An intelligent transportation vehicle scheduling method that balances user and system requirements

Abstract

The present invention discloses an intelligent traffic vehicle scheduling method that balances user and system requirements, including: step 1, abstracting network modeling based on the traffic network of the target area; step 2, performing data sorting and cleaning of traffic data, road information, etc. based on the network conditions of the target area; step 3, constructing an objective function based on user equilibrium and system optimization; step 4, setting conditional constraints for road rationalization; step 5, allocating OD traffic based on a constrained multi-objective optimization algorithm to maximize the system-optimal and individual-optimal road traffic distribution. Starting from the perspective of traffic flow distribution and combining the constrained multi-objective optimization method, the present invention can effectively optimize the user optimal balance and system optimality in the traffic system at the same time, providing decision makers with more diverse decision-making options, thereby reducing urban road congestion.

Description

Technical Field

[0001] The present invention relates to the fields of complex intelligent transportation networks and evolutionary computing, and more particularly to an intelligent transportation vehicle scheduling method based on a constrained multi-objective optimization algorithm to balance user and system requirements. Background Art

[0002] With the continuous advancement of urbanization and the expansion of transportation networks, the efficient operation of road transportation networks has become a key factor in the sustainable development of modern cities. Currently, due to the continued growth in the number of vehicles, traffic congestion has an increasingly serious impact on our daily travel. This not only results in significant economic losses, but also increases fuel consumption due to congestion, further exacerbating air pollution and carbon emissions.

[0003] Nowadays, there are many advanced methods that can predict traffic flow more accurately. It is particularly necessary to rationally regulate traffic flow through travel statistics and policy guidance. Wardrop proposed two principles for the definition of traffic equilibrium, which laid the foundation for traffic flow distribution. Wardrop's first principle is also called User Equilibrium (UE), which means that when road users are clearly aware of the traffic status of the network and try to choose the shortest path, the network will reach a state of equilibrium. Wardrop's second principle is also called System Optimization (SO), which means that under the condition of system equilibrium, traffic flow on a congested road network should be distributed based on the minimum average or total travel cost. The first principle reflects a criterion for road users to choose routes, while the second principle reflects a network design goal.

[0004] In reality, users tend to choose the best travel option for themselves. After understanding traffic conditions, they tend to choose routes that are more favorable to them, that is, the shortest distance or the shortest time. This can be detrimental to the overall system. Therefore, allocating traffic flow to ensure that the shortest travel time is achieved for most users while also improving system road utilization and reducing congestion is a key issue in traffic planning and management.

[0005] Currently, traditional traffic flow allocation methods focus only on optimizing a single objective and are mainly divided into the following two categories: one is the Frank-Wolfe algorithm, and the other is the Gradient Projection algorithm. Both of these algorithms are mathematical programming algorithms. Although they can both calculate results well, they can only optimize for two single objectives: user equilibrium and system optimization. They are unable to reasonably balance these two conflicting objectives and find the optimal vehicle scheduling solution in the complex real-world situations. Summary of the Invention

[0006] In order to overcome the shortcomings of the existing technology, the present invention proposes an intelligent traffic vehicle scheduling method based on balancing user and system requirements, so as to achieve optimal user balance in the traffic network while minimizing system congestion, thereby better solving the traffic flow distribution optimization problem and reducing urban road congestion.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] The intelligent traffic vehicle dispatching method for balancing user and system requirements of the present invention is characterized in that it includes the following steps:

[0009] Step 1. Abstract each road intersection in the target area into a road node, and the roads between each road intersection into an edge, so as to abstractly model the target area and obtain the real traffic network graph G = (N,E) of the target area, where N represents the set of road nodes in the real traffic network graph G, and N = {n1, n2, … n i …n m}, where n i represents the i-th road node, m is the total number of road nodes; E represents the edge set in the real traffic network graph G, and E = {e1, e2,… e a … e M}, where e a represents the ath road in E; M represents the total number of roads;

[0010] Step 2. Calculate the ath road e in the real road network graph G a Maximum vehicle capacity C a , road a a Free-pass time FFT a and the ath road e a The travel time t a ;

[0011] Step 2.1. Calculate the ath road e using formula (1) a Maximum vehicle capacity C a :

[0012] C a =m a ×l a (1)

[0013] In formula (1), l a Indicates the ath road e a Length, m a Indicates the ath road e a the number of lanes on the

[0014] Step 2.2. Calculate the ath road e using formula (2) a Free-pass time FFT a :

[0015] (2)

[0016] In formula (2), v a Indicates the ath road e a speed limit;

[0017] Step 2.3. Calculate the ath road e according to formula (3) a The travel time t a :

[0018] (3)

[0019] In formula (3), flow a Indicates the ath road e a The actual traffic volume, and There are two fixed parameters;

[0020] Step 3. Build an intelligent traffic scheduling optimization model;

[0021] Step 3.1. Define the first objective function F1 of the intelligent traffic scheduling optimization model under vehicle equilibrium according to formula (4):

[0022] (4)

[0023] Step 3.2. Define the second objective function F2 of the intelligent traffic scheduling optimization model under the optimal traffic network condition according to formula (5):

[0024] (5)

[0025] Step 4. Establish road capacity constraints and real path constraints based on real traffic conditions.

[0026] Step 4.1: Use Equation (6) to establish the road capacity constraint of the intelligent traffic scheduling optimization model:

[0027]

[0028] Step 4.2. Establish real path constraints for the intelligent traffic scheduling optimization model, including: there is no loop structure in the real path, and the real path is a connected path from the source to the destination;

[0029] Step 5. Solve the intelligent traffic scheduling optimization model based on a constrained multi-objective optimization algorithm to obtain a set of vehicle scheduling solutions that balance user and system requirements.

[0030] Step 5.1. Randomly initialize and generate an initial population that satisfies the constraints.

[0031] Define and initialize the current number of iterations t = 0, and set the maximum number of iterations to t max , randomly initialize and generate P vehicle scheduling solutions that meet the constraints and form the t-th generation population ;in, is the t-th generation population S t The i-th individual in ;

[0032] Each individual is encoded as a Y×M binary number, representing the i-th vehicle dispatch plan that includes all vehicles in the real traffic network graph G; where Y represents the number of vehicles, and each vehicle in each individual corresponds to an M-bit binary number, where each bit in the M-bit binary number represents whether the corresponding vehicle is driving on the road at the corresponding bit;

[0033] Step 5.2. Calculate the population S of generation t t After calculating the crowding distance and target value of each individual in the population, the operator is selected from the t-th generation population S through a bidding competition. t Select parent individuals and use a simulated binary crossover operator to The parent individuals are crossed, and the remaining The parent individuals mutate, so that parent individuals and the mutated The parent individuals form the t-th generation intermediate population Q t ;

[0034] Step 5.3. For the intermediate population Q t Perform crossover and mutation operations to generate a new population Z of generation t t ;

[0035] Step 5.4. Set the tth generation population S t and the new population Z of generation t t After merging, the t-th generation joint population R with dimension 2P is formed t ;

[0036] Step 5.5. Calculate the joint population R of generation t t The crowding distance and target value of each individual in R are then sorted by non-dominated sorting. t Select P individuals that meet the road capacity constraint and the real path constraint to form the t+1 generation population S t+1 ;

[0037] Step 5.6. After assigning t+1 to t, return to step 5.2 until t>t max So far, we get the tth max Generation population ,

[0038] Step 5.7. After each individual in the non-dominated sort is performed, the Pareto optimal individuals are selected to form a set of optimal vehicle scheduling solutions S best , S best Each individual in is an optimal vehicle scheduling solution that balances user and system requirements.

[0039] The electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the intelligent traffic vehicle scheduling method, and the processor is configured to execute the program stored in the memory.

[0040] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program executes the steps of the intelligent traffic vehicle scheduling method when the computer program is executed by a processor.

[0041] Compared with the existing technology, the beneficial effects of the present invention are embodied in:

[0042] 1. This invention proposes a method that comprehensively considers both user and system needs. Traditional traffic flow allocation algorithms often focus on a single objective: user balance or system optimization. This results in neglecting one objective while optimizing the other, and fails to simultaneously meet the needs of individual users and the overall system. This invention, however, optimizes both user balance and system optimization simultaneously, improving the individual travel experience while achieving optimal efficiency for the entire transportation network, thereby achieving a more balanced and comprehensive optimization effect.

[0043] 2. This method improves overall transportation network efficiency. By comprehensively optimizing user balance and system optimization, it can effectively reduce overall travel time and fuel consumption, thereby reducing the economic losses and environmental pollution caused by traffic congestion. This method helps to rationally distribute traffic flow during peak hours, avoiding excessive congestion on some roads and underutilization on others, thereby improving the operational efficiency of the entire urban transportation network.

[0044] 3. This invention can flexibly adapt to different traffic conditions. The multi-objective optimization method offers greater flexibility and adaptability, enabling dynamic adjustments based on real-time traffic conditions and historical data. Compared to traditional algorithms with fixed rules or parameters, this method is more adaptable to the complex and ever-changing urban traffic environment. Through real-time optimization and regulation, it improves the responsiveness and accuracy of traffic management, ensuring optimal flow distribution under various traffic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Flowchart of the method of the present invention. DETAILED DESCRIPTION

[0046] In this embodiment, an intelligent traffic scheduling optimization method that balances user and system requirements is provided. Figure 1 As shown, the following steps are included:

[0047] Step 1. Abstract each road intersection in the target area into a road node, and the roads between each road intersection into an edge, so as to abstractly model the target area and obtain the real traffic network graph G = (N,E) of the target area, where N represents the set of road nodes in the real traffic network graph G, and N = {n1, n2, … n i …n m}, where n i represents the i-th road node, m is the total number of road nodes; E represents the edge set in the real traffic network graph G, and E = {e1, e2,… e a … e M}, where e a represents the ath road in E; M represents the total number of roads;

[0048] Step 2. Calculate the ath road e in the real road network graph G a Maximum vehicle capacity C a , road a a Free-pass time FFT a and the ath road e a The travel time t a ;

[0049] Step 2.1. Calculate the ath road e using formula (1) a Maximum vehicle capacity C a :

[0050] C a =m a ×l a (1)

[0051] In formula (1), l aIndicates the ath road e a Length, m a Indicates the ath road e a the number of lanes on the

[0052] Step 2.2. Calculate the ath road e using formula (2) a Free-pass time FFT a :

[0053] (2)

[0054] In formula (2), v a Indicates the ath road e a speed limit;

[0055] Step 2.3. Calculate the ath road e according to formula (3) a The travel time t a :

[0056] (3)

[0057] In formula (3), flow a Indicates the ath road e a The actual traffic volume, and There are two fixed parameters;

[0058] Step 3. Build an intelligent traffic scheduling optimization model;

[0059] Step 3.1. Define the first objective function F1 of the intelligent traffic scheduling optimization model under vehicle equilibrium according to formula (4):

[0060] (4)

[0061] Step 3.2. Define the second objective function F2 of the intelligent traffic scheduling optimization model under the optimal traffic network condition according to formula (5):

[0062] (5)

[0063] In formula (5), a represents a road in the network graph G. This means that the objective function F2 is calculated by accumulating the results for all roads in the network. The formulas in steps 3.2 and 3.3 are derived from the Beckmann transform based on Wardrop's first and second principles, further integrated, and combined with the formula for calculating travel time in step 2.

[0064] Step 4. Establish road capacity constraints and real path constraints based on real traffic conditions.

[0065] Step 4.1: Use Equation (6) to establish the road capacity constraint of the intelligent traffic scheduling optimization model:

[0066]

[0067] Step 4.2. Establish real path constraints for the intelligent traffic scheduling optimization model, including: there is no loop structure in the real path, and the real path is a connected path from the source to the destination;

[0068] Step 5. Solve the intelligent traffic scheduling optimization model based on a constrained multi-objective optimization algorithm to obtain a set of vehicle scheduling solutions that balance user and system requirements.

[0069] Step 5.1. Randomly initialize and generate an initial population that satisfies the constraints.

[0070] Define and initialize the current number of iterations t = 0, and set the maximum number of iterations to t max , randomly initialize and generate P vehicle scheduling solutions that meet the constraints and form the t-th generation population ;in, is the t-th generation population S t The i-th individual in ;

[0071] Each individual is encoded as a Y×M binary number, representing the i-th vehicle scheduling plan that includes all vehicles in the real traffic network graph G; where Y represents the number of vehicles, and each vehicle in each individual corresponds to an M-bit binary number, and each bit in the M-bit binary number represents whether the corresponding vehicle is driving on the road at the corresponding bit; the actual physical meaning of M is the number of roads in the section, and the M-bit binary number reflects a driving path of a vehicle in the section. Stacking M to form a Y×M binary number can reflect the formal path of each vehicle in a scheduling plan.

[0072] Step 5.2. Calculate the population S of generation t t After calculating the crowding distance and target value of each individual in the population, the operator is selected from the t-th generation population S through a bidding competition. t Select parent individuals and use a simulated binary crossover operator to The parent individuals are crossed, and the remaining The parent individuals mutate, so that parent individuals and the mutated The parent individuals form the t-th generation intermediate population Q t .

[0073] Step 5.3. For the intermediate population Q t Perform crossover and mutation operations to generate a new population Z of generation tt ;

[0074] Step 5.4. Set the tth generation population S t and the new population Z of generation t t After merging, the t-th generation joint population R with dimension 2P is formed t ;

[0075] Step 5.5. Calculate the joint population R of generation t t The crowding distance and target value of each individual in R are then sorted by non-dominated sorting. t Select P individuals that meet the road capacity constraint and the real path constraint to form the t+1 generation population S t+1 .

[0076] Step 5.6. After assigning t+1 to t, return to step 5.2 until t>t max So far, we get the tth max Generation population ,

[0077] Step 5.7. After each individual in the non-dominated sort is performed, the Pareto optimal individuals are selected to form a set of optimal vehicle scheduling solutions S best , S best Each individual in is an optimal vehicle scheduling solution that balances user and system requirements.

[0078] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0079] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

Claims

1. An intelligent transportation vehicle scheduling method that balances user and system requirements, characterized in that: The steps include: Step 1. Abstract each road intersection in the target area into a road node, and the roads between each road intersection into an edge, so as to abstractly model the target area and obtain the real traffic network graph G = (N,E) of the target area, where N represents the set of road nodes in the real traffic network graph G, and N = {n1, n2, … n i …n m }, where n i represents the i-th road node, m is the total number of road nodes; E represents the edge set in the real traffic network graph G, and E = {e1, e2, …e a … e M }, where e a represents the ath road in E; M represents the total number of roads; Step 2. Calculate the ath road e in the real road network graph G a Maximum vehicle capacity C a , road a a Free-pass time FFT a and the ath road e a The travel time t a ; Step 3. Build an intelligent traffic scheduling optimization model; Step 3.

1. Define the first objective function F1 of the intelligent traffic scheduling optimization model under vehicle equilibrium according to formula (4): (4) In formula (4), flow a Indicates the ath road e a The actual traffic volume, and There are two fixed parameters; Step 3.

2. Define the second objective function F2 of the intelligent traffic scheduling optimization model under the optimal traffic network condition according to formula (5): (5) Step 4. Establish road capacity constraints and real path constraints based on real traffic conditions. Step 4.1: Use Equation (6) to establish the road capacity constraint of the intelligent traffic scheduling optimization model: (6) Step 4.

2. Establish real path constraints for the intelligent traffic scheduling optimization model, including: there is no loop structure in the real path, and the real path is a connected path from the source to the destination; Step 5. Solve the intelligent traffic scheduling optimization model based on a constrained multi-objective optimization algorithm to obtain a set of vehicle scheduling solutions that balance user and system requirements. Step 5.

1. Randomly initialize and generate an initial population that satisfies the constraints. Define and initialize the current number of iterations t = 0, and set the maximum number of iterations to t max , randomly initialize and generate P vehicle scheduling solutions that meet the constraints and form the t-th generation population ;in, is the t-th generation population S t The i-th individual in ; Each individual is encoded as a Y×M binary number, representing the i-th vehicle dispatch plan that includes all vehicles in the real traffic network graph G; where Y represents the number of vehicles, and each vehicle in each individual corresponds to an M-bit binary number, where each bit in the M-bit binary number represents whether the corresponding vehicle is driving on the road at the corresponding bit; Step 5.

2. Calculate the population S of generation t t After calculating the crowding distance and target value of each individual in the population, the operator is selected from the t-th generation population S through a bidding competition. t Select parent individuals and use a simulated binary crossover operator to The parent individuals are crossed, and the remaining The parent individuals mutate, so that parent individuals and the mutated The parent individuals form the t-th generation intermediate population Q t ; Step 5.

3. For the intermediate population Q t Perform crossover and mutation operations to generate a new population Z of generation t t ; Step 5.

4. Set the tth generation population S t and the new population Z of generation t t After merging, the t-th generation joint population R with dimension 2P is formed t ; Step 5.

5. Calculate the joint population R of generation t t The crowding distance and target value of each individual in R are then sorted by non-dominated sorting. t Select P individuals that meet the road capacity constraint and the real path constraint to form the t+1 generation population S t+1 ; Step 5.

6. After assigning t+1 to t, return to step 5.2 until t>t max So far, we get the tth max Generation population , Step 5.

7. After each individual in the non-dominated sort is performed, the Pareto optimal individuals are selected to form a set of optimal vehicle scheduling solutions S best , S best Each individual in is an optimal vehicle scheduling solution that balances user and system requirements.

2. The intelligent transportation vehicle scheduling method for balancing user and system requirements according to claim 1, characterized in that: The step 2 comprises the following steps: Step 2.

1. Calculate the ath road e using formula (1) a Maximum vehicle capacity C a : C a =m a ×l a (1) In formula (1), l a Indicates the ath road e a Length, m a Indicates the ath road e a the number of lanes on the Step 2.

2. Calculate the ath road e using formula (2) a Free-pass time FFT a : (2) In formula (2), v a Indicates the ath road e a speed limit; Step 2.

3. Calculate the ath road e according to formula (3) a The travel time t a : (3)。 3. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the intelligent transportation vehicle scheduling method according to any one of claims 1-2, and the processor is configured to execute the program stored in the memory.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent transportation vehicle scheduling method according to any one of claims 1 to 2 are executed.

Images