1. A modular vehicle operation path and combination strategy collaborative optimization method is characterized in that: the method comprises the following specific steps:

s1, acquiring urban traffic physical network and candidate operation route information, and constructing a modularized vehicle operation path connectivity balance constraint;

s2, acquiring the number of the passenger demands, the passenger positions and the time window information, and constructing the passenger demand full coverage constraint;

s3, constructing passenger on-the-way transfer constraints according to the running paths and lines of the urban traffic modular vehicles, the required number of passengers, the positions and the time window information;

s4, constructing a target function based on the use cost of the modular vehicle and the generalized transportation cost of the passenger travel time according to the running path of the urban traffic modular vehicle and the passenger demand information;

s5, establishing a hybrid integer linear programming model of the modularized vehicle running path and the vehicle combination strategy collaborative optimization for realizing on-the-road transfer according to the constraint set and the objective function;

and S6, solving the mixed integer linear programming model to obtain an optimized modular vehicle running path and a vehicle combination strategy for passenger on-the-way transfer.

2. The modular vehicle travel path and combination strategy collaborative optimization method of claim 1, wherein: the step S1 specifically includes the following steps:

s11: the set of modular vehicles is denoted as V; the set of arcs representing the candidate travel paths of the modular vehicle in the spatiotemporal state network is denoted A_v；

S12: the modular vehicle travel path connectivity flow balance constraint is expressed as:

wherein the content of the first and second substances,representing a candidate operating path (i, j, t, t ', w, w') epsilon A of the modular vehicle as a decision variable_vWhether selected by a modular vehicle; when in useWhen, it means that the modular vehicle is passing this path, otherwise it is not; v is a modular vehicle; i is a node in a road network and represents a starting point of a certain road section; j is a node in the road network and represents the terminal of a certain road section; t is a time variable, T belongs to T, and T is a time set; t 'is the time when the passenger arrives at node j, t' ═ t + TT_i,j；TT_i,jThe time spent from i to j for passengers taking the modular vehicle is known, and the road network information, namely the time parameter spent on each road section is known, so that the time spent from the node i to the node j can be calculated; w ═ w₁,...,w_d,...,w_|D|]Denotes a set of passenger states in the vehicle, w_dIs the amount of passengers to the destination d; w' represents an in-vehicle passenger state after the modular vehicle services a passenger; w is a⁰The method comprises the steps of (1) initializing an in-vehicle passenger state for the modular vehicle; o_vIs taken as a starting point; d_vIs the end point; e.g. of the type_vIs the left end point of the vehicle time window; l_vIs the right end point of the vehicle time window; t' is the time of the previous time-space state node entering the intermediate point in the intermediate point flow balance constraint; w' is the passenger state in the vehicle before other vehicles serve the passengers; (j, t ', w') is the terminal spatio-temporal state, and (j ', t ", w') isThe time of the previous node of the middle point and the passenger state in the vehicle are balanced by the inflow, namely the space-time state of the previous node of the middle point; j' is the node preceding the flow balancing midpoint.

3. The modular vehicle travel path and combination strategy collaborative optimization method of claim 1, wherein: the step S2 includes the following steps:

S21：n_prepresenting the required number of passengers to be served at a certain station p; w represents a set of passenger loading conditions in the modular vehicle; (w' -w) represents the amount of passenger state transition before and after the modular vehicle services the passenger; Ψ_pIs a set of the arc of the passenger;

s22: the passenger demand full coverage constraint is expressed as:

wherein, w, w', n_pThe values of (A) are respectively as follows:

w＝[w₁,...，w_d,...,w_|D|]

w′＝[w′₁,...,w′_d,...,w′_|D|]

wherein D is a destination set;indicating the total passenger demand, V, at station p to destination d^*Is a virtual spare vehicle collection.

4. The modular vehicle travel path and combination strategy collaborative optimization method of claim 1, wherein: step S3 specifically includes the following:

the in-transit transfer constraint for a passenger riding the modular vehicle is expressed as:

v∈V，t∈T，t′＝t+TT_i，j

wherein, aggregateA set of transfer arcs representing the arrival of the modular vehicle v at point b at time t; TT_i，jRepresents the time spent by a passenger in a modular vehicle from i to j;

representing a candidate operating path (i, j, t, t ', w, w') epsilon A of the modular vehicle as a decision variable_vWhether selected by a modular vehicle; when in useWhen, it means that the modular vehicle is passing this path, otherwise it is not;

w' ″ is the passenger state in the vehicle after other vehicles serve the passengers; v' is a spare modular vehicle.

5. The modular vehicle travel path and combination strategy collaborative optimization method of claim 1, wherein: the step S4 includes the following steps:

s41: the modular vehicle travel time cost may be expressed as

Wherein the parameter TT_i,jRepresents the time spent by a passenger in a modular vehicle from i to j;

s42: the passenger trip cost can be expressed as

Wherein the parametersIndicating modular vehicle origin o_vThe time taken to reach j;

the parameter pi represents the cost of use of the modular vehicle.

6. The modular vehicle travel path and combination strategy collaborative optimization method of claim 1, wherein: step S5, the hybrid integer linear programming model for the modular vehicle operation path and the vehicle combination strategy collaborative optimization for implementing the in-transit transfer is represented as: -

subject to

The objective function is to minimize the weighted total operating cost and the passenger trip cost.

7. The system for realizing the modular vehicle operation path and combination strategy collaborative optimization method of claim 1 comprises the following steps:

the modularized vehicle and operation path module is used for acquiring a modularized vehicle set and an arc set of a modularized vehicle candidate operation path, and constructing modularized vehicle operation path connectivity flow balance constraint according to urban traffic physical network and candidate operation line information;

the passenger demand acquisition module is used for acquiring the passenger demand quantity, the passenger position and the time window information and constructing passenger demand full-coverage constraint;

the passenger on-the-way transfer module is used for constructing passenger on-the-way transfer constraints according to the running paths and lines of the urban traffic modular vehicle, the required quantity of passengers, positions and time window information;

the objective function module is used for constructing an objective function based on the use cost of the modular vehicle and the generalized transportation cost of the passenger travel time according to the running path of the urban traffic modular vehicle and the passenger demand information;

and the optimization module is used for establishing a hybrid integer linear programming model for the modularized vehicle running path and the vehicle combination strategy collaborative optimization for realizing the on-the-road transfer according to the constraint set and the objective function, solving the hybrid integer linear programming model and obtaining the optimized modularized vehicle running path and the vehicle combination strategy for passenger on-the-road transfer.

Background

The modularized vehicle has the advantages of small size, high flexibility, timely response, energy conservation, environmental protection, high service level and the like, can assist the construction of future smart cities in China, effectively improve the passenger trip experience, and make a contribution to supporting city circulation. As is known, the traveling demands of passengers have strong time-varying property and fluctuation, so that the conditions of crowding, no-load and the like often occur to the vehicles, and in order to adapt to the continuously increasing and changing traveling demands of the passengers, the modularized vehicle running path capable of responding to the demands of the passengers in real time is designed, the vehicle combination strategy is cooperatively optimized, the passengers are transferred on the way through the combination and separation of the vehicle units, and a practical foundation is laid for the joint optimization of the vehicle capacity, the running path and the driving schedule.

The design of the modular vehicle operation scheme mainly comprises two parts of main contents: firstly, setting a vehicle running path, namely determining the running path of the vehicle, wherein each line comprises the number of stations, the time spent and the number of passengers served along the way; and the other is vehicle combination strategy design, namely the position and time of the vehicle combination separation and the passenger transfer number are determined. The length of an operation line is determined by the operation path, and the total travel time of passengers is influenced. The vehicle combination strategy influences the transfer times and time of passengers and influences the use condition of the vehicle. How to cooperatively optimize the operation path of the modular vehicles and the combination strategy depends on the path distribution result of the modular vehicles in the road network. In some existing route planning methods, the in-transit transfer of the vehicle can be rarely considered, so that the optimization of the travel time cost of passengers cannot be realized. Therefore, how to make a running path scheme for the modular bus can meet the high-quality travel demand of passengers in time, realize in-transit transfer, save the total travel time of the passengers, and have higher research value and practical significance.

Disclosure of Invention

In view of the problems in the introduction of the background art, the present invention aims to provide a method and a system for collaborative optimization of a modular vehicle operation path and a combination strategy based on a spatio-temporal state network, which can meet the travel demand of a passenger, shorten the travel route as much as possible, and reduce the travel cost of the passenger.

The technical scheme adopted by the invention is as follows:

a modular vehicle operation path and combination strategy collaborative optimization method specifically comprises the following steps:

s1, acquiring urban traffic physical network and candidate operation route information, and constructing a modularized vehicle operation path connectivity balance constraint;

s2, acquiring the number of the passenger demands, the passenger positions and the time window information, and constructing the passenger demand full coverage constraint;

s3, constructing passenger on-the-way transfer constraints according to the running paths and lines of the urban traffic modular vehicles, the required number of passengers, the positions and the time window information;

s4, constructing a target function based on the use cost of the modular vehicle and the generalized transportation cost of the passenger travel time according to the running path of the urban traffic modular vehicle and the passenger demand information;

s5, establishing a hybrid integer linear programming model of the modularized vehicle running path and the vehicle combination strategy collaborative optimization for realizing on-the-road transfer according to the constraint set and the objective function;

and S6, solving the mixed integer linear programming model to obtain an optimized modular vehicle running path and a vehicle combination strategy for passenger on-the-way transfer.

Further, step S1 specifically includes the following steps:

s11: the set of modular vehicles is denoted as V; spatiotemporal state networksWherein the arc set representing the candidate operating paths of the modular vehicle is represented as A_v；

S12: the modular vehicle travel path connectivity flow balance constraint is expressed as:

wherein the content of the first and second substances,representing a candidate operating path (i, j, t, t ', w, w') epsilon A of the modular vehicle as a decision variable_vWhether selected by a modular vehicle; when in useWhen, it means that the modular vehicle is passing this path, otherwise it is not; v is a modular vehicle; i is a node in a road network and represents a starting point of a certain road section; j is a node in the road network and represents the terminal of a certain road section; t is a time variable, T belongs to T, and T is a time set; t 'is the time when the passenger arrives at node j, t' ═ t + TT_i，j；TT_i，jThe time spent from i to j for passengers taking the modular vehicle is known, and the road network information, namely the time parameter spent on each road section is known, so that the time spent from the node i to the node j can be calculated; w ═ w_t，…，w_d,...，w_|D|]Denotes a set of passenger states in the vehicle, w_dIs the amount of passengers to the destination d; w' represents an in-vehicle passenger state after the modular vehicle services a passenger; w is a⁰The method comprises the steps of (1) initializing an in-vehicle passenger state for the modular vehicle; o_vIs taken as a starting point; d_vIs the end point; e.g. of the type_vAs the left end point of the vehicle time window；l_vIs the right end point of the vehicle time window; t' is the time of the previous time-space state node entering the intermediate point in the intermediate point flow balance constraint; w' is the passenger state in the vehicle before other vehicles serve the passengers; (j, t ', w ') is the terminal space-time state, (j ', t ", w") is the time of the previous node entering the flow balance intermediate point and the passenger state in the vehicle, i.e. the space-time state of the previous point of the intermediate point; j' is the node preceding the flow balancing midpoint.

Further, step S2 specifically includes the following steps:

S21：n_prepresenting the required number of passengers to be served at a certain station p; w represents a set of passenger loading conditions in the modular vehicle; (w' -w) represents the amount of passenger state transition before and after the modular vehicle services the passenger; Ψ_pIs a set of the arc of the passenger;

s22: the passenger demand full coverage constraint is expressed as:

wherein, w, w', n_pThe values of (A) are respectively as follows:

w＝[w_t，...，w_d，...，w_|D|]

w'＝[w'₁，...，w'_d，...，w'_|D|]

wherein D is a destination set;represents the total passenger demand at station p to destination d; v^*Is a virtual spare vehicle collection.

Further, step S3 specifically includes the following steps:

the in-transit transfer constraint for a passenger riding the modular vehicle is expressed as:

v∈V,t∈T,t'＝t+TT_i,j

wherein, aggregateA set of transfer arcs representing the arrival of the modular vehicle v at point b at time t; TT_i,jRepresents the time spent by a passenger in a modular vehicle from i to j;

representing a candidate operating path (i, j, t, t ', w, w') epsilon A of the modular vehicle as a decision variable_vWhether selected by a modular vehicle; when in useWhen, it means that the modular vehicle is passing this path, otherwise it is not;

w' ″ is the passenger state in the vehicle after other vehicles serve the passengers; v' is a spare modular vehicle.

Further, step S4 specifically includes the following steps:

s41: the modular vehicle travel time cost may be expressed as

Wherein the parameter TT_i，jRepresents the time spent by a passenger in a modular vehicle from i to j;

s42: the passenger trip cost can be expressed as

Wherein the parametersIndicating modular vehicle origin o_vThe time taken to reach j;

the parameter pi represents the cost of use of the modular vehicle.

Further, in step S5, the hybrid integer linear programming model for the modular vehicle operation path and the vehicle combination strategy collaborative optimization for implementing the in-transit transfer is represented as:

subject to

the objective function is to minimize the weighted total operating cost and the passenger trip cost.

The system of the modularized vehicle running path and combination strategy collaborative optimization method comprises

The modularized vehicle and operation path module is used for acquiring a modularized vehicle set and an arc set of a modularized vehicle candidate operation path, and constructing modularized vehicle operation path connectivity flow balance constraint according to urban traffic physical network and candidate operation line information; the passenger demand acquisition module is used for acquiring the passenger demand quantity, the passenger position and the time window information and constructing passenger demand full-coverage constraint;

the passenger on-the-way transfer module is used for constructing passenger on-the-way transfer constraints according to the running paths and lines of the urban traffic modular vehicle, the required quantity of passengers, positions and time window information;

the objective function module is used for constructing an objective function based on the use cost of the modular vehicle and the generalized transportation cost of the passenger travel time according to the running path of the urban traffic modular vehicle and the passenger demand information;

and the optimization module is used for establishing a hybrid integer linear programming model for the modularized vehicle running path and the vehicle combination strategy collaborative optimization for realizing the on-the-road transfer according to the constraint set and the objective function, solving the hybrid integer linear programming model and obtaining the optimized modularized vehicle running path and the vehicle combination strategy for passenger on-the-road transfer.

The invention has the beneficial effects that: the modularized vehicle operation network connectivity, the flow balance, the passenger travel requirements, the vehicle carrying capacity and other practical factors are comprehensively considered, the modularized vehicle operation path and the combination strategy thereof are optimized, the high-time-variation passenger requirements can be responded in real time, the driving route is greatly shortened through the combination and the separation of the modularized vehicle units, and the service efficiency and the service level of the modularized vehicle are improved. In addition, through weighing passenger trip time cost and modularization vehicle use cost, can the effective control passenger at car time, latency and modularization vehicle rate of utilization to the very big degree improves convenience and the satisfaction that the passenger took the modularization vehicle trip, more closes to actual problem, improves practical application and worth.

Drawings

FIG. 1 is a flow chart of the present invention.

FIG. 2 is a block diagram of a modular vehicle physical network topology of the present invention.

Fig. 3 is a table of candidate service line information according to the present invention.

FIG. 4 is a schematic diagram of the modular vehicle operation path and vehicle combination strategy of different scenarios obtained by the present invention.

Detailed Description

The present invention is further illustrated by the following examples, which are not intended to limit the invention to these embodiments. It will be appreciated by those skilled in the art that the present invention encompasses all alternatives, modifications and equivalents as may be included within the scope of the claims.

The description of the parameters involved in the present invention is shown in Table 1.

TABLE 1

Example one

Referring to fig. 1, the present embodiment provides a method for collaborative optimization of a modular vehicle operation path and a combination strategy, the method includes the following steps;

s1, acquiring urban traffic physical network and candidate operation route information, and constructing a modularized vehicle operation path connectivity balance constraint;

in the present embodiment, a given modular vehicle physical network topology such as nodes, stations, passengers, etc. is shown in fig. 2, and includes 8 nodes, where the node 6 and the node 8 are two destinations respectively, there are passenger demands and corresponding time windows to the two destinations on the three transportation arcs 1 → 3, 2 → 4 and 3 → 4, and the given candidate operation route information is shown in fig. 3.

In order to better study the requirements of the limiting conditions such as passenger demand information on the results and verify the feasibility of the model, 5 different scenarios are designed, and the condition settings are shown in table 2.

TABLE 2

The method specifically comprises the following steps of connecting flow balance constraint of a modular vehicle running path:

s11: the set of modular vehicles is denoted as V; the set of arcs representing the candidate travel paths of the modular vehicle in the spatiotemporal state network is denoted A_v。

S12: the modular vehicle travel path connectivity flow balance constraint is expressed as:

the formula represents the balance of the starting point flow, and when a certain vehicle starts from a starting point 1 node in an initial state, the sum of all candidate path selection results is equal to 1; summing all candidate arcs derived from node one, wherein two candidate paths are 1 → 2 and 1 → 3 respectively; a constraint of 1 ensures that the vehicle only starts one candidate arc at that time; o_vIs 1, e_vAlso 1, are initial parameter values.

The formula represents the balance of the terminal flow, and when a certain vehicle arrives at the terminal 8 node in a final state, the sum of all candidate path selection results is equal to 1; is to sum all candidate arcs arriving at the pointing node 8Two selection paths are provided, namely 6 → 8 and 7 → 8; a constraint of 1 ensures that only one candidate arc arrives for this vehicle at that time; d_vIs 8, l_vAlso 20, are initial parameter values.

The formula represents the balance of the middle point flow, namely, a certain vehicle enters a certain node at the middle point except the starting point and the end point, and the number of candidate arcs pointing out the certain node is the same, namely, the candidate arcs enter the node and exit the node, so that the constraint is 0. Since the summation is for a certain node, and x represents an arc.

Wherein the content of the first and second substances,representing a candidate operating path (i, j, t, t ', w, w') epsilon A of the modular vehicle as a decision variable_vWhether selected by a modular vehicle; when in useBy time, it is meant that the modular vehicle passes this path, and not vice versa.

S2, acquiring the number of the passenger demands, the passenger positions and the time window information, and constructing the passenger demand full coverage constraint;

the specific implementation process is as follows;

S21：n_prepresenting the required number of passengers to be served at a certain station p; w represents a set of passenger loading conditions in the modular vehicle; (w' -w) represents the amount of passenger state transition before and after the modular vehicle services the passenger; Ψ_pIs a set of the arc of the catcher.

S22: the passenger demand full coverage constraint is expressed as:

the formula shows that the passenger demands of a certain station occur on all selected candidate arcsThe sum of the state transitions should equal the number of passenger demands. V is within the range of V and N^*Representing the collection of all modular vehicles including the spare vehicle.

Wherein, w, w', n_pThe values of (A) are respectively as follows:

w＝[w₁，…，w_d，…，w_|D|]

w'＝[w'₁，...，w'_d,...,w'_|D|]

wherein D is a destination set;representing the total passenger demand at station p to destination d.

S3, generating passenger on-the-way transfer constraints according to the information such as the running paths and lines of the urban traffic modular vehicle, the required number of passengers, the position, the time window and the like, wherein the specific implementation process is as follows;

the in-transit transfer constraint for a passenger riding the modular vehicle is expressed as:

the present formula indicates that the number of passengers rolling in from a modular vehicle should be equal to the number of passengers rolling out therefrom. V' is E.V.U.V^*L { v } indicates that the passenger is another vehicle in the set of all vehicles other than the vehicle v in which the passenger was located before the transfer. w, w': (i, j, t, t ', w, w ') indicates that the occupant's state transition occurs in accordance with the state transition arc.

v∈V，t∈T，t'＝t+TT_i，j

Wherein, aggregateA set of transfer arcs representing the arrival of the modular vehicle v at point b at time t; TT_i，jRepresents the time spent by a passenger in a modular vehicle from i to j;

representing a candidate operating path (i, j, t, t ', w, w') epsilon A of the modular vehicle as a decision variable_vWhether selected by a modular vehicle; when in useBy time, it is meant that the modular vehicle passes this path, and not vice versa.

S4, generating an objective function based on the use cost of the modular vehicle and the generalized transportation cost of the passenger travel time according to the information such as the running path of the urban traffic modular vehicle, the passenger demand and the like;

s41: the modular vehicle travel time cost may be expressed as

The time cost is determined based on given road network information, and represents the time taken from node i to node j, such as route 1 → 3 → 4 → 5 → 7 → 8, and the time cost is 5; v is belonged to V and U^*Represents the set of all modular vehicles, including the spare vehicle; (i, j, t, t ', w, w'). epsilon.A_v/v∈V^*,(o_v，j,e_v,t'，w₀And w') indicates a case where the backup vehicle is not used.

Wherein the parameter TT_i，jRepresents the time spent by a passenger in a modular vehicle from i to j;

s42: the passenger trip cost can be expressed as

The formula shows that the original vehicle resources are insufficient, and the standby vehicle in the standby vehicle set is needed, so that when the vehicle starts from the initial state at the starting point, the extra payment of the cost pi of using a new vehicle is needed

Wherein the parametersIndicating modular vehicle origin o_vThe time taken to reach j;

the parameter pi represents the cost of use of the modular vehicle.

S5, establishing a hybrid integer linear programming model of modularized vehicle running paths and vehicle combination strategy collaborative optimization for realizing on-the-road transfer according to the constraint set and the objective function, wherein the specific implementation process is as follows;

the formula is an objective function of the model and aims to minimize the travel time of the passenger

subject to

The objective function is to minimize the weighted total operating cost and the passenger trip cost.

S6, solving the mixed integer linear programming model to obtain the modular vehicle running paths and the vehicle combination strategies under different situations, wherein the modular vehicle running paths and the vehicle combination strategies are shown in FIG. 4.

Wherein, the optimal modularized vehicle operation path and vehicle combination strategy is scenario 3, and the objective function value is 11.

According to the modularized vehicle traffic route optimization method, actual factors such as modularized vehicle operation network connectivity, flow balance, passenger travel requirements and vehicle carrying capacity are comprehensively considered, and the modularized vehicle operation route and the combination strategy thereof are optimized, so that the high-time-variation passenger requirements can be responded in real time, the driving route is greatly shortened through combination and separation of modularized vehicle units, and the service efficiency and level of the modularized vehicle are improved. In addition, through weighing passenger trip time cost and modularization vehicle use cost, can the effective control passenger at car time, latency and modularization vehicle rate of utilization to the very big degree improves convenience and the satisfaction that the passenger took the modularization vehicle trip, more closes to actual problem, improves practical application and worth.

Example two

The embodiment provides a system for implementing the modular vehicle operation path and combination strategy collaborative optimization method based on the spatio-temporal state network, which comprises the following steps:

the modularized vehicle and operation path module is used for acquiring a modularized vehicle set and an arc set of a modularized vehicle candidate operation path, and constructing modularized vehicle operation path connectivity flow balance constraint according to urban traffic physical network and candidate operation line information; the passenger demand acquisition module is used for acquiring the passenger demand quantity, the passenger position and the time window information and constructing passenger demand full-coverage constraint;

the passenger on-the-way transfer module is used for constructing passenger on-the-way transfer constraints according to the running paths and lines of the urban traffic modular vehicle, the required quantity of passengers, positions and time window information;

the objective function module is used for constructing an objective function based on the use cost of the modular vehicle and the generalized transportation cost of the passenger travel time according to the running path of the urban traffic modular vehicle and the passenger demand information;

and the optimization module is used for establishing a hybrid integer linear programming model for the modularized vehicle running path and the vehicle combination strategy collaborative optimization for realizing the on-the-road transfer according to the constraint set and the objective function, solving the hybrid integer linear programming model and obtaining the optimized modularized vehicle running path and the vehicle combination strategy for passenger on-the-road transfer.

According to the modularized vehicle traffic route optimization method, actual factors such as modularized vehicle operation network connectivity, flow balance, passenger travel requirements and vehicle carrying capacity are comprehensively considered, and the modularized vehicle operation route and the combination strategy thereof are optimized, so that the high-time-variation passenger requirements can be responded in real time, the driving route is greatly shortened through combination and separation of modularized vehicle units, and the service efficiency and level of the modularized vehicle are improved. In addition, through weighing passenger trip time cost and modularization vehicle use cost, can the effective control passenger at car time, latency and modularization vehicle rate of utilization to the very big degree improves convenience and the satisfaction that the passenger took the modularization vehicle trip, more closes to actual problem, improves practical application and worth.

The above description is only for the purpose of clearly illustrating the present invention, and is not intended to limit the embodiments of the present invention, and the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention will not be exhaustive of all the embodiments, so that all the changes and modifications obvious to the technical scheme of the present invention shall be covered within the scope of the present invention.