A method for dynamically allocating the number of long-distance bus vehicles and related devices

Abstract

The present application discloses a method for dynamically allocating the number of long-distance buses and a related device. The method comprises: obtaining the number of regions sent by a long-distance bus station and the total number of buses of the long-distance bus station; constructing an optimization model for obtaining the allocation of buses to each region in this week d according to the number of regions and the total number of buses, wherein the optimization model is used to minimize the sum of squares of the difference between the number of passengers that can be transported in each region in this week d and the number of passengers actually transported in last week d under the constraint condition, d=1, 2, ..., 7; solving the optimization model to obtain the allocation of buses to each region in this week d, thereby improving the technical problem of low utilization rate of buses in the prior art.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a method for dynamically allocating the number of long-distance buses and related devices. Background Art

[0002] In recent years, with the rapid development of the economy, population mobility has become more frequent. Although the transportation industry is also developing rapidly, for many suburban long-distance passenger stations, their departure areas and the number of vehicles sent to the area are generally fixed. This has caused many buses to run empty, resulting in low utilization of buses, waste of resources, and traffic congestion. Summary of the invention

[0003] The present application provides a method and related device for dynamically allocating the number of long-distance buses, which are used to improve the technical problem of low bus utilization rate in the prior art.

[0004] In view of this, the first aspect of the present application provides a method for dynamically allocating the number of long-distance buses, comprising:

[0005] Get the number of areas that the long-distance bus station sends to and the total number of buses at the long-distance bus station;

[0006] An optimization model for obtaining the allocation of public transport vehicles to each region in this week d is constructed according to the number of regions and the total number of public transport vehicles, wherein the optimization model is used to minimize the sum of squares of the difference between the number of passengers that can be transported in each region in this week d and the number of passengers actually transported in last week d under the constraint condition;

[0007] The optimization model is solved to obtain the allocation of public transport vehicles in each area in this week d.

[0008] Optionally, the objective function of the optimization model is:

[0009]

[0010] when When , the constraints of the optimization model are:

[0011]

[0012]

[0013] Ψ id(z-1) *N≥x idz , i∈[1,K];

[0014] when When , the constraints of the optimization model are:

[0015]

[0016] x idz ≤Ψ id(z-1) *N,i∈[1,K];

[0017] In the formula, z is this week, z-1 is last week, and x idz is the number of buses allocated to the ith area in week d, m is the full load of the bus, K is the number of areas, and f id(z-1) is the number of passengers actually transported by the ith area in the last week, N is the total number of public transport vehicles, ψ id(z-1) is the maximum regional allocation coefficient of the ith region in last week d.

[0018] Optionally, the calculation process of the regional maximum allocation coefficient of each region in the previous week is:

[0019] Collect the historical number of passengers transported in each region in each week d in the most recent preset time period to obtain d data matrices;

[0020] Calculate the ratio of each element in each of the data matrices to the sum of the corresponding row elements to obtain d new matrices;

[0021] The average value of the elements in each column of the new matrix is ​​calculated to obtain the regional maximum allocation coefficient of each region in the previous week d.

[0022] The second aspect of the present application provides a device for dynamically allocating the number of long-distance buses, comprising:

[0023] An acquisition unit, used for acquiring the number of areas sent by the long-distance bus station and the total number of buses of the long-distance bus station;

[0024] A model building unit, used to build an optimization model for obtaining the allocation amount of public transport vehicles for each area in this week d according to the number of areas and the total number of public transport vehicles, wherein the optimization model is used to minimize the sum of squares of the difference between the number of passengers that can be transported in each area in this week d and the number of passengers actually transported in last week d under the constraint condition;

[0025] The model solving unit is used to solve the optimization model to obtain the public transport vehicle allocation quantity of each area in this week d.

[0026] Optionally, the objective function of the optimization model is:

[0027]

[0028] when When , the constraints of the optimization model are:

[0029]

[0030]

[0031] Ψ id(z-1) *N≥x idz , i∈[1,K];

[0032] when When , the constraints of the optimization model are:

[0033]

[0034] x idz ≤Ψ id(z-1) *N,i∈[1,K];

[0035] In the formula, z is this week, z-1 is last week, and x idz is the number of buses allocated to the ith area in week d, m is the full load of the bus, K is the number of areas, and f id(z-1) is the number of passengers actually transported by the ith area in the last week, N is the total number of public transport vehicles, ψ id(z-1) is the maximum regional allocation coefficient of the ith region in last week d.

[0036] Optionally, the calculation process of the regional maximum allocation coefficient of each region in the previous week is:

[0037] Collect the historical number of passengers transported in each region in each week d in the most recent preset time period to obtain d data matrices;

[0038] Calculate the ratio of each element in each of the data matrices to the sum of the corresponding row elements to obtain d new matrices;

[0039] The average value of the elements in each column of the new matrix is ​​calculated to obtain the regional maximum allocation coefficient of each region in the previous week d.

[0040] A third aspect of the present application provides a device for dynamically allocating the number of long-distance buses, the device comprising a processor and a memory;

[0041] The memory is used to store program code and transmit the program code to the processor;

[0042] The processor is used to execute the method for dynamically allocating the number of long-distance buses described in any one of the first aspects according to the instructions in the program code.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, which is used to store program code. When the program code is executed by a processor, it implements the method for dynamically allocating the number of long-distance buses described in any one of the first aspects.

[0044] It can be seen from the above technical solutions that this application has the following advantages:

[0045] The present application provides a method for dynamically allocating the number of long-distance buses, comprising: obtaining the number of regions to which a long-distance bus station sends buses and the total number of buses of the long-distance bus station; constructing an optimization model for obtaining the number of buses allocated to each region in this week d according to the number of regions and the total number of buses, the optimization model being used to minimize the sum of squares of the difference between the number of passengers that can be transported in each region in this week d and the number of passengers actually transported in last week d under the condition of satisfying constraints; solving the optimization model to obtain the number of buses allocated to each region in this week d.

[0046] In the present application, an optimization model for obtaining the allocation of buses to each region on week d is constructed according to the number of regions sent by long-distance passenger stations and the total number of buses. The allocation of buses to each region on week d is obtained by solving an optimization model with the minimum sum of squares of the difference between the number of passengers that can be transported by each region on week d and the number of passengers actually transported on last week d under the constraint condition. By analyzing the dynamically updated historical data, a set of real-time vehicle allocation plans are dynamically provided for each region in the coming week to avoid the situation where vehicles run empty when a fixed number of vehicles is used, thereby improving the utilization rate of buses and improving the technical problem of low utilization rate of buses in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A schematic diagram of a flow chart of a method for dynamically allocating the number of long-distance public transport vehicles provided in an embodiment of the present application;

[0049] Figure 2 A structural schematic diagram of a device for dynamically allocating the number of long-distance buses provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] For easier understanding, see Figure 1 The present application embodiment provides a method for dynamically allocating the number of long-distance buses, including:

[0052] Step 101: Obtain the number of areas to which a long-distance bus station is sent and the total number of buses at the long-distance bus station.

[0053] Once the long-distance bus station is determined, the total number of buses N of the long-distance bus station and the number of regions K (i.e. the number of destinations) to which the long-distance bus station is sent can also be determined. Due to the long distance, each long-distance bus only performs long-distance transportation once a day. The number of people going to each region is constantly changing with seasonal and other objective conditions, so it is necessary to reasonably arrange the number of long-distance buses in each region to avoid wasting resources.

[0054] Step 102: construct an optimization model for obtaining the allocation of public transport vehicles to each region in this week d based on the number of regions and the total number of public transport vehicles. The optimization model is used to minimize the sum of squares of the difference between the number of passengers that can be transported in each region in this week d and the number of passengers actually transported in last week d under the constraints.

[0055] The number of buses allocated to the ith area in week d is denoted by x idz , d=1,2,...,7, d represents the number of days of the week, z represents this week, usually the number of buses in each long-distance bus terminal remains unchanged, so the first constraint can be established, the sum of the number of buses allocated to each area in this week d is less than or equal to the total number of buses in the long-distance bus terminal, that is:

[0056]

[0057] The number of buses allocated by the long-distance bus station to each area must satisfy the requirement that the number of buses allocated by the long-distance bus station to the area on week d multiplied by the full load of the bus m must be greater than or equal to the actual number of passengers transported by the area on week d, resulting in the second constraint condition, that is, the number of passengers that can be transported by each area this week must be greater than or equal to the actual number of passengers transported by the area on week d. Through research on long-distance passenger buses, it is found that the actual number of passengers fluctuates greatly during the seven days of the week, and it is difficult to use the previous day's ride situation to predict the next day's ride situation. Therefore, the actual number of passengers transported by each area on week d f is used in the embodiment of the present application. id(z-1) To predict the allocation of the area in this week d, that is, the number of passengers that can be transported in each area in this week d must be greater than or equal to the actual number of passengers transported in each area last week d, that is, the second constraint is:

[0058] x idz ×m≥f id(z-1) ,i∈[1,K].

[0059] In the formula, z-1 means that last week, the full load of a bus was generally between 25 and 35, that is, m was between 25 and 35, and m is a constant.

[0060] The embodiment of the present application takes into account that for urban and rural passenger stations, the total number of vehicles is unchanged, but due to some special factors, the number of passengers in a certain area in the previous week has increased dramatically. At this time, if the actual number of passengers last week is still used to predict the number of passengers this week, it is not in line with the actual situation. Therefore, the embodiment of the present application sets a maximum allocation for each area. If the maximum number of buses allocated to each area is allocated according to the total number of buses in the long-distance passenger station, the maximum number of buses that can be allocated to each area is β id(z-1) *N, β id(z-1) is the regional maximum allocation coefficient of the ith region in the last week d, and the regional maximum allocation coefficient β id(z-1) is a number greater than 0 and less than 1, and the sum of the regional maximum allocation coefficients of all regions in last week d is 1. Therefore, the number of buses at the long-distance bus station must satisfy the requirement that the number of buses allocated by the long-distance bus station to each region in this week d must be less than or equal to the regional maximum allocation of each region in last week d, that is:

[0061] β id(z-1) *N≥x idz ,i∈[1,K];

[0062] 0<β id(z-1) <1;

[0063]

[0064] How to maximize the rational use of bus resources at the long-distance bus station is an optimization problem. In order to better utilize the bus resources at the long-distance bus station, it is necessary to utilize all the buses at the long-distance bus station as much as possible under the constraints. Therefore, in the embodiment of the present application, the objective function is to minimize the sum of the squares of the difference between the number of passengers that can be transported in each area this week d and the number of passengers actually transported last week d, that is, the objective function is:

[0065]

[0066] The preliminary optimization model constructed through the above process is:

[0067]

[0068]

[0069] x idz ×m≥f id(z-1) ,i∈[1,K];

[0070] β id(z-1) *N≥x idz ,i∈[1,K];

[0071] 0<β id(z-1) <1;

[0072]

[0073] For the above preliminary optimization model, the number of public transport vehicles allocated to the ith area in week d is x idz and the regional maximum allocation coefficient β of the i-th region in the last week d id(z-1) is an unknown quantity, through the constraint condition and 0<β id(z-1) <1 can be introduced Therefore, the constraint It can be eliminated.

[0074] The embodiment of the present application also considers that, for long-distance buses, although the passenger transportation situation this week is similar to that of last week, some special cases may cause abnormal passenger transportation data of last week. It is not instructive to use abnormal data to predict the vehicle allocation situation this week, so the embodiment of the present application introduces the maximum allocation of the region. idz ×m≥f id(z-1) ,i∈[1,K] and β id(z-1) *N≥x idz ,i∈[1,K], we can get the following new constraints:

[0075]

[0076] The maximum regional allocation coefficient β in the new constraint condition id(z-1) is a variable, so the regional maximum allocation cannot normally suppress abnormal data. In order to better obtain a reasonable optimization model, the regional maximum allocation coefficient needs to be a fixed value that can truly reflect the actual capacity of each area, so it is necessary to solve the regional maximum allocation coefficient of each area. The embodiment of the present application calculates the regional maximum allocation coefficient of each area in the last week d based on the historical number of passengers transported by each area on each day in the most recent preset time period, and obtains the regional maximum allocation coefficient of each area by objectively analyzing the historical data.

[0077] Taking a certain urban and rural passenger transport bus terminal as an example, first, the historical number of passengers transported by the urban and rural passenger transport bus terminal in each week in the recent preset time period (such as the last 30 days, 60 days, etc.) is collected, and the collected historical data is statistically analyzed as follows:

[0078] Table 1

[0079]

[0080] The data from Monday to Sunday are counted separately. Some of the historical data may be abnormal data, but the proportion of the total historical data will be very small. The data obtained from the statistics in Table 1 can be regarded as a j×K data matrix represents the day of the week, and correspondingly d data matrices are generated, where b = 1, 2, ..., j and i = 1, 2, ..., K.

[0081] Calculate the ratio of each element in each data matrix to the sum of the corresponding row elements to obtain d new matrices Right now:

[0082]

[0083] Calculate the average value of each column element in each new matrix to obtain the maximum regional allocation coefficient of each region in the last week, that is:

[0084]

[0085] The above formula can be used to calculate [Ψ 1d ,Ψ 2d ,...,Ψ K-1d ,Ψ Kd ] (z-1) , that is, ψ id(z-1) =β id(z-1) It is understandable that the number of historical passengers collected changes with time, that is, the regional maximum allocation coefficient ψ of each region last week d id(z-1) It also changes over time.

[0086] The above preliminary optimization model can be expressed as:

[0087]

[0088]

[0089] x idz ×m≥f id(z-1) ,i∈[1,K];

[0090] Ψ id(z-1) *N≥x idz ,i∈[1,K].

[0091] For the constraint x idz ×m≥f id(z-1) , i∈[1,K] and the constraint Ψ id(z-1) *N≥x idz ,i∈[1,K], we can get:

[0092]

[0093] If the data of a certain area in last week d is abnormal, the constraint condition i∈[1,K] does not hold, resulting in an empty solution set for the constraints of the entire optimization model. Therefore, when establishing the entire optimization model, it is necessary to compare the actual number of passengers in each area with the maximum number of regional allocations in the area, as follows:

[0094] when When , the data in area i is normal;

[0095] when When , the data in region i is abnormal.

[0096] When the ratio of the actual number of passengers in a certain area last week to the full load of long-distance buses is greater than the maximum number of vehicles allocated in the area, the data in the area is considered abnormal. The abnormal data in the area can be directly deleted and the maximum number of vehicles allocated in the area Ψ is used. id(z-1) *N is used to replace the ratio of the actual number of passengers in the area last week to the vehicle load. id(z-1) / m, the final optimization model is:

[0097]

[0098] when When , the constraints are:

[0099]

[0100]

[0101] Ψ id(z-1) *N≥x idz , i∈[1,K];

[0102] when When , the constraints are:

[0103]

[0104] x idz ≤Ψ id(z-1) *N,i∈[1,K];

[0105] Step 103: Solve the optimization model to obtain the allocation of public transport vehicles in each area in this week d.

[0106] The objective function and constraints of the final optimization model are convex functions, which can easily solve the bus allocation x for each area in week d. idzThe existing technology can be used to directly solve the problem, such as using the CVX toolkit of Matlab software to directly solve the problem, and obtain the bus allocation x for each area in week d. id The method in the embodiment of the present application can be used to predict the number of public transport vehicles to be distributed in the next week. After determining the number of public transport vehicles to be distributed in each area, the departure situation of each area can also be determined, that is, passengers can book tickets within 7 days in advance, which is highly practical.

[0107] In the embodiment of the present application, an optimization model for obtaining the bus allocation amount for each area on week d is constructed according to the number of areas sent by the long-distance bus station and the total number of buses. The bus allocation amount for each area on week d is obtained by solving the optimization model with the minimum sum of the squares of the difference between the number of passengers that can be transported for each area this week and the number of passengers actually transported on week d under the constraint conditions. By analyzing the dynamically updated historical data, a set of real-time vehicle allocation plans are dynamically provided for each area every day to avoid the situation where vehicles run empty due to a fixed number of vehicles, thereby improving the utilization rate of buses and improving the technical problem of low utilization rate of buses in the prior art.

[0108] The above is an embodiment of a method for dynamically allocating the number of long-distance buses provided by the present application, and the following is an embodiment of a device for dynamically allocating the number of long-distance buses provided by the present application.

[0109] Please refer to Figure 2 , a device for dynamically allocating the number of long-distance buses provided in an embodiment of the present application includes:

[0110] An acquisition unit, used for acquiring the number of areas sent by the long-distance bus station and the total number of buses of the long-distance bus station;

[0111] A model building unit, used to build an optimization model for obtaining the allocation amount of public transport vehicles for each region in this week d according to the number of regions and the total number of public transport vehicles, wherein the optimization model is used to minimize the sum of squares of the difference between the number of passengers that can be transported in each region in this week d and the number of passengers actually transported in last week d under the constraint condition;

[0112] The model solving unit is used to solve the optimization model to obtain the allocation of public transport vehicles in each area in this week d.

[0113] As a further improvement, the objective function of the optimization model is:

[0114]

[0115] when When , the constraints of the optimization model are:

[0116]

[0117]

[0118] Ψ id(z-1) *N≥x idz , i∈[1,K];

[0119] when When , the constraints of the optimization model are:

[0120]

[0121] x idz ≤Ψ id(z-1) *N,i∈[1,K];

[0122] In the formula, z is this week, z-1 is last week, and x idz is the number of buses allocated to the ith area in week d, m is the full load of the bus, K is the number of areas, and f id(z-1) is the number of passengers actually transported by the ith area in the last week, N is the total number of public transport vehicles, ψ id(z-1) is the maximum regional allocation coefficient of the ith region in last week d.

[0123] As a further improvement, the calculation process of the regional maximum allocation coefficient of each region in the last week is:

[0124] Collect the historical number of passengers transported in each region in each week d in the most recent preset time period to obtain d data matrices;

[0125] Calculate the ratio of each element in each of the data matrices to the sum of the corresponding row elements to obtain d new matrices;

[0126] Calculate the average value of each column element in each new matrix to obtain the regional maximum allocation coefficient of each region in the last week d.

[0127] In the embodiment of the present application, an optimization model for obtaining the bus allocation amount for each area in this week d is constructed according to the number of areas sent by the long-distance bus station and the total number of buses. The bus allocation amount for each area in this week d is obtained by solving the optimization model with the minimum sum of the squares of the difference between the number of passengers that can be transported in each area this week and the number of passengers actually transported last week d under the constraints. By analyzing the dynamically updated historical data, a set of real-time vehicle allocation plans can be dynamically provided for each area in the next week to avoid the situation where vehicles run empty when a fixed number of vehicles is used, thereby improving the utilization rate of buses and improving the technical problem of low utilization rate of buses in the prior art.

[0128] The embodiment of the present application also provides a device for dynamically allocating the number of long-distance buses, the device comprising a processor and a memory;

[0129] The memory is used to store the program code and transmit the program code to the processor;

[0130] The processor is used to execute the method for dynamically allocating the number of long-distance buses in the aforementioned method embodiment according to the instructions in the program code.

[0131] The embodiment of the present application also provides a computer-readable storage medium, which is used to store program code. When the program code is executed by a processor, the method for dynamically allocating the number of long-distance buses in the aforementioned method embodiment is implemented.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0134] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0135] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0136] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0139] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for dynamically allocating the number of long-distance buses, characterized in that: include: Get the number of areas that the long-distance bus station sends to and the total number of buses at the long-distance bus station; An optimization model for obtaining the allocation of public transport vehicles for each region in this week d is constructed according to the number of regions and the total number of public transport vehicles, wherein the optimization model is used to minimize the sum of squares of the difference between the number of passengers that can be transported for each region in this week d and the number of passengers actually transported for last week d under the constraint condition, d=1, 2, ..., 7; Solving the optimization model to obtain the allocation of public transport vehicles in each area in this week d; The objective function of the optimization model is: when When , the constraints of the optimization model are: P id(z-1) *N≥x idz ,i∈[1,K]; when When , the constraints of the optimization model are: x idz ≤Ψ id(z-1) *N,i∈[1,K]; In the formula, z is this week, z-1 is last week, and x idz is the number of buses allocated to the ith area in week d, m is the full load of the bus, K is the number of areas, and f id(z-1) is the number of passengers actually transported by the ith area in the last week, N is the total number of public transport vehicles, ψ id(z-1) is the maximum regional allocation coefficient of the ith region in the last week d; The calculation process of the regional maximum allocation coefficient of each region last week is: Collect the historical number of passengers transported in each region in each week d in the most recent preset time period to obtain d data matrices; Calculate the ratio of each element in each of the data matrices to the sum of the corresponding row elements to obtain d new matrices; The average value of the elements in each column of the new matrix is ​​calculated to obtain the regional maximum allocation coefficient of each region in the previous week d.

2. A device for dynamically allocating the number of long-distance buses, characterized in that: include: An acquisition unit, used for acquiring the number of areas sent by the long-distance bus station and the total number of buses of the long-distance bus station; A model building unit, used for building an optimization model for obtaining the allocation amount of public transport vehicles for each region in this week d according to the number of regions and the total number of public transport vehicles, wherein the optimization model is used for minimizing the sum of squares of the difference between the number of passengers that can be transported in each region in this week d and the number of passengers actually transported in last week d under the constraint condition, d=1, 2, ..., 7; A model solving unit, used for solving the optimization model to obtain the allocation of public transport vehicles in each area in this week d; The objective function of the optimization model is: when When , the constraints of the optimization model are: P id(z-1) *N≥x idz ,i∈[1,K]; when When , the constraints of the optimization model are: x idz ≤Ψ id(z-1) *N,i∈[1,K]; In the formula, z is this week, z-1 is last week, and x idz is the number of buses allocated to the ith area in week d, m is the full load of the bus, K is the number of areas, and f id(z-1) is the number of passengers actually transported by the ith area in the last week, N is the total number of public transport vehicles, ψ id(z-1) is the maximum regional allocation coefficient of the ith region in the last week d; The calculation process of the regional maximum allocation coefficient of each region last week is: Collect the historical number of passengers transported in each region in each week d in the most recent preset time period to obtain d data matrices; Calculate the ratio of each element in each of the data matrices to the sum of the corresponding row elements to obtain d new matrices; The average value of the elements in each column of the new matrix is ​​calculated to obtain the regional maximum allocation coefficient of each region in the previous week d.

3. A device for dynamically allocating the number of long-distance buses, characterized in that: The device comprises a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the method for dynamically allocating the number of long-distance buses according to claim 1 according to the instructions in the program code.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and when the program codes are executed by a processor, the method for dynamically allocating the number of long-distance buses described in claim 1 is implemented.

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