighted. By comparing it with the passenger congestion situation in the standard mode, it is shown that the method used in this invention can indeed effectively reduce the number of congested passengers and improve passenger satisfaction.

[0145] like Figure 5 As shown, the Beijing Subway Yizhuang Line starts at Songjiazhuang Station and ends at Yizhuang Railway Station. The first phase of the project (from Songjiazhuang to Ciqu Station) officially opened on December 30, 2010. It is the first subway line in my country to use a domestically produced CBTC signaling system.

[0146] like Figure 6 As shown, the Beijing Subway Yizhuang Line, Phase I, has a total of 13 stations and 26 platforms. Under normal circumstances, the Beijing Subway Yizhuang Line adopts a standard mode, with its station dwell time and interval travel time information sourced from the Beijing Rail Transit Control Center (TCC). The interval travel time includes constant speed time, acceleration time, and deceleration time. In this embodiment, the acceleration time and deceleration time on all lines are set to 25 seconds. The train's constant speed time and turnaround time are shown in Table 1. The minimum train interval between two trains is set to 90 seconds. Since the minimum dwell time at each platform is only related to the station, the minimum dwell time at two platforms of the same station in both directions is equal, as shown in Table 1 below:

[0147] Table 1 Minimum Dwell Time (Unit: seconds)

[0148]

[0149] The time t0 = 0 is set to begin when the line obstacle ends and recovery begins. Assume 20 trains serve the track, with 10 serving in the up direction and 10 in the down direction. Initially, all 20 trains are already dispatched. At time t0, all trains simultaneously resume operation according to the optimized train schedule based on the skip-stop pattern. By assumption, if a train is a non-departing train, the maximum number of skipped stops S... max =3, meaning the train can skip a maximum of 3 platforms in subsequent travel; if the train has already departed and at time t0 the number of platforms it has reached has exceeded half, that is, the departing train on the up line has passed 7 platforms and the departing train on the down line has passed 20 platforms, then the maximum number of platforms the train can skip is set to S. nax =1; if not more than half, then set the maximum number of stops skipped by the train to S. max =2.

[0150] This embodiment sets the simulation experiment time to 7:00 to 10:00 AM, as this period is the morning rush hour, and passenger congestion and delays are more likely to occur after a fault. Based on passenger data from Beijing TCC, data is collected every 15 minutes. The passenger arrival rate of each station from 7:00 to 10:00 AM is shown in Tables 2 and 3. We set the fault recovery time to 7:30 AM, and the fault duration to 30 minutes, as shown in Tables 2 and 3 below:

[0151] Table 2 Passenger Arrival Rate, Part 1 (Unit: people / s)

[0152]

[0153] Table 3 Passenger Arrival Rate, Part 2 (Unit: people / s)

[0154]

[0155]

[0156] The train has a capacity of 1200 passengers. Under normal circumstances, without malfunctions, the 20 trains on the line operating according to the standard train schedule are sufficient to meet the morning rush hour demand, and there will be no stranded passengers. However, in the event of a malfunction, the train will stop at an adjacent platform, at which point the doors will open, and passengers can continue to board until the train is full. For platforms where no train makes an emergency stop, newly arrived passengers will wait on the platform. At the time t0 after the malfunction is resolved, since the trains were operating under the standard schedule, the number of passengers P waiting on the platform will be... j,m And the passengers U inside the car i,mThe values ​​can all be calculated from passenger data. During the recovery period, trains receive an optimized train schedule before departure and operate according to the new schedule using skip-stop measures. However, when a train arrives at its final destination (platform 13 or 26) at a time different from the planned departure time... Figure 1 When this happens, subsequent trains will no longer use the skip-stop measure after completing the up or down turnaround, and will automatically return to the standard mode.

[0157] This embodiment employs an iterative algorithm, setting α=β=1, N=800, and P=800, to calculate the arrival and departure times of trains under the skip-stop measure, thus obtaining the train timetable. To compare the effectiveness of the iterative algorithm, this embodiment also calculates the train timetable under the standard mode. Simultaneously, comparing the passenger flow diagram using the iterative algorithm under the skip-stop mode with the passenger flow diagram under the standard mode, it is found that the simulation time required based on the iterative algorithm under the skip-stop mode is 201 seconds.

[0158] like Figure 7 As shown, Figure 7 This is the train schedule in standard mode.

[0159] like Figure 8 As shown, Figure 8 This is a train operation diagram based on an iterative algorithm in the skip-stop mode.

[0160] This embodiment is based on observation. Figure 7 and Figure 8 ,Discover Figure 8 The trains in the train system used a skip-stop mechanism. And... Figure 4 The first northbound train, departing from platform 1, skipped platforms 2, 3, and 4. The northbound train, departing from platform 7, skipped platforms 9 and 10. Observing the arrival times of the trains at their final platforms shows that the train travel time in the skip-stop mode based on the iterative algorithm is shorter than that in the standard mode. Simulation experiments in this embodiment demonstrate that in the standard mode... Figure 7 As shown, the last southbound train departs station 1 (platform 26) and enters the turnaround time of 4902 seconds, while in the skip-stop mode, the iterative algorithm as follows... Figure 8 As shown, the last southbound train departs station 1 (platform 26) and enters the turnaround phase in 4376 seconds. The calculation shows that the total train travel time saved by the iterative algorithm in the skip-stop mode compared to the standard mode is as follows:

[0161] like Figure 9 As shown, Figure 9 This is a passenger flow map in the standard mode.

[0162] like Figure 10 As shown, Figure 10This is a passenger flow map based on an iterative algorithm under the skip-stop mode.

[0163] This embodiment compares the passenger flow map in the standard mode with the passenger flow map based on the iterative algorithm in the skip-stop mode, and it can be concluded that the passenger flow based on the iterative algorithm in the skip-stop mode is significantly reduced. Among these, Figure 9 A small number of passengers remained gathered on platform 5 from 0 to 34 minutes onwards. Figure 10 This completely solved the congestion problem for this group of passengers.

[0164] This embodiment, by adjusting the weights α and β of stranded passengers and train load rate in the passenger optimization model, reveals the total travel time of the last outgoing train and the percentage of savings compared to the standard mode, as shown in Table 4:

[0165] Table 4 Comparison of results for different α and β values

[0166]

[0167] Among them, the standard is that the weights of stranded passengers and train load rate are equal. When the weight α of stranded passengers is not equal to the weight β of train load rate, the total running time of the last down-bound train that has not yet departed will increase, and the number of stranded passengers will also increase. Therefore, in order to achieve a balance between the total train running time and the number of stranded passengers, and to reduce both the total train running time and the total number of stranded passengers, the weights of stranded passengers and train load rate should be set to equal values. In addition, the number of iterations between the traffic flow model and the passenger flow model also affects the optimization results. The relationship between the number of iterations and the optimization results based on the iterative algorithm is shown in Table 5:

[0168] Table 5 Comparison of results for different iteration numbers

[0169]

[0170] Table 5 shows that in the actual simulation results, when the number of iterations N is less than 8, the more iterations there are, the fewer stranded passengers there will be. However, when the number of iterations N is greater than 8, increasing the number of iterations will not help reduce the total train travel time or the total number of stranded passengers. Therefore, in order to obtain the optimal result and save the computation time of the iterative algorithm, the number of iterations is set to 8.

[0171] like Figure 11 As shown, Figure 11 This is a passenger congestion graph, which compares the number of passengers congested at all stations served by each train under the standard mode with the passenger congestion situation optimized by the iterative algorithm for each train service. Figure 11The horizontal axis represents the train service number, and the vertical axis represents the number of stranded passengers. The black solid line represents the stranded passenger situation under the standard mode, and the gray solid line represents the number of stranded passengers optimized using an iterative algorithm. And from... Figure 11 As can be seen, compared with the standard mode, the iterative algorithm in the skip mode can significantly reduce the number of stranded passengers.

[0172] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0173] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for constructing urban rail transit train timetables based on skip-stop measures, characterized in that, Includes the following steps: S1. Based on the skip-stop index, a traffic flow optimization model is constructed according to the complete operation process of the train from the starting station to the terminal station, specifically as follows: Construct a train skip-stop index to limit the total number of platforms a train skips and the range of consecutive skip-stops; and assume that when three consecutive trains pass through the same platform, at least two trains must stop at the same platform. Construct the train's dwell time at the station and calculate the train's departure time at the platform; Establish the relationship between train departure time at the platform and train skipping indicators; Calculate the train's travel time within the section; Calculate the train's arrival time at the platform; Construct the minimum turnaround time for a train to return to the down direction and the minimum turnaround time for a train to return to the up direction, and construct the minimum running interval between trains; Based on the four categories of trains that have departed, have not yet departed, have departed, and have not yet departed after the fault recovery period, calculate the total running time of trains after the fault recovery period, and calculate the total running time of all trains based on the total running time of trains after the fault recovery period. A traffic flow optimization model is established with the goal of minimizing the total travel time of all trains. S2. Based on the stop-and-go index in step S1, construct a passenger flow optimization model according to the changes in the number of passengers getting on and off the bus, specifically as follows: S21. Based on the stop-and-go indicator, a dynamic model of passenger disembarkation quantity changes is constructed according to the passenger disembarkation time sequence, specifically as follows: Calculate the number of passengers waiting on the platform to take a train to their destination platform; Calculate the number of passengers waiting on the platform to board the train; Based on the skip-stop index, we construct the relationship between the number of passengers on the platform who want to take a train to their destination platform and the number of passengers on the platform waiting to take a train to their destination platform. Calculate the number of passengers who want to take the train at the platform; If the train has not yet departed, then count the number of passengers disembarking at the platform. If the train has already departed, calculate the number of passengers who disembarked at the platform. S22. Based on the stop-and-go indicator, a dynamic model of passenger boarding quantity changes is constructed according to the passenger boarding time sequence, specifically as follows: Calculate the number of passengers on the train when it departs from the platform; Calculate the remaining capacity inside the train; If the train has sufficient capacity, the number of passengers who can board the train on the platform depends on the number of passengers who want to board the train on the platform. If the train capacity is insufficient, the number of passengers who can board the train at the platform depends on the remaining capacity inside the train. Calculate the number of passengers stranded due to insufficient train capacity, both for trains that have not yet departed and for trains that have already departed. S23. Based on the dynamic model of passenger disembarkation changes in step S21 and the passenger boarding changes in step S22, a passenger flow optimization model is constructed, specifically as follows: The passenger load factor is calculated based on the number of passengers on the train when it departs from the platform and the train's capacity. A passenger flow optimization model is established with the weighted sum of the passenger flow optimization target of dwell rate and passenger load rate as the objective. S3. Based on the traffic flow optimization model in step S1 and the passenger flow optimization model in step S2, use an iterative algorithm to solve for the optimal solutions of the traffic flow optimization model and the passenger flow optimization model, and output the optimal train operation schedule.

2. The method for constructing an urban rail transit train timetable based on skip-stop measures according to claim 1, characterized in that, Step S1 specifically includes: S11. Construct a train skip-stop index to limit the total number of platforms a train skips and the range of consecutive skip-stops; and assume that when three consecutive trains pass through the same platform, at least two trains must stop at the same platform, i.e.: in, Indicates train Is it in Stop at the platform. Indicates the number of platforms on the rail line. This indicates the upper limit of the number of platforms a train can skip on the track. Indicates train Is it in Stop at the platform. Indicates train Is it in Platform stop; S12. Construct the train's stopping time at the station and calculate the train's departure time at the platform, i.e.: in, Indicates train Skip or stop The platform's dwell time. Indicates that the train is The minimum dwell time at the platform. Indicates train exist Departure time at the platform, Indicates train exist Arrival time at the platform; S13. Based on the train skip-stop index in step S11 and the train arrival time, departure time, and dwell time at the platform in step S12, construct the relationship between the train departure time and the train skip-stop index, i.e.: ； S14. Based on the train's tripping index in step S11, calculate the train's travel time within the section, i.e.: in, Indicates train exist The running time of the interval, Indicates that the train is Average running time of the interval Indicates the train acceleration time. Indicates the train deceleration time; S15. Based on the train's travel time within the section as calculated in step S14, calculate the train's arrival time at the platform, i.e.: in, Indicates train exist Arrival time at the platform; S16. Based on the train's arrival time at the platform in step S15 and the train's departure time at the platform in step S13, construct the minimum turnaround time for the train to return to the down direction and the minimum turnaround time for the train to return to the up direction, and construct the minimum running interval between trains, i.e.: in, Indicates train exist Arrival time at the platform. Indicates train exist Departure time at the platform, This indicates the minimum turnaround time for the train to return to the down direction. Indicates train exist Arrival time at the platform. Indicates train exist Departure time at the platform, Indicates the number of trains on the track. This indicates the minimum turnaround time for the train to return to the upward direction. Indicates train exist Departure time at the platform, Indicates the minimum operating interval between trains; S17. Based on the four categories of trains that have departed (upbound), have not yet departed (upbound), have departed (downbound), and have not yet departed (downbound) after the fault recovery period, calculate the total running time of all trains after the fault recovery period. Then, based on the total running time of all trains after the fault recovery period, calculate the total running time of all trains. In step S17, the train Total uptime after the fault recovery period The calculation formula is: in, Indicates train Total uptime after the fault recovery period , This indicates the initial departure time of the first train during the fault recovery period. Trains that have already departed refer to train services that departed from the originating station and are running on the track before the fault occurred, while trains that have not yet departed refer to train services that have not yet departed from the originating station before the fault occurred. Indicates train exist Departure time at the platform, Indicates train exist Departure time at the platform, This represents the total travel time of all trains. Indicates the number of trains in service; S18. Based on the total travel time of all trains in step S17. Establish a traffic flow optimization model, namely: 。 3. The method for constructing an urban rail transit train timetable based on skip-stop measures according to claim 1, characterized in that, Step S21 specifically includes the following steps: S211. Calculate the number of passengers waiting on the platform to take the train to their destination platform, i.e.: in, Indicates in Waiting on the platform to board the train The destination is The number of passengers on the platform. Indicates that the fault has been recovered. Waiting on the platform and arriving at the destination The number of passengers on the platform. Indicates in Waiting on the platform and arriving at the destination Passenger arrival rate at the platform This indicates the initial departure time of the first train during the fault recovery period. Indicates the arrival platform train exist Departure time at the platform, Indicates in Waiting on the platform to board the train The destination is However, due to train capacity limitations and excessive passenger flow, a large number of passengers were stranded on the platform. Indicates the arrival platform train exist Departure time from the platform; S212. Based on the number of passengers waiting on the platform to board the train to their destination platform as stated in step S211, calculate the total number of passengers waiting on the platform to board the train, i.e.: in, Indicates in Waiting on the platform to board the train The number of passengers, Indicates the number of platforms on the track line; S213. Based on the skip-stop index, construct the relationship between the number of passengers on the platform who want to take a train to their destination platform and the number of passengers waiting on the platform to take a train to their destination platform, that is: in, Indicates in I want to take the train from the platform. The destination is The number of passengers on the platform. Indicates train Is it in Stop at the platform. Indicates train Is it in Platform stop; S214. Based on the relationship established in step S213 between the number of passengers on the platform who want to take a train to their destination platform and the number of passengers waiting on the platform to take a train to their destination platform, calculate the number of passengers on the platform who want to take a train, i.e.: in, Indicates in I want to take the train from the platform. The number of passengers; S215. If the train has not yet departed, calculate the number of passengers disembarking at the platform, i.e.: in, Indicates train exist The number of passengers disembarking at the platform. Indicates train exist Boarding at the station, destination is The number of passengers on the platform; S216. If the train has already departed, calculate the number of passengers disembarking at the platform, i.e.: in, This indicates that the passenger had boarded the train before the fault was resolved. And the destination is The number of passengers on the platform. This indicates that the passenger had boarded the train before the fault was resolved. And the destination is The number of passengers on the platform. This indicates whether passengers will need to choose to arrive in advance if their destination platform is skipped. Get off at the platform, and .

4. The method for constructing an urban rail transit train timetable based on skip-stop measures according to claim 3, characterized in that, Step S22 specifically includes: S221. Calculate the number of passengers on the train when it departs from the platform, i.e.: in, Indicates train from The number of passengers on the train when it departs from the platform. Indicates train from The number of passengers on the train when it departs from the platform. Indicates train exist The number of passengers disembarking at the platform. Indicates in The platform allows passengers to board trains. The number of passengers; S222. Calculate the remaining capacity inside the train, i.e.: in, Indicates train exist The remaining capacity inside the train on the platform. Indicates train capacity; S223. If the train capacity is sufficient, the number of passengers who can board the train on the platform depends on the number of passengers who want to board the train on the platform; if the train capacity is insufficient, the number of passengers who can board the train on the platform depends on the remaining capacity inside the train, that is: in, Indicates in The platform allows passengers to board trains. The number of passengers, This indicates taking the minimum value; S224. If the ratio of the number of passengers who board the train at the platform and arrive at the destination platform to the number of passengers who can board the train at the platform is equal to the ratio of the number of passengers who want to board the train at the platform and arrive at the destination platform to the number of passengers who want to board the train at the platform, then the number of passengers who board the train at the platform and arrive at the destination platform is calculated as follows: in, Indicates in Boarding the train at the platform And the destination is The number of passengers on the platform; S225. If the capacity of the undeparted trains is insufficient, then based on the number of passengers who boarded the train on the platform and arrived at the destination platform in step S224 and the number of passengers waiting on the platform to board the train and arrive at the destination platform in step S211, calculate the number of passengers who are unable to board the train on the platform, i.e.: in, Indicates in Waiting on the platform to board the train The destination is However, due to train capacity limitations and excessive passenger flow, a large number of passengers were stranded on the platform. S226. Based on the number of passengers waiting to board the train on the platform as stated in step S212 and the number of passengers who can board the train on the platform as stated in step S223, calculate the total number of passengers stranded on the train, i.e.: in, Indicates in Waiting on the platform to board the train However, due to train capacity limitations and excessive passenger flow, the total number of passengers stranded was [number missing]. S227. If the capacity of the departing trains is insufficient, calculate the number of passengers stranded on the platform based on the number of passengers who boarded the train at the platform and arrived at the destination platform in step S224 and the number of passengers waiting on the platform to board the train to the destination platform in step S211. 。 5. The method for constructing an urban rail transit train timetable based on skip-stop measures according to claim 4, characterized in that, Step S23 specifically includes: S231. Calculate the train's passenger load factor based on the number of passengers on the train and the train's capacity when the train departs from the platform, i.e.: in, Indicates train exist Platform passenger load factor Indicates train capacity. Indicates train from The number of passengers on the train when it departs from the platform; S232. Based on the number of stranded passengers on the train in step S226 and the train occupancy rate in step S231, establish a passenger flow optimization model, namely: in, This represents the weighted sum of the target passenger flow optimization parameters: dwell rate and load factor. Indicates the number of trains in service. Indicates the number of platforms. The weighted proportions representing train delay rates Indicates in Waiting on the platform to board the train However, due to train capacity limitations and excessive passenger flow, the total number of passengers stranded was [number missing]. The nominal value, Indicates train exist Platform passenger load factor The nominal value, The proportional weight representing the train's passenger load factor.

6. The method for constructing an urban rail transit train timetable based on skip-stop measures according to claim 1, characterized in that, Step S3 specifically includes: S31. Initialization: Input the number of trains, the number of trains serving, the number of platforms, the fault recovery time, the location of the dispatched train at the platform, the number of passengers waiting at the platform and arriving at the destination platform when the fault is recovered, the passenger arrival rate waiting at the platform and arriving at the destination platform, the train running interval set before the fault, the minimum stop time of the train at the platform, the average running time of the train in the section, the train acceleration time, the train deceleration time, the minimum turnaround time of the train returning to the down direction, the minimum turnaround time of the train returning to the up direction, the train capacity, the proportional weight of the train delay rate, the proportional weight of the train passenger load factor, the maximum number of iterations, and the maximum number of delayed passengers. S32. Set the iteration index to 1 and assign the train running interval set before the fault to the train running interval. S33. Set the initial skip-stop index to stop at all stations, and input it into the traffic flow optimization model constructed in step S1 to calculate the train departure time and arrival time at the platform, and obtain the standard train operation diagram. S34. Based on the standard train operation diagram obtained in step S33 and the passenger flow optimization model in step S2, calculate the nominal value of the total number of passengers stranded on the train and the nominal value of the train passenger load factor required by the passenger flow optimization model. S35. Determine if the iteration index is greater than the maximum number of iterations. If it is, output the current optimal train schedule; otherwise, proceed to step S36 to continue iterating. S36. Based on the standard train operation diagram in step S33, calculate the total number of passengers stranded on all platforms for all trains. S37. Determine if there is a platform where the total number of passengers stranded on trains is greater than the maximum number of stranded passengers. If so, solve the passenger flow optimization model in step S2 and calculate the optimized train interval. Increment the iteration index by one, and use the optimized train interval obtained in step S37 to calculate the traffic flow optimization model in step S1 to obtain a new train schedule. Otherwise, output the current optimal train schedule. S38. Return to step S35 until the iteration index is greater than the maximum number of iterations, then the iteration ends and the current optimal train schedule is output.

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