A method and system for allocating public transport passenger flow

Abstract

This invention relates to the field of public transport passenger flow allocation technology, and specifically to a public transport passenger flow allocation method and system. The method includes acquiring first information, which includes a set of bus routes, a set of stations, and a set of bus trips; constructing a public transport network based on the first information; constructing an event-activity extended network based on the public transport network, the event-activity extended network including spatiotemporal nodes, time-independent nodes, and arcs connecting the nodes, with arcs having different priorities; constructing equilibrium conditions for user equilibrium priorities based on the event-activity extended network; and establishing a passenger flow allocation model based on the equilibrium conditions for user equilibrium priorities. The passenger flow allocation model is used to calculate the passenger flow allocation results. This invention, by solving the passenger flow allocation model, enables passengers' route selection and traffic distribution to reach a stable equilibrium state, thereby achieving a rational allocation of public transport passenger flow.

Description

Technical Field

[0001] This invention relates to the field of public transport passenger flow allocation technology, and more specifically, to a public transport passenger flow allocation method and system. Background Technology

[0002] In the field of traffic engineering, traffic assignment has long been a core issue, with existing technologies mainly falling into two categories: frequency-based traffic assignment and timetable-based traffic assignment. Timetable-based traffic assignment, in particular, has shortcomings in practical applications, especially when based on static models. It often uses an all-or-nothing approach to assign passengers, ignoring passenger priorities and queuing conditions when vehicles are fully loaded, leading to inaccurate estimates of actual passenger flow and waiting times. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for allocating public transport passenger flow to improve the above-mentioned problems.

[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0005] On the one hand, embodiments of this application provide a method for allocating public transport passenger flow, the method comprising:

[0006] Obtain first information, which includes a set of bus routes, a set of stations, and a set of bus trips;

[0007] Construct a public transportation network based on the first information;

[0008] An event-activity extended network is constructed based on the public transportation network. The event-activity extended network includes spatiotemporal nodes, time-independent nodes, and arcs connecting the nodes, and the arcs have different priorities.

[0009] Based on the event-activity extended network, a user priority balancing condition is constructed;

[0010] A passenger flow allocation model is established based on the equilibrium conditions of the user equilibrium priority, and the passenger flow allocation model is used to calculate the passenger flow allocation result.

[0011] Secondly, embodiments of this application provide a public transport passenger flow allocation system, the system comprising:

[0012] The first acquisition module is used to acquire first information, which includes a set of bus routes, a set of stations, and a set of bus trips.

[0013] The first processing module is used to construct a public transportation network based on the first information;

[0014] The second processing module is used to construct an event-activity extended network based on the public transportation network. The event-activity extended network includes spatiotemporal nodes, time-independent nodes, and arcs connecting the nodes, and the arcs have different priorities.

[0015] The third processing module is used to construct the balancing conditions for user balancing priorities based on the event-activity extended network;

[0016] The fourth processing module is used to establish a passenger flow allocation model based on the equilibrium conditions of the user equilibrium priority, and the passenger flow allocation model is used to calculate the passenger flow allocation result.

[0017] Thirdly, embodiments of this application provide a public transport passenger flow allocation device, the device including a memory and a processor. The memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the above-described public transport passenger flow allocation method.

[0018] Fourthly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described bus passenger flow allocation method.

[0019] The beneficial effects of the present invention are:

[0020] This invention establishes an event-activity extended network and constructs equilibrium conditions for user priority based on the event-activity extended network. Then, it establishes a passenger flow allocation model based on the equilibrium conditions for user priority and solves the passenger flow allocation model to achieve a stable equilibrium state between passenger route selection and flow allocation, thereby realizing the rational allocation of public transport passenger flow. In addition, the passenger flow allocation model in this invention can directly simulate the impact of passenger priority on allocation without relying on complex dynamic network loading, thereby greatly reducing the computational cost.

[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1This is a schematic diagram of the bus passenger flow allocation method described in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the bus passenger flow distribution system described in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the bus passenger flow distribution equipment described in an embodiment of the present invention.

[0026] Figure 4 A diagram illustrating the event-activity extended network.

[0027] The diagram is labeled as follows: 800, Bus passenger flow distribution equipment; 801, Processor; 802, Memory; 803, Multimedia component; 804, I / O interface; 805, Communication component; 901, First acquisition module; 902, First processing module; 903, Second processing module; 904, Third processing module; 905, Fourth processing module. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0029] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] Example 1:

[0031] This embodiment provides a method for allocating public transport passenger flow. It can be understood that this embodiment can be used to set up a scenario, such as: in a metropolis, public transport passenger flow is large and routes are complex. This invention is used to calculate the distribution of public transport passenger flow, accurately obtain the passenger load of vehicles running at any time, and rationally plan public transport resources based on this data.

[0032] See Figure 1The figure shows that the method includes steps S1, S2, S3, S4 and S5.

[0033] Step S1: Obtain first information, which includes a set of bus routes, a set of stations, and a set of bus trips;

[0034] Step S2: Construct a public transportation network based on the first information;

[0035] In this step, the public transport network constructed is as follows: ,in Indicates a collection of bus routes, Indicates meeting at the station. This represents the collection of bus routes, for each bus line. Consists of a series of orderly stations Composition, dividing the line into There are 10 segments, each segment is denoted as 1. ,in, and These correspond to the end and beginning stations of a section, respectively. The journey is a specific example of a bus traveling along a route according to a predetermined timetable.

[0036] Step S3: Construct an event-activity extended network based on the public transportation network. The event-activity extended network includes spatiotemporal nodes, time-independent nodes, and arcs connecting the nodes, with the arcs having different priorities.

[0037] In this step, the event-activity extension network is specifically as follows: Figure 4 As shown, the spatiotemporal nodes include train arrival nodes. , , , and passenger entrance nodes , Time-independent nodes include the starting point r and the ending point s; spacetime nodes. The timestamp is ,in, Represents a node (corresponding to the time of the event), the passenger entry node is used to model departure time selection, and its timestamp is usually aligned with the departure time of the train.

[0038] It should be noted that arcs in the event-activity extended network are divided into two categories: system entry arcs and time-lapse arcs. System entry arcs include access arcs and exit arcs. Among them, the access arc represents the arc from the starting point. To any entry node The initial activity of passengers entering the public transport network from their departure point. The departure arc represents the arrival node from the departure point. To the finish line The time progression arc represents the activities of passengers after leaving the public transportation system and arriving at their destination. It includes the in-vehicle arc, the stop arc, the walking arc, the transfer arc, and the boarding arc. The in-vehicle arc represents the movement of the bus between two stops. The stop arc represents the stopping process at a stop. The walking arc represents the arrival node connecting adjacent stations. With the entry node ,satisfy , For the station to The transfer arc represents the time spent walking between different stations for transferring between different bus lines. The boarding arc represents the process from waiting to actually boarding the target vehicle.

[0039] Arc cost calculation is the core mechanism for determining passenger behavior decisions. Each type of arc has a corresponding cost calculation method, reflecting the actual experience of passengers moving within the network. Basic arc costs mainly include three categories: in-vehicle arcs, departure arcs, and access arcs. The specific calculation process is as follows:

[0040] For the inner arc of the car The cost calculation formula is:

[0041] ;

[0042] This formula contains three components:

[0043] Time cost: , indicating travel time Multiply by the time value factor Ticket cost: This indicates the ticket price for that segment of the journey; overcrowding penalty: When the occupancy rate exceeds the congestion threshold This will increase additional costs. This represents the congestion penalty coefficient. Indicates the flow rate within the arc of the vehicle. Indicates the capacity of the inner arc of the vehicle. This indicates the passenger load factor.

[0044] For the departure arc Cost is related to the time to reach the destination:

[0045] ;

[0046] in, Indicates passenger group The earliest expected arrival time Indicates passenger group The expected latest arrival time, Indicates from node To the node The walking time, due to this place It is an exit arc, therefore the node This indicates that passengers will disembark at the last stop on the route and begin walking to their destination; This indicates how much earlier the passenger arrived than their expected earliest arrival time. This indicates how much later a passenger arrives compared to their expected latest arrival time. This formula considers three cases: Early arrival penalty: when the arrival time is earlier than the lower limit of the expected arrival time window... Early arrival penalty coefficient; Late arrival penalty: When the arrival time is later than the upper limit of the expected arrival time window, Late arrival penalty coefficient; On-time arrival: only walking time cost is calculated. Indicates passenger group The expected arrival time window can be divided equally at fixed intervals.

[0047] For access arc Cost is related to the latest departure time:

[0048] ;

[0049] in, Indicates passenger group The latest departure time is the latest time a passenger must depart to ensure they arrive at their destination within the expected arrival time window. This indicates the penalty coefficient for departing early; This indicates the departure delay penalty, which is how much time a passenger departed earlier than the latest departure time.

[0050] Understandably, for all types of arcs other than the basic arc, only the time cost needs to be calculated.

[0051] Step S4: Construct the equilibrium conditions for user equilibrium priorities based on the event-activity extended network;

[0052] In this step, the in-vehicle arc flow is limited by the in-vehicle arc capacity. To characterize the priority rules, nodes are defined. The ordered set of the incoming arc is composed of nodes The set of all predecessor nodes is denoted as:

[0053] ;

[0054] Among them, the stop arc has the highest priority; this type of arc indicates that the vehicle is at the station. After a stop, the train continues to the next station, prioritizing passengers already on board. Other types of arcs are ordered by passenger arrival time; the earlier a passenger arrives, the higher their arc appears in the set. For example, τ(a) arrives earlier than τ(c), which arrives earlier than τ(d). Figure 4 As shown, the ordered set of node e entering the arc is: .

[0055] It is understandable that passenger route selection is constrained by dynamic priority rules, specifically including:

[0056] 1. Retention Priority: Seats are automatically reserved for passengers already on the train (e.g., from train number [missing information]). Passengers do not need to queue again upon departure.

[0057] 2. First-Come, First-Served (FCFS): Passengers queue in order of arrival time, with those arriving earlier having priority to board. To quantify the impact of priority, available capacity is defined. :

[0058] ;

[0059] in For the inner arc of the car capacity, For higher priority Incoming arc flow rate, Represents a node The ordered set of the incoming arc, This indicates priority. Under a priority system, passengers board in a first-come, first-served order. Passengers with higher priority use the available capacity first, while passengers with lower priority can only use the remaining capacity. The priority is calculated by subtracting the flow of higher-priority passengers. The available capacity that passengers can actually use.

[0060] path Available capacity The minimum available capacity for all arcs related to boarding:

[0061] ;

[0062] in It belongs to the path The collection of all stops, transfers and boarding arcs.

[0063] To make the model easy to quantify, for each passenger group Introduced cost boundary to indicate The highest acceptable path cost in path selection within the group, and the equilibrium conditions that define user equilibrium priorities, are expressed as follows:

[0064] 1. If the path Available ( ), its cost No less than passenger group Cost boundary ;

[0065] 2. If the path Used ( ), its cost Not higher than .

[0066] Cost Boundary Essentially, it is a dynamically adjusted cost boundary, with its value falling between the maximum cost of used paths and the minimum cost of available paths. It plays a crucial role in the model, continuously adjusting to meet the equilibrium conditions of user priority as the model iterates, thereby achieving a stable balance in passenger route selection and traffic allocation, and ultimately realizing the rational distribution of public transport passenger flow.

[0067] Step S5: Establish a passenger flow allocation model based on the equilibrium conditions of the user equilibrium priority. The passenger flow allocation model is used to calculate the passenger flow allocation result.

[0068] Step S5 further includes steps S51, S52, S53, and S54, which specifically include:

[0069] Step S51: Obtain the path cost gap vector and anxiety cost vector;

[0070] It should be noted that the anxiety cost vector By all composition, The anxiety cost corresponding to arc (i, j) is the anxiety cost in this invention. It is not predetermined, but determined through the solution process. Its value satisfies the condition of the available arc capacity. Complementary relationship:

[0071] ;

[0072] This means that when the arc has remaining capacity ( The cost of anxiety is zero. When the cost of anxiety is positive ( Arc capacity has been exhausted. The specific calculation process is as follows:

[0073] 1. Initially set the anxiety cost for all arc segments. ;

[0074] 2. Calculate the generalized arc cost based on the current anxiety cost;

[0075] 3. Obtain arc flow by performing traffic assignment. ;

[0076] 4. Calculate the available capacity for each arc. ;

[0077] 5. Update the cost of anxiety To make them satisfy the complementary condition;

[0078] 6. Repeat steps 2-5 until convergence.

[0079] Step S52: Based on the path cost gap vector, transform the equilibrium condition of the user equilibrium priority into a first nonlinear complementary problem model;

[0080] In this step, the equilibrium condition for user priority is transformed into a first nonlinear complementary problem model, specifically as follows:

[0081] ;

[0082] in, This is a path flow vector, representing the number of passengers allocated to each path; Traffic situation is The path cost vector at time; M is the path cost difference vector; For example, a path-passenger group association matrix. Used to indicate the path Belongs to group 1 indicates yes, 0 indicates no; d represents the cost boundary for each group; d is the traffic demand vector. Traffic situation is The available capacity vector of the path. Formula 1 ensures that passengers only choose the path with the lowest generalized cost, Formula 2 ensures that the total flow of each group of passengers equals its demand, and Formula 3 is used to quantify the orthogonal relationship between the available capacity of the path and the path cost difference vector.

[0083] Step S53: Optimize the first nonlinear complementarity problem model based on the anxiety cost vector to obtain the second nonlinear complementarity problem model;

[0084] Because the first nonlinear complementarity problem model suffers from problems such as non-additivity of generalized costs and unclear physical meaning of variables, in order to solve this problem, the path-dependent path cost gap vector is decomposed onto arc segments, and arc-dependent anxiety cost variables are introduced to obtain the second nonlinear complementarity problem model, as follows:

[0085] ;

[0086] In the above formula, Indicates the available capacity of the arc segment; Representing the path-arc correlation matrix, for example Used to indicate arc segments Belongs to path 1 indicates yes, 0 indicates no; This represents the anxiety cost vector for passengers waiting at the station.

[0087] Step S54: Construct a passenger flow allocation model based on the second nonlinear complementary problem model.

[0088] Step S54 further includes steps S541, S542, and S543, which specifically include:

[0089] Step S541: Preset the complementary residual function of a single arc in the public transportation network;

[0090] In this step, the complementary residual function of a single arc is specifically as follows:

[0091] ;

[0092] In the above formula, The residual function representing a single arc. and Let $\mathbf$ and $\mathbf$ represent the anxiety cost and available capacity, respectively. It should be noted that the complementary residual function is the Fischer-Burmeister (FB) value function.

[0093] Step S542: Construct the objective function based on the complementary residual function of the single arc;

[0094] In this step, the objective function is the sum of squares of all arc residuals, specifically including:

[0095] ;

[0096] In the above formula, This represents the objective function.

[0097] Step S543: Construct the passenger flow allocation model based on the objective function.

[0098] In this step, the constraints represented by the second nonlinear complementarity problem model are further transformed, specifically including:

[0099] ;

[0100] In the above formula, Let represent the generalized path cost vector. The generalized path cost is the sum of the costs of the generalized arcs that make up the path. It should be noted that the calculation process for the generalized arc cost is as follows:

[0101] ;

[0102] In the above formula, Indicating anxiety costs, Indicates arc cost, This represents the set of all stops, transfers, and boarding arcs along the route. This represents the generalized arc cost.

[0103] Following step S5, steps S6, S7, and S8 are further included, which specifically include:

[0104] Step S6: Obtain second information, which includes the generalized cost corresponding to each arc of each path in the public transportation network;

[0105] Step S7: Based on the second information, determine whether the generalized cost corresponding to each arc in the current path is strictly increasing, and obtain the first judgment result;

[0106] Step S8: Determine the solution method for the passenger flow allocation model based on the first judgment result.

[0107] Step S8 further includes steps S81, S82, and S83, which specifically include:

[0108] Step S81: When the first judgment result is that the generalized cost corresponding to each arc in the current path is strictly increasing, determine in turn whether the generalized cost corresponding to each arc in each path is strictly increasing;

[0109] Step S82: If the generalized cost corresponding to each arc in each path is strictly increasing, then the implicit method is used to solve the passenger flow allocation model.

[0110] Step S82 further includes steps S821, S822, S823, S824, and S825, which specifically include:

[0111] Step S821: Calculate the gradient of the objective function with respect to the anxiety cost vector;

[0112] In this step, the gradient calculation formula is as follows:

[0113] ;

[0114] In the above formula, This represents the gradient of the objective function with respect to the anxiety cost. and Representing arcs respectively Sum of arcs The corresponding anxiety cost vector; Represents arc The corresponding available capacity.

[0115] Among them, for a single element The calculation process is as follows:

[0116] ;

[0117] Arc available capacity pair The partial derivatives are:

[0118] ;

[0119] To solve for these partial derivatives, sensitivity analysis is required, which includes:

[0120] (10);

[0121] (11);

[0122] In the above formula, Indicates passenger The set of routes that can be used in a public transport network. This indicates the number of passengers allocated to each route. Indicates passenger Traffic demand, Indicates passenger The cost boundary, This represents the generalized arc cost.

[0123] Regarding formulas 10 and 11 Taking the derivative, we get:

[0124] ;

[0125] ;

[0126] In the above formula, Indicates arc flow rate.

[0127] This is equivalent to the optimal condition for the following quadratic programming problem:

[0128] ;

[0129] ;

[0130] ;

[0131] In the above formula, G represents the passenger group; A represents the universal set of all arcs. This represents a path-arc association indicator variable, indicating whether arc (i,j) belongs to a passenger group. The path p is defined as 1 for yes and 0 for no. Since the generalized cost is strictly increasing, the quadratic programming problem can be solved using the gradient projection algorithm. The required partial derivatives can be obtained, and then substituted into the gradient calculation formula to obtain the gradient.

[0132] Step S822: Search for a new anxiety cost vector along the negative gradient method;

[0133] Step S823: After determining the new anxiety cost vector, calculate the objective function and available capacity to obtain the calculation results;

[0134] Step S824: Determine whether the calculation result satisfies the descent condition according to the Armijo criterion, and obtain the second judgment result;

[0135] Step S825: Determine whether the calculation result is the optimal solution based on the second judgment result.

[0136] In this step, when a new solution satisfies the descent condition, the current optimal solution is updated and the convergence condition is checked. If the convergence condition is met, the algorithm terminates and outputs the current solution; otherwise, the iteration count k is incremented and a new anxiety cost vector is searched for to continue iterating.

[0137] Step S83: If there exists a path in which the generalized cost of each arc is not strictly increasing, then a nonlinear programming method is used to solve the passenger flow allocation model.

[0138] In this step, the nonlinear programming method is used to solve the passenger flow allocation model, which is a technical solution well known to those skilled in the art, and therefore will not be described in detail here.

[0139] In this embodiment, the implicit method is applicable to cases where the generalized costs are strictly increasing, in which case the path flow solution for the lower-level problem... Group cost boundary For a given anxiety cost vector It exists uniquely. Implicit functional relationships can be used to simplify complex, multi-level problems into ones that only concern... This method greatly reduces computational costs for single-level optimization problems. Nonlinear programming methods are suitable for situations where generalized costs are not strictly increasing or nonconvex, resulting in no unique solution for the lower-level problems. In such cases, implicit methods may fail. This method directly transforms the original complementary problem into a standard nonlinear programming problem and solves it using sequential quadratic programming techniques.

[0140] First, compared to traditional Dynamic Network Loading (DNL) methods that require extensive time-segmentation and iterative simulations, this invention accurately simulates passenger priority without a complex dynamic loading process, significantly reducing computational costs. Second, by introducing the concept of passenger anxiety cost and decomposing path-related variables onto arc segments, the generalized cost becomes additive, simplifying the solution process and improving the model's interpretability. Numerical experiments show that this method obtains sufficiently accurate solutions for user equilibrium priority in both benchmark and real-world networks, validating the invention's significant advantages in computational efficiency and accuracy. This novel model provides an efficient and feasible solution for addressing passenger allocation problems in large-scale public transportation networks.

[0141] Example 2:

[0142] like Figure 2 As shown, this embodiment provides a public transport passenger flow allocation system. The system includes a first acquisition module 901, a first processing module 902, a second processing module 903, a third processing module 904, and a fourth processing module 905, specifically including:

[0143] The first acquisition module is used to acquire first information, which includes a set of bus routes, a set of stations, and a set of bus trips.

[0144] The first processing module is used to construct a public transportation network based on the first information;

[0145] The second processing module is used to construct an event-activity extended network based on the public transportation network. The event-activity extended network includes spatiotemporal nodes, time-independent nodes, and arcs connecting the nodes, and the arcs have different priorities.

[0146] The third processing module is used to construct the balancing conditions for user balancing priorities based on the event-activity extended network;

[0147] The fourth processing module is used to establish a passenger flow allocation model based on the equilibrium conditions of the user equilibrium priority, and the passenger flow allocation model is used to calculate the passenger flow allocation result.

[0148] In one specific embodiment of this disclosure, the fourth processing module further includes a first acquisition unit, a first processing unit, a second processing unit, and a third processing unit, specifically comprising:

[0149] The first acquisition unit is used to acquire the path cost gap vector and the anxiety cost vector;

[0150] The first processing unit is used to transform the equilibrium condition of the user equilibrium priority into a first nonlinear complementary problem model based on the path cost gap vector.

[0151] The second processing unit is used to optimize the first nonlinear complementarity problem model based on the anxiety cost vector to obtain a second nonlinear complementarity problem model.

[0152] The third processing unit is used to construct a passenger flow allocation model based on the second nonlinear complementary problem model.

[0153] In one specific embodiment of this disclosure, the third processing unit further includes a fourth processing unit, a fifth processing unit, and a sixth processing unit, specifically comprising:

[0154] The fourth processing unit is used to pre-determine the complementary residual function of a single arc in the public transportation network;

[0155] The fifth processing unit is used to construct the objective function based on the complementary residual function of the single arc;

[0156] The sixth processing unit is used to construct the passenger flow allocation model based on the objective function.

[0157] In one specific embodiment of this disclosure, the fourth processing module is followed by a second acquisition module, a judgment module, and a fifth processing module, specifically including:

[0158] The second acquisition module is used to acquire second information, which includes the generalized cost corresponding to each arc of each path in the public transportation network;

[0159] The judgment module is used to determine whether the generalized cost corresponding to each arc in the current path is strictly increasing based on the second information, and to obtain the first judgment result;

[0160] The fifth processing module is used to determine the solution method of the passenger flow allocation model based on the first judgment result.

[0161] In one specific embodiment of this disclosure, the fifth processing module further includes a seventh processing unit, an eighth processing unit, and a ninth processing unit, specifically comprising:

[0162] The seventh processing unit is used to determine whether the generalized cost corresponding to each arc in each path is strictly increasing when the first judgment result is that the generalized cost corresponding to each arc in the current path is strictly increasing.

[0163] The eighth processing unit is used to solve the passenger flow allocation model using an implicit method if the generalized cost corresponding to each arc in each path is strictly increasing.

[0164] The ninth processing unit is used to solve the passenger flow allocation model using a nonlinear programming method if there exists a path where the generalized cost corresponding to each arc is not strictly increasing.

[0165] In one specific embodiment of this disclosure, the eighth processing unit further includes a tenth processing unit, an eleventh processing unit, a twelfth processing unit, a judgment unit, and a thirteenth processing unit, specifically including:

[0166] The tenth processing unit is used to calculate the gradient of the objective function with respect to the anxiety cost vector;

[0167] The eleventh processing unit is used to search for a new anxiety cost vector along the negative gradient method;

[0168] The twelfth processing unit is used to calculate the objective function and available capacity after determining the new anxiety cost vector, and obtain the calculation results;

[0169] The judgment unit is used to determine whether the calculation result satisfies the descent condition according to the Armijo criterion, and to obtain a second judgment result;

[0170] The thirteenth processing unit is used to determine whether the calculation result is the optimal solution based on the second judgment result.

[0171] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0172] Example 3:

[0173] Corresponding to the above method embodiments, this embodiment also provides a bus passenger flow allocation device. The bus passenger flow allocation device described below and the bus passenger flow allocation method described above can be referred to each other.

[0174] Figure 3 This is a block diagram illustrating a public transport passenger flow distribution device 800 according to an exemplary embodiment. Figure 3 As shown, the bus passenger flow distribution device 800 may include: a processor 801 and a memory 802. The bus passenger flow distribution device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0175] The processor 801 controls the overall operation of the bus passenger flow allocation device 800 to complete all or part of the steps in the aforementioned bus passenger flow allocation method. The memory 802 stores various types of data to support the operation of the bus passenger flow allocation device 800. This data may include, for example, instructions for any application or method used to operate on the bus passenger flow allocation device 800, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the public transport passenger distribution device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0176] In an exemplary embodiment, the bus passenger flow allocation device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the bus passenger flow allocation method described above.

[0177] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the bus passenger flow allocation method described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the bus passenger flow allocation device 800 to complete the bus passenger flow allocation method described above.

[0178] Example 4:

[0179] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in relation to the public transport passenger flow allocation method described above.

[0180] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the public transport passenger flow allocation method described in the above method embodiments.

[0181] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0182] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0183] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for allocating passenger flow on public transport, characterized in that, include: Obtain first information, which includes a set of bus routes, a set of stations, and a set of bus trips; Construct a public transportation network based on the first information; An event-activity extended network is constructed based on the public transportation network. The event-activity extended network includes spatiotemporal nodes, time-independent nodes, and arcs connecting the nodes. The arcs have different priorities. The spatiotemporal nodes include train arrival nodes and passenger entrance nodes. The time-independent nodes include start points and end points. Based on the event-activity extended network, a user priority balancing condition is constructed; A passenger flow allocation model is established based on the equilibrium condition of the user equilibrium priority. The passenger flow allocation model is used to calculate the passenger flow allocation result. Specifically, the passenger flow allocation model is as follows: ; In the above formula, This is a path flow vector, representing the number of passengers allocated to each path; For the path-passenger group association matrix, Used to indicate the path Belongs to group 1 indicates yes, 0 indicates no; d represents the highest acceptable path cost for each group in path selection; d is the traffic demand vector. This represents the anxiety cost vector of passengers waiting at the station. Let represent the generalized path cost vector. The generalized path cost is the sum of the costs of the generalized arcs that make up the path. It should be noted that the calculation process for the generalized arc cost is as follows: ; In the above formula, Indicating anxiety costs, Indicates arc cost, This represents the set of all stops, transfers, and boarding arcs along the route. This represents the generalized arc cost.

2. A public transport passenger flow distribution system, characterized in that, include: The first acquisition module is used to acquire first information, which includes a set of bus routes, a set of stations, and a set of bus trips. The first processing module is used to construct a public transportation network based on the first information; The second processing module is used to construct an event-activity extended network based on the public transportation network. The event-activity extended network includes spatiotemporal nodes, time-independent nodes, and arcs connecting the nodes. The arcs have different priorities. The spatiotemporal nodes include train arrival nodes and passenger entrance nodes. The time-independent nodes include start points and end points. The third processing module is used to construct the balancing conditions for user balancing priorities based on the event-activity extended network; The fourth processing module is used to establish a passenger flow allocation model based on the equilibrium conditions of the user equilibrium priority. The passenger flow allocation model is used to calculate the passenger flow allocation result. Specifically, the passenger flow allocation model is as follows: ; In the above formula, This is a path flow vector, representing the number of passengers allocated to each path; For the path-passenger group association matrix, Used to indicate the path Belongs to group 1 indicates yes, 0 indicates no; d represents the highest acceptable path cost for each group in path selection; d is the traffic demand vector. This represents the anxiety cost vector of passengers waiting at the station. Let represent the generalized path cost vector. The generalized path cost is the sum of the costs of the generalized arcs that make up the path. It should be noted that the calculation process for the generalized arc cost is as follows: ; In the above formula, Indicating anxiety costs, Indicates arc cost, This represents the set of all stops, transfers, and boarding arcs along the route. This represents the generalized arc cost.

Images