Last bus timetable optimization method of intelligent rail and bus collaborative transportation service network
By optimizing the last bus schedule of smart rails and bus lines and using an integer planning model, the problem of failure of last bus transfer is solved, efficient operational coordination is achieved, and passenger experience and system efficiency are improved.
Patent Information
- Application Number
- CN202510486885.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-18
- Publication Date
- 2025-08-01
AI Technical Summary
The last bus schedule of smart rail and bus routes has not been effectively coordinated, resulting in the failure of the last bus transfer, affecting the passenger's travel experience, and leading to waste of operational resources and increasing costs.
By building a dual-mode public transportation network, the last bus schedule for smart rails and bus routes is optimized, and an integer planning model is adopted to maximize the number of successful transfers, minimize the number of transfer waiting times and reissue shifts, and a timetable for collaborative operation is formulated.
It improves the success rate of last bus transfer, reduces passenger waiting time and waste of operational resources, and improves the efficiency and passenger experience of the public transportation system.
Smart Images
Figure CN120409784A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of coordinated scheduling of public transportation, and particularly to an optimization method for the last bus schedule of an intelligent rail and bus coordinated transportation service network. Background Art
[0002] With the acceleration of the urbanization process and the increasing growth of residents' travel demands, the urban public transportation system plays an increasingly important role in alleviating traffic congestion, reducing carbon emissions, and improving travel efficiency. The Autonomous Rail Rapid Transit (ART), also known as intelligent rail, is a new type of medium and low-capacity public transportation system. The intelligent rail has the advantages of low investment cost, high transportation efficiency, and strong flexibility, and is being introduced in more and more cities. After the introduction of the intelligent rail, how to improve the operation coordination between the intelligent rail and the existing public transportation, especially the transfer connection of the last bus, is of great significance for enhancing the travel experience of passengers when transferring between different public transportation modes, and is an urgent problem to be solved.
[0003] For passengers taking the last bus, when transferring between different lines, due to the poor connection of the last bus schedule, the transfer of the last bus fails, which greatly affects the travel experience of passengers. At present, due to the poor coordination of the schedules of the intelligent rail and bus lines, there are often problems such as inconsistent operation end times of the last bus and too long transfer connection times, which further deteriorate the travel experience of passengers during the last bus period.
[0004] In addition, the intelligent rail and bus often operate independently and lack an effective coordinated scheduling mechanism. Especially in the evening or during the last bus period, the travel demands of passengers are relatively concentrated. However, due to the system's failure to flexibly schedule according to the real-time traffic flow and the number of passengers, there are often problems such as mismatched departure times of the last bus or too long waiting times for passengers to transfer. This not only reduces the travel experience of passengers but also reduces the overall efficiency of public transportation. The unreasonable connection between the intelligent rail and bus lines also leads to a waste of a lot of last bus capacity resources and empty running of vehicles, further increasing the operation cost of the urban public transportation system. Summary of the Invention
[0005] In view of the above deficiencies in the prior art, the present invention provides an optimization method for the last bus schedule of an intelligent rail and bus coordinated transportation service network, aiming to achieve the efficient connection of the last bus of the intelligent rail and bus service network by accurately analyzing and coordinately adjusting the last bus schedules of each line. Through this method, not only can the success rate of transfer connection be improved, but also while reducing resource waste, the travel experience of passengers and the operation efficiency of the system can be enhanced, enabling passengers to reach their destinations smoothly as much as possible, and promoting the healthy and sustainable development of the urban public transportation system.
[0006] To achieve the above-mentioned invention objective, the technical solution adopted by the present invention is as follows:
[0007] An optimization method for the last bus schedule of an intelligent rail and bus collaborative transportation service network, comprising the following steps:
[0008] Construct a dual-mode public transportation network based on the intelligent rail and bus lines with the last bus service to be optimized, and determine all transfer stations therein;
[0009] According to the time period of the last bus service to be optimized and the operation times of the intelligent rail and buses, determine the departure intervals of the intelligent rail and bus lines within the last bus time period;
[0010] According to the historical passenger flow data of transfers between each line, determine the passenger flow volume that each shift of each line needs to transfer to other lines;
[0011] Taking the maximization of the number of successful transfers of the last bus as the optimization objective and the departure times of the last buses of each line as decision variables, construct a first single-objective integer programming model and solve it to obtain the number of successful transfers of the last bus;
[0012] According to the number of successful transfers of the last bus, taking the minimization of the transfer waiting time of successful transfer passengers and the minimization of the number of additional trips of each line as the optimization objectives, construct a dual-objective integer programming model and solve it to obtain the transfer waiting time of successful transfer passengers, the number of additional trips of each line, and the departure times of the last buses of each line;
[0013] According to the transfer waiting time of successful transfer passengers and the number of additional trips of each line, taking the minimization of the transfer waiting time of failed transfer passengers as the optimization objective, construct a second single-objective integer programming model and solve it to obtain the transfer waiting time of failed transfer passengers transferring to the additional trips and the departure times of each bus of the additional trip lines.
[0014] Preferably, constructing the first single-objective integer programming model includes:
[0015] Construct a first objective function according to the number of passengers on the last bus of each line who need to transfer to other lines at the transfer station and a 0-1 auxiliary variable indicating whether it is a feasible transfer;
[0016] Taking the maximization of the number of successful transfers of the last bus as the optimization objective, the departure times of the last buses of each line as decision variables, and the arrival time of the last bus for transfer being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the departure time of the last bus, and the value range of the auxiliary variable as constraints.
[0017] Preferably, the specifically constructed first single-objective integer programming model is:
[0018]
[0019] Among them, max represents the maximum value function, z1 represents the first objective function, and p kls represents the number of passengers who need to transfer from the last bus of line l to line k at the transfer station s, and y kls represents a 0-1 auxiliary variable indicating whether it is a feasible transfer, De ks represents the departure time of line k at the transfer station s for the passengers who need to transfer, Ar ls represents the arrival time when the passengers arrive at the transfer station s after getting off the bus of line l on foot, M represents a set positive integer, and S t represents the set of transfer stations, T represents the end point of the last bus optimization period, and h k represents the headway of line k, and x k represents the departure time of the last bus of line k at the starting station, and h l represents the headway of line l, and x l represents the departure time of the last bus of line l at the starting station, and N represents the set of all lines.
[0020] Preferably, the calculation method of the arrival time when the passengers arrive at the transfer station after getting off the bus of the bus line on foot is:
[0021]
[0022] Among them, t lm represents the running time of line l between station m-1 and station m during the last bus optimization period, and tw lm represents the stop time of line l at station m during the last bus optimization period, and t n represents the walking time of the passengers;
[0023] The calculation method of the departure time of the line that the passengers need to transfer at the transfer station is:
[0024]
[0025] Among them, t km represents the running time of line k between station m-1 and station m during the last bus optimization period, and tw km represents the stop time of line k at station m during the last bus optimization period.
[0026] Preferably, constructing a bi-objective integer programming model includes:
[0027] Construct a second objective function according to the number of passengers who need to transfer from the last bus of each line to other lines at the transfer station, the transfer waiting time for the passengers to transfer from the last bus of the line to other lines, and the 0-1 auxiliary variable indicating whether it is a feasible transfer;
[0028] Taking the minimum transfer waiting time of successful transfer passengers as the optimization objective, and using the number of successful transfers of the last bus, the transfer waiting time, the departure time of the last bus that the passenger needs to transfer to being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the departure time of the last bus, and the value range of the auxiliary variable as the constraints;
[0029] Construct the third objective function according to the sum of the number of additional trips required for the intelligent rail and bus lines;
[0030] Taking the number of additional trips required for the corresponding line that accepts transfers when there are failed transfers of the last bus, the departure time of the last bus that the passenger needs to transfer to being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the departure time of the last bus, and the value range of the auxiliary variable as the constraints.
[0031] Preferably, the constructed bi-objective integer programming model is specifically:
[0032]
[0033] minz3=μ∑ k∈A δ k +∑ k∈B δ k ;
[0034]
[0035] Among them, min represents the minimum value function, z2 represents the second objective function, z3 represents the third objective function, p kls represents the number of passengers on the last bus of line l who need to transfer to line k at transfer station s, y kls represents a 0-1 auxiliary variable indicating whether it is a feasible transfer, w kls represents the transfer waiting time for a passenger to transfer from the last bus of line l to line k, μ represents the coefficient for converting intelligent rail trips to bus trips, δ k represents the number of additional trips required for line k, A represents the set of intelligent rail lines, B represents the set of bus lines, P represents the number of successful transfers of the last bus, De ks represents the departure time of line k at transfer station s that the passenger needs to transfer to, Ar ls represents the arrival time of the passenger at transfer station s after getting off line l and walking, h k represents the headway of line k, max represents the maximum value function, represents the ceiling function, M represents a set positive integer, S t represents the set of transfer stations, T represents the end point of the optimization period of the last bus, h k represents the headway of line k, x k represents the departure time of the last bus of line k at the starting station, hl denotes the headway of line l, x l denotes the departure time of the last bus of line l at the starting station, and N denotes the set of all lines.
[0036] Preferably, constructing the second single-objective integer programming model includes:
[0037] Construct a fourth objective function according to the number of passengers transferring from each trip of each line to other lines at the transfer station, the transfer waiting time of passengers transferring from each trip of each line to other lines at the transfer station, and the 0-1 auxiliary variable indicating whether it is a feasible transfer;
[0038] With the optimization goal of minimizing the transfer waiting time of transfer-failed passengers, the constraints include the difference between the departure time of the vehicle that the passenger needs to transfer to and the arrival time of the passenger at the transfer station, the transfer waiting time, the headway of each line, the line of transfer-failed passengers, the departure time of the first supplementary trip of the line that needs to supplement trips being later than the departure time of the last bus, and the value range of the auxiliary variable.
[0039] Preferably, the constructed second single-objective integer programming model is specifically:
[0040]
[0041] Among them, min represents the function of taking the minimum value, z4 represents the fourth objective function, p kiljs denotes the number of passengers transferring from the j-th trip of line l to the i-th trip of line k at the transfer station s, W kiljs denotes the transfer waiting time of passengers from the j-th trip of line l transferring to the i-th trip of line k at the transfer station s, y kiljs denotes the 0-1 auxiliary variable indicating whether it is a feasible transfer, De kis denotes the departure time of the i-th trip of line k that the passenger needs to transfer to at the transfer station s, Ar ljs denotes the arrival time of the passenger walking to the transfer station s after getting off the j-th trip of line l, M represents a set positive integer, W represents the upper limit of the transfer time window, x lj denotes the departure time of the j-th trip of line l at the starting station, x l(j-1) denotes the departure time of the (j - 1)-th trip of line l at the starting station, h l denotes the headway of line l, x ki denotes the departure time of the i-th trip of line k at the starting station, x k(i-1) denotes the departure time of the (i - 1)-th trip of line k at the starting station, h k denotes the headway of line k, denotes the δ-th of line l lThe departure time of the shift bus at the starting station, x l Indicates the departure time of the last bus of line l at the starting station, x k1 Indicates the departure time of the first shift bus of line k at the starting station, x k Indicates the departure time of the last bus of line k at the starting station. N represents the set of all lines, I k Indicates the set of additional trips of line k, I l Indicates the set of trips corresponding to the passengers who fail to transfer at line l, S t Indicates the set of transfer stations.
[0042] Preferably, the calculation method for the arrival time when a passenger continues to walk to the transfer station after getting off the bus on a bus line is as follows:
[0043]
[0044] Among them, t lm Indicates the running time between station m - 1 and station m of line l during the last bus optimization period, tw lm Indicates the stopping time of line l at station m during the last bus optimization period, t n Indicates the walking time of the passenger;
[0045]
[0046] Among them, t km Indicates the running time between station m - 1 and station m of line k during the last bus optimization period, tw km Indicates the stopping time of line k at station m during the last bus optimization period.
[0047] Preferably, determine the departure schedule of the last bus of each line according to the departure time of the last bus of each line;
[0048] Determine the departure schedule of each bus of the additional line according to the departure time of each bus of the additional line;
[0049] Obtain the optimized departure schedule of the last bus of each line according to the departure schedule of the last bus of each line and the departure schedule of each bus of the additional line.
[0050] The present invention has the following beneficial effects:
[0051] (1) The present invention specifically addresses the problem of the transfer connection of the last buses in the intelligent rail and bus service network, comprehensively considers various factors related to the optimization of the last bus schedule, and through the solution of the optimization model, realizes the optimization of the last bus schedule, provides an efficient collaborative transfer schedule compilation for operation and dispatching personnel, and ensures the success rate of transfer.
[0052] (2) The present invention proposes three models related to the optimization of the last bus schedule. The first single-objective integer programming model provides the maximum number of successful transfers; the second two-objective integer model minimizes the transfer waiting time while also minimizing the number of departure trips of the supplementary lines; the third single-objective integer programming model ensures the minimum waiting time for passengers who fail to transfer when taking the supplementary vehicles of the lines.
[0053] (3) The models proposed by the present invention not only minimize the transfer waiting time for passengers who succeed in transferring during the last bus, but also consider the supplementary trips to enable all passengers who fail to transfer to succeed in transferring; and the models also comprehensively consider the costs of the operating agencies to minimize the number of departure trips of the supplementary lines.
[0054] In summary, the method for optimizing the last bus schedule of the intelligent rail and bus collaborative transportation service network proposed by the present invention is very suitable for solving the problem of collaborative optimization of the last bus schedules of the intelligent rail and bus networks. BRIEF DESCRIPTION OF THE DRAWINGS
[0055] Figure 1 It is a schematic flowchart of a method for optimizing the last bus schedule of the intelligent rail and bus collaborative transportation service network. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0056] The following describes the specific embodiments of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions made using the concept of the present invention are within the scope of protection.
[0057] As Figure 1 shown, a method for optimizing the last bus schedule of the intelligent rail and bus collaborative transportation service network provided by an embodiment of the present invention includes the following steps S1 to S6:
[0058] S1. Construct a dual-mode public transportation network based on the intelligent rail and bus lines of the last bus service to be optimized, and determine all transfer stations therein;
[0059] In an optional embodiment of the present invention, step S1 constructs a dual-mode public transportation network based on the intelligent rail and bus lines of the last bus service to be optimized, which is expressed as:
[0060] G = {N, S t}, N = {A, B};
[0061] wherein, N represents the set of all lines included in the dual-mode public transportation network, and S tLet \(S\) represent the set of transfer stations, \(A\) represent the set of ART lines, and \(B\) represent the set of bus lines.
[0062] Determine all stations involving ART and bus transfers in the dual-mode public transportation network.
[0063] S2. According to the time period of the last bus service to be optimized and the operation times of ART and buses, determine the departure intervals of ART and bus lines within the last bus time period;
[0064] In an optional embodiment of the present invention, step S2 first obtains the time period of the last bus service to be optimized, determines the end point \(T\) of the last bus optimization time period; then, according to the operation times of ART and buses, determines the respective departure intervals \(h\) of all lines to be optimized within the last bus optimization time period l , the departure interval \(h\) l is assumed to be a constant value within the planning time \(T\).
[0065] S3. According to the historical passenger flow data of transfers between lines, determine the passenger flow of each trip of each line that needs to transfer to other lines;
[0066] S4. Taking maximizing the number of successful transfers of the last bus as the optimization goal and the departure times of the last buses of each line as decision variables, construct a first single-objective integer programming model and solve it to obtain the number of successful transfers of the last bus;
[0067] In an optional embodiment of the present invention, step S3 establishes a first single-objective integer programming model by introducing the departure times of the last buses of each line as decision variables and 0-1 auxiliary variables representing whether a transfer is feasible, aiming to maximize the number of successful transfers of the last bus.
[0068] This embodiment constructs the first single-objective integer programming model including:
[0069] Construct a first objective function according to the number of passengers on the last buses of each line who need to transfer to other lines at transfer stations and 0-1 auxiliary variables representing whether a transfer is feasible;
[0070] Taking maximizing the number of successful transfers of the last bus as the optimization goal, the departure times of the last buses of each line as decision variables, and the arrival time of the last bus that the passenger needs to transfer being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the departure time of the last bus, and the value range of the auxiliary variable as constraints.
[0071] The first objective function constructed in this embodiment is:
[0072]
[0073] where, max represents the maximum value function, \(z1\) represents the first objective function, \(p\)kls The number of passengers who need to transfer from line l to line k at the transfer station s for the last bus of line l, y kls A 0-1 auxiliary variable indicating whether it is a feasible transfer. When the departure time of the last bus of line k at the transfer station s is later than the arrival time of the passengers on line l at the transfer station s, the value is 1, indicating that the last bus transfer is feasible; otherwise, the value is 0, that is, the last bus transfer is not feasible.
[0074] The constraint conditions constructed in this embodiment are:
[0075]
[0076] Among them, De ks Indicates the departure time of line k that the passenger needs to transfer at the transfer station s, Ar ls Indicates the arrival time when the passenger walks to the transfer station s after getting off line l, M represents a set positive integer, S t Indicates the set of transfer stations, T represents the end point of the last bus optimization period, h k Indicates the departure interval of line k, x k Indicates the departure time of the last bus of line k at the starting station, x l Indicates the departure time of the last bus of line l at the starting station. Here, line k and line l only represent different lines with transfer requirements, and the passenger transfers from line l to line k; the lines with transfers can come from the same transportation mode or different ones. Therefore, k, l ∈ N, and it is limited in the model that k ≠ l; h l Indicates the departure interval of line l, N represents the set of all lines. The first constraint condition means that if the departure time of the last bus that the passenger needs to transfer is later than the arrival time of the passenger at the transfer station, then the transfer is successful. The second and third constraint conditions represent the value range of the decision variable of the last bus departure time. The fourth constraint condition represents the value range of the auxiliary variable.
[0077] Among them, the calculation method of the arrival time when the passenger walks to the transfer station after getting off the bus line is:
[0078]
[0079] The calculation method of the departure time of the line that the passenger needs to transfer at the transfer station is:
[0080]
[0081] Among them, t lm Indicates the driving time between station m-1 and station m of line l during the last bus optimization period, t kmDenote the running time of line k between station m - 1 and station m during the last - train optimization period, which is a fixed parameter; \(t_{w}\) lm Denote the dwell time of line l at station m during the last - train optimization period, \(t_{w}\) km Denote the dwell time of line k at station m (\(m\geq2\)), which is a fixed parameter; \(t\) n Denote the walking time of passengers.
[0082] S5. Based on the number of passengers who successfully transfer during the last - train period, with the goal of minimizing the transfer waiting time of successfully - transferred passengers and minimizing the number of additional trips of each line, construct a bi - objective integer programming model and solve it to obtain the transfer waiting time of successfully - transferred passengers, the number of additional trips of each line, and the departure time of the last - train of each line;
[0083] In an alternative embodiment of the present invention, step S5 establishes a bi - objective integer programming model according to the maximized number of successfully - transferred passengers obtained in step S4, aiming to minimize the transfer waiting time of successfully - transferred passengers and the number of additional trips of the line. By solving the model, the departure schedule of the last - train of each line and the minimized number of additional trips of the lines that need to be supplemented can be obtained.
[0084] The bi - objective integer programming model constructed in this embodiment includes:
[0085] Construct a second objective function according to the number of passengers who need to transfer to other lines at the transfer station for the last - train of each line, the transfer waiting time of passengers transferring from the last - train of the line to other lines, and a 0 - 1 auxiliary variable indicating whether it is a feasible transfer;
[0086] With the goal of minimizing the transfer waiting time of successfully - transferred passengers, using the number of passengers who successfully transfer during the last - train period, the transfer waiting time, the arrival time of the last - train that the passenger needs to transfer being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the last - train departure time, and the value range of the auxiliary variable as constraints;
[0087] Construct a third objective function according to the sum of the number of additional trips required for the APM and bus lines;
[0088] With the number of additional trips of the line that accepts the transfer when there is a failure in the last - train transfer, the arrival time of the last - train that the passenger needs to transfer being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the last - train departure time, and the value range of the auxiliary variable as constraints.
[0089] The second objective function constructed in this embodiment is:
[0090]
[0091] where min represents the minimum value function, z2 represents the second objective function, and p kls represents the number of passengers who need to transfer from the last bus of line l to line k at the transfer station s, and y kls represents a 0-1 auxiliary variable indicating whether it is a feasible transfer, and W kls represents the transfer waiting time for passengers to transfer from the last bus of line l to line k, which is only calculated when the transfer is feasible.
[0092] The constraint conditions constructed in this embodiment are as follows:
[0093]
[0094] where P represents the number of passengers who successfully transfer at the last bus, which is obtained by optimizing the model in step S4; De ks represents the departure time of line k, which the passenger needs to transfer to, at the transfer station s, and Ar ls represents the arrival time when the passenger arrives at the transfer station s after getting off line l on foot, and h k represents the headway of line k. The first constraint condition means restricting the number of passengers who successfully transfer to the maximum value, that is, taking the optimization result of step S4 as the constraint condition. The second constraint condition means calculating the transfer waiting time, and only when the transfer of the last bus is feasible, the corresponding transfer waiting time will be calculated. This model also includes all the constraint conditions in the model described in step S4.
[0095] The third objective function constructed in this embodiment is as follows:
[0096] min z3 = μ∑ k∈A δ k +∑ k∈B δ k ;
[0097] where min represents the minimum value function, z3 represents the third objective function, μ represents the coefficient for converting the intelligent rail service frequency to the bus service frequency, and δ k represents the number of additional service trips that line k needs to make, which is only calculated when the transfer of the last bus is not feasible.
[0098] The constraint conditions constructed in this embodiment are as follows:
[0099]
[0100] where max represents the maximum value function, S t represents the set of transfer stations, N represents the set of all lines, represents the ceiling function. The first constraint condition means calculating the number of additional service trips that the corresponding line accepting the transfer needs to make when there is a failure in transferring the last bus. This model also includes all the constraint conditions in the model described in step S4.
[0101] Integrating the above two models, the bi-objective integer programming model described in step S5 can be obtained.
[0102] S6. Based on the transfer waiting time of the passengers who have successfully transferred and the number of additional trips for each line, with the goal of minimizing the transfer waiting time of the passengers who fail to transfer, a second single-objective integer programming model is constructed and solved to obtain the transfer waiting time for the passengers who fail to transfer to the additional shuttle buses and the departure times of each shuttle bus on the additional lines.
[0103] In an optional embodiment of the present invention, step S6 establishes a second single-objective integer programming model based on the number of additional trips for each line obtained in step S5 and the departure time of the last bus for each line, aiming to optimize the transfer waiting time for the passengers who fail to transfer to the additional trips. Thus, an optimized departure schedule for each additional trip can be obtained.
[0104] The construction of the second single-objective integer programming model in this embodiment includes:
[0105] Construct a fourth objective function based on the number of passengers transferring from each bus of each line to other lines at the transfer station, the transfer waiting time for passengers transferring from each bus of each line to other lines at the transfer station, and a 0-1 auxiliary variable indicating whether it is a feasible transfer;
[0106] With the goal of minimizing the transfer waiting time of the passengers who fail to transfer, the constraints include the difference between the departure time of the vehicle that the passenger needs to transfer to and the arrival time of the passenger at the transfer station, the transfer waiting time, the headway of each line, the line of the passengers who fail to transfer, the departure time of the first additional bus on the line that needs to be supplemented is later than the departure time of the last bus, and the value range of the auxiliary variable.
[0107] The fourth objective function constructed in this embodiment is:
[0108]
[0109] Among them, min represents the minimum value function, z4 represents the fourth objective function, p kiljs represents the number of passengers transferring from the j-th bus of line l to the i-th bus of line k at the transfer station s. During the optimized period of the last bus, at the same transfer station s, for each bus of line l, the number of passengers transferring to a certain bus of line k is the same, which is the same as the number of passengers transferring from the last bus of line l to line k, and is regarded as evenly distributed. Specifically, which bus of line k it will transfer to is determined according to the optimization result; w kiljs represents the transfer waiting time for passengers from the j-th bus of line l transferring to the i-th bus of line k at the transfer station s, which is only calculated when the transfer is feasible, that is, when y kiljsThe transfer waiting time will only be calculated when the value is 1; y kiljs A 0-1 auxiliary variable indicating whether it is a feasible transfer. When the difference between the departure time of the i-th vehicle of line k at the transfer station s and the arrival time of the passengers of the j-th vehicle of line l at the transfer station s is within the time window [0, W], the value is 1, indicating that the transfer is feasible; otherwise, the value is 0, that is, the transfer is not feasible.
[0110] The constraint conditions constructed in this embodiment are:
[0111]
[0112] Among them, De kis Represents the departure time of the i-th shift of line k that the passenger needs to transfer at the transfer station s, Ar ljs Represents the arrival time when the passenger walks to the transfer station s after getting off the j-th vehicle of line l. M represents a set positive integer, which is a very large positive integer; W represents the upper limit of the transfer time window, x lj Represents the departure time of the j-th vehicle of line l at the starting station, x ki Represents the departure time of the i-th vehicle of line k at the starting station, which is a decision variable; x l(j-1) Represents the departure time of the (j - 1)-th vehicle of line l at the starting station, h l Represents the headway of line l, x k(i-1) Represents the departure time of the (i - 1)-th vehicle of line k at the starting station, h k Represents the headway of line k, Represents the departure time of the δ l -th vehicle of line l at the starting station, x l Represents the departure time of the last vehicle of line l at the starting station, x k1 Represents the departure time of the first vehicle of line k at the starting station, x k Represents the departure time of the last vehicle of line k at the starting station, N represents the set of all lines, I k Represents the set of additional trips of line k, calculated by step S5, I k ={1, 2,..., δ k}; I l Represents the set of trips corresponding to the passengers who fail to transfer on line l, calculated from the results of step S5, I k ={1, 2,..., δ l}, where δ l Is the number of trips corresponding to the passengers who fail to transfer on line l, and the calculation method is The δ l -th vehicle of line l is the last vehicle; S tDenote the set of transfer stations. The first and second constraint conditions represent the difference between the departure time of the vehicle that the passenger needs to transfer to and the arrival time of the passenger at the transfer station. If the difference is within the time window [0, W], the transfer is successful, and the variable y kiljs takes a value of either 1 or 0; otherwise, the transfer fails, and the variable y kiljs takes a value of 0. The third constraint condition represents the calculation of the transfer waiting time, and the corresponding transfer waiting time is calculated only when the transfer is feasible, that is, when the variable y kiljs takes a value of 1. The fourth and fifth constraint conditions indicate that the headway of each line is uniform. The sixth constraint condition represents the line l with passengers who fail to transfer, and the last failed trip of the line is the last trip of the line. The seventh constraint condition represents the line that needs to add trips, and the first added trip should be later than the departure time of the last trip. The eighth constraint condition represents the value range of the auxiliary variable.
[0113] Among them, the calculation method of the arrival time of the passenger who continues to walk to the transfer station after getting off the bus on the bus line is as follows:
[0114]
[0115] Among them, t lm represents the running time between station m - 1 and station m of line l during the last trip optimization period, tw lm represents the stopping time of line l at station m during the last trip optimization period, t n represents the walking time of the passenger;
[0116]
[0117] Among them, t km represents the running time between station m - 1 and station m of line k during the last trip optimization period, tw km represents the stopping time of line k at station m during the last trip optimization period.
[0118] By solving the first single-objective integer programming model of the present invention, the maximum number of passengers who succeed in transferring at the last trip can be obtained; substituting the optimization result of the first single-objective integer programming model into the bi-objective integer programming model for solution, a series of Pareto optimal solutions regarding the total transfer waiting time of the passengers who succeed in transferring and the number of additional trips of the line can be obtained; substituting the optimization result of the bi-objective integer programming model into the second single-objective integer programming model, the transfer waiting time of the passengers who fail to transfer to the additional trips can be minimized. At the same time, the bi-objective integer programming model can calculate the departure time of the last trip of each line, and the second single-objective integer programming model can calculate the departure time of each trip of the additional line. After integration, the optimized departure time table of the last trip can be obtained.
[0119] All three integer programming models mentioned above can be directly solved efficiently using commercial optimization solvers. "The model in step S5 can obtain the departure schedules of the last buses on each route and the number of departure trips for the routes that need to be supplemented", which means that the optimization result of the step S5 model can directly determine the departure times of the last buses on each route, i.e., x k , x l values, and calculate the number of departure trips for each supplemented route, i.e., δ k values; "The model in step S6 can obtain the departure schedules of each supplemented trip", which means that the optimization result of step S6 can determine the departure times of each bus on the supplemented route, so as to ensure that passengers taking the supplemented trips can leave the transfer station as soon as possible. Through the above steps S1 - S6, a departure schedule finally composed of the departure times of the last buses on each route and each bus on the supplemented route is obtained.
[0120] The present invention aims at a dual - mode public transportation network with the co - existence of intelligent rail transit and buses, comprehensively considers multiple objectives such as maximizing the number of successful transfers, minimizing the number of departure trips of the supplemented routes, and minimizing the transfer waiting time, and proposes corresponding optimization models. Through the optimization algorithm, the optimal solutions of the optimization models are obtained, so as to assist decision - makers in formulating the schedules for the coordinated transfer of the last buses of intelligent rail transit and buses.
[0121] The present invention can provide an optimized departure schedule for each route for the service during the last bus hours of intelligent rail transit and buses. The schedule generated by the optimization model can achieve the three objectives of maximizing the number of successful transfers, minimizing the transfer waiting time, and minimizing the number of departure trips of the supplemented routes.
[0122] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processors of general - purpose computers, special - purpose computers, embedded processors, or other programmable data - processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data - processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.
[0123] These computer program instructions can also be stored in a computer - readable memory that can direct a computer or other programmable data - processing device to work in a specific manner, so that the instructions stored in the computer - readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions in Figure 1 one process or multiple processes and / or blocksFigure 1 The functions specified in one or more boxes.
[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one or more processes and / or one or more boxes. Figure 1 One or more processes and / or Figure 1 The steps of the functions specified in one or more boxes.
[0125] Specific embodiments are used in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.
[0126] Those of ordinary skill in the art will realize that the embodiments described herein are for helping the reader understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.
Claims
1. An optimization method for the last bus schedule of an intelligent rail transit and bus collaborative transportation service network, characterized in that, It includes the following steps: Construct a dual-mode public transportation network based on the intelligent rail and bus lines with the last bus service to be optimized, and determine all transfer stations therein; Determine the departure intervals of the intelligent rail and bus lines during the last bus service period according to the period of the last bus service to be optimized and the operation times of the intelligent rail and buses; Determine the passenger flow volume that each shift of each line needs to transfer to other lines according to the historical passenger flow data of transfers between lines; Taking the maximization of the number of successful transfers of the last bus as the optimization goal and the departure times of the last buses of each line as decision variables, construct a single-objective integer programming model and solve it to obtain the number of successful transfers of the last bus; According to the number of successful transfers of the last bus, taking the minimization of the transfer waiting time of successful transfer passengers and the minimization of the number of additional trips of lines as optimization goals, construct a bi-objective integer programming model and solve it to obtain the transfer waiting time of successful transfer passengers, the number of additional trips of each line, and the departure times of the last buses of each line; According to the transfer waiting time of successful transfer passengers and the number of additional trips of each line, taking the minimization of the transfer waiting time of failed transfer passengers as the optimization goal, construct a single-objective integer programming model and solve it to obtain the transfer waiting time of failed transfer passengers transferring to additional trips and the departure times of each bus of the additional lines; 2. The optimization method for the last bus schedule of an intelligent rail and bus collaborative transportation service network according to claim 1, characterized in that Constructing the first single-objective integer programming model includes: Construct the first objective function according to the number of passengers on the last bus of each line who need to transfer to other lines at the transfer station and the 0-1 auxiliary variable indicating whether it is a feasible transfer; Taking the maximization of the number of successful transfers of the last bus as the optimization goal, taking the departure times of the last buses of each line as decision variables, and taking the arrival time of the last bus that the passenger needs to transfer to being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the departure time of the last bus, and the value range of the auxiliary variable as constraints; 3. An optimization method for the last bus schedule of an intelligent rail transit and bus collaborative transportation service network according to claim 1 or 2, characterized in that The specific first single-objective integer programming model constructed is: s.t. Among them, max represents the maximum value function, z1 represents the first objective function, p kls represents the number of passengers who need to transfer from the last bus of line l to line k at the transfer station s, y kls represents a 0-1 auxiliary variable indicating whether it is a feasible transfer, De ks represents the departure time of line k, which the passenger needs to transfer to, at the transfer station s, Ar ls represents the arrival time when the passenger arrives at the transfer station s after getting off line l on foot, M represents a set positive integer, S t represents the set of transfer stations, T represents the end point of the last bus optimization period, h k represents the headway of line k, x k represents the departure time of the last bus of line k at the starting station, h l represents the headway of line l, x l represents the departure time of the last bus of line l at the starting station, N represents the set of all lines.
4. An optimization method for the last bus schedule of an intelligent rail transit and bus collaborative transportation service network according to claim 3, characterized in that, The calculation method of the arrival time when a passenger walks to the transfer station after getting off the bus on the bus line is: where, t lm represents the running time of line l between station m-1 and station m during the last train optimization period, tw lm represents the dwell time of line l at station m during the last train optimization period, t n represents the walking time of passengers; The calculation method of the departure time of the line that the passenger needs to transfer to at the transfer station is: where t km represents the running time of line k between station m - 1 and station m during the last train optimization period, and tw km represents the dwell time of line k at station m during the last train optimization period.
5. The optimization method for the last bus schedule of an intelligent rail and bus collaborative transportation service network according to claim 1, wherein Constructing the bi-objective integer programming model includes: Construct the second objective function according to the number of passengers on the last bus of each line who need to transfer to other lines at the transfer station, the transfer waiting time of the passengers transferring from the last bus of the line to other lines, and the 0-1 auxiliary variable indicating whether it is a feasible transfer; Taking the minimization of the transfer waiting time of successful transfer passengers as the optimization goal, taking the number of successful transfers of the last bus, the transfer waiting time, the arrival time of the last bus that the passenger needs to transfer to being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the departure time of the last bus, and the value range of the auxiliary variable as constraints; Construct the third objective function according to the sum of the number of additional trips required for the intelligent rail and bus lines; Taking the number of additional trips of the line corresponding to accepting the transfer when there is a failed transfer of the last bus, the arrival time of the last bus that the passenger needs to transfer to being later than the arrival time of the passenger at the transfer station, the value range of the decision variable of the departure time of the last bus, and the value range of the auxiliary variable as constraints; 6. The optimizing method for the last bus schedule of an intelligent rail transit and bus collaborative transportation service network according to claim 1 or 5, characterized in that The specific bi-objective integer programming model constructed is: min z3 = μ∑ k∈A δ k + ∑ k∈B δ k ; s.t. Among them, min represents the minimum value function, z2 represents the second objective function, z3 represents the third objective function, and p kls represents the number of passengers who need to transfer from the last bus of line l to line k at the transfer station s, and y kls represents a 0-1 auxiliary variable indicating whether it is a feasible transfer, and w kls represents the transfer waiting time for passengers to transfer from the last bus of line l to line k, μ represents the coefficient for converting the number of intelligent rail trips to bus trips, and δ k represents the number of additional trips that need to be issued for line k, A represents the set of intelligent rail lines, B represents the set of bus lines, P represents the number of passengers who have successfully transferred at the last bus, and De ks represents the departure time of line k, which the passengers need to transfer to, at the transfer station s, and Ar ls represents the arrival time when the passengers arrive at the transfer station s after getting off line l on foot, and h k represents the headway of line k, and max represents the maximum value function, represents the ceiling function, M represents a set positive integer, and S t represents the set of transfer stations, T represents the end point of the optimization period of the last bus, and h k represents the headway of line k, and x k represents the departure time of the last bus of line k at the starting station, and h l represents the headway of line l, and x l represents the departure time of the last bus of line l at the starting station, and N represents the set of all lines.
7. An optimization method for the last bus schedule of an intelligent rail and bus collaborative transportation service network according to claim 1, characterized in that Constructing the second single-objective integer programming model includes: Constructing the fourth objective function based on the number of passengers transferring from each bus of each line to other lines at the transfer station, the transfer waiting time of passengers transferring from each bus of each line to other lines at the transfer station, and the 0-1 auxiliary variable indicating whether it is a feasible transfer; Taking minimizing the transfer waiting time of transfer-failed passengers as the optimization objective, with the difference between the departure time of the vehicle that the passenger needs to transfer to and the arrival time of the passenger at the transfer station, the transfer waiting time, the headway of each line, the lines of transfer-failed passengers, the departure time of the first bus of the supplementary line being later than the departure time of the last bus, and the value range of the auxiliary variable as the constraint conditions.
8. The optimization method for the last bus schedule of an intelligent rail transit and bus collaborative transportation service network according to claim 1 or 7, characterized in that The specifically constructed second single-objective integer programming model is: s.t. Among them, min represents the minimum value function, z4 represents the fourth objective function, p kiljs represents the number of passengers transferring from the j-th bus of line l to the i-th bus of line k at the transfer station s, w kiljs represents the transfer waiting time of passengers from the j-th bus of line l transferring to the i-th bus of line k at the transfer station s, y kiljs represents a 0-1 auxiliary variable indicating whether it is a feasible transfer, De kis represents the departure time of the i-th bus of line k that the passenger needs to transfer to at the transfer station s, Ar ljs represents the arrival time when the passenger walks to the transfer station s after getting off the j-th bus of line l. M represents a set positive integer, W represents the upper limit of the transfer time window, x lj represents the departure time of the j-th bus of line l at the starting station, x l(j-1) represents the departure time of the (j - 1)-th bus of line l at the starting station, h l represents the headway of line l, x ki represents the departure time of the i-th bus of line k at the starting station, x k(i-1) represents the departure time of the (i - 1)-th bus of line k at the starting station, h k represents the headway of line k, represents the departure time of the δ l -th bus of line l at the starting station, x l represents the departure time of the last bus of line l at the starting station, x k1 represents the departure time of the first bus of line k at the starting station, x k represents the departure time of the last bus of line k at the starting station, N represents the set of all lines, I k represents the set of supplementary trips of line k, I l represents the set of trips corresponding to passengers who fail to transfer on line l, S t represents the set of transfer stations.
9. The optimization method for the last bus schedule of an intelligent rail transit and bus collaborative transportation service network according to claim 8, characterized in that, The calculation method of the arrival time of passengers continuing to walk to the transfer station after getting off the bus on the bus line is: Among them, t lm represents the running time of line l between station m - 1 and station m during the last train optimization period, tw lm represents the stopping time of line l at station m during the last train optimization period, t n represents the walking time of passengers; where t km represents the running time of line k between station m - 1 and station m during the last train optimization period, and tw km represents the dwell time of line k at station m during the last train optimization period.
10. The optimized method for the last bus schedule of the intelligent rail and bus collaborative transportation service network according to claim 1, characterized in that, Determining the departure time schedule of the last bus of each line according to the departure time of the last bus of each line; Determining the departure time schedule of each bus of the supplementary line according to the departure time of each bus of the supplementary line; Obtaining the optimized departure time schedule of the last bus of each line based on the departure time schedule of the last bus of each line and the departure time schedule of each bus of the supplementary line.