Method, system, device and medium for optimizing a schedule to improve utilization of station parking

Abstract

The application provides a schedule optimization method, system, device and medium for improving station berth utilization, comprising the following steps: obtaining bus line operation information and station information of all stations in a stop corridor; obtaining an initial departure schedule considering the target of transfer coordination; establishing a decision path for departure schedule adjustment optimization, and constructing multiple decision paths with each station in the corridor as a selectable decision point; obtaining an adjustment optimization scheme of the initial departure schedule under each path with the target of improving berth utilization; comparing the total benefits of each path, selecting the most optimal decision path and the corresponding departure schedule adjustment scheme, and obtaining a final departure schedule; and the application can make vehicles arrive at the platform in a formation that maximizes the utilization of platform berth space, and realize the coordinated optimization of platform service capacity and scheduling.

Description

Technical Field

[0001] This invention belongs to the field of intelligent public transport operation management technology, specifically involving timetable optimization methods, systems, equipment, and media for improving station parking space utilization. Background Technology

[0002] Platform service capacity and vehicle scheduling are two key factors influencing the efficiency of Bus Rapid Transit (BRT) systems, and they are mutually influential and restrictive. On the one hand, platform service capacity is not only related to the planning and design of the platforms but is also greatly affected by bus scheduling. Reasonable bus scheduling can improve platform service capacity by increasing the utilization rate of platform parking spaces. On the other hand, if bus scheduling does not consider matching with platform doors and their service capacity, the expected effect of the scheduling plan will be significantly reduced. Therefore, as an important sub-issue of bus scheduling, the departure timetable needs to consider not only passenger time costs and bus operating costs but also its coordination with platform service capacity when formulating it.

[0003] Currently, most cities' BRT corridors adopt a mixed vehicle operation mode, with platforms using a straight-line parking layout that prohibits overtaking. However, this setup fails to fully utilize the platform's service capacity and increases vehicle waiting time, thus interfering with the effective implementation of vehicle scheduling plans. Firstly, due to the matching requirements between platform doors and vehicle doors and the differences in door positions among different vehicle types, using fixed platform doors limits the combination of vehicles that a station can serve simultaneously. Secondly, the fixed-size parking space does not take into account the differences between different vehicle types; short and long vehicles occupy the same parking space length, resulting in a waste of parking space. Invention patents CN113284363A, CN114529222A, 202211462164.7, 202310029269.1, and 202310073097.8 increase berth space utilization and improve station service capacity by optimizing station facilities. Specifically, this is achieved by configuring adaptive platform doors to enable flexible matching between vehicle doors and platform doors, as well as dynamic allocation of berths. Furthermore, existing research has shown that buses operating in platoons can not only reduce overall fuel consumption but also help improve the stability of bus operations. Therefore, it is necessary to propose a collaborative optimization method for departure timetables for mixed operations of multiple routes and vehicle types. Combining the aforementioned invention patents, this method considers both the conventional fixed berth operation mode and the adaptive berth operation mode proposed in the aforementioned invention patents, enabling vehicles to arrive at the station in platoons that maximize the use of station berth space, thereby achieving collaborative optimization of station service capacity and scheduling.

[0004] Existing literature on departure timetable optimization mostly focuses on passenger transfer coordination for multi-route coordination problems. Some literature also incorporates the goal of reducing public transport operating costs in the scenario of mixed operation of multiple vehicle types. However, none of them have addressed the consideration of platform service capacity or berth utilization. Since there is a mutual influence and constraint relationship between platform service capacity and departure timetable, timetables that do not take into account platform service capacity lack a certain degree of reliability, thus limiting the effectiveness of timetables in the public transport system. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a timetable optimization method, system, equipment, and medium for improving the utilization rate of station berths. It can achieve coordinated optimization of platform service capacity and scheduling by improving the berth utilization rate of platforms under multi-vehicle operation.

[0006] This invention is achieved through the following technical solution:

[0007] A timetable optimization method to improve station berth utilization includes the following steps:

[0008] S1: Obtain bus route operation information and station information for all stops in the corridor;

[0009] S2: Considering the goal of transfer coordination, the initial departure timetable is obtained;

[0010] S3: Establish decision paths for adjusting and optimizing departure timetables, and construct multiple decision paths with each station in the corridor as an optional decision point;

[0011] S4: To improve berth utilization, an adjustment and optimization scheme for the initial departure timetable under each route is obtained;

[0012] S5: Compare the total benefits of each route, select the route with the best benefits and the corresponding departure timetable adjustment plan, and obtain the final departure timetable.

[0013] Furthermore, the operational information includes the vehicle type information for each route, the operating time of each route, and the vehicle travel time between stations; the station information includes the routes served by each station, the berth configuration, and the available train formation types.

[0014] Furthermore, when optimizing the timetable of the corridor network, priority is determined based on the public transport traffic volume of each corridor, with corridors with high public transport traffic volume having higher priority than corridors with low public transport traffic volume.

[0015] Furthermore, the decision path construction process in step S3 is as follows:

[0016] S31: Label all stations in the corridor according to the direction of vehicle travel as b, select the station with the most service lines and classify it into the set of starting decision points, denoted as B, b∈B. If there are stations in this set that serve the same lines, then retain any one of the stations.

[0017] S32: Using each station in set B as the starting point of the decision tree, if set B has n stations, then there are n starting points for decision trees and n decision trees are generated. Let... Let be the starting point of the a-th decision tree;

[0018] S33: Select the next decision point for each decision tree starting point. The selection range of the next decision point is all stations in the corridor. The selection principle of the next decision point is: prioritize the station with the fewest and non-zero lines to be adjusted. If there are multiple stations with the same number of lines to be determined, generate multiple decision branches.

[0019] S34: Based on the selection principle of the next decision point, continuously select the next decision point. When there is no route to be adjusted in each decision path, stop selecting the next decision point and terminate the decision path.

[0020] Furthermore, in step S4, the adjustment and optimization scheme of the initial departure timetable for each route is obtained with the goal of improving berth utilization. This is achieved by establishing a station-based departure timetable adjustment and optimization model sequentially according to the station order in each route. The specific process is as follows:

[0021] S41: List all marshalling types that the current platform can serve and construct a set of marshalling types;

[0022] S42: Determine the initial arrival time and order of the vehicles based on the initial timetable or the results of the previous decision point;

[0023] S43: Construct decision variables;

[0024] S44: Constraints for constructing the optimization model;

[0025] S45: Calculate the offset from the initial timetable and berth space utilization;

[0026] S46: Construct an objective equation with the goal of maximizing berth space utilization;

[0027] S47: Based on the objective equation, decision variables, and constraints, an optimization model is obtained. The arrival times of the vehicles are solved from the optimization model, and the departure timetable of the routes served by the station is derived by reverse calculation.

[0028] Furthermore, the process of listing all the grouping types that the platform can serve and constructing a grouping type set in step S41 is as follows: Let the platform serve n pIf there are several grouping types, then p∈P={1,2,…,n} p A group of type p can be represented as in The number of vehicle types y1 in a group of type p;

[0029] The process in step S42 of determining the initial arrival time and arrival order of vehicles from the initial timetable or the result of the previous decision point is as follows: The initial arrival time of vehicle q is... Form a set F, Where yi represents the vehicle type q, and assuming the platform serves three types of vehicle types, then y i ∈Y={y1,y2,y3}, l represents the route to which vehicle q belongs, y i Let l be an attribute of vehicle q, determined by the initial departure timetable; the arrival order of vehicles is q∈Q={1,2,…,n}. q If any vehicle q in set Q exists on a route whose departure time has been optimized and adjusted within this decision path, then mark the corresponding vehicle q as q. * q * ∈Q, n q This represents the total number of vehicles arriving at the station during the operating hours.

[0030] Step S43 involves constructing decision variables, including:

[0031] Decision variable one: Discretize the operating time into k points, k∈K={0,1,2,…,n} k Set up a binary decision variable S. kp If S kp =1, which means that the train of type p arrives at the platform at time node k; otherwise, S kp =0; group j∈J={1,2,…,n j} represents the marshalling and arrival process, j is the marshalling and arrival order, and n is the total number of marshalling groups arriving at the station. j for:

[0032]

[0033] S kp ∈{0,1}# (2)

[0034] Arrival time d of train group j j for;

[0035] d j =ξk j #(3)

[0036] The type of group j is θ j for:

[0037]

[0038] Where ξ is the time interval between discrete points, k j Let k be the discrete time point of the j-th order group's arrival at the station. j ∈K;

[0039] Decision Variable Two: Setting up a Binary Decision Variable If vehicle q is assigned to group j, then otherwise

[0040]

[0041] Step S44 constructs the constraints of the optimization model, the constraints including:

[0042] Constraint 1: Each train can only be included in one trainset.

[0043]

[0044] Constraint 2: The time interval between the arrival of adjacent train sets is greater than the minimum time interval:

[0045] d min ≤d j -d j-1 #(7)

[0046] Constraint 3: The optimized vehicle arrival time must equal the initial arrival time plus the adjustment time.

[0047]

[0048] Constraint 4: The optimized order of vehicle arrivals must be consistent with the order of their initial arrival times.

[0049]

[0050] Constraint 5: Vehicles grouped together must arrive at the platform simultaneously.

[0051]

[0052] Constraint 6: The number of each vehicle type in the train is:

[0053]

[0054] Constraint 7: The optimized arrival group type must be one of the group types that the station can serve.

[0055]

[0056] Constraint 8: The departure timetables of routes that have already been optimized and adjusted in this decision path will not be adjusted again.

[0057]

[0058] Constraint 9: The adjustable time must not exceed the maximum limit.

[0059]

[0060] Where, d min This is the minimum arrival time interval between stations in the marshalling yard; The time required to adjust train number q for inclusion in train group j, in minutes; e max This is the maximum time adjustment value; This indicates the arrival time after optimization and adjustment; For binary variables, if group j is of type p, then otherwise

[0061] The process of calculating the offset from the initial timetable and the berth space utilization rate in step S45 is as follows: Assuming the offset from the initial timetable is e and the platform berth space utilization rate is ρ, then the offset of the adjusted timetable from the initial timetable is:

[0062]

[0063] The ratio of the average arriving train length to the platform berth space length is the platform berth utilization rate:

[0064]

[0065] Among them, l b l is the length of the platform berth space. P ly is the length of group p. i For y i The length of the vehicle is measured in meters (m).

[0066] S46: Considering minimizing the offset from the initial departure timetable, construct an objective equation to maximize berth space utilization:

[0067]

[0068] Where γ1 and γ2 are coefficients used to represent the difference in importance and dimensions between the two targets, and γ1 + γ2 = 1;

[0069] S47: An improved genetic algorithm is used to solve an optimization model with equation (18) as the objective equation and equations (1) to (17) as constraints. Then, based on the vehicle arrival times obtained from the optimization model, the departure timetable of the line served by the station is derived in reverse. The process is expressed as follows:

[0070]

[0071] in, This represents the departure time of vehicle q, where y i , l is an attribute of vehicle q, determined by the initial departure timetable. This indicates the average travel time from the departure point to the platform on line l to which vehicle q belongs.

[0072] Furthermore, the total benefit of each path is Among them, e t This is the sum of the offsets of all lines. This represents the average berth space utilization rate at stations along the decision path.

[0073] A collaborative optimization system for departure timetables based on berth utilization includes:

[0074] The data acquisition module is used to obtain bus route operation information and station information for all stops in the transit corridor;

[0075] The initial timetable acquisition module is used to obtain the initial departure timetable considering the transfer coordination target;

[0076] The decision path construction module is used to establish decision paths for adjusting and optimizing departure timetables, and to construct multiple decision paths with each station in the corridor as an optional decision point;

[0077] The adjustment and optimization module aims to maximize berth utilization and obtains the initial departure timetable adjustment and optimization scheme for each route.

[0078] The output module is used to compare the total benefits of each route, select the decision route with the best benefits and the corresponding departure timetable adjustment plan, and obtain the final departure timetable.

[0079] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the timetable optimization method for improving station berth utilization.

[0080] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the timetable optimization method for improving station berth utilization.

[0081] Compared with the prior art, the present invention has the following beneficial technical effects:

[0082] This invention provides a timetable optimization method, system, device, and medium to improve station berth utilization, comprising the following steps: acquiring bus route operation information and station information for all stops in the transit corridor; considering the goal of transfer coordination, obtaining an initial departure timetable; establishing decision paths for timetable adjustment and optimization, constructing multiple decision paths with each station in the corridor as optional decision points; obtaining adjustment and optimization schemes for the initial departure timetable under each path with the goal of improving berth utilization; comparing the total benefits of each path, selecting the decision path with the best benefits and the corresponding timetable adjustment scheme, and obtaining the final departure timetable; This application enables vehicles to arrive at the platform in convoy formations that maximize the use of platform berth space, achieving coordinated optimization of platform service capacity and scheduling. Furthermore, the station-based departure timetable optimization model adjusts and optimizes only the routes served by a single station at a time, resulting in low computational load. Secondly, by selecting the path with the best overall benefit from multiple decision paths, it avoids, to some extent, the local optima problem caused by station-based optimization models. Finally, this application can be directly applied to linear bus stops with fixed platform screen doors, which are currently used in most cities, and is also applicable to linear bus stops with adaptive platform screen doors, demonstrating strong versatility. Attached Figure Description

[0083] Figure 1 This is a flowchart of the timetable optimization method for improving station berth utilization according to the present invention;

[0084] Figure 2 This is a schematic diagram of the decision tree and decision path of the present invention;

[0085] Figure 3 This is a schematic diagram of the corridor and route in an embodiment of the present invention;

[0086] Figure 4 This is a schematic diagram of a linear platform berth with fixed platform doors in an embodiment of the present invention;

[0087] Figure 5 This is a schematic diagram of a linear platform berth configured with adaptive platform doors in an embodiment of the present invention;

[0088] Figure 6 This is a schematic diagram of the decision tree and decision path in an embodiment of the present invention;

[0089] Figure 7 This is a schematic diagram of vehicle arrival times at each station in decision path 1, which is an application scenario of the present invention. Detailed Implementation

[0090] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0091] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0092] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0093] This invention provides a timetable optimization method to improve the utilization rate of station parking spaces, such as... Figure 1 As shown, it includes the following steps:

[0094] S1: Obtain bus route operation information and station information for all stops in the corridor;

[0095] S2: Considering the goal of transfer coordination, the initial departure timetable is obtained;

[0096] S3: Establish decision paths for adjusting and optimizing departure timetables, and construct multiple decision paths with each station in the corridor as an optional decision point;

[0097] S4: To improve berth utilization, an adjustment and optimization scheme for the initial departure timetable under each route is obtained;

[0098] S5: Compare the total benefits of each route, select the route with the best benefits and the corresponding departure timetable adjustment plan, and obtain the final departure timetable.

[0099] Preferably, when optimizing the timetable of the corridor network, priority is determined based on the traffic volume of each corridor, with corridors with high traffic volume having higher priority than those with low traffic volume. According to the timetable optimization method of this invention, each corridor is optimized sequentially according to priority. For stations traversed by multiple corridors, multiple single-corridor optimization processes are performed. When optimizing a low-priority corridor, the optimized vehicle arrival time of that corridor must not conflict with the vehicle arrival time optimized by a high-priority corridor. Furthermore, for routes stopping at multiple corridors, the optimization result obtained in the high-priority corridor serves as a fixed condition for optimizing the next priority corridor.

[0100] It should be noted that the initial departure timetable is obtained by considering the goal of transfer coordination. There is already a large body of literature on transfer coordination in the existing technology, which is not within the scope of protection of this application and will not be elaborated here.

[0101] Preferably, the operational information includes the vehicle type information for each route, the operating time of each route, and the vehicle travel time between stations; the station information includes the routes served by each station, the berth configuration, and the available train formation types.

[0102] Preferably, the decision path construction process in step S3 is as follows:

[0103] S31: Label all stations in the corridor according to the direction of vehicle travel as b, select the station with the most service lines and classify it into the set of starting decision points, denoted as B, b∈B. If there are stations in this set that serve the same lines, then retain any one of the stations.

[0104] S32: Using each station in set B as the starting point of the decision tree, if set B has n stations, then there are n starting points for decision trees and n decision trees are generated. Let... Let be the starting point of the a-th decision tree;

[0105] S33: Select the next decision point for each decision tree starting point. The selection range of the next decision point is all stations in the corridor. The selection principle of the next decision point is: prioritize the station with the fewest and non-zero lines to be adjusted. If there are multiple stations with the same number of lines to be determined, generate multiple decision branches.

[0106] S34: Based on the selection principle of the next decision point, continuously select the next decision point. When there is no route to be adjusted in any of the decision paths, stop selecting the next decision point and terminate that decision path. Specifically, such as... Figure 2 As shown, assuming the initial decision point set contains n stations, n decision trees are generated. The decision trees and decision paths are illustrated below. Figure 1 As shown, for example Let n be a decision path in the decision tree n.

[0107] Preferably, in step S4, with the goal of improving berth utilization, the adjustment and optimization scheme for the initial departure timetable under each route can be as follows: A station-based departure timetable optimization and adjustment model is constructed sequentially according to the station order in each route. The specific process is as follows:

[0108] S41: List all marshalling types that the current platform can serve and construct a set of marshalling types. Let n be the number of marshalling types that the platform can serve. p If there are several grouping types, then p∈P={1,2,…,n} p A group of type p can be represented as in This represents the number of vehicle types y1 in a group of type p.

[0109] S42: Determine the initial arrival time and order of vehicles based on the initial timetable or the result of the previous decision point. The initial arrival time of vehicle q is... Form a set F, Among them, y i Let y represent the vehicle type q. Assume the platform serves three types of vehicle types. i ∈Y={y1,y2,y3}, l represents the route to which vehicle q belongs, y i Let l be an attribute of vehicle q, determined by the initial departure timetable; the arrival order of vehicles is q∈Q={1,2,…,n}. q If any vehicle q in set Q exists on a route whose departure time has been optimized and adjusted within this decision path, then mark the corresponding vehicle q as q. * q * ∈Q, n q This represents the total number of vehicles arriving at the station during the operating hours.

[0110] S43: Construct decision variables. The optimization model has two decision variables, as described below:

[0111] Decision variable one: Discretize the operating time into k points, k∈K={0,1,2,…,n} k Set up a binary decision variable S. kp If S kp =1, which means that the train of type p arrives at the platform at time node k; otherwise, S kp =0; group j∈J={1,2,…,n j} represents the marshalling and arrival process, j is the marshalling and arrival order, and n is the total number of marshalling groups arriving at the station. j for:

[0112]

[0113] S kp∈{0,1}#(2)

[0114] Arrival time d of train group j j for;

[0115] d j =ξk j #(3)

[0116] The type of group j is θ j for:

[0117]

[0118] Where ξ is the time interval between discrete points, k j Let k be the discrete time point of the j-th order group's arrival at the station. j ∈K;

[0119] Decision Variable Two: Setting up a Binary Decision Variable If vehicle q is assigned to group j, then otherwise

[0120]

[0121] S44: Construct the constraints for the optimization model, the constraints including:

[0122] Constraint 1: Each train can only be included in one trainset.

[0123]

[0124] Constraint 2: The time interval between the arrival of adjacent train sets is greater than the minimum time interval:

[0125] d min ≤d j -d j-1 #(7)

[0126] Constraint 3: The optimized vehicle arrival time must equal the initial arrival time plus the adjustment time.

[0127]

[0128] Constraint 4: The optimized order of vehicle arrivals must be consistent with the order of their initial arrival times.

[0129]

[0130] Constraint 5: Vehicles grouped together must arrive at the platform simultaneously.

[0131]

[0132] Constraint 6: The number of each vehicle type in the train is:

[0133]

[0134] Constraint 7: The optimized arrival group type must be one of the group types that the station can serve.

[0135]

[0136] Constraint 8: The departure timetables of routes that have already been optimized and adjusted in this decision path will not be adjusted again.

[0137]

[0138] Constraint 9: The adjustable time must not exceed the maximum limit.

[0139]

[0140] Where, d min This is the minimum arrival time interval between stations in the marshalling yard; The time required to adjust train number q for inclusion in train group j, in minutes; e max This is the maximum time adjustment value; This indicates the arrival time after optimization and adjustment; For binary variables, if group j is of type p, then otherwise

[0141] S45: Calculate the offset from the initial timetable and the berth space utilization rate. Assuming the offset from the initial timetable is e, and the platform berth space utilization rate is ρ, then the offset of the adjusted timetable from the initial timetable is:

[0142]

[0143] The ratio of the average arriving train length to the platform berth space length is the platform berth utilization rate:

[0144]

[0145] Among them, l b l is the length of the platform berth space. P ly is the length of group p. i For y i The length of the vehicle is measured in meters (m).

[0146] S46: Considering minimizing the offset from the initial departure timetable, construct an objective equation to maximize berth space utilization:

[0147]

[0148] Where Y1 and γ2 are coefficients representing the difference in importance and dimensions between the two targets, and γ1 + γ2 = 1.

[0149] S47: Use an improved genetic algorithm to solve the optimization model with equation (18) as the objective equation and equations (1) to (17) as constraints. Then, based on the vehicle arrival time obtained from the model, the departure timetable of the line served by the station is obtained by reverse deduction.

[0150]

[0151] in, This represents the departure time of vehicle q, where y i , l is an attribute of vehicle q, determined by the initial departure timetable. This indicates the average travel time from the departure point to the platform on line l to which vehicle q belongs.

[0152] Preferably, the total benefit of each path is Among them, e t This is the sum of the offsets of all lines. This represents the average berth space utilization rate at stations along the decision path.

[0153] This invention provides an embodiment with a single corridor as the background and including two application scenarios:

[0154] S1: Obtain the operation information and station information of bus routes passing through the corridor:

[0155] There are six stops in the bus corridor, and four bus routes stop at these stops. Specific operational information is as follows: Figure 3 As shown, the operating hours selected in this embodiment are (8:00 a.m. to 9:00 a.m.). Route 1 and Route 2 operate two types of vehicles, namely 11.98-meter-long vehicles and 31.64-meter-long vehicles, while Route 3 and Route 4 only operate 11.98-meter-long vehicles.

[0156] The berth space length at each station in the corridor is 74.5 meters. In application scenario one, all bus stops in the corridor are straight-line bus stops equipped with fixed platform screen doors. Each station contains two fixed berths, each 32 meters long. Let y1 be a vehicle 11.98 meters long and y2 be a vehicle 31.64 meters long. Stations with fixed berths can serve six train formation types: {y1}, {y1, y1}, {y2}, {y1, y2}, and {y2, y2}. In application scenario two, all bus stops in the corridor are equipped with fixed platform screen doors. The linear bus platform with adaptive station doors can adaptively adjust the number and length of parking spaces according to the arrival of vehicles. It can serve 10 train formation types: {y1}, {y2}, {y1, y1}, {y1, y2}, {y2, y2}, {y1, y1, y1}, {y1, y1, y2}, {y1, y1, y1, y1}, {y1, y1, y1, y1}. The parking space configurations for both types of platforms are as follows: Figure 4 and Figure 5 As shown.

[0157] S2: Considering the goal of transfer coordination, the initial departure timetable is obtained, as shown in Table 1:

[0158] Table 1

[0159]

[0160] S3: Establish decision paths for optimizing departure timetables. Multiple decision paths are constructed using stations in the corridor as selectable decision points. All stations in the corridor are labeled 'b' according to the vehicle's direction of travel. The station with the most service routes is selected and assigned to the starting decision point set B. In this embodiment, the stations with the most service routes are stations 2, 3, and 6, and the routes served by each station are not entirely the same. Therefore, b∈B={2, 3, 6}. Using each station in set B as the starting point of the decision tree, and based on the principle of decision path generation, the decision tree and decision paths in this embodiment are as follows: Figure 6 As shown.

[0161] S4: With the goal of improving berth utilization, we obtained the adjustment and optimization schemes for the initial departure timetables under each route.

[0162] In this embodiment, considering the differences in importance and dimensions between berth space utilization and offset, the objective equation... Let γ1 = 0.4 and γ2 = 0.6. An improved genetic algorithm is used to solve for each decision path, and the total benefits of each path are compared to select the optimal decision path and the corresponding timetable adjustment scheme.

[0163] For decision path 1, firstly, taking station 2 as the decision station, adjust the departure timetables for routes 1 and 3. The specific steps are as follows:

[0164] First, list all the marshalling types that the current platform can serve and construct the corresponding sets. The following sections describe all the marshalling types that the platform can serve under two different application scenarios:

[0165] Application Scenario 1: The berth space at each station in the corridor is 74.5 meters long, and each station contains two fixed berths with a length of 32 meters.

[0166] In this scenario, the decision-making station can serve six grouping types: {y1}, {y1, y1}, {y2}, {y1, y2}, and {y2, y2}. Let {y1} be grouping type 1 and {y2, y2} be grouping type 6. Grouping type 1 can be represented as Grouping type 6 can be represented as The representations for other grouping types can be obtained similarly.

[0167] Application Scenario 2: The berth space length of each station in the corridor is 74.5 meters, and all stations are straight bus stops equipped with adaptive platform screen doors.

[0168] In this scenario, the decision-making platform can serve 10 group types, namely {y1}, {y2}, {y1, y1}, {y1, y2}, {y2, y2}, {y1, y1, y1}, {y1, y1, y2}, {y1, y1, y1, y1}, and {y1, y1, y1, y1, y1}. Let {y1} be group 1 and {y1, y1, y1, y2} be group 10. Grouping type 1 can be represented as Group type 10 can be represented as The representations for other grouping types can be obtained similarly.

[0169] Secondly, based on the initial departure timetable, calculate the arrival time of vehicles on routes 1 and 3 at station 2, and sort the initial arrival times of the vehicles.

[0170] Secondly, construct the decision variable S. kp and Let d min =2min, e max =9min, construct the corresponding constraints. And with the objectives of maximizing berth space utilization and minimizing the offset from the initial timetable, construct the objective equation.

[0171] Finally, by improving the genetic algorithm to solve the model, the arrival time of each vehicle was obtained, and the departure timetables of routes 1 and 3 were deduced.

[0172] The next decision station in decision path 1 is designated as station 3, which is then modeled and solved as the target station. Station 3 serves routes 1 and 2. Route 1 has already been adjusted and optimized in this decision path, so it is used as a fixed condition for adjusting and optimizing route 2. The other steps are the same as the adjustment process for station 2, and will not be repeated here.

[0173] The optimization and adjustment process for other decision-making paths can be obtained in the same way, and will not be elaborated here.

[0174] S5: Calculate and compare the total benefits of the 6 decision paths, select the decision path with the best total benefit and the corresponding departure timetable adjustment plan, and obtain the final departure timetable.

[0175] Taking decision path 1 in application scenario 1 as an example, the solution results for its departure timetable are shown in Table 2:

[0176] Table 2

[0177]

[0178] The total offset for route 1 is 0 minutes, for route 2 it is 21 minutes, for route 3 it is 8 minutes, and for route 4 it is 10 minutes. Furthermore, in application scenario one, the vehicle arrival times at each station in decision route 1 are as follows: Figure 7 As shown in the diagram. Station 2 has 12 arriving train formations with an average formation length of 29.87m and a berth utilization rate of 0.40; Station 3 has 9 arriving train formations with an average formation length of 29.85m and a berth utilization rate of 0.40; Station 6 has 7 arriving train formations with an average formation length of 28.88m and a berth utilization rate of 0.39. Therefore, the total benefit of decision path 1 in application scenario 1 is calculated as follows:

[0179]

[0180] Similarly, the total benefits of each decision path under both application scenarios can be obtained. In application scenario one, the total benefit of decision path 1 is the best, at 1.82. Therefore, the result of this path is used as the final departure timetable for application scenario one, as shown in Table 2. In application scenario two, the total benefit of decision path 3 is the best, at 1.78. Therefore, the result of this path is used as the final departure timetable for application scenario two, as shown in Table 3.

[0181] Table 3

[0182]

[0183] This invention provides a collaborative optimization system for departure timetables based on berth utilization, comprising:

[0184] The data acquisition module is used to obtain bus route operation information and station information for all stops in the transit corridor;

[0185] The initial timetable acquisition module is used to obtain the initial departure timetable considering the transfer coordination target;

[0186] The decision path construction module is used to build decision paths for adjusting and optimizing the departure timetable based on the initial departure timetable, and to construct multiple decision paths with each station in the corridor as an optional decision point;

[0187] The adjustment and optimization module aims to maximize berth utilization and obtains the initial departure timetable adjustment and optimization scheme for each route.

[0188] The output module is used to compare the total benefits of each route, select the decision route with the best benefits and the corresponding departure timetable adjustment plan, and obtain the final departure timetable.

[0189] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or function. The processor described in this embodiment of the present invention can be used to operate a timetable optimization method to improve the utilization rate of station berths.

[0190] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the timetable optimization method for improving station berth utilization in the above embodiments.

[0191] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0192] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0193] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0194] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0195] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A timetable optimization method to improve the utilization rate of station berths, characterized in that, Includes the following steps: S1: Obtain bus route operation information and station information for all stops in the corridor; S2: Considering the goal of transfer coordination, the initial departure timetable is obtained; S3: Establish decision paths for adjusting and optimizing departure timetables, and construct multiple decision paths with each station in the corridor as an optional decision point; The decision path construction process is as follows: S31: All stations within the corridor are numbered according to the direction of vehicle travel. The station with the most service lines is selected and grouped into the initial decision point set, denoted as . , If any stations in the set serve the same routes, then any one of those stations will be retained. S32: Using sets respectively Each station in the set serves as the starting point of the decision tree. There is For each site, there are A decision tree starting point and generate A decision tree, set For the first The starting point of a decision tree; S33: Select the next decision point for each decision tree starting point. The selection range of the next decision point is all stations in the corridor. The selection principle of the next decision point is: prioritize the station with the fewest and non-zero lines to be adjusted. If there are multiple stations with the same number of lines to be determined, generate multiple decision branches. S34: Based on the selection principle of the next decision point, continuously select the next decision point. When there is no route to be adjusted in each decision path, stop selecting the next decision point and terminate the decision path. S4: To improve berth utilization, an adjustment and optimization scheme for the initial departure timetable under each route is obtained; To improve berth utilization, an adjustment and optimization scheme for the initial departure timetable under each route was obtained. This was achieved by sequentially establishing a station-based departure timetable adjustment and optimization model according to the order of stations along each route. The specific process is as follows: S41: List all marshalling types that the current platform can serve and construct a set of marshalling types; S42: Determine the initial arrival time and order of the vehicles based on the initial timetable or the results of the previous decision point; S43: Construct decision variables; S44: Constraints for constructing the optimization model; S45: Calculate the offset from the initial timetable and berth space utilization; S46: Construct an objective equation with the goal of maximizing berth space utilization; S47: Based on the objective equation, decision variables and constraints, an optimization model is obtained. Based on the vehicle arrival time obtained from the optimization model, the departure timetable of the line served by the station is derived in reverse. S5: Compare the total benefits of each route, select the route with the best benefits and the corresponding departure timetable adjustment plan, and obtain the final departure timetable.

2. The timetable optimization method for improving station berth utilization according to claim 1, characterized in that, The operational information includes the vehicle type information for each route, the operating time of each route, and the vehicle travel time between stations; the station information includes the routes served by each station, the berth configuration, and the available train formation types.

3. The timetable optimization method for improving station berth utilization according to claim 1, characterized in that, When optimizing the timetable of the corridor network, priority is determined based on the public transport traffic volume of each corridor, with corridors with high public transport traffic volume having higher priority than corridors with low public transport traffic volume.

4. The timetable optimization method for improving station berth utilization according to claim 1, characterized in that, The process of listing all marshalling types that the platform can serve and constructing a set of marshalling types in step S41 is as follows: Assume that the platform can serve Grouping type, then , type The grouping can be represented as ,in for Number of car models; The process in step S42 of determining the initial arrival time and arrival order of the vehicle from the initial timetable or the result of the previous decision point is as follows: Vehicle Initial arrival time is , forming a set , ,in, Indicates vehicle If there are three types of vehicle types available for platform service, then... , Indicates vehicle The route it belongs to, , For vehicles The attributes are determined by the initial departure timetable; the arrival order of the vehicles is... ,gather If a route already included in this decision path has had its departure times optimized and adjusted, then the corresponding vehicles will be... Marked as , , This represents the total number of vehicles arriving at the station during the operating hours. Step S43 involves constructing decision variables, including: Decision variable one: Discretize the operating time into k points. , Set up binary decision variables ,like If it is true, it means that the train of type p arrives at the platform at time node k; otherwise... Grouping This indicates the process of marshalling and arriving at the station. For the order of arrival of marshalling trains, the total number of marshalling trains arriving at each station. for: （1） （2） Grouping Arrival time for; （3） Grouping The type is for: （4） in, The time interval between discrete points. For the first Discrete time points of arrival at the station in sequence grouping. ; Decision Variable Two: Setting up a binary decision variable If the vehicle Incorporated into the group In the middle, then ,otherwise , （5） Step S44 constructs the constraints of the optimization model, the constraints including: Constraint 1: Each train can only be included in one trainset. （6） Constraint 2: The time interval between the arrival of adjacent train sets is greater than the minimum time interval: （7） Constraint 3: The optimized vehicle arrival time must equal the initial arrival time plus the adjustment time. （8） Constraint 4: The optimized order of vehicle arrivals must be consistent with the order of their initial arrival times. （9） Constraint 5: Vehicles grouped together must arrive at the platform simultaneously. （10） Constraint 6: The number of each vehicle type in the train is: （11） Constraint 7: The optimized arrival group type must be one of the group types that the station can serve. （12） （13） Constraint 8: The departure timetables of routes that have already been optimized and adjusted in this decision path will not be adjusted again. （14） Constraint 9: The adjustable time must not exceed the maximum limit. （15） in, This refers to the minimum time interval between arrival stations between train sets; for Train number included in formation The time to be adjusted is in minutes; This is the maximum time adjustment value; This indicates the arrival time after optimization and adjustment; If it is a binary variable, if grouped For type but ,otherwise ; The process of calculating the offset from the initial timetable and the berth space utilization rate in step S45 is as follows: assuming the offset from the initial timetable is... The utilization rate of platform berth space is The offset between the adjusted timetable and the initial timetable is: （16） The ratio of the average arriving train length to the platform berth space length is the platform berth utilization rate: （17） in, The length of the platform berth space. For grouping Length, ; S46: Considering minimizing the offset from the initial departure timetable, construct an objective equation to maximize berth space utilization: （18） in, and , where is a coefficient used to represent the difference in importance and dimensions between two objectives, and ; S47: An improved genetic algorithm is used to solve an optimization model with equation (18) as the objective equation and equations (1) to (17) as constraints. Then, based on the vehicle arrival times obtained from the optimization model, the departure timetable of the line served by the station is derived in reverse. The process is expressed as follows: （19） in, Indicates vehicle Departure time, among which , For vehicles The attributes are determined by the initial departure timetable; Indicates vehicle Line The average travel time from the departure point to the platform.

5. The timetable optimization method for improving station berth utilization according to claim 4, characterized in that, The total benefit of each path is ,in, This is the sum of the offsets of all lines. This represents the average berth space utilization rate at stations along the decision path.

6. A collaborative optimization system for departure timetables based on berth utilization, characterized in that, The timetable optimization method for improving station berth utilization based on any one of claims 1-5 includes: The data acquisition module is used to obtain bus route operation information and station information for all stops in the transit corridor; The initial timetable acquisition module is used to obtain the initial departure timetable considering the transfer coordination target; The decision path construction module is used to establish decision paths for adjusting and optimizing departure timetables, and to construct multiple decision paths with each station in the corridor as an optional decision point; The adjustment and optimization module aims to maximize berth utilization and obtains the initial departure timetable adjustment and optimization scheme for each route. The output module is used to compare the total benefits of each route, select the decision route with the best benefits and the corresponding departure timetable adjustment plan, and obtain the final departure timetable.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the timetable optimization method for improving station berth utilization as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the timetable optimization method for improving station berth utilization as described in any one of claims 1-5.

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