Elastic Bus Operation Scheduling Method, Device, Electronic Equipment and Storage Medium

Abstract

The present invention relates to the technical field of bus dispatching, and provides an elastic bus operation dispatching method, device, electronic device and storage medium. The method includes: obtaining elastic bus operation parameters, static passenger demand data and dynamic passenger demand data; substituting the static passenger demand data into a first operation dispatching model with operation cost as the objective to generate an initial dispatching plan including an initial path and an initial charging plan; substituting any dynamic passenger demand data and all static passenger demand data into a second operation dispatching model with operation cost as the objective to generate an adjusted dispatching plan corresponding to the scenario of any dynamic passenger demand data; determining a target dispatching plan based on the initial dispatching plan and each adjusted dispatching plan. Since the adjusted dispatching plan takes into account the dynamic passenger demand, it achieves the effect of optimizing the operation cost of the elastic bus system while meeting the dynamic passenger demand, and also improves the flexibility of the entire elastic bus system.

Description

Technical Field

[0001] The present invention relates to the technical field of bus dispatching, and particularly relates to a flexible bus operation dispatching method, device, electronic device and storage medium. Background Art

[0002] Under the background of the era of traffic low-carbonization and sharing, the application scenarios of electric buses are gradually innovated, and a flexible, efficient and diversified flexible bus service mode is introduced. Flexible buses can comprehensively consider the information of passengers' reservation needs and real-time needs, provide a more flexible service mode, thereby reducing the negative impact brought by the range anxiety of electric vehicles, breaking the time and space limitations of traditional buses with fixed stops, fixed routes and fixed schedules, and enhancing the competitiveness of public transport services.

[0003] At present, existing flexible buses analyze the vehicle operation and line passenger flow rules based on technical means such as artificial intelligence, Internet of Things and big data to achieve the static operation dispatching of flexible buses, that is, plan the operation routes of flexible buses (including driving nodes and the lines between driving nodes) according to the pre-obtained static passenger demand data. However, with the wide popularity of intelligent mobile terminals, public transport travelers have more convenient ways to express their travel needs in real time. However, the existing technology does not consider optimizing the dynamic passenger demand (the demand other than the static passenger demand, which generally refers to the passenger demand obtained after the static operation dispatching plan is determined in practical applications). Flexible buses only run according to the stops (i.e., driving nodes) and lines planned by the static operation dispatching, and the flexibility is poor. Moreover, the existing static operation dispatching does not consider the charging plan either, which may lead to the problem of insufficient power during the operation of flexible buses.

[0004] Therefore, how to combine the dynamic passenger demand to achieve an operation dispatching plan including the operation route and charging plan of flexible buses is a technical problem to be solved urgently at present. Summary of the Invention

[0005] The present invention provides a flexible bus operation dispatching method, device, electronic device and storage medium to solve the above technical problems existing in the prior art.

[0006] The present invention provides a flexible bus operation dispatching method, including:

[0007] Obtain the flexible bus operation parameters, static passenger demand data and dynamic passenger demand data;

[0008] Substitute the static passenger demand data into a first operation dispatching model with the operation cost as the objective to generate an initial dispatching plan including an initial route and an initial charging plan, where the first operation dispatching model is constructed based on the flexible bus operation parameters;

[0009] Substitute any of the dynamic passenger demand data and all the static passenger demand data into the second operation and scheduling model with the operation cost as the objective, to generate an adjusted scheduling plan corresponding to the scenario of any of the dynamic passenger demand data, where the adjusted scheduling plan includes an adjusted route and an adjusted charging plan, and the second operation and scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data;

[0010] Based on the initial scheduling plan and each of the adjusted scheduling plans, determine the final target scheduling plan.

[0011] According to an elastic bus operation and scheduling method provided by the present invention, the first operation and scheduling model includes: a first total utility objective function of the elastic bus constructed based on elastic bus operation parameters and a first set of constraint conditions; the first total utility objective function is used to represent the total cost of any scheduling plan of all elastic buses that meet the static passenger demand data; the first set of constraint conditions includes:

[0012] The first bus route constraint: ensure that the boarding point should be visited before the alighting point;

[0013] The first time constraint: ensure the continuity of the elastic bus time;

[0014] The first bus capacity constraint: ensure that the elastic bus is not overloaded;

[0015] The first bus power constraint: ensure that the power of the elastic bus meets the power required by the initial scheduling plan.

[0016] According to an elastic bus operation and scheduling method provided by the present invention, the first total utility objective function is:

[0017] ;

[0018] Wherein, is the value of the first total utility objective function; is the usage cost of the elastic bus; is the charging cost of the elastic bus; is the detour cost of the passenger; is the penalty cost for the passenger waiting for the elastic bus; is the set of driving nodes of the elastic bus; is the set of passenger boarding points; is the set of electric elastic bus charging stations; is the set of elastic bus vehicles; is the set of scenarios corresponding to the dynamic passenger demand data; is two driving nodes and [[ID= fifty-four ]]The driving duration between; is the average maximum waiting time of all passengers at boarding point \(i\) in the static passenger demand data; is the occurrence probability of the scenario; is a variable taking values of 0 or 1, indicating whether the flexible bus passes through the driving node to the driving node ; is an integer variable, indicating the charging duration of the flexible bus at the charging station ; is an integer variable, representing the average additional detour duration of all passengers boarding at boarding point taking the flexible bus compared to directly driving or taking a taxi to reach the destination; is an integer variable, indicating the duration from the departure time for the flexible bus to reach the driving node ; is the expected cost considering scenario \(s\), and \(\min()\) is the function to find the minimum value.

[0019] According to a flexible bus operation and scheduling method provided by the present invention, the second operation and scheduling model includes: a second total utility objective function of the flexible bus and a second set of constraint conditions. The second total utility objective function is constructed based on the initial scheduling plan and the dynamic passenger demand data, and is used to characterize the total cost of all flexible buses in any scheduling plan that meets the scenarios of the dynamic passenger demand data. The second set of constraint conditions includes:

[0020] Second bus route constraint: Ensure that the boarding and alighting points in the static passenger demand data must be served, and the boarding and alighting points in the dynamic passenger demand data must also be served;

[0021] Second time constraint: Ensure that the line detour duration generated by adjusting the flexible bus route is not greater than the additional detour duration of passengers in the scenarios corresponding to the dynamic passenger demand data;

[0022] Second bus capacity constraint: Ensure that the flexible bus is not overloaded;

[0023] Second bus power constraint: Ensure that the power of the flexible bus meets the power required for the adjusted scheduling plan.

[0024] According to a flexible bus operation and scheduling method provided by the present invention, the second total utility objective function is:

[0025] ;

[0026] where is the usage cost of the flexible bus; is the charging cost of the flexible bus; is the detour cost of the passengers; is the penalty cost for passengers waiting for the flexible bus; is the set of driving nodes of the flexible bus in the scenario corresponding to the dynamic passenger demand data; is the set of passenger boarding points in the scenario corresponding to the dynamic passenger demand data; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; are two driving nodes and is the driving duration between them; is the average value of the maximum waiting time of all passengers at boarding point i in the scenario corresponding to the dynamic passenger demand data; is a variable with a value of 0 or 1, indicating whether the path of the flexible bus k from driving node i to j is added in the scenario s corresponding to the dynamic passenger demand data; is a variable with a value of 0 or 1, indicating whether the path of the flexible bus k from driving node i to j is deleted in the scenario s corresponding to the dynamic passenger demand data; is an integer variable, indicating the charging duration of the flexible bus k at the charging station c in the scenario s corresponding to the dynamic passenger demand data; is an integer variable, indicating the boarding point is the average value of the additional detour duration of all passengers taking the flexible bus relative to driving directly or taking a taxi to the destination in the scenario s corresponding to the dynamic passenger demand data; is an integer variable, indicating the flexible bus in the scenario s corresponding to the dynamic passenger demand data starting from the departure time to reach the driving node is the duration; min() is the function to find the minimum value.

[0027] According to a flexible bus operation scheduling method provided by the present invention, based on the initial scheduling plan and each of the adjusted scheduling plans, a final target scheduling plan is determined, including:

[0028] For each scenario, calculate the similarity matrix between the adjusted scheduling plan and the initial scheduling plan of each scenario, and establish the association relationship between the adjusted path and the initial path in the scenario according to the similarity matrix;

[0029] Generate the membership degree matrix according to the similarity matrix, the association relationship and the initial path of the initial scheduling plan, and allocate the driving nodes in the static passenger demand data to the initial path according to the membership degree matrix;

[0030] Calculate a position matrix based on the membership matrix, the scenario probabilities, and the positions of driving nodes in the adjusted paths corresponding to different scenarios in the static passenger demand data, and determine the order of the driving nodes in the static passenger demand data in the path of the final target scheduling plan based on the position matrix, so as to determine the final target scheduling plan.

[0031] The present invention also provides a flexible bus operation scheduling device, including:

[0032] A data acquisition module, configured to acquire flexible bus operation parameters, static passenger demand data, and dynamic passenger demand data;

[0033] An initial scheduling plan generation module, configured to substitute the static passenger demand data into a first operation scheduling model with the operation cost as the target, and generate an initial scheduling plan including an initial path and an initial charging plan, where the first operation scheduling model is constructed based on the flexible bus operation parameters;

[0034] An adjusted scheduling plan generation module, configured to substitute any of the dynamic passenger demand data and all the static passenger demand data into a second operation scheduling model with the operation cost as the target, and generate an adjusted scheduling plan corresponding to the scenario of any of the dynamic passenger demand data, where the adjusted scheduling plan includes an adjusted path and an adjusted charging plan, and the second operation scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data;

[0035] A target scheduling plan determination module, configured to determine a final target scheduling plan based on the initial scheduling plan and each of the adjusted scheduling plans.

[0036] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor, where when the processor executes the program, the flexible bus operation scheduling method as described in any one of the above is implemented.

[0037] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the flexible bus operation scheduling method as described in any one of the above is implemented.

[0038] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the flexible bus operation scheduling method as described in any one of the above is implemented.

[0039] The elastic bus operation scheduling method, device, electronic device and storage medium provided by the present invention generate an initial scheduling plan through a first operation scheduling model and static passenger demand data, and consider the dynamically emerging demands in actual applications. Based on the initial scheduling plan and dynamic passenger demand data, a second operation scheduling model with the operation cost as the objective is constructed to generate an adjusted scheduling plan for the scenario corresponding to any of the dynamic passenger demand data. Then, according to the initial scheduling plan and each adjusted scheduling plan, a final target scheduling plan with better robustness is determined. Since the adjusted scheduling plan takes into account the dynamic passenger demand, the final target scheduling plan considering the dynamic passenger demand is realized, enabling the operation cost of the elastic bus system to reach a better effect. Moreover, when dynamic passenger demands occur, it is also possible to switch to the corresponding adjusted scheduling plan at low cost, thereby improving the flexibility of the entire elastic bus system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a flowchart of the elastic bus operation scheduling method provided by the present invention.

[0042] Figure 2 It is a diagram of the initial path and charging plan of the initial scheduling plan in the elastic bus operation scheduling method provided by the present invention.

[0043] Figure 3 It is a diagram of the path and charging plan of the adjusted scheduling plan corresponding to the first scenario in the elastic bus operation scheduling method provided by the present invention.

[0044] Figure 4 It is a diagram of the path and charging plan of the adjusted scheduling plan corresponding to the second scenario in the elastic bus operation scheduling method provided by the present invention.

[0045] Figure 5 It is a diagram of the path and charging plan of the adjusted scheduling plan corresponding to the third scenario in the elastic bus operation scheduling method provided by the present invention.

[0046] Figure 6 It is a diagram of the initial path and charging plan of the target scheduling plan in the elastic bus operation scheduling method provided by the present invention.

[0047] Figure 7 It is a schematic diagram of matrix calculation when determining the final target scheduling plan in the elastic bus operation scheduling method provided by the present invention.

[0048] Figure 8 It is a schematic structural diagram of the elastic bus operation and dispatching device provided by the present invention.

[0049] Figure 9 It is a schematic structural diagram of the electronic device provided by the present invention. Specific embodiments

[0050] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0051] The elastic bus operation and dispatching method according to the embodiment of the present invention, as Figure 1 shown, includes the following steps S110 to step S140.

[0052] Step S110: Obtain elastic bus operation parameters, static passenger demand data and dynamic passenger demand data. The elastic bus operation parameters include:

[0053] 1) The usage cost of the elastic bus, for example: ¥5 / period.

[0054] 2) The charging cost of the elastic bus, for example: ¥25 / period.

[0055] 3) The detour cost of passengers, for example: ¥5 / period.

[0056] 4) The penalty cost for passengers waiting for the elastic bus, for example: ¥25 / period.

[0057] 5) The maximum passenger capacity of the elastic bus, for example: 24 people.

[0058] 6) The maximum power of the electric elastic bus, for example: 50 kWh.

[0059] 7) The minimum power of the electric elastic bus, for example: 15 kWh.

[0060] 8) The charging rate, for example: 5 kWh / period.

[0061] 9) The service duration (the duration of stopping for passengers to get on and off) is 1 period.

[0062] The above period is a period of a preset duration, for example: 1 - 5 minutes. Those less than the length of one period are calculated as one period. All duration concepts in this article can be counted in units of the said period.

[0063] Static passenger demand data and dynamic passenger demand data can be obtained from online car-hailing orders, mainly including information such as the boarding point, the alighting point, and the maximum waiting time (the time from the departure time of the flexible bus to the boarding point of the passenger). The static passenger demand data and dynamic passenger demand data obtained from the online car-hailing orders in a certain area are shown in Table 1 below.

[0064] Table 1 Static passenger demand data and dynamic passenger demand data

[0065]

[0066] In the above Table 1, the boarding point and the alighting point corresponding to each static passenger demand data are the driving nodes that the flexible bus needs to pass through (i.e., the stations where passengers get on and off the bus on the flexible bus route). The maximum waiting time is in the unit of the above-defined time period. For example, if the time period is 5 minutes and the maximum waiting time is 10, then 10×5 = 50 minutes, indicating that the passenger expects the time from the departure time of the flexible bus to the boarding point of the passenger to be 50 minutes.

[0067] Step S120: Substitute the static passenger demand data into the first operation scheduling model with the operation cost as the objective to generate an initial scheduling plan including an initial route and an initial charging plan. The first operation scheduling model is constructed based on the flexible bus operation parameters. In this step, by pre-constructing the first operation scheduling model with the operation cost as the objective and solving the first operation scheduling model according to the static passenger demand data, the operation cost is minimized, and the scheduling plan corresponding to the minimum operation cost is used as the initial scheduling plan, that is, the flexible bus is scheduled according to the initial route and the initial charging plan in the initial scheduling plan, so that the operation cost of the entire flexible bus system is minimized.

[0068] It should be noted that: the initial route and the initial charging plan are the initial routes and initial charging plans of each flexible bus in the flexible bus system.

[0069] It can be understood that: the initial charging plan includes charging or not charging. When the initial route contains the node of the charging station, it means charging is required. When the initial route does not contain the node of the charging station, it means charging is not required.

[0070] Step S130: Substitute any one of the dynamic passenger demand data and all the static passenger demand data into the second operation scheduling model with the operation cost as the objective to generate an adjusted scheduling plan corresponding to the scenario of any one of the dynamic passenger demand data. The adjusted scheduling plan includes an adjusted route and an adjusted charging plan. The second operation scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data.

[0071] Each piece of dynamic passenger demand data and all static passenger demand data correspond to a scenario, namely the dynamic demand scenario. That is, each piece of dynamic passenger demand data corresponds to a scenario. As shown in Table 1 above, there are three dynamic demand scenarios. Dynamic passenger demands 8, 9, and 10 respectively form three dynamic demand scenarios with static passenger demand data 1 - 7.

[0072] In this step, through the pre - constructed second operation scheduling model with the operation cost as the goal, and solving the second operation scheduling model according to the dynamic passenger demand data and all static passenger demand data, the operation cost is minimized under the corresponding scenario. The scheduling plan corresponding to the minimum operation cost is used as the adjusted scheduling plan. That is, the flexible bus is scheduled according to the adjusted path and adjusted charging plan in the adjusted scheduling plan, so that the operation cost of the entire flexible bus system is minimized under the corresponding scenario.

[0073] Step S140: Determine the final target scheduling plan based on the initial scheduling plan and each adjusted scheduling plan.

[0074] In the flexible bus operation scheduling method of this embodiment, an initial scheduling plan is generated through the first operation scheduling model and static passenger demand data, and considering the randomly occurring dynamic demands in actual applications, based on the initial scheduling plan and dynamic passenger demand data, a second operation scheduling model with the operation cost as the goal is constructed to generate adjusted scheduling plans corresponding to the scenarios of each piece of dynamic passenger demand data. Then, according to the initial scheduling plan and each adjusted scheduling plan, a final target scheduling plan with better robustness is determined. Since the adjusted scheduling plan takes into account the dynamic passenger demand, the final scheduling plan considering the dynamic passenger demand is realized, making the operation cost of the flexible bus system reach a better effect. Moreover, when dynamic passenger demands occur, it can also be switched to the corresponding adjusted scheduling plan at low cost, thereby improving the flexibility of the entire flexible bus system.

[0075] In some embodiments, the first operation scheduling model includes: the first total utility objective function of the flexible bus constructed based on the flexible bus operation parameters and the first constraint condition set. The first total utility objective function is used to represent the total cost of any scheduling plan of all flexible buses that meet the static passenger demand data. Among them, the first constraint condition set includes the following constraint conditions.

[0076] The first bus path constraint: Ensure that the boarding point should be visited before the alighting point.

[0077] The first time constraint: Ensure the continuity of the flexible bus time.

[0078] The first bus capacity constraint: Ensure that the flexible bus is not overloaded.

[0079] The first bus power constraint: Ensure that the power of the flexible bus meets the power required by the initial scheduling plan.

[0080] Specifically, the formula for the first total utility objective function is as follows:

[0081] (1).

[0082] Where, is the value of the first total utility objective function; is the cost of using the flexible bus; is the charging cost of the flexible bus; is the cost of passenger detour; is the penalty cost for passengers waiting for the flexible bus; is the set of driving nodes of the flexible bus (i.e., the set of stops of the flexible bus); is the set of passenger boarding points; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; is the set of scenarios corresponding to the dynamic passenger demand data; are two driving nodes and the driving duration between them; is the average maximum waiting duration at boarding point i in the static passenger demand data; is the scenario the occurrence probability of; is a variable taking values of 0 or 1, indicating whether the flexible bus passes through the driving node to the driving node , if so, takes the value of 1, otherwise 0; is an integer variable, indicating the charging duration of the flexible bus at the charging station ; is an integer variable, indicating the average value of the additional detour duration of all passengers boarding at boarding point taking the flexible bus relative to driving directly or taking a taxi to the destination; is an integer variable, indicating the duration from the departure time when the flexible bus arrives at the driving node ; is the expected cost considering scenario s, and min() is the minimum function. Among them, the set V contains the nodes in the sets P and C, as well as the depot stops of the flexible bus.

[0083] ]>It should be noted that: Since the dynamic passenger demand data is not considered temporarily when generating the initial scheduling plan, the last term in the first total utility objective function It can take the value of 0.

[0084] In this embodiment, the above first total utility objective function includes this term, that is, the average maximum waiting time of all passengers at boarding point i in the static passenger demand data, and also includes , that is, the flexible bus at the charging station charging duration. By adding multiple objective items such as charging duration and waiting duration, the time window of the first total utility objective function becomes more flexible, more in line with the actual situation, and also improves the operation efficiency and accuracy of the model.

[0085] The first constraint condition set is used to impose relevant restrictions on the planned path based on objective conditions such as bus path, time, bus capacity, and bus power during the scheduling planning with the minimum of the first total utility objective function as the goal.

[0086] Specifically, the formula expressions of the constraint conditions in the first constraint condition set are as follows:

[0087] The first bus path constraint is used to ensure that the boarding point should be visited before the alighting point:

[0088] (2).

[0089] In the formula, is the set of passenger boarding points, represents the duration for the flexible bus to reach the boarding point starting from the departure time, represents the duration for the flexible bus to reach the alighting point starting from the departure time, is the service duration of the flexible bus at the driving node, are two driving nodes and the driving duration between them.

[0090] The first time constraint is used to ensure the continuity of the flexible bus time, initialize the departure time of the flexible bus, and calculate the bus arrival time according to the following formula to achieve bus time continuity:

[0091] (3);

[0092] (4);

[0093] (5);

[0094] (6).

[0095] Among them, is the set of boarding and alighting points in the static passenger demand data, is the set of stations for flexible buses, is an integer variable representing the flexible bus departure time from station . is the waiting time of the flexible bus at the station (it should be noted that: the departure time of the flexible bus from station is not determined by the moment, but is determined by the departure time of the flexible bus from station , that is, the waiting time before departure. Both and are calculated in time periods. For example, a time period is 5 minutes, and there are 12 time periods in one hour), is an integer variable representing the flexible bus travel time from the departure time to the driving node . is the service time of the flexible bus at the driving node, is a value of infinity.

[0096] The first bus capacity constraint is used to ensure that the flexible bus is not overloaded. The number of passengers is calculated according to the following formula to meet the first bus capacity constraint:

[0097] (7);

[0098] (8).

[0099] Among them, represents the number of people on the flexible bus at the driving node , represents the number of people on the flexible bus at the driving node . is the number of people getting on / off at the driving node . If the driving node is a boarding point, then takes a positive value. If the driving node is an alighting point, then takes a negative value.

[0100] The first bus power constraint is used to ensure that the power of the flexible bus meets the power required by the initial scheduling plan. The charging time is calculated according to the following formula:

[0101] (9);

[0102] (10).

[0103] Among them, is the driving duration between the driving node and the charging station ; represents the flexible bus from the departure time to the charging station duration; [[ID=2l]] is a variable with a value of 0 or 1, indicating whether the flexible bus departs from the charging station to the driving node .

[0104] The above formulas (1)-(10) are the first operation scheduling model. In this embodiment, substituting the static passenger demand data into the above formulas, and using Python to call Cplex to solve the first operation scheduling model, an initial scheduling plan including the initial path and the initial charging plan is obtained. The initial scheduling plan solved based on the static passenger demand data in Table 1 above is as Figure 2 shown. Figure 2 Among them, taking two flexible buses as an example, the initial scheduling plans of the two flexible buses are shown. Among them, the boarding point locations are 1-7 (corresponding to boarding points 1-7 in Table 1), the alighting point locations are 11-17 (corresponding to alighting points 11-17 in Table 1), D represents the bus yard, and S1 and S2 respectively represent two charging stations.

[0105] In some embodiments, the second operation scheduling model includes: the second total utility objective function of the flexible bus and the second constraint condition set. The second total utility objective function is constructed based on the initial scheduling plan and the dynamic passenger demand data, and is used to characterize the total cost of any scheduling plan of all flexible buses in the scenario that meets the dynamic passenger demand data; the second constraint condition set includes:

[0106] The second bus path constraint: Ensure that the boarding points and alighting points in the static passenger demand data must be served, and the boarding points and alighting points in the dynamic passenger demand data must also be served.

[0107] The second time constraint: Ensure that the line detour duration generated by the flexible bus adjusting the path is not greater than the additional detour duration of the passengers in the scenario corresponding to the dynamic passenger demand data.

[0108] The second bus capacity constraint: Ensure that the flexible bus is not overloaded.

[0109] The second bus power constraint: Ensure that the power of the flexible bus meets the power required for the adjusted scheduling plan.

[0110] Specifically, the formula of the second total utility objective function is as follows:

[0111] (11).

[0112] Among them, is the cost of using flexible buses; is the charging cost of flexible buses; is the detour cost of passengers; is the penalty cost for passengers waiting for flexible buses; is the set of driving nodes of flexible buses in the scenario corresponding to the dynamic passenger demand data; is the set of passenger boarding points in the scenario corresponding to the dynamic passenger demand data; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; are two driving nodes and is the driving duration between them; is the average maximum waiting duration of all passengers at boarding point i in the scenario corresponding to the dynamic passenger demand data; is a variable taking a value of 0 or 1, indicating whether the path of flexible bus k from driving node i to j is added in the scenario s corresponding to the dynamic passenger demand data; is a variable taking a value of 0 or 1, indicating whether the path of flexible bus k from driving node i to j is deleted in the scenario s corresponding to the dynamic passenger demand data; is an integer variable representing the charging duration of flexible bus k at charging station c in the scenario s corresponding to the dynamic passenger demand data; is an integer variable representing the average additional detour duration of all passengers boarding at boarding point in the scenario s corresponding to the dynamic passenger demand data when taking the flexible bus compared to driving directly or taking a taxi to the destination; is an integer variable representing the duration from the departure time when flexible bus reaches driving node ; min() is the function to find the minimum value. Among them, the set contains the nodes in the set and C, as well as the depot points of flexible buses. The variables and represent the adjustment of the driving route. For example, in scenario 1 corresponding to Figure 3 , boarding point 8 and alighting point 18 in the dynamic passenger demand data are inserted between boarding point 2 and boarding point 6 in the initial route. Then , , and When the dynamic passenger demand data is obtained, any unserved driving nodes on the initial path can be adjusted.

[0113] Specifically, the formula expressions of the constraints in the second constraint set are as follows.

[0114] The second bus path constraint is used to ensure that the boarding points and alighting points in the static passenger demand data must be served, and the boarding points and alighting points in the dynamic passenger demand data must also be served. The formula is as follows:

[0115] (12).

[0116] Among them, is the set of passenger boarding points and alighting points in the dynamic passenger demand data, is the set of boarding points and alighting points in the static passenger demand data.

[0117] The second time constraint is used to ensure that the line detour duration generated by the flexible bus adjustment path is not greater than the additional detour duration of passengers in the corresponding scenario of the dynamic passenger demand data. Calculate the line detour duration generated by the flexible bus adjustment path according to the following formula and compare it with the additional detour duration of passengers in the corresponding scenario of the dynamic passenger demand data:

[0118] (13).

[0119] Among them, represents the average value of the additional detour duration of all passengers boarding at the boarding point in the corresponding scenario s of the dynamic passenger demand data when taking the flexible bus compared with directly driving or taking a taxi to reach the destination, is the set of dynamic demand boarding points, is in the corresponding scenario s of the dynamic passenger demand data, for the flexible bus starting from the departure time to reach the corresponding alighting point of the duration, is the service duration of the flexible bus at the driving node in the corresponding scenario s of the dynamic passenger demand data, is the driving duration between two driving nodes and .

[0120] The second bus capacity constraint is used to ensure that the flexible bus is not overloaded. The formula is as follows:

[0121] (14).

[0122] Among them, is a variable with a value of 0 or 1, indicating that in the corresponding scenario s of the dynamic passenger demand data, for the flexible bus The number of passengers on the vehicle at the driving node is the rated passenger capacity of the flexible bus. The second bus power constraint is used to ensure that the power of the flexible bus meets the power required for the adjusted scheduling plan.

[0123] Among them,

[0124] (15).

[0125] (16).

[0126] where is the power consumed by the flexible bus k from the driving node to the charging station ; is the charging rate of the flexible bus, is the rated capacity of the flexible bus battery, is an integer variable representing the power of the flexible bus when it reaches the driving node in the scenario s corresponding to the dynamic passenger demand data; is an integer variable representing the power of the flexible bus when it arrives at the charging station in the scenario s corresponding to the dynamic passenger demand data; is the set of stations of the flexible bus, is a variable taking a value of 0 or 1, indicating whether the flexible bus goes from the charging station to the driving node ; is a variable taking a value of 0 or 1, indicating whether the path of the flexible bus k from the charging station to the driving node j is added in the scenario s corresponding to the dynamic passenger demand data; is a variable taking a value of 0 or 1, indicating whether the path of the flexible bus k from the charging station to the driving node j is deleted in the scenario s corresponding to the dynamic passenger demand data.

[0127] The above formulas (11)-(16) are the second operation scheduling model. In this embodiment, the static passenger demand data and the dynamic passenger demand data are substituted into the above formulas, and Python is used to call Cplex to solve the second operation scheduling model, obtaining an adjusted scheduling plan including the adjusted path and the adjusted charging plan. The adjusted scheduling plan obtained based on the dynamic passenger demand data in Table 1 above is as Figures 3 - 5 shown, Figures 3 - 5 in which, taking two flexible buses as an example, the initial scheduling plans of the two flexible buses are shown. Figure 3The adjusted scheduling plan diagram of the dynamic scenario formed by the dynamic passenger demand data corresponding to demand number 8 in Table 1 and the static passenger demand data 1-7 Figure 4 The adjusted scheduling plan diagram of the dynamic scenario formed by the dynamic passenger demand data corresponding to demand number 9 in Table 1 and the static passenger demand data 1-7 Figure 5 The adjusted scheduling plan diagram of the dynamic scenario formed by the dynamic passenger demand data corresponding to demand number 10 in Table 1 and the static passenger demand data 1-7

[0128] In some embodiments, step S140 specifically includes the following steps 1 to 3

[0129] Step 1: For each scenario, calculate the similarity matrix between the adjusted scheduling plan of each scenario and the initial scheduling plan, and establish the association relationship between the adjusted path and the initial path in the scenario according to the similarity matrix. Specifically, as shown in (a) below, obtain the adjusted path Figure 7 and the adjusted charging plan of each dynamic passenger demand data corresponding scenario s (the path including the charging station node in the adjusted path contains the adjusted charging plan), and obtain the initial path and the initial charging plan, where represents the adjusted path of flexible bus k under scenario s, represents the initial path of flexible bus k. For the data in Table 1, as shown in (b) below, for each scenario, calculate the similarity matrix between the adjusted scheduling plan of each scenario and the initial scheduling plan, that is, compare the number of nodes with the same position and the same order in the two scheduling plans, and construct the similarity matrix of the two scheduling plans based on the number of nodes with the same position and the same order. Establish the association relationship between the path and the initial path Figure 7 in the scenario according to the similarity matrix. Specifically, for each row in the similarity matrix, the adjusted path corresponding to the larger element and the initial path are associated. For example: in the similarity matrix, there are two driving nodes (i.e., yard D and boarding point 2) with the same position and the same order between the adjusted path in the adjusted scheduling plan and the initial path in the initial scheduling plan (the elements corresponding to in the similarity matrix are 2, while the elements corresponding to and are 1), so the adjusted path and are associated with the initial path and . That is, in the similarity matrix, for each row corresponding to , the adjusted path is associated with the column where the largest element in this row is located , and the initial path is associated with the column where the largest element in this row is located Association

[0130] Step 2: Generate a membership degree matrix (MDM) based on the similarity matrix, the association relationship, and the initial path of the initial scheduling plan. As shown in (c) below, allocate the driving nodes (boarding points and corresponding alighting points) in the static passenger demand data to the initial path according to the membership degree matrix. For example: Adjust the path Figure 7 associated with the initial path That is associated, that is The driving node i in is calculated according to the following formula (17) and is likely to be allocated to the initial path .

[0131] Membership degree matrix An entry in contains a score (0 and 1 in the membership degree matrix), which is calculated by summing the scenario probabilities associated with the scenario path The scenario probability is a preset value. For example: The probabilities of three scenarios are all 1 / 3 .

[0132] (17).

[0133] Where, if , and is associated with , then . According to the similarity matrix and the membership degree matrix, allocate the driving node i in each static passenger demand data to the path . As shown in (c) below, in the membership degree matrix, an element of 1 indicates that the corresponding boarding point and alighting point are allocated to the corresponding initial path. For example: The boarding point 1 and the alighting point 11 are allocated to the initial path Figure 7 . .

[0134] Step 3: As shown in (d) below, based on the membership degree matrix, the scenario probability, and the positions of the driving nodes in the static passenger demand data in the adjusted paths corresponding to different scenarios, calculate a position matrix, and determine the order of the driving nodes in the static passenger demand data in the path of the final target scheduling plan based on the position matrix to determine the final target scheduling plan, that is, a robust scheduling plan with a better service order. Specifically, the formula for calculating the position matrix is as follows Figure 7 .

[0135] (18).

[0136] indicates that the driving node i in the static passenger demand data belongs to In the case of, the position of driving node i in the static passenger demand data in

[0137] Based on the data of the corresponding region in Table 1, the scheduling plans obtained in the above steps S120 to S140 are shown in Table 2 below, and the final target scheduling plan is as Figure 6 shown.

[0138] Table 2 Test Results of Online Car-hailing Data in a Certain Region

[0139]

[0140] In Table 2, the detour cost, operation cost, charging cost, and waiting cost are all time costs, calculated in units of the above preset time period. For the amount cost, it can be obtained by multiplying the amount per unit time by the time cost. Specifically, in different scenarios, the flexible bus fleet plans to adopt different adjustment methods. In Scenario 1, the bus adopts an additional charging cost to serve the real-time demand, while in Scenario 2, the fleet changes to cooperate with each other to meet all demands. According to the solution of this embodiment, the final target scheduling plan is obtained. The final total target cost (Formula (1)) is 1853, and the average target value gap is less than 6.38%. That is, the total target cost of the two scheduling paths V1 and V2 of the final target scheduling plan obtained in step S140 in Table 2 is 1853, with a lower cost, and it can switch to the corresponding adjusted scheduling plan at a lower cost in any scenario of Scenarios 1-3.

[0141] The flexible bus operation scheduling device provided by the present invention will be described below. The flexible bus operation scheduling device described below can be correspondingly referred to the flexible bus operation scheduling method described above.

[0142] The flexible bus operation scheduling device according to the embodiment of the present invention, as Figure 8 shown, includes the following modules 810 to 840.

[0143] The data acquisition module 810 is configured to acquire flexible bus operation parameters, static passenger demand data, and dynamic passenger demand data.

[0144] The initial scheduling plan generation module 820 is configured to substitute the static passenger demand data into the first operation scheduling model with the operation cost as the target, and generate an initial scheduling plan including an initial path and an initial charging plan. The first operation scheduling model is constructed based on the flexible bus operation parameters.

[0145] The adjusted scheduling plan generation module 830 is configured to substitute any of the dynamic passenger demand data and all the static passenger demand data into a second operation scheduling model with the operation cost as the objective, and generate an adjusted scheduling plan corresponding to the scenario of any of the dynamic passenger demand data. The adjusted scheduling plan includes an adjusted route and an adjusted charging plan. The second operation scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data.

[0146] The target scheduling plan determination module 840 is configured to determine a final target scheduling plan based on the initial scheduling plan and each adjusted scheduling plan.

[0147] The flexible bus operation scheduling device in this embodiment generates an initial scheduling plan through the first operation scheduling model and the static passenger demand data, and considers the randomly occurring dynamic demands in actual applications. Based on the initial scheduling plan and the dynamic passenger demand data, a second operation scheduling model with the operation cost as the objective is constructed to generate an adjusted scheduling plan corresponding to the scenario of any of the dynamic passenger demand data. Then, according to the initial scheduling plan and each adjusted scheduling plan, a final target scheduling plan with better robustness is determined. Since the adjusted scheduling plan takes into account the dynamic passenger demand, the final scheduling plan considering the dynamic passenger demand is realized, enabling the operation cost of the flexible bus system to reach a better effect. Moreover, when dynamic passenger demands occur, it can also be switched to the corresponding adjusted scheduling plan at low cost, thereby improving the flexibility of the entire flexible bus system.

[0148] In some embodiments, the first operation scheduling model includes: a first total utility objective function of the flexible bus constructed based on the flexible bus operation parameters and a first set of constraint conditions; the first total utility objective function is used to represent the total cost of any scheduling plan of all flexible buses that meets the static passenger demand data; the first set of constraint conditions includes:

[0149] The first bus route constraint: ensuring that the boarding point should be visited before the alighting point.

[0150] The first time constraint: ensuring the continuity of the flexible bus time.

[0151] The first bus capacity constraint: ensuring that the flexible bus is not overloaded.

[0152] The first bus power constraint: ensuring that the power of the flexible bus meets the power required for the initial scheduling plan.

[0153] In some embodiments, the first total utility objective function is:

[0154] ;

[0155] Wherein, is the first total utility objective function value; is the flexible bus usage cost; is the flexible bus charging cost; is the passenger detour cost; is the penalty cost for passengers waiting for the flexible bus; is the set of flexible bus driving nodes; is the set of passenger boarding points; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; is the set of scenarios corresponding to the dynamic passenger demand data; are two driving nodes and the driving duration between them; is the average maximum waiting time of all passengers at boarding point i in the static passenger demand data; is the scenario appearance probability; is a variable taking values of 0 or 1, indicating whether the flexible bus passes from driving node to driving node ; is an integer variable, indicating the charging duration of the flexible bus at the charging station ; is an integer variable taking values, indicating the average additional detour duration of all passengers boarding at boarding point when taking the flexible bus compared to directly driving or taking a taxi to reach the destination; is an integer variable, indicating the duration from the departure time when the flexible bus arrives at the driving node ; is the expected cost considering scenario s, and min() is the function to find the minimum value.

[0156] In some embodiments, the second operation scheduling model includes: the second total utility objective function of the flexible bus and the second set of constraint conditions. The second total utility objective function is constructed based on the initial scheduling plan and the dynamic passenger demand data, and is used to characterize the total cost of all flexible buses in any scheduling plan that meets the scenarios corresponding to the dynamic passenger demand data. The second set of constraint conditions includes:

[0157] Second bus path constraint: Ensure that the boarding and alighting points in the static passenger demand data must be served, and the boarding and alighting points in the dynamic passenger demand data must also be served.

[0158] Second time constraint: Ensure that the detour duration of the flexible bus adjustment path is no greater than the additional detour duration of passengers in the corresponding scenario of the dynamic passenger demand data.

[0159] Second bus capacity constraint: Ensure that the flexible bus is not overloaded.

[0160] Second bus power constraint: Ensure that the power of the flexible bus meets the power required for the adjusted scheduling plan.

[0161] In some embodiments, the second total utility objective function is:

[0162] ;

[0163] Wherein, is the usage cost of the flexible bus; is the charging cost of the flexible bus; is the passenger detour cost; is the penalty cost for passengers waiting for the flexible bus; is the set of driving nodes of the flexible bus in the scenario corresponding to the dynamic passenger demand data; is the set of passenger boarding points in the scenario corresponding to the dynamic passenger demand data; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; are two driving nodes and The driving duration between; is the average maximum waiting time of all passengers at boarding point i in the scenario corresponding to the dynamic passenger demand data; is a variable taking a value of 0 or 1, indicating whether the path of the flexible bus k from driving node i to j is added in the scenario s corresponding to the dynamic passenger demand data; is a variable taking a value of 0 or 1, indicating whether the path of the flexible bus k from driving node i to j is deleted in the scenario s corresponding to the dynamic passenger demand data; is an integer variable representing the charging duration of the flexible bus k at the charging station c in the scenario s corresponding to the dynamic passenger demand data; is an integer variable representing the average additional detour duration of all passengers boarding at boarding point taking the flexible bus relative to driving directly or taking a taxi to reach the destination in the scenario s corresponding to the dynamic passenger demand data; is an integer variable representing the flexible bus in the scenario s corresponding to the dynamic passenger demand data The duration from the departure time to reach the driving node ; min() is the minimum value function.

[0164] In some embodiments, the target scheduling plan determination module 840 specifically includes:

[0165] For each scenario, calculate the similarity matrix between the adjusted scheduling plan and the initial scheduling plan of each scenario, and establish the association relationship between the adjusted path and the initial path in the scenario according to the similarity matrix.

[0166] Generate a membership degree matrix according to the similarity matrix, the association relationship, and the initial path of the initial scheduling plan, and allocate the driving nodes in the static passenger demand data to the initial path according to the membership degree matrix.

[0167] Calculate the position matrix based on the membership degree matrix, the scenario probability, and the positions of the driving nodes in the static passenger demand data in the adjusted paths corresponding to different scenarios, and determine the order of the driving nodes in the static passenger demand data in the path of the final target scheduling plan based on the position matrix, so as to determine the final target scheduling plan.

[0168] Figure 9 An entity structure diagram of an electronic device is exemplified, as Figure 9 shown. The electronic device may include: a processor 910, a communication interface 920, a memory 930, and a communication bus 940. Among them, the processor 910, the communication interface 920, and the memory 930 complete mutual communication through the communication bus 940. The processor 910 can call the logical instructions in the memory 930 to execute the flexible bus operation scheduling method, and the method includes:

[0169] Obtain flexible bus operation parameters, static passenger demand data, and dynamic passenger demand data.

[0170] Substitute the static passenger demand data into the first operation scheduling model with the operation cost as the target to generate an initial scheduling plan including an initial path and an initial charging plan, and the first operation scheduling model is constructed based on the flexible bus operation parameters.

[0171] Substitute any one of the dynamic passenger demand data and all the static passenger demand data into the second operation scheduling model with the operation cost as the target to generate an adjusted scheduling plan corresponding to any one of the dynamic passenger demand data scenarios. The adjusted scheduling plan includes an adjusted path and an adjusted charging plan, and the second operation scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data.

[0172] Determine the final target scheduling plan based on the initial scheduling plan and each adjusted scheduling plan.

[0173] In addition, when the logical instructions in the above-mentioned memory 930 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0174] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that 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 flexible bus operation scheduling method provided by the above-mentioned various methods. The method includes:

[0175] Obtain flexible bus operation parameters, static passenger demand data, and dynamic passenger demand data.

[0176] Substitute the static passenger demand data into a first operation scheduling model with the operation cost as the target to generate an initial scheduling plan including an initial path and an initial charging plan. The first operation scheduling model is constructed based on the flexible bus operation parameters.

[0177] Substitute any one of the dynamic passenger demand data and all the static passenger demand data into a second operation scheduling model with the operation cost as the target to generate an adjusted scheduling plan corresponding to the scenario of any one of the dynamic passenger demand data. The adjusted scheduling plan includes an adjusted path and an adjusted charging plan. The second operation scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data.

[0178] Based on the initial scheduling plan and each of the adjusted scheduling plans, determine the final target scheduling plan.

[0179] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the flexible bus operation scheduling method provided by the above-mentioned various methods. The method includes:

[0180] Obtain flexible bus operation parameters, static passenger demand data, and dynamic passenger demand data.

[0181] Substitute the static passenger demand data into a first operation scheduling model with the operation cost as the objective to generate an initial scheduling plan including an initial path and an initial charging plan, where the first operation scheduling model is constructed based on the flexible bus operation parameters.

[0182] Substitute any of the dynamic passenger demand data and all the static passenger demand data into a second operation scheduling model with the operation cost as the objective to generate an adjusted scheduling plan corresponding to the scenario of any of the dynamic passenger demand data, where the adjusted scheduling plan includes an adjusted path and an adjusted charging plan, and the second operation scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data.

[0183] Determine a final target scheduling plan based on the initial scheduling plan and each of the adjusted scheduling plans.

[0184] The device embodiments described above are merely illustrative. 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 to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0185] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment 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 essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing 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.

[0186] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An elastic bus operation scheduling method, characterized in that Including: Obtain flexible bus operation parameters, static passenger demand data, and dynamic passenger demand data; Substitute the static passenger demand data into a first operation scheduling model with operation cost as the objective to generate an initial scheduling plan including an initial route and an initial charging plan. The first operation scheduling model is constructed based on the flexible bus operation parameters; Substitute any of the dynamic passenger demand data and all the static passenger demand data into a second operation scheduling model with operation cost as the objective to generate an adjusted scheduling plan corresponding to the scenario of any of the dynamic passenger demand data. The adjusted scheduling plan includes an adjusted route and an adjusted charging plan. The second operation scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data; Based on the initial scheduling plan and each adjusted scheduling plan, determine the final target scheduling plan; The first operation scheduling model includes: a first total utility objective function of the flexible bus constructed based on the flexible bus operation parameters. The first total utility objective function is used to represent the total cost of any scheduling plan of all flexible buses that meet the static passenger demand data. The first total utility objective function is: ； Among them, is the first total utility objective function value; is the flexible bus usage cost; is the flexible bus charging cost; is the passenger detour cost; is the penalty cost for passengers waiting for the flexible bus; is the set of flexible bus driving nodes; is the set of passenger boarding points; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; is the set of scenarios corresponding to dynamic passenger demand data; are two driving nodes and is the driving duration between them; is the average maximum waiting time of all passengers at boarding point i in the static passenger demand data; is the scenario is the occurrence probability; is a variable taking values of 0 or 1, indicating whether the flexible bus passes through the driving node to the driving node ; is an integer variable, indicating the charging duration of the flexible bus at the charging station ; is an integer variable taking values, indicating the average additional detour duration of all passengers boarding at boarding point when taking the flexible bus compared to directly driving or taking a taxi to the destination; is an integer variable, indicating the duration from the departure time for the flexible bus to reach the driving node ; is the expected cost considering scenario s, and min() is the minimum value function; The second operation scheduling model includes: a second total utility objective function constructed based on the initial scheduling plan and the dynamic passenger demand data. The second total utility objective function is used to represent the total cost of any scheduling plan of all flexible buses that meet the scenario corresponding to the dynamic passenger demand data. The second total utility objective function is: ； Among them, is the usage cost of flexible bus; is the charging cost of flexible bus; is the detour cost of passengers; is the penalty cost for passengers waiting for the flexible bus; is the set of driving nodes of the flexible bus in the scenario corresponding to the dynamic passenger demand data; is the set of passenger boarding points in the scenario corresponding to the dynamic passenger demand data; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; are two driving nodes and is the driving duration between them; is the average value of the maximum waiting time of all passengers at boarding point i in the scenario corresponding to the dynamic passenger demand data; is a variable with a value of 0 or 1, indicating whether the path of flexible bus k from driving node i to j is added in the scenario s corresponding to the dynamic passenger demand data; is a variable with a value of 0 or 1, indicating whether the path of flexible bus k from driving node i to j is deleted in the scenario s corresponding to the dynamic passenger demand data; is an integer variable, indicating the charging duration of flexible bus k at charging station c in the scenario s corresponding to the dynamic passenger demand data; is an integer variable, indicating the average value of the additional detour duration of all passengers boarding at boarding point in the scenario s corresponding to the dynamic passenger demand data when taking the flexible bus relative to directly driving or taking a taxi to the destination; is an integer variable, indicating that in the scenario s corresponding to the dynamic passenger demand data, flexible bus takes the departure time as the starting point to reach driving node The duration of; min() is the function to find the minimum value.

2. The elastic bus operation scheduling method according to claim 1, characterized in that The first operation scheduling model further includes: a first set of constraint conditions; the first set of constraint conditions includes: First bus route constraint: Ensure that the boarding point should be visited before the alighting point; First time constraint: Ensure the continuity of the flexible bus time; First bus capacity constraint: Ensure that the flexible bus is not overloaded; First bus power constraint: Ensure that the power of the flexible bus meets the power required by the initial scheduling plan.

3. The elastic bus operation scheduling method according to claim 1, characterized in that The second operation scheduling model further includes: a second set of constraint conditions. The second set of constraint conditions includes: Second bus route constraint: Ensure that the boarding points and alighting points in the static passenger demand data must be served, and the boarding points and alighting points in the dynamic passenger demand data must also be served; Second time constraint: Ensure that the line detour duration generated by the adjusted route of the flexible bus is not greater than the additional detour duration of passengers in the scenario corresponding to the dynamic passenger demand data; Second bus capacity constraint: Ensure that the flexible bus is not overloaded; Second bus power constraint: Ensure that the power of the flexible bus meets the power required by the adjusted scheduling plan.

4. The elastic bus operation scheduling method according to any one of claims 1 to 3, characterized in that, Based on the initial scheduling plan and each adjusted scheduling plan, determining the final target scheduling plan includes: For each scenario, calculate the similarity matrix between the adjusted scheduling plan of each scenario and the initial scheduling plan, and establish the association relationship between the adjusted route and the initial route in the scenario according to the similarity matrix; Generate a membership degree matrix according to the similarity matrix, the association relationship, and the initial route of the initial scheduling plan, and allocate the driving nodes in the static passenger demand data to the initial route according to the membership degree matrix; Calculate a position matrix based on the membership matrix, scenario probabilities, and the positions of driving nodes in the adjusted paths corresponding to different scenarios in the static passenger demand data, and determine the order of driving nodes in the static passenger demand data in the path of the final target scheduling plan based on the position matrix to determine the final target scheduling plan.

5. An elastic bus operation and dispatching device, characterized in that, Including: A data acquisition module for acquiring elastic bus operation parameters, static passenger demand data, and dynamic passenger demand data; An initial scheduling plan generation module for substituting the static passenger demand data into a first operation scheduling model with the operation cost as the target to generate an initial scheduling plan including an initial path and an initial charging plan, where the first operation scheduling model is constructed based on the elastic bus operation parameters; An adjusted scheduling plan generation module for substituting any one of the dynamic passenger demand data and all the static passenger demand data into a second operation scheduling model with the operation cost as the target to generate an adjusted scheduling plan corresponding to the scenario of any one of the dynamic passenger demand data, where the adjusted scheduling plan includes an adjusted path and an adjusted charging plan, and the second operation scheduling model is constructed based on the initial scheduling plan and the dynamic passenger demand data; A target scheduling plan determination module for determining the final target scheduling plan based on the initial scheduling plan and each of the adjusted scheduling plans; The first operation scheduling model includes: a first total utility objective function of the elastic bus constructed based on the elastic bus operation parameters, and the first total utility objective function is used to characterize the total cost of any scheduling plan of all elastic buses that meet the static passenger demand data; the first total utility objective function is: ； wherein, is the first total utility objective function value; is the cost of using flexible buses; is the charging cost of flexible buses; is the detour cost of passengers; is the penalty cost for passengers waiting for flexible buses; is the set of driving nodes of flexible buses; is the set of passenger boarding points; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; is the set of scenarios corresponding to dynamic passenger demand data; are two driving nodes and the driving duration between them; is the average maximum waiting duration of all passengers at boarding point i in static passenger demand data; is scenario the occurrence probability of; is a variable taking values of 0 or 1, indicating whether flexible bus passes through driving node to driving node ; is an integer variable, indicating the charging duration of flexible bus at charging station ; is an integer variable taking values, indicating the average additional detour duration of all passengers boarding at boarding point when taking the flexible bus compared to directly driving or taking a taxi to the destination; is an integer variable, indicating the duration from the departure time when flexible bus arrives at driving node ; is the expected cost considering scenario s, and min() is the minimum value function; The second operation scheduling model includes: a second total utility objective function, which is constructed based on the initial scheduling plan and the dynamic passenger demand data, and the second total utility objective function is used to characterize the total cost of any scheduling plan of all elastic buses that meet the scenario corresponding to the dynamic passenger demand data; the second total utility objective function is: ； Among them, is the usage cost of flexible bus; is the charging cost of flexible bus; is the detour cost of passengers; is the penalty cost for passengers waiting for the flexible bus; is the set of driving nodes of the flexible bus in the scenario corresponding to the dynamic passenger demand data; is the set of passenger boarding points in the scenario corresponding to the dynamic passenger demand data; is the set of electric flexible bus charging stations; is the set of flexible bus vehicles; are two driving nodes and is the driving duration between them; is the average value of the maximum waiting time of all passengers at boarding point i in the scenario corresponding to the dynamic passenger demand data; is a variable with a value of 0 or 1, indicating whether the path of flexible bus k from driving node i to j is added in the scenario s corresponding to the dynamic passenger demand data; is a variable with a value of 0 or 1, indicating whether the path of flexible bus k from driving node i to j is deleted in the scenario s corresponding to the dynamic passenger demand data; is an integer variable, indicating the charging duration of flexible bus k at charging station c in the scenario s corresponding to the dynamic passenger demand data; is an integer variable, indicating the average value of the additional detour duration of all passengers boarding at boarding point taking the flexible bus compared to driving directly or taking a taxi to the destination in the scenario s corresponding to the dynamic passenger demand data; is an integer variable, indicating that the flexible bus takes the departure time as the starting point to reach the driving node The duration of; min() is the function to find the minimum value.

6. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the elastic bus operation scheduling method according to any one of claims 1 to 4.

7. 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, it implements the elastic bus operation scheduling method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the elastic bus operation scheduling method according to any one of claims 1 to 4.

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