Modular vehicle planning method and device based on morning peak commuting and school special line

Abstract

The present invention provides a module bus planning method and device based on a morning peak commuting school special line, the method comprising: obtaining passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the special line passing stations, the running time between stations and the upper limit of the module berths at each station, and the module information includes the module rated passenger capacity and the number of available modules; according to the passenger boarding demand, fare, special line route information and module information, the module planning decision result is obtained. The present invention obtains the module planning decision result by considering the passenger boarding demand, fare, special line route information and module information, etc., and serves as many commuting school passengers as possible while ensuring the maximization of the profits of the bus company, thereby providing theoretical guidance and decision support for the operation and management of the commuting school special line of the bus company, and has a very broad application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic planning, and in particular to a method and device for planning a modular vehicle based on a morning peak commuting and school-related special line. Background Art

[0002] With the booming economy, many large cities in my country have experienced serious traffic congestion, especially during the morning rush hour, which has become increasingly "normalized", making residents feel very unhappy about commuting to school. Therefore, some urban bus companies have launched commuting and school special lines to transport corporate employees and students to the same destination safely and efficiently, such as work units and schools.

[0003] At present, existing research on the application of modular vehicles in the field of transportation is mostly focused on public transportation systems, with the goal of minimizing the total system cost to optimize bus departure schedules and module formations at each station. However, such research rarely considers the module scale and station berth constraints, and the results are difficult to directly apply to actual operational scheduling. Summary of the invention

[0004] The present invention provides a modular vehicle planning method and device based on a morning peak commuting and school-related special line, which is used to solve the defect that the existing technology is constrained by actual transportation capacity and is difficult to be directly applied to actual operation scheduling work, and continuously maintains a high vehicle utilization rate during operation to reduce operating costs.

[0005] The present invention provides a module vehicle planning method based on a morning peak commuting and school special line, comprising: obtaining passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the special line passing stations, the running time between stations and the upper limit of module berths at each station, and the module information includes the module rated passenger capacity and the number of available modules; according to the passenger boarding demand, the fare, the special line route information and the module information, a module planning decision result is obtained.

[0006] According to a modular bus planning method based on a morning rush hour commuting and school special line provided by the present invention, a modular planning decision result is obtained according to the passenger boarding demand, the fare, the special line route information and the module information, including: constructing a mixed integer nonlinear programming model according to the passenger boarding demand, the fare, the special line route information and the module information; linearizing the mixed integer nonlinear programming model to obtain a linearized model; according to the linearized model, combining with the branch and bound method, obtaining a modular planning decision result; the modular planning decision result includes site planning information characterizing whether each site is a reorganization site, the corresponding module reserve information when arriving at each site, and the number of modules added when arriving at each site.

[0007] According to a modular vehicle planning method based on a morning peak commuting and school special line provided by the present invention, a mixed integer nonlinear programming model is constructed according to the passenger boarding demand, the fare, the special line route information and the module information, including: constructing a station boarding passenger prediction sub-model based on the passenger boarding demand, the module rated passenger capacity, the number of available modules and the upper limit of module berths at each station; constructing an actual stop time prediction sub-model based on the special line route information and the preset fixed stop time; constructing a mixed integer nonlinear programming model based on the fare, the station boarding passenger prediction sub-model, the actual stop time prediction sub-model, the module rated passenger capacity, the number of available modules, the upper limit of module berths at each station, the preset scheduling cost and the preset adjacent station operating cost, in combination with the preset station adding module constraint condition and the preset station module reserve constraint condition; wherein the preset station adding module constraint condition is used to characterize the module quantity constraint added to each station, and the preset station module reserve constraint condition is used to characterize the module quantity constraint reserved for each station.

[0008] According to a modular vehicle planning method based on a morning rush hour commuting and schooling line provided by the present invention, the preset site module adding constraint condition includes a first site module adding quantity constraint and a first numerical constraint, the first site module adding quantity constraint is used to limit the number of modules added to the corresponding site according to the preset storage module quantity of each site, and the first numerical constraint is used to limit the number of modules added to each site to an integer; the preset site module reserve constraint condition includes a first site module reserve constraint, a second site module reserve constraint and a second numerical constraint, the first site module reserve constraint is used to limit the number of modules reserved at the corresponding site according to the available module quantity, the second site module reserve constraint is used to limit the number of modules reserved at the corresponding site according to the module berth upper limit of each site, and the second numerical constraint is used to limit the number of modules reserved at each site to an integer.

[0009] According to a modular vehicle planning method based on a morning peak commuting and school-related special line provided by the present invention, the mixed integer nonlinear programming model is expressed as:

[0010]

[0011]

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] y1=1,y J =0

[0019]

[0020]

[0021] Among them, z represents the profit of the module bus operation during the morning peak; α1 represents the weight coefficient of the bus company's fare revenue; α2 represents the weight coefficient of the bus company's operating cost, and F p represents the passenger fare, that is, the fare paid by the enterprise (or school) for each passenger; J represents the number of stations along the commuter-to-school line; s j represents the number of passengers boarding at station j; B j represents the running time from site j to the next adjacent site j+1; g j represents the number of modules leaving site j; t j represents the actual stop time at station j; F d represents the scheduling cost required to reserve modules in advance at the reorganization site, d j represents the module reserve at site j when arriving at site j; t j represents the actual stop time at station j; T j represents the preset fixed stop time at station j; y j represents a binary variable. When the selected site j is a module recombination site, its value is 1, otherwise it is 0; Q j represents the boarding demand of passengers at station j; C represents the rated passenger capacity of the module; b j represents the number of passengers on the bus when it arrives at station j; p j represents the number of modules added when arriving at site j; F m Indicates the upper limit of the number of modules that the module vehicle owner can own; F j represents the upper limit of module berths at site j; Represents a set of integers.

[0022] According to a modular vehicle planning method based on a morning rush hour commuting and school line provided by the present invention, the linearization processing of the mixed integer nonlinear programming model includes: based on preset auxiliary variables and preset binary auxiliary variables, linearization processing of the mixed integer nonlinear programming model to obtain a linearized model.

[0023] According to a modular vehicle planning method based on a morning peak commuting and school-related special line provided by the present invention, the linearized model is expressed as:

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[0040] s″ j ≤Q j j=1,2,...,J

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[0060] y1=1,y J =0

[0061]

[0062]

[0063] Among them, z represents the profit of the module bus operation during the morning peak; α1 represents the weight coefficient of the bus company's fare revenue; α2 represents the weight coefficient of the bus company's operating cost, and F p represents the passenger fare, that is, the fare paid by the enterprise (or school) for each passenger; J represents the number of stations along the commuter-to-school line; s j represents the number of passengers boarding at station j; B j represents the running time from site j to the next adjacent site j+1; g j represents the number of modules leaving site j; F d represents the scheduling cost required to reserve modules in advance at the reorganization site, d j represents the module reserve at site j when arriving at site j; g′ j , s′ j , s″ j , d′ j Indicates preset auxiliary variables; T j represents the preset fixed stop time at station j; y jrepresents a binary variable. When the selected site j is a modular recombination site, its value is 1, otherwise it is 0; M represents the maximum value; t j represents the actual stop time at station j; q j represents the number of remaining passengers on the bus when it arrives at station j; g j represents the boarding demand of passengers at station j; C represents the rated passenger capacity of the module; b j represents the number of passengers on the bus when it arrives at station j; v j and u j represents the preset binary auxiliary variable; p j represents the number of modules added when arriving at site j; F m Indicates the upper limit of the number of modules that the module vehicle owner can own; F j represents the upper limit of module berths at site j; Represents a set of integers.

[0064] The present invention also provides a modular vehicle planning device based on a morning peak commuting and school special line, comprising: a data acquisition unit, which acquires passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the special line passing stations, the operating time between stations and the upper limit of module berths at each station, and the module information includes the module rated passenger capacity and the number of available modules; a planning unit, which obtains the module planning decision result according to the passenger boarding demand, the fare, the special line route information and the module information.

[0065] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any of the above-mentioned methods for planning modular vehicles based on morning rush hour commuting and school special lines are implemented.

[0066] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the modular vehicle planning method based on the morning peak commuting and school special line as described in any one of the above are implemented.

[0067] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned modular vehicle planning methods based on morning peak commuting and school special lines.

[0068] The modular bus planning method and device based on the morning rush hour commuter and school special line provided by the present invention obtains the modular planning decision result by considering the passengers' boarding demand, fare, special line route information and module information, so as to serve as many commuter and school passengers as possible while ensuring the maximization of profits of the bus company, thereby providing theoretical guidance and decision-making support for the operation and management of the bus company's commuter and school special line, and has a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 It is a flow chart of a module vehicle planning method based on a morning peak commuting and schooling special line provided by the present invention;

[0071] Figure 2 This is a schematic diagram of dynamic module access provided by the present invention;

[0072] Figure 3 It is a structural schematic diagram of a module vehicle planning device based on a morning peak commuting and school special line provided by the present invention;

[0073] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] Figure 1 A flow chart of a module vehicle planning method based on a morning peak commuting and school special line is shown, the method comprising:

[0076] S11, obtaining passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the special line passing stations, the running time between stations and the upper limit of the module berths at each station; the module information includes the module rated passenger capacity and the number of available modules;

[0077] S12, obtaining module planning decision results according to passenger boarding demand, fare, dedicated line route information and module information.

[0078] It should be noted that the S1N in this specification does not represent the order of the module vehicle planning method based on the morning peak commuting and school special line. Figure 2 The present invention describes a modular vehicle planning method based on a morning peak commuting and school-related special line.

[0079] The modular bus for commuting and schooling during the morning rush hour stops at regular stations to facilitate smooth boarding of passengers. Before the modular bus arrives at the reorganization station, passengers waiting at the reorganization station board the modules already in the station (i.e., modules reserved in advance). These modules will be quickly moved to the modular bus when the modular bus arrives at the station to complete the boarding process. Figure 2 It should be noted that the destinations of the passengers on the morning peak commuter and school special lines are the same (i.e., there are only passengers getting on the bus during the journey), so the modular vehicle only has access operations at the reorganization station. Due to the variability of the rated passenger capacity of the modular vehicle, it can always maintain a high vehicle utilization rate during operation, thereby reducing operating costs.

[0080] Step S11, obtaining passenger boarding requirements, fares, special line route information and module information; wherein the special line route information includes the special line passing stations, the running time between stations and the upper limit of the module berths at each station, and the module information includes the module rated passenger capacity and the number of available modules.

[0081] It should be noted that obtaining passenger boarding demand, fares, dedicated route information and module information includes: obtaining passenger residence information and destination information; determining fares, passenger boarding demand and dedicated route information based on passenger residence information and destination information; obtaining module rated passenger capacity and number of available modules from the bus company; determining the geographical location of each station based on the dedicated route information, thereby determining the operating time between stations and the upper limit of station module berths.

[0082] Step S12, obtaining module planning decision results according to passenger boarding demand, fare, dedicated line route information and module information.

[0083] In this embodiment, according to the passenger boarding demand, the fare, the dedicated line route information and the module information, the module planning decision result is obtained, including:

[0084] S121, constructing a mixed integer nonlinear programming model according to passenger boarding demand, fare, dedicated line route information and module information.

[0085] Specifically, a mixed integer nonlinear programming model is constructed according to passenger boarding demand, fare, dedicated line route information and module information, including: constructing a station boarding passenger prediction sub-model based on passenger boarding demand, module rated passenger capacity, number of available modules and upper limit of module berths at each station; constructing an actual stop time prediction sub-model based on dedicated line route information and preset fixed stop times; constructing a mixed integer nonlinear programming model based on fare, station boarding passenger prediction sub-model, actual stop time prediction sub-model, module rated passenger capacity, number of available modules, upper limit of module berths at each station, preset scheduling cost and preset adjacent station operating cost, in combination with preset station module joining constraint condition and preset station module reserve constraint condition; wherein, the preset station module joining constraint condition is used to characterize the constraint on the number of modules added to each station, and the preset station module reserve constraint condition is used to characterize the constraint on the number of modules reserved at each station.

[0086] Furthermore, the preset site module adding constraint conditions include a first site module adding quantity constraint and a first numerical constraint. The first site module adding quantity constraint is used to limit the number of modules added to the corresponding site according to the preset number of storage modules at each site, and the first numerical constraint is used to limit the number of modules added to each site to an integer; the preset site module reserve constraint conditions include a first site module reserve constraint, a second site module reserve constraint and a second numerical constraint. The first site module reserve constraint is used to limit the number of modules reserved at the corresponding site according to the number of available modules, the second site module reserve constraint is used to limit the number of modules reserved at the corresponding site according to the upper limit of module berths at each site, and the second numerical constraint is used to limit the number of modules reserved at each site to an integer.

[0087] It should be noted that, unlike the public welfare of regular bus services, the design of customized bus services needs to focus on profitability. The operating profit of the commuter and school special line service is equal to the line fare revenue minus the line operating cost. Among them, the line operating cost includes the operating time cost of the module car and the scheduling cost of reserving modules in advance at the reorganization station. Based on the residential location reported by the employees (or students) and the fare for each employee (or student) negotiated and determined by the bus company and the enterprise (or school), the bus company customizes a suitable commuter and school special line service for the enterprise (or school) to meet the passengers' boarding needs as much as possible while ensuring the maximization of profits. Therefore, the mixed integer nonlinear programming model can be expressed as:

[0088]

[0089] Among them, z represents the profit of the module bus operation during the morning peak; α1 represents the weight coefficient of the bus company's fare revenue; α2 represents the weight coefficient of the bus company's operating cost, and F prepresents the passenger fare, that is, the fare paid by the enterprise (or school) for each passenger; J represents the number of stations along the commuter-to-school line; s j represents the number of passengers boarding at station j; B j represents the running time from site j to the next adjacent site j+1; g j represents the number of modules leaving site j; t j represents the actual stop time at station j; F d represents the scheduling cost required to reserve modules in advance at the reorganization site, d j It represents the module reserve of site j when arriving at site j.

[0090] During the operation of the modular vehicle, when passing through a regular station j, the vehicle stops normally to allow passengers to board the vehicle, and the stop time is fixed at T j ; Before the module car arrives at the reorganization station, the passengers waiting at the reorganization station board the pre-stored modules in the station in advance. These modules will move quickly and connect to the module car when the module car arrives at the station to complete the passenger boarding process. Since the whole process is carried out while the vehicle is moving, the module car's parking time is zero. It can be seen that the actual parking time at station j is expressed as:

[0091]

[0092] Among them, t j represents the actual stop time at station j; T j represents the preset fixed stop time at station j; y j represents a binary variable, and its value is 1 when the selected site j is a modular recombination site, otherwise it is 0;

[0093] If the station is a regular station, the number of passengers boarding the bus at the station should be the smaller value between the passenger boarding demand at the station and the number of passengers remaining on the bus; if the station is a modular reorganization station, the number of passengers boarding the bus at the station should be the smaller value between the passenger boarding demand at the station and the number of passengers remaining on the bus. Therefore, the number of passengers boarding the bus at station j is s j , expressed as:

[0094]

[0095] Among them, Q j represents the boarding demand of passengers at station j; C represents the rated passenger capacity of the module; g j represents the number of modules leaving site j; b j represents the number of passengers on the bus when it arrives at station j; y j represents a binary variable, when the selected site j is a module recombination site, its value is 1, otherwise it is 0; p jRepresents the number of modules added when arriving at site j.

[0096] The number of people on the bus when leaving the station is the cumulative number of people on the bus from the first station to this station, expressed as:

[0097]

[0098] Among them, b j represents the number of passengers on the bus when it arrives at station j; s k represents the number of passengers boarding at station k.

[0099] Then, the calculation of passenger-related variables during vehicle operation is performed. Since only the reorganization site can store modules, the modules can only be dynamically connected when the module vehicle arrives at the reorganization site, but the number of modules to be added must be determined based on the preset number of storage modules in the site, that is, the number constraint of the modules added at the first site is expressed as:

[0100]

[0101] Among them, p j represents the number of modules added when arriving at site j; d j It represents the module reserve of site j when arriving at site j.

[0102] In addition, since the number of modules that the bus company can provide is limited, the total amount of modules pre-reserved at the reorganization station cannot exceed the number of available modules, that is, the module reserve constraint of the first station is expressed as:

[0103]

[0104] Among them, d j F represents the module reserve of site j when arriving at site j; m Indicates the upper limit of the number of modules that a module vehicle owner can own.

[0105] At the same time, due to the different geographical conditions of each site, the upper limit of module berths in different sites is also different, so the number of modules stored in a site should not exceed the upper limit of the available berths of the site, that is, the module reserve constraint of the second site is expressed as:

[0106]

[0107] Among them, F j Indicates the upper limit of module berths at site j.

[0108] It should be noted that the modular train only allows module access at the reorganization site, and the module marshaling at the regular site remains unchanged. Therefore, the module marshaling scheme of the train at each site is expressed as:

[0109]

[0110] Among them, g j represents the number of modules leaving site j; p k Represents the number of modules added when reaching site k.

[0111] It should be added that, according to the actual situation, the first station of the commuter-to-school line must be a reorganized station and the last station must be a regular station. Whether the remaining stations are reorganized stations will be scientifically decided by the model with the help of binary variables. j =1, then site j is a recombination site, otherwise it is a regular site, that is:

[0112] y1=1,y J =0

[0113]

[0114] In addition, all relevant variables representing the number of modules are integer variables, that is, the first numerical constraint and the second numerical constraint are expressed as:

[0115]

[0116] Among them, p j represents the number of modules added when arriving at site j; d j represents the module reserve at site j when arriving at site j; Represents a set of integers.

[0117] S122, linearizing the mixed integer nonlinear programming model to obtain a linearized model.

[0118] Specifically, the mixed integer nonlinear programming model is linearized, including: based on preset auxiliary variables and preset binary auxiliary variables, the mixed integer nonlinear programming model is linearized to obtain a linearized model.

[0119] Furthermore, the linearized model is expressed as:

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[0136] s″ j ≤Q j j=1,2,...,J

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[0156] y1=1,y J =0

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[0159] Among them, z represents the profit of the module bus operation during the morning peak; α1 represents the weight coefficient of the bus company's fare revenue; α2 represents the weight coefficient of the bus company's operating cost, and F p represents the passenger fare, that is, the fare paid by the enterprise (or school) for each passenger; J represents the number of stations along the commuter-to-school line; s j represents the number of passengers boarding at station j; B j represents the running time from site j to the next adjacent site j+1; g j represents the number of modules leaving site j; F d represents the scheduling cost required to reserve modules in advance at the reorganization site, d j represents the module reserve at site j when arriving at site j; g′ j , s′ j , s″ j , d′ j Indicates preset auxiliary variables; T j represents the preset fixed stop time at station j; y j represents a binary variable. When the selected site j is a modular recombination site, its value is 1, otherwise it is 0; M represents the maximum value; t j represents the actual stop time at station j; q j represents the number of remaining passengers on the bus when it arrives at station j; Q j represents the boarding demand of passengers at station j; C represents the rated passenger capacity of the module; b j represents the number of passengers on the bus when it arrives at station j; v j and u j represents the preset binary auxiliary variable; p j represents the number of modules added when arriving at site j; F m Indicates the upper limit of the number of modules that the module vehicle owner can own; F j represents the upper limit of module berths at site j; Represents a set of integers.

[0160] S123, according to the linearized model, combined with the branch and bound method, the module planning decision result is obtained; the module planning decision result includes site planning information indicating whether each site is a reorganization site, the corresponding module reserve information when arriving at each site, and the number of modules added when arriving at each site.

[0161] It should be noted that the module planning decision result also includes the number of passengers boarding at each station. In addition, the branch and bound method can be implemented by some commercial solvers, such as Gurobi, Cplex, etc., which are not further limited here.

[0162] To sum up, the embodiment of the present invention obtains the module planning decision result by considering the information such as passengers' boarding demand, fare, special line route information and module information, so as to serve as many commuting and school passengers as possible while ensuring the maximization of profits of the bus company, thereby providing theoretical guidance and decision-making support for the operation and management of the commuting and school special lines of bus companies, and has a very broad application prospect.

[0163] The following is a description of the modular vehicle planning device based on the morning rush hour commuting and school dedicated line provided by the present invention. The modular vehicle planning device based on the morning rush hour commuting and school dedicated line described below and the modular vehicle planning method based on the morning rush hour commuting and school dedicated line described above can be referenced to each other.

[0164] Figure 3 A modular vehicle planning device based on a morning peak commuting and school-related special line is shown, the device comprising:

[0165] The data acquisition unit 31 acquires passenger boarding demand, fare, special line route information and module information; the special line route information includes the special line passing stations, the running time between stations and the upper limit of the module berths at each station; the module information includes the module rated passenger capacity and the number of available modules;

[0166] The planning unit 32 obtains the module planning decision result according to the passengers' boarding demand, the fare, the dedicated line route information and the module information.

[0167] In this embodiment, the data acquisition unit 31 includes: an address information acquisition unit, which acquires the passenger's residence information and destination information; a first data acquisition unit, which determines the fare, passenger boarding demand and special line route information based on the passenger's residence information and destination information; a second data acquisition unit, which acquires the module's rated passenger capacity and the number of available modules from the bus company; a third data acquisition unit, which determines the geographical location of each station based on the special line route information, thereby determining the operating time between stations and the upper limit of the station module berths.

[0168] In addition, the planning section 32 includes: a model building unit, which builds a mixed integer nonlinear programming model according to passenger boarding demand, fare, dedicated line route information and module information; a linearization processing unit, which linearizes the mixed integer nonlinear programming model to obtain a linearized model; a module planning unit, which obtains a module planning decision result based on the linearized model in combination with the branch and bound method; the module planning decision result includes site planning information that characterizes whether each site is a reorganization site, the corresponding module reserve information when arriving at each site, and the number of modules added when arriving at each site.

[0169] Specifically, the model construction unit includes: a first sub-model construction sub-unit, which constructs a station boarding passenger prediction sub-model based on passenger boarding demand, module rated passenger capacity, number of available modules and upper limit of module berths at each station; a second sub-model construction sub-unit, which constructs an actual stop time prediction sub-model according to special line route information and preset fixed stop time; a model construction sub-unit, which constructs a mixed integer nonlinear programming model according to fare, station boarding passenger prediction sub-model, actual stop time prediction sub-model, module rated passenger capacity, number of available modules, upper limit of module berths at each station, preset scheduling cost and preset adjacent station operating cost, and in combination with preset station module joining constraint conditions and preset station module reserve constraint conditions; wherein, the preset station module joining constraint conditions are used to characterize the constraint conditions on the number of modules added to each station, and the preset station module reserve constraint conditions are used to characterize the constraint conditions on the number of modules reserved at each station.

[0170] Furthermore, the preset site module adding constraint conditions include a first site module adding quantity constraint and a first numerical constraint. The first site module adding quantity constraint is used to limit the number of modules added to the corresponding site according to the preset number of storage modules at each site, and the first numerical constraint is used to limit the number of modules added to each site to an integer; the preset site module reserve constraint conditions include a first site module reserve constraint, a second site module reserve constraint and a second numerical constraint. The first site module reserve constraint is used to limit the number of modules reserved at the corresponding site according to the number of available modules, the second site module reserve constraint is used to limit the number of modules reserved at the corresponding site according to the upper limit of module berths at each site, and the second numerical constraint is used to limit the number of modules reserved at each site to an integer.

[0171] It should be noted that, unlike the public welfare of regular bus services, the design of customized bus services needs to focus on profitability. The operating profit of the commuter and school special line service is equal to the line fare revenue minus the line operating cost. Among them, the line operating cost includes the operating time cost of the module car and the scheduling cost of reserving modules in advance at the reorganization station. Based on the residential place reported by the employees (or students) and the fare for each employee (or student) determined by the bus company and the enterprise (or school), the bus company customizes a suitable commuter and school special line service for the enterprise (or school) to meet the passengers' boarding needs as much as possible while ensuring maximum profit.

[0172] In addition, during the operation of the modular vehicle, when passing through a regular station j, the vehicle stops normally to allow passengers to board the vehicle, and the stop time is fixed at T j Before the module car arrives at the reorganization station, the passengers waiting at the reorganization station board the pre-stored modules in the station in advance. These modules will move quickly and connect to the module car when the module car arrives at the station to complete the passengers' boarding process. Since the whole process is carried out while the vehicle is moving, the module car's stop time is zero.

[0173] In addition, the linearization processing unit includes: a linearization processing sub-unit, which performs linearization processing on the mixed integer nonlinear programming model based on preset auxiliary variables and preset binary auxiliary variables to obtain a linearized model.

[0174] To sum up, the embodiment of the present invention obtains information such as passenger boarding demand, fare, special line route information and module information through the data acquisition module, so that the planning module can obtain the module planning decision results, while ensuring the maximization of the profit of the bus company and serving as many commuting and school passengers as possible, thereby providing theoretical guidance and decision-making support for the operation and management of the bus company's commuting and school special lines, and has a very broad application prospect.

[0175] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As shown, the electronic device may include: a processor 41, a communication interface 42, a memory 43 and a communication bus 44, wherein the processor 41, the communication interface 42 and the memory 43 communicate with each other through the communication bus 44. The processor 41 may call the logic instructions in the memory 43 to execute a module vehicle planning method based on a morning peak commuting and school special line, the method comprising: obtaining passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the special line passing stations, the running time between stations and the upper limit of the module berths at each station, and the module information includes the module rated passenger capacity and the number of available modules; according to the passenger boarding demand, fare, special line route information and module information, the module planning decision result is obtained.

[0176] In addition, the logic instructions in the above-mentioned memory 43 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the 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 a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0177] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the modular vehicle planning method based on the morning rush hour commuting and school special line provided by the above-mentioned methods, the method including: obtaining passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the special line passage stations, the operating time between stations and the upper limit of module berths at each station, and the module information includes the module rated passenger capacity and the number of available modules; according to the passenger boarding demand, fare, special line route information and module information, the module planning decision result is obtained.

[0178] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the modular vehicle planning method based on the morning rush hour commuting and school special line provided by the above-mentioned methods, the method comprising: obtaining passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the stations passed by the special line, the operating time between stations and the upper limit of module berths at each station, and the module information includes the rated passenger capacity of the module and the number of available modules; according to the passenger boarding demand, fare, special line route information and module information, the module planning decision result is obtained.

[0179] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0180] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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 invention.

Claims

1. A modular vehicle planning method based on a morning peak commuting and school-related special line, characterized in that: include: Obtaining passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the special line passing stations, the running time between stations and the upper limit of the module berths at each station; the module information includes the module rated passenger capacity and the number of available modules; Obtaining a module planning decision result according to the passenger boarding demand, the fare, the dedicated line route information and the module information; The module planning decision result includes site planning information indicating whether each site is a reorganization site, module reserve information corresponding to each site when arriving at each site, and information on the number of modules added when arriving at each site; The step of obtaining a module planning decision result according to the passenger boarding demand, the fare, the dedicated route information and the module information includes: constructing a mixed integer nonlinear programming model according to the passenger boarding demand, the fare, the dedicated route information and the module information; A mixed integer nonlinear programming model is constructed according to the passenger boarding demand, the fare, the dedicated route information and the module information, including: constructing a station boarding passenger prediction submodel based on the passenger boarding demand, the module rated passenger capacity, the number of available modules and the upper limit of module berths at each station; constructing an actual stop time prediction submodel according to the dedicated route information and the preset fixed stop time; constructing a mixed integer nonlinear programming model according to the fare, the station boarding passenger prediction submodel, the actual stop time prediction submodel, the module rated passenger capacity, the number of available modules, the upper limit of module berths at each station, the preset scheduling cost and the preset adjacent station operating cost, in combination with the preset station adding module constraint condition and the preset station module reserve constraint condition; wherein the preset station adding module constraint condition is used to characterize the module quantity constraint added to each station, and the preset station module reserve constraint condition is used to characterize the module quantity constraint reserved for each station.

2. The modular vehicle planning method based on the morning peak commuting and school special line according to claim 1 is characterized in that: The module planning decision result is obtained according to the passenger boarding demand, the fare, the dedicated line route information and the module information, including: Linearizing the mixed integer nonlinear programming model to obtain a linearized model; According to the linearized model, combined with the branch and bound method, the module planning decision result is obtained.

3. The modular vehicle planning method based on the morning peak commuting and school special line according to claim 1 is characterized in that: The preset site module adding constraint condition includes a first site module adding quantity constraint and a first numerical constraint, wherein the first site module adding quantity constraint is used to limit the number of modules added to the corresponding site according to the preset number of storage modules of each site, and the first numerical constraint is used to limit the number of modules added to each site to an integer; The preset site module reserve constraint condition includes a first site module reserve constraint, a second site module reserve constraint and a second numerical constraint. The first site module reserve constraint is used to limit the number of modules reserved at the corresponding site according to the number of available modules. The second site module reserve constraint is used to limit the number of modules reserved at the corresponding site according to the upper limit of module berths at each site. The second numerical constraint is used to limit the number of modules reserved at each site to an integer.

4. The modular vehicle planning method based on the morning peak commuting and school special line according to claim 1 is characterized in that: The mixed integer nonlinear programming model is expressed as: y1=1,y J =0 Among them, z represents the profit of the module bus operation during the morning peak; α1 represents the weight coefficient of the bus company's fare revenue; α2 represents the weight coefficient of the bus company's operating cost, and F p represents the passenger fare, that is, the fare paid by the enterprise or school for each passenger; J represents the number of stations along the commuter-to-school line; s j represents the number of passengers boarding at station j; B j represents the running time from site j to the next adjacent site j+1; g j represents the number of modules leaving site j; t j represents the actual stop time at station j; F d represents the scheduling cost required to reserve modules in advance at the reorganization site, d j represents the module reserve of site j when arriving at site j; T j represents the preset fixed stop time at station j; y j represents a binary variable. When the selected site j is a module recombination site, its value is 1, otherwise it is 0; Q j represents the boarding demand of passengers at station j; C represents the rated passenger capacity of the module; b j represents the number of passengers on the bus when it arrives at station j; p j represents the number of modules added when arriving at site j; F m Indicates the upper limit of the number of modules that the module vehicle owner can own; F j represents the upper limit of module berths at site j; Represents a set of integers.

5. The modular vehicle planning method based on the morning peak commuting and school special line according to claim 2 is characterized in that: The linearizing the mixed integer nonlinear programming model comprises: Based on the preset auxiliary variables and the preset binary auxiliary variables, the mixed integer nonlinear programming model is linearized to obtain a linearized model.

6. The modular vehicle planning method based on the morning peak commuting and school special line according to claim 2 is characterized in that: The linearized model is expressed as: y1=1,y J =0 Among them, z represents the profit of the module bus operation during the morning peak; α1 represents the weight coefficient of the bus company's fare revenue; α2 represents the weight coefficient of the bus company's operating cost, and F p represents the passenger fare, that is, the fare paid by the enterprise or school for each passenger; J represents the number of stations along the commuter-to-school line; s j represents the number of passengers boarding at station j; B j represents the running time from site j to the next adjacent site j+1; g j represents the number of modules leaving site j; F d represents the scheduling cost required to reserve modules in advance at the reorganization site, d j represents the module reserve at site j when arriving at site j; g′ j , s′ j , s″ j , d′ j Indicates preset auxiliary variables; T j represents the preset fixed stop time at station j; y j represents a binary variable. When the selected site j is a modular recombination site, its value is 1, otherwise it is 0; M represents the maximum value; t j represents the actual stop time at station j; q j represents the number of remaining passengers on the bus when it arrives at station j; Q j represents the boarding demand of passengers at station j; C represents the rated passenger capacity of the module; b j represents the number of passengers on the bus when it arrives at station j; v j and u j represents the preset binary auxiliary variable; p j represents the number of modules added when arriving at site j; F m Indicates the upper limit of the number of modules that the module vehicle owner can own; F j represents the upper limit of module berths at site j; Represents a set of integers.

7. A modular vehicle planning device based on a morning peak commuting and school-related special line, characterized in that: include: A data acquisition unit, which acquires passenger boarding demand, fare, special line route information and module information; wherein the special line route information includes the special line passing stations, the running time between stations and the upper limit of the module berths at each station; the module information includes the module rated passenger capacity and the number of available modules; A planning unit, which obtains a module planning decision result according to the passenger boarding demand, the fare, the dedicated line route information and the module information; The module planning decision result includes site planning information indicating whether each site is a reorganization site, module reserve information corresponding to each site when arriving at each site, and information on the number of modules added when arriving at each site; The step of obtaining a module planning decision result according to the passenger boarding demand, the fare, the dedicated route information and the module information includes: constructing a mixed integer nonlinear programming model according to the passenger boarding demand, the fare, the dedicated route information and the module information; A mixed integer nonlinear programming model is constructed according to the passenger boarding demand, the fare, the dedicated route information and the module information, including: constructing a station boarding passenger prediction submodel based on the passenger boarding demand, the module rated passenger capacity, the number of available modules and the upper limit of module berths at each station; constructing an actual stop time prediction submodel according to the dedicated route information and the preset fixed stop time; constructing a mixed integer nonlinear programming model according to the fare, the station boarding passenger prediction submodel, the actual stop time prediction submodel, the module rated passenger capacity, the number of available modules, the upper limit of module berths at each station, the preset scheduling cost and the preset adjacent station operating cost, in combination with the preset station adding module constraint condition and the preset station module reserve constraint condition; wherein the preset station adding module constraint condition is used to characterize the module quantity constraint added to each station, and the preset station module reserve constraint condition is used to characterize the module quantity constraint reserved for each station.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the modular vehicle planning method based on the morning peak commuting and school dedicated line are implemented as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the modular vehicle planning method based on the morning peak commuting and school dedicated line as described in any one of claims 1 to 6 are implemented.

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