A demand response bus optimization scheduling method for cyclic drop-off and pick-up passenger and freight combined transportation

Abstract

The application discloses a kind of circulating fling and hang passenger and freight intermodal transport demand response bus optimization scheduling method, belong to traffic control field.Method includes: the passenger trip information of reservation trip, freight demand information and the state information of modular demand response bus are collected;Bus scheduling optimization model is constructed while minimizing the operating cost and time cost of modular fleet;The constraint condition of the bus scheduling optimization model is constructed;Based on the bus scheduling optimization model, two-stage modeling is carried out, different scenarios are divided into sub-problems for different dynamic demands, and real-time demand is inserted into the pre-planned path set according to the cost generated by each scenario.The application realizes efficient scheduling of passenger and freight intermodal transport and flexible combination of fleet by combining the circulating fling and hang mechanism and the modular fleet, and considers the impact of real-time random demand on the scheduling system, which can significantly improve the scheduling efficiency and reduce the operating cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent traffic scheduling, specifically, a demand response public transportation optimization scheduling method for circulating drop-and-pick-up passenger and freight combined transportation. BACKGROUND

[0002] In the traditional scheduling mode, passenger transportation and freight transportation are often carried out separately, and the scheduling technology often uses fixed routes and timetables, which is difficult to flexibly respond to real-time demand.

[0003] Modular vehicles allow flexible configuration of vehicle structure and function according to specific transportation needs, such as seat layout, cargo capacity, and technical facilities. This design not only improves the adaptability and flexibility of the vehicle, but also enables the bus to better serve different use scenarios and user needs, for example, when facing freight transportation needs, the modular fleet can perform drop-and-pick-up operations of freight module vehicles through automatic coupling or separation.

[0004] Integrating the concept of modular vehicles into the public transportation system of circulating drop-and-pick-up passenger and freight combined transportation can achieve efficient use of vehicles and reduce operating costs. Through optimization scheduling methods, it can ensure that vehicles meet passenger travel needs while efficiently transporting goods.

[0005] The passenger and freight combined transportation mode improves the efficiency of resource utilization by transporting passengers and goods on the same vehicle. In the circulating drop-and-pick-up system, vehicles can load and unload goods at different stations without waiting for passengers to get on and off, which can further improve transportation efficiency and reduce waiting time.

[0006] However, there are some challenges to achieve rapid demand response of circulating drop-and-pick-up passenger and freight combined transportation:

[0007] 1. Data accuracy and processing: Although modern technology makes data collection and processing more convenient, ensuring data accuracy is crucial for scheduling decisions. Unstable data sources or sensors may introduce errors into the results, affecting the accuracy of scheduling.

[0008] 2. Algorithm optimization: In a complex urban public transportation network, there are many factors to consider, including traffic conditions, vehicle status, etc. Therefore, it is necessary to continuously optimize the algorithm to improve scheduling efficiency and ensure efficient operation of public transportation vehicles.

[0009] 3. Traffic management and planning: Scheduling needs to consider the city's traffic management and planning, including road restrictions, traffic flow, etc. Coordination and integration with other transportation modes are also a challenge, requiring cross-departmental and cross-disciplinary cooperation. SUMMARY

[0010] In view of the deficiencies of the prior art, the application provides a demand response bus optimization scheduling method for cyclic drop and hang passenger and freight combined transport.

[0011] The technical means adopted by the application are as follows:

[0012] The application discloses a demand response bus optimization scheduling method for cyclic drop and hang passenger and freight combined transport, comprising the following steps:

[0013] Step 1: Collecting passenger trip information, freight demand information and state information of modular demand response buses for pre-arranged trips, wherein the passenger trip information comprises passenger number, boarding and alighting station position and time window; the freight demand information comprises freight volume, drop and hang station position and time window; and the state information of the modular demand response buses comprises initial station position of the vehicle and average driving time of each station.

[0014] Step 2: Constructing a bus scheduling optimization model for minimizing the operation cost and time cost of the modular vehicle fleet simultaneously, wherein the objective function of the bus scheduling optimization model comprises operation cost of the vehicle fleet, vehicle fleet trip time cost, penalty cost caused by violation of soft time window constraints and detour cost caused by real-time random demand; and performing parameter initialization on the objective function.

[0015] Step 3: Constructing constraint conditions of the bus scheduling optimization model, wherein the constraint conditions of the bus scheduling optimization model comprise vehicle path constraints, time window constraints, capacity constraints and modular related constraints.

[0016] Step 4: Performing two-stage modeling based on the bus scheduling optimization model, wherein real-time demand in different scenarios is considered in the two-stage modeling process, and on the basis of meeting the path constraints, time constraints, capacity constraints and modular related constraints, the real-time demand meeting the requirements is dynamically inserted into the vehicle fleet driving plan to realize robust optimization of the driving route of the modular vehicle fleet.

[0017] Compared with the prior art, the application has the following advantages:

[0018] 1.The present application relates to a kind of circulating demand response public transport optimization scheduling method of drop and pick up passenger and freight combined transport, which realizes the efficient scheduling of passenger and freight combined transport by the flexible combination of modular vehicle fleet and circulating drop and pick up mechanism;On the basis of meeting the passenger transport demand, the module vehicle is simultaneously transported by drop and pick up using modular vehicle fleet in the freight demand along the original bus line.Specifically, the mode minimizes the operating cost of vehicle trip, vehicle fleet trip time cost and the penalty cost generated due to violation of soft time window constraint as the target, realizes the scheduling optimization of modular demand response public transport.

[0019] 2,The present application realizes not only the efficient scheduling of passenger and freight combined transport and the flexible combination of vehicle fleet by the combination of circulating drop and pick up mechanism and modular vehicle fleet, but also can respond to the dynamic demand of passenger and freight, can significantly improve scheduling efficiency and reduce operating cost. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 A flow chart of the present application, a circulating drop and pick up passenger and freight combined transport demand response public transport optimization scheduling method. DETAILED DESCRIPTION

[0022] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] As Figure 1 indicated, the present application provides a demand response bus optimization scheduling method for circulating drop and hang passenger and freight intermodal transport, comprising the following steps:

[0025] Step 1: Collecting passenger trip information, freight demand information and state information of modular demand response buses for pre-booking trips; the passenger trip information includes passenger number, boarding and alighting station location and time window; the freight demand information includes freight volume, freight drop and hang station location and time window; the state information of the modular demand response buses includes initial station location of the vehicle and average travel time of each station.

[0026] Step 2: Constructing a bus scheduling optimization model that minimizes the operating cost and time cost of the modular vehicle fleet at the same time, the objective function of the bus scheduling optimization model includes operating cost of the vehicle fleet trip, vehicle fleet trip time cost, penalty cost due to violation of soft time window constraints and detour cost caused by real-time random demand; parameter initialization is performed on the objective function.

[0027] Specifically, the objective function of the bus scheduling optimization model is:

[0028]

[0029] In the formula, total objective function; a cost coefficient related to operating cost is pre-set when actually used, is a set of all modular vehicle fleets, indicates whether the vehicle fleet is in an operating state, and a value of 1 indicates that the vehicle fleet is in an operating state, and a value of 0 indicates that the vehicle fleet is in a non-operating state; a cost coefficient related to travel time cost is pre-set when actually used, denotes a set of all stations, denotes a set of the number of modules of the modular vehicle fleet, denotes a set of the operating cost reduction factor when the modules are coupled, which is pre-set in actual use, denotes a set of stations between which the travel time is calculated, is a 0,1 variable, and takes the value of 1 to indicate that the number of modules is of the vehicle fleet travels between the stations , and takes the value of 0 to indicate that the number of modules is of the vehicle fleet does not travel between the stations ; denotes a set of penalty cost coefficients for violating the soft time window, which is pre-set in actual use, denotes a set of all freight drop-and-pickup stations, denotes the freight volume for drop-and-pickup operation at the station , represents a penalty function related to the violation of the freight soft time window. Specifically, it is defined as:

[0030]

[0031] wherein, denotes an early arrival penalty coefficient for arriving before the soft time window, denotes a late arrival penalty coefficient for arriving after the soft time window, denotes the upper and lower limits of the time window at the station , denotes the time at which the vehicle fleet arrives at the station , denotes a set of scenarios of real-time random demand, denotes the probability of occurrence of each scenario of real-time random demand, denotes a recourse function, which specifically indicates the optimal expected cost of the second-stage problem after dynamically adjusting the scheduling plan considering the real-time random demand of passenger and freight in the second-stage modeling, and is used to calculate the cost caused by the detour of the vehicle after the addition of the real-time demand in the second stage.

[0032] Step 3: constructing the constraint conditions of the bus scheduling optimization model, wherein the constraint conditions of the bus scheduling optimization model include vehicle path constraints, time window constraints, capacity constraints, and modularization-related constraints.

[0033] Specifically, the constraint conditions of the bus scheduling optimization model are constructed, including constructing vehicle path constraints, constructing passenger path constraints, and constructing capacity and modularization-related constraints.

[0034] The vehicle routing constraints include the following.

[0035] (1)

[0036] Constraint (1) indicates that each station can only be visited once by the same fleet to ensure the uniqueness of the route. Wherein, is a 0,1 variable, taking the value of 1 indicates that the fleet is in sequence between the stations , and the value of 0 indicates that the fleet is not in sequence between the stations .

[0037] (2)

[0038] Constraint (2) indicates that the same fleet is used to send passengers or goods after receiving passengers or goods. Wherein, represents the set of passenger and goods pickup stations.

[0039] (3)

[0040] Constraint (3) ensures the flow conservation of each station. Wherein represents the set of passenger and goods drop-off stations. Wherein represents the set of passenger and goods drop-off stations.

[0041] (4)

[0042] (5)

[0043] (6)

[0044] Constraint (4) ensures that the fleet cannot detour or circulate within the same station. Constraints (5) and (6) ensure that the fleet departs from the station and finally returns to the station. Wherein, represents the set of all stations, represents the set of all passenger stations in the service range.

[0045] (7)

[0046] (8)

[0047] (9);

[0048] Wherein, constraints (7) to (9) are a series of logical constraints involving auxiliary variables , which represent the auxiliary variables and the decision variables and The relationship between the auxiliary variable and the decision variable is guaranteed by the constraint (7). The constraint (8) indicates that the demand can only be satisfied by the fleet when it is in operation. The constraint (9) ensures that the value of the auxiliary variable is determined by and only when and , is 1.

[0049] The construction of the passenger path constraints includes the following.

[0050] (10)

[0051] The constraint (10) is the time window constraint for the passenger demand at the corresponding station. The passenger demand must follow the corresponding hard time window, and the fleet must pick up the passenger within this hard time window, denotes the waiting time of the fleet at the station , which is defined as: .

[0052] (11)

[0053] The constraint (11) limits the maximum travel time of the entire trip of the fleet, where denotes the departure time of the fleet from the depot, denotes the arrival time of the fleet at the terminal, denotes the specified maximum time of the trip.

[0054] (12)

[0055] The constraint (12) ensures that all boarding points of the demand are served before the corresponding alighting point . Where denotes the travel time between the stations .

[0056] (13)

[0057] (14)

[0058] The constraints (13) and (14) together ensure the continuity of the service time of boarding and alighting within the trip. Where denotes a sufficiently large constant, whose value is usually a positive integer greater than or equal to .​​

[0059] The construction capacity and the modularization related constraints include the following.

[0060]

[0061] , (15)

[0062]

[0063] , (16)

[0064] Constraints (15) and (16) represent the passenger flow recursive relationship between each station. Among them, represents the number of passengers on the car of the car fleet arriving at the station , represents the number of passengers getting on and off at the station .

[0065] (17)

[0066] Constraint (17) ensures that the number of passengers on the car cannot exceed the capacity of the module car in the car fleet. Among them, represents the maximum passenger capacity of each module car.

[0067] (18)

[0068] (19)

[0069] Constraints (18) and (19) ensure the conservation of the number of modules at each station. Among them, represents the number of module cars included in the car fleet when leaving the station , represents the number of module cars in the car fleet carrying out the drop-off operation at the station .

[0070] (20)

[0071] Constraint (20) ensures that the number of module cars in the car fleet throughout the trip will not exceed . Among them, represents the maximum number of modules of the car fleet that meets the requirements.

[0072] In the scheduling method of the present application, in order to convert the soft time window penalty function ​The following linearization constraints are introduced to incorporate into the objective function and optimize. These constraints convert the original piecewise function into linear form by introducing auxiliary variables and logical conditions, thus facilitating the solution. Based on this, the following linearization constraints are constructed.

[0073] (21)

[0074] (22)

[0075] Constraints (21) and (22) ensure that the early arrival penalty value of the vehicle fleet arriving at the freight drop and pick-up site before the soft time window and the late arrival penalty value of the vehicle fleet arriving at the freight drop and pick-up site after the soft time window can be correctly calculated. Among them, represents the early arrival penalty value of the vehicle fleet at the site , represents the late arrival penalty value of the vehicle fleet at the site , is a 0,1 variable, taking the value of 1 to indicate that the vehicle fleet arrives at the site earlier than the soft time window, and taking the value of 0 to indicate that the vehicle fleet does not arrive at the site earlier than the soft time window, is a 0,1 variable taking the value of 1 to indicate that the vehicle fleet arrives at the site later than the soft time window, and taking the value of 0 to indicate that the vehicle fleet does not arrive at the site later than the soft time window.

[0076] (23)

[0077] (24)

[0078] (25)

[0079] Constraint (23) ensures that the vehicle fleet cannot appear at the same time at the site in both early arrival and late arrival cases. Constraints (24)-(25) ensure the logical correctness of the early arrival and late arrival of the vehicle fleet. When the vehicle fleet arrives at the site before the soft time window, the arrival time of the vehicle fleet at the site needs to satisfy , and when the vehicle fleet arrives at the site after the soft time window, the arrival time of the vehicle fleet at the site needs to satisfy .

[0080] Step 4: Perform two-stage modeling based on the bus dispatch optimization model. In the two-stage modeling process, real-time demand under different scenarios is considered. On the basis of satisfying path constraints, time constraints and capacity constraints, different dynamic demands are divided into sub-problems of different scenarios. Real-time demands are inserted into the pre-planned path set according to the cost generated by each scenario.

[0081] The recourse weight function involved in step 2 This represents the optimal expected cost of the second-stage problem after dynamically adjusting the scheduling plan, taking into account real-time stochastic demand for passenger and freight transport. The specific definition is as follows:

[0082]

[0083] in, This represents the set of stations for real-time demand. and These are two variables, one 0 and one 1, representing the scene. In the middle, the number of modules is convoy Should there be any additions or deletions on the site? The path between; Indicates in the scene In the middle, at the site The volume of freight transported via trailer swapping operations. The penalty function related to violations of the freight soft time window in the second phase is specifically defined as follows:

[0084]

[0085] in, Indicates in the scene In the middle, the site upper and lower limits of the time window Indicates in the scene In the middle, the team Arrival Station Time, This represents the penalty cost coefficient for not accepting real-time demands; it is preset during actual use. It is a 0,1 variable, representing the site corresponding to the real-time demand. The value is 1 when no service is being provided, and the value corresponds to the site in real-time demand. The value is 0 when the service is being provided.

[0086] For different real-time stochastic demand scenarios, the constraints involved in the second stage include constructing vehicle routing constraints, travel time constraints, capacity and modularity-related constraints, and linearization constraints.

[0087] The construction of vehicle path constraints includes the following.

[0088] (26)

[0089] Constraint (26) guarantees that the first-stage appointment demands must be satisfied. Among them, and are two 0,1 variables, representing whether the vehicle fleet increases or reduces the path between sites .

[0090] (27)

[0091] (28)

[0092]

[0093] , (29)

[0094] (30)

[0095] (31)

[0096] Constraint (27) represents the permission to refuse real-time demand. Constraint (28) ensures that the system traffic remains balanced when considering real-time demand and detours. Constraint (29) guarantees that in each scenario, all passenger and cargo appointment demands and real-time demands are delivered by the same vehicle fleet after pick-up and drop-off. Constraint (30) avoids reducing the route that does not originally exist in the first-stage pre-planned path in each scenario considering real-time demand. Constraint (31) avoids adding a route that already exists in the first-stage journey in the second stage.

[0097] The construction of travel time constraints includes the following.

[0098] (32)

[0099] Constraint (32) is similar to constraint (10) and sets a time window for generating new passenger and freight demands in the second stage. Among them, represents the waiting time of the vehicle fleet at the site in scenario . It is defined as: .

[0100] (33)

[0101] where​ denotes the maximum time of a trip. In the pick-up and drop-off problem, pick-up sites are 1, 2, …, n; drop-off sites are n+1, n+2, …, 2n, and 2n+1 is generally regarded as the terminal, which is a commonly known definition in the field.

[0102] (34)

[0103] (35)

[0104] (36);

[0105] Constraint (33) is similar to constraint (11), which limits the maximum travel time of each vehicle fleet in scenario Constraint (34) ensures that all pick-up points of real-time demand are served before the corresponding drop-off points in scenario Constraints (35) - (36) are similar to constraints (13) - (14), which ensure the continuity of service time at each site within the trip after adjusting the trip in scenario

[0106] The capacity and modularization related constraints in the second stage model are similar to those in the first stage, specifically to ensure that after considering the real-time random demand of passengers and goods in scenario , the reasonable allocation of modular vehicle fleets can still be met and the capacity constraints of the vehicle fleets can still be met. The capacity and modularization related constraints constructed based on this include the following.

[0107] , (37)

[0108] , (38)

[0109] Constraints (37) and (38) represent the passenger flow recursive relationship between each site in scenario . Among them, denotes the set of drop-off sites under real-time demand, denotes the number of passengers on the vehicle of the fleet arriving at site in scenario ; denotes the number of passengers boarding and alighting at site in scenario .

[0110] ​​​​​(39)

[0111] (40)

[0112] (41)

[0113] (42);

[0114] Constraint (39) guarantees that in the scenario In this scenario, the number of passengers on the vehicle cannot exceed the capacity of the modular vehicles in the fleet. Constraints (40) and (41) guarantee that in the scenario... In this process, the number of modules in the fleet remains constant at each station. Among them, Indicates in the scene In the middle, the team Leave the station At that time, the number of modular vehicles included in the convoy. Indicates in the scene In the middle, the team On the site The number of modular vehicles performing the swapping operation. Constraint (42) ensures that in the scenario... Throughout the entire journey, the number of modular vehicles in the convoy will not exceed [number missing]. .

[0115] Constructing linearization constraints includes the following.

[0116] The linearization constraints in the second stage are similar to those in the first stage, specifically designed to reduce the soft time window penalty function in the second stage. Incorporating the recourse weight function in the second phase In the process, optimization was performed, and the following linearization constraints were introduced, as detailed below:

[0117] (43)

[0118] (44)

[0119] Constraints (43) and (44) ensure that, after considering real-time random requirements in the second stage, the scenario... In this system, the early and late penalties for fleets arriving at freight drop-off points before the soft time window are correctly calculated. and They represent the scenes respectively. In the middle, the team On the site Early arrival and late arrival penalties; and These are variables of 0 and 1, representing the team respectively. Arriving at the site earlier or later than the soft time window .

[0120] (45)

[0121] (46)

[0122] (47)

[0123] Constraint (45) guarantees that the fleet will not appear at the site at the same time in both the early arrival and late arrival cases. Constraints (46)-(47) guarantee the logical correctness of the fleet early arrival and late arrival. Respectively represent the time when the fleet arrives at the site before or after the soft time window in the scenario , the time when the fleet arrives at the site needs to satisfy and respectively.

[0124] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A demand response bus optimization scheduling method for cyclic intermodal passenger and freight transportation, characterized in that, The method comprises the following steps: Step 1: collecting passenger travel information, freight demand information and state information of the modular demand responsive bus for the passengers who make travel reservations; The passenger travel information includes the number of passengers, the location and time window of the boarding and alighting stations; the freight demand information includes the freight volume, the location and time window of the freight drop-off and pick-up stations; and the state information of the modular demand responsive bus includes the initial location of the vehicle and the average travel time of each station; Step 2: constructing a bus scheduling optimization model which minimizes the operating cost and time cost of the modular vehicle fleet simultaneously, wherein the objective function of the bus scheduling optimization model includes the operating cost of the vehicle fleet, the travel time cost of the vehicle fleet, the penalty cost caused by the violation of the soft time window constraint and the detour cost caused by the real-time random demand; the objective function is initialized; and the objective function of the bus scheduling optimization model is as follows: In the formula, Represent the overall objective function; This represents a cost coefficient related to operating costs. It is a collection of all modular racing teams. Indicates the team Whether it is in operation, a value of 1 indicates that the fleet is in operation. The fleet is in operation; a value of 0 indicates that it is in operation. It is in a non-operational state; This represents a cost coefficient related to travel time costs. Represents the set of all sites. This represents the set of the number of modular vehicles in a modular fleet. This represents the operating cost reduction factor when modular vehicles are coupled. Indicates site Travel time between It is a 0, 1 variable, where a value of 1 indicates that the number of modules is 1. convoy On the site Traveling between them, a value of 0 indicates that the number of modules is [missing information]. convoy Not on site Travel between; This represents the penalty cost coefficient for violating the soft time window, set This represents the collection of all freight drop-off and trailer stations. Indicates on the site The volume of freight transported via trailer swapping operations. The penalty function related to violations of the freight soft time window is specifically defined as follows: wherein, represents an early arrival penalty coefficient for arriving before the soft time window, represents a late arrival penalty coefficient for arriving after the soft time window, represents the upper and lower bounds of the time window at the site , represents the time of arrival of the vehicle fleet at the site , represents a set of scenarios of real-time stochastic demand, represents the probability of occurrence of each scenario of real-time stochastic demand, represents a recourse function, in particular the optimal expected cost of the second stage problem after dynamically adjusting the schedule plan after taking into account the real-time stochastic demand of passengers and freight in the second stage modeling; Step 3: constructing the constraint conditions of the bus scheduling optimization model, wherein the constraint conditions of the bus scheduling optimization model include vehicle path constraints, time window constraints, capacity constraints and modular related constraints; Step 4: performing two-stage modeling based on the bus scheduling optimization model, wherein the real-time demand in different scenarios is considered in the two-stage modeling process, and on the basis of meeting the path constraints, the time window constraints, the capacity constraints and the modular related constraints, the real-time demand meeting the requirements is dynamically inserted into the vehicle fleet travel plan to realize the robust optimization of the travel route of the modular vehicle fleet. 2.The demand responsive bus optimization scheduling method of a cyclic drop-and-go intermodal passenger and freight transportation according to claim 1, wherein, The constraint conditions of the bus scheduling optimization model are constructed, including: The following vehicle path constraints are constructed: wherein is a 0,1 variable taking the value 1 if the vehicle fleet is driving in sequence between stations and the value 0 if the vehicle fleet is not driving in sequence between stations , wherein, represents a set of passenger and freight loading sites, wherein denotes a set of passenger and freight drop-off sites, wherein, denotes the set of all stations, denotes the set of all passenger stations in the service area, ； The following passenger path constraints are constructed: wherein, representing a fleet of vehicles at a site waiting time, defined as: , wherein, representing a vehicle fleet the time of departure from the depot, representing the time of arrival of the vehicle fleet at the destination, representing the prescribed maximum time of the trip, wherein, representing travel time between sites ​ wherein represents an integer greater than or equal to an integer greater than or equal to 1. The following capacity and modular related constraints are constructed: in, Indicates the number of modules. convoy Arrival Station The number of passengers on the train Indicates on the site The number of passengers getting on and off the bus. wherein, represents the maximum passenger capacity of each modular vehicle, wherein, represents a fleet of module cars leaving a site the number of module cars comprised in the fleet, represents a fleet of module cars at a site the number of module cars performing drop and pick up operations, wherein, represents the maximum number of modules of the vehicle fleet that meet the requirements; The following linearization constraints are constructed: in, Indicates the team On the site Early arrival penalty value, Indicates the team On the site Late arrival penalty value, It is a 0,1 variable, with a value of 1 indicating the team. Arrive at the station earlier than the soft time window A value of 0 indicates that the team No arrival at the station before the soft time window , It is a variable with values ​​of 0 and 1, where a value of 1 represents the team. Arriving at the station later than the soft time window A value of 0 indicates that the team No arrival at the station later than the soft time window , When the fleet arrives at the site before the soft time window, the time at which the fleet arrives at the site needs to satisfy When the fleet arrives at the site after the soft time window, the time at which the fleet arrives at the site needs to satisfy . 3.The demand responsive bus optimization scheduling method of a cyclic drop-and-go intermodal passenger and freight transportation according to claim 2, wherein, The recourse function is as follows: in, This represents the set of stations for real-time demand. and These are two variables, one 0 and one 1, representing the scene. In the middle, the number of modules is convoy Should there be any additions or deletions on the site? The path between; Indicates in the scene In the middle, at the site The volume of freight transported via trailer swapping operations. The penalty function related to violations of the freight soft time window in the second phase is specifically defined as follows: wherein, denotes the time window upper limit of the site in the scenario , denotes the time of the vehicle fleet arrival at the site in the scenario , denotes the penalty cost coefficient for not accepting the real-time demand, is a 0,1 variable that takes the value 1 if the site corresponding to the real-time demand is not served and the value 0 if the site corresponding to the real-time demand is served.

4. The demand responsive bus optimization scheduling method for cyclic drop-and-go intermodal passenger and freight transportation according to claim 3, wherein, For different real-time random demand scenario sub-problems, the constraint conditions of the second-stage modeling are constructed, including: The vehicle path constraints are constructed: wherein, and are two 0,1 variables, indicating whether the path between the sites is increased or decreased by the fleet in the scenario , ； The travel time constraints are constructed: in, Indicates in the scene In the middle, the team On the site The waiting time is defined as: , wherein, denotes the maximum time specified by the itinerary, ； The capacity and modular related constraints are constructed: in, This represents the set of stations based on real-time demand. Indicates in the scene In the middle, the number of modules is convoy Arrival Station The number of passengers on the train Indicates in the scene In the middle, at the site The number of passengers getting on and off the bus. ； The linearization constraints are constructed: wherein, and respectively represent the arrival of a vehicle fleet at a site early and late, and are 0,1 variables, respectively representing that a vehicle fleet arrived at a site early or late, ​ 。

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