Route traffic flow induction method considering crowd effect of travelers

Abstract

The invention provides a path traffic flow induction method considering the crowd effect of travelers. A plurality of paths are selected as induction paths, path subsidy information is provided for part of travelers, the part of travelers are attracted to select the induction paths to go out, and other travelers are guided to select the induction paths under the influence of the conformity effect. Therefore, a bilevel planning model is constructed as a traffic flow regulation and control model, the upper layer model takes the minimization of the total travel time cost and the path subsidy cost of the system as a target function, and the lower layer model is a dynamic evolution model considering the conformity effect under the given induction path. Through genetic algorithm solution and instance analysis, the model can effectively reduce the total travel time cost of the whole network system and alleviate traffic congestion to a certain extent. Meanwhile, due to the existence of the crowd effect, the subsidy cost can be reduced while the total travel time cost of a road network system is reduced, and the effect of the traffic flow regulation and control strategy based on path subsidy is further improved.

Description

Technical Field

[0001] The invention belongs to a traffic flow induction method, and in particular relates to a path traffic flow induction method taking into account the herd effect of travelers. Background Art

[0002] In the past decade, the number of motor vehicles in my country has been rising steadily. The huge number of motor vehicles has caused tremendous pressure on my country's urban traffic. Urban traffic congestion has become a major problem that needs to be solved urgently. The traffic flow control strategy aims to optimize the spatiotemporal distribution of traffic flow on the road network, and to achieve effective relief of traffic congestion by scientifically and rationally adjusting the traffic flow state, thus becoming an important means to improve traffic conditions. In order to better formulate traffic flow control strategies and induce residents to choose appropriate routes for travel, the government can provide appropriate transportation subsidies. Traffic subsidies refer to any funding or benefit subsidies given by the government to certain specific enterprises, industries or individuals. Appropriate transportation subsidies can attract people to choose routes that are more beneficial to the entire transportation system, thereby alleviating traffic congestion. However, excessive transportation subsidies will put pressure on government finances, so the present invention considers further improving the effect of subsidies through the herd effect. The herd effect refers to the psychological change process in which people consciously or unconsciously use the opinions of the majority as a criterion to make judgments and form impressions. In the field of transportation, the travel behavior of travelers is also affected by the herd effect. Therefore, the government only needs to provide transportation subsidies to some travelers, and other travelers will follow the crowd and choose travel routes that are more beneficial to the entire transportation system due to the herd effect, thereby reducing the total travel time cost of the road network system, alleviating traffic congestion and reducing transportation subsidy costs.

[0003] Regarding the research field of the present invention, the problems and defects of existing research are as follows: Currently in the field of transportation science, the relevant research on the herd effect mainly focuses on the two aspects of "evacuation of dense crowds" and "pedestrians crossing the street illegally", while the impact of the herd effect on travelers' path selection has not been systematically studied. In terms of traffic congestion charges and subsidy strategies, most studies are aimed at traffic congestion charges, while there are fewer studies on subsidy strategies, and fewer studies on path subsidy mechanisms. Based on the above shortcomings, the present invention first considers the establishment of a traffic flow dynamic evolution model that takes into account the herd effect, and on this basis, studies the traffic flow control strategy based on path subsidies, aiming to optimize the path flow distribution, realize the effective utilization of road resources in the transportation network, and alleviate traffic congestion. The present invention is helpful to provide important reference basis and guiding value for relevant government departments to scientifically and rationally formulate traffic flow control strategies. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present invention provides a path traffic flow induction method taking into account the herd effect of travelers.

[0005] Technical solution: A path traffic flow induction method considering the herd effect of travelers of the present invention comprises the following steps:

[0006] The present invention assumes that the government only provides path subsidy information to some travelers in the transportation network. The government first selects several paths that have the potential to achieve system optimization as induced paths, and then releases path subsidy information to some travelers through information platforms and other means. Under the incentive of the path subsidy information, a considerable number of these travelers will choose induced paths. Under the influence of the herd effect, the remaining travelers will follow suit and choose induced paths, thereby achieving the purpose of optimizing the traffic distribution on the transportation network. It is worth pointing out that only travelers who have received path subsidy information and choose to travel on induced paths will receive corresponding subsidies; travelers who have not received path subsidy information will not receive subsidies even if they choose induced paths. In this case, the government does not need to provide subsidies to all travelers who choose induced paths, and can achieve a better traffic flow control effect, thereby effectively reducing the government's path subsidy costs.

[0007] Under the above assumptions, step 1, based on the relevant theories of the Logit type random user equilibrium model, considering that travelers are simultaneously affected by the bandwagon effect and path subsidy information, a generalized travel impedance function considering the bandwagon effect under the induced path is defined; step 2, based on the travel impedance function, a dynamic evolution model of traffic flow considering the bandwagon effect under the induced path is determined; step 3, taking the dynamic evolution model as the lower model, and taking minimizing the total travel time cost of the system and the path subsidy cost as the objective function as the upper model, a two-level programming model for traffic flow control is established; step 4, a genetic algorithm is designed to solve the two-level programming model to quickly and accurately solve the above problem.

[0008] In step 1, according to the relevant theories of the Logit type stochastic user equilibrium model, when travelers are affected by both the herd effect and the path subsidy information, the generalized travel impedance function considering the herd effect under the induced path is defined as:

[0009]

[0010] For travelers who do not receive path subsidy information, the generalized travel impedance function is:

[0011]

[0012] in is the actual travel time of path r in the k+1th stage, r∈R w ,w∈W, is defined as the "attractiveness" of path r to travelers in the k+1th stage, β is an important parameter that relates the "attractiveness" of the path and the travel time of the path, θ is a parameter greater than 0, which is used to describe the familiarity of travelers in the traffic network with the road network, and is inversely proportional to the familiarity of travelers with the road network, φ is an important parameter for converting the path subsidy into the generalized travel impedance of the path, and ρ rw is a relation variable. If the path r between OD pairs w∈W is an induced path, then ρ rw is 1, otherwise it is 0, χ is the unit subsidy standard of the induced path, L rw is the path length of path r between OD pair w∈W, ε rw is a random term in the utility function;

[0013] It is easy to know that this problem is a convex programming problem. According to the relevant theory of the Logit model, the optimal solution of the problem (f 1,rw ,f 2,rw ) is unique; therefore, if the travelers in the k+1th stage choose the path with the smallest generalized travel impedance according to the travel impedance function defined above, the traffic flow solution when the system reaches a stable state at the end of this stage is the minimum point of the model.

[0014] In step 2, specifically: determine the traffic flow dynamic evolution model considering the herd effect under the induced path;

[0015] According to the travel impedance function determined in claim 2, the dynamic evolution process and dynamic evolution model of traffic flow can be further derived.

[0016] The specific expression of the dynamic evolution process framework of traffic flow considering the herd effect under the induced path can be simplified as follows:

[0017]

[0018] Where Ω is:

[0019]

[0020] in, and They represent the path flow of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the system reaches a steady state at the end of the k+1th stage, respectively. f is the path flow, f∈Ω, and f 1,rw and f 2,rw They represent the path flows of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the system reaches a steady state at the end of the k+1th stage, respectively. A is the set of road segments, a∈A, W is the set of OD pairs generated by travel demand between OD pairs, w∈W, R w is the set of feasible paths between OD pairs w, r∈Rw , x a is the traffic flow on road section a, t a is the travel impedance of section a, δ arw is a relation variable. If the road segment a is in the path r between the OD pair w, then δ arw is 1, otherwise it is 0, ψ is the proportion of travelers who receive path subsidy information, q w is the travel demand between OD pairs w∈W.

[0021] Step 3 is as follows: construct a two-level planning model for traffic flow control under the induced path considering the herd effect;

[0022] A two-level planning model for traffic flow regulation considering the herd effect under the induced path is constructed. By optimizing the proportion of travelers who receive path subsidy information (referred to as the subsidy ratio) and the path subsidy standard, a better traffic flow regulation effect is achieved while the path subsidy cost is relatively small.

[0023] The lower model of the two-level planning model for traffic flow control is an evolution model of traffic flow that follows the dynamic evolution law of traffic flow under the herd effect (i.e., the model constructed in step 2); the upper model of the two-level planning model for traffic flow control takes minimizing the total travel time cost and path subsidy cost of the system as the objective function, and the cost is calculated based on the dynamic evolution result of traffic flow in the lower model. At the same time, the government's path subsidy cost budget constraint must be considered to ensure that the path subsidy cost is lower than the government's path subsidy cost budget;

[0024] Upper model:

[0025] The objective function of the upper model consists of two items. The first item is the total travel time cost of the system, which is defined here as the sum of all path flows multiplied by path impedance. The second item is the path subsidy cost. At the same time, the government's path subsidy cost budget constraint must be considered to ensure that the path subsidy cost is lower than the government's path subsidy cost budget. In summary, the upper model of the two-level planning model for traffic flow control can be expressed by the following formula:

[0026]

[0027] The constraints are:

[0028]

[0029] where f 1,rw and f 2,rw are the path flows of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the lower model, i.e., the dynamic evolution model of traffic flow considering the herd effect under the induced path, finally reaches a steady state under the traffic flow control strategy of the upper model. λ is an important parameter for converting path subsidy cost into time cost. ρrw is a relation variable. If the path r between OD pairs w∈W is an induced path, then ρ rw =1, otherwise 0, χ is the path unit subsidy standard, its unit is χ yuan / km, D is the upper limit of the path subsidy cost; Formula (6) represents the constraint of the path subsidy cost, ensuring that the path subsidy cost is lower than the government's path subsidy cost budget; Formula (7) represents that the time equivalent cost of the path subsidy on any path is not greater than the travel time of the path;

[0030] Lower model:

[0031] The lower model is a traffic flow dynamic evolution model considering the herd effect under a given induced path; where (f1, f2) is the path flow obtained after the following traffic flow dynamic evolution model has evolved over a period of time:

[0032]

[0033] in

[0034]

[0035] Where ψ is the proportion of travelers who receive path subsidy information, i.e., the subsidy ratio, q w is the traffic demand between OD pairs w∈W;

[0036] In summary, the traffic flow control strategy considering the herd effect under the induction path in the present invention is to solve the two-level programming problem that satisfies the upper-level goal as formula (6) and the lower-level model is the traffic flow dynamic evolution model considering the herd effect under the induction path.

[0037] Step 4 is specifically as follows: solving the two-level programming model of traffic flow control considering the herd effect under the induced path;

[0038] A genetic algorithm is used to solve the bi-level programming model of traffic flow control under the induced path considering the herd effect (i.e., the model constructed in step 2); the algorithm can improve the convergence efficiency of the algorithm iteration and solve the above problem quickly and accurately. The specific steps are as follows:

[0039] Step 4-1: Perform double-layer encoding on the traffic flow control problem considering the herd effect under the induced path, and generate an initial population to form an initial traffic flow induction scheme candidate set;

[0040] The genetic algorithm maps the solution of the optimization problem into individuals for iterative search through gene coding. Combined with the specific situation of the constructed two-level planning model for traffic flow control, a two-level coding method is used for chromosomes, and each level represents the path unit subsidy standard and the proportion of travelers who receive path subsidy information. To facilitate the design of genetic operations such as crossover and mutation, binary sequences are used to represent related real numbers, and all genotypes are composed of a binary symbol set {0,1}. Taking the path unit subsidy standard as an example, the solution space of the actual problem is recorded as [a,b], and the chromosome length is set to N, then the search space is {0,1,2L,2 N -1}; if the binary gene sequence is converted into a decimal number, it is n, then the path unit subsidy standard represented by it after decoding is The same is true for the proportion of travelers who received path subsidy information;

[0041] Take the population size as g, generate the initial population according to the range of the path unit subsidy standard and the proportion of travelers who receive path subsidy information (i.e., subsidy ratio), and set the selection crossover probability P of the genetic algorithm. c and genetic variation probability P m , set the maximum number of iterations Maxgen;

[0042] Step 4-2: Pass the chromosome to the lower model;

[0043] Extract the path unit subsidy standard represented by the chromosome in the population and the proportion of travelers who receive path subsidy information (i.e., subsidy ratio), and solve the traffic flow dynamic evolution model considering the herd effect under the current path unit subsidy standard and subsidy ratio until it finally reaches the steady state solution, i.e., the path flow solution for the two types of travelers;

[0044] Step 4-3: The lower model feeds back the traffic flow evolution results to the upper model;

[0045] Step 4-4: Calculate the fitness function value according to the traffic flow dynamic evolution result of the lower model;

[0046] The objective function of the upper model of the two-level programming model for traffic flow control under the induced path considering the herd effect is to minimize the sum of the total travel time cost of the system and the total path subsidy cost. It is easy to know that the value of the objective function is always positive, so the inverse of the objective function value can be mapped to the fitness value. The smaller the objective function value, the greater the fitness value. At the same time, in order to ensure that the path subsidy cost is lower than a certain value, when the path subsidy cost is higher than a certain value, a penalty function is added to make its fitness value smaller. According to the principle of genetic algorithm, the individual with a larger fitness value is selected as the parent and inherited to the next generation. On the contrary, the individual with a smaller fitness value will be gradually eliminated as the population continues to evolve and develop. The fitness function Fit is shown in formula (10):

[0047]

[0048] Among them, F is the penalty function, flag is the relationship variable, if the path subsidy cost is greater than D, then flag is 1, otherwise flag is 0;

[0049] Step 4-5: Roulette wheel selection strategy;

[0050] The roulette wheel selection strategy is used to select the better individuals in the population to form a new population;

[0051] According to the individual fitness value calculated in the above formula, the selection probability of individuals in a population of size g is further determined, and the calculation formula is:

[0052]

[0053] Among them, P i is the selection probability of individuals in the population, Fit i is the fitness value of individuals in the population;

[0054] Calculate the cumulative probability Q of individuals in the population i , and its calculation formula is:

[0055]

[0056] Generate g uniformly distributed random numbers ζ in [0,1], if Q i ≤ζ≤Q i+1 , then select individual i;

[0057] Step 4-6: Crossover operation;

[0058] Using the two-point crossover method, with a certain crossover probability P c , randomly set two crossover points in the coding strings of two chromosomes that are paired with each other in the parent population, and exchange the parts of the two chromosomes between the two set crossover points;

[0059] Steps 4-7: genetic variation operations;

[0060] Using the basic bit mutation method, with a certain mutation probability P m , generate a random number r∈[0,1] in the chromosome of each offspring in the crossover offspring set, if r≤P m , then randomly mutate the value of the number or some bits on the chromosome. For the individuals represented by binary coded symbol strings in this chapter, if the value of a gene to be mutated is 0, the mutation operation will change it to 1; conversely, if the original gene value is 1, the mutation operation will change it to 0;

[0061] Repeat steps 4-2 to 4-7 until the number of iterations of the genetic algorithm reaches the set maximum number of iterations Maxgen, then stop running and output the optimal path unit subsidy standard and optimal subsidy ratio.

[0062] In summary, the present invention proposes a path traffic flow induction method that takes into account the herd effect of travelers, and constructs a two-level planning model for traffic flow regulation. The model selects several paths as induced paths, and provides path subsidy information to a part of travelers, so as to attract these travelers to choose the induced paths, and guide the remaining travelers to choose the induced paths under the influence of the herd effect. In this two-level planning model, the upper model takes minimizing the total travel time cost of the system and the path subsidy cost as the objective function, and the lower model is a dynamic evolution model that considers the herd effect under a given induced path. The model is solved by a genetic algorithm, and the results show that the traffic flow regulation model that considers the herd effect can effectively reduce the total travel time cost of the entire network system and alleviate traffic congestion to a certain extent. At the same time, the existence of the herd effect can reduce the subsidy cost while reducing the total travel time cost of the road network system, further improving the effect of the traffic flow regulation strategy based on path subsidies.

[0063] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0064] (1) This paper studies the impact of the herd effect on travelers' path selection and points out that the herd mentality of travelers in a transportation network will lead travelers to tend to choose the path chosen by the majority of people in the group. It also finds that the existence of the herd effect can further enhance the effect of path subsidies.

[0065] (2) Traditional traffic charging / subsidy strategies are usually based on road section charging / subsidy strategies. The present invention uses the discovery that the bandwagon effect can further enhance the effect of path subsidies, proposes a traffic flow control strategy based on path subsidies under the bandwagon effect, and explores the rationality and feasibility of the strategy.

[0066] (3) The present invention establishes a two-level planning model for traffic flow regulation under the induced path considering the herd effect. The model provides path subsidy information to some travelers to attract them to choose the induced path, and guides the remaining travelers to choose the induced path under the influence of the herd effect, so as to achieve the traffic flow regulation effect while reducing the path subsidy cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flow chart of a path traffic flow induction method considering the herd effect of travelers provided by the present invention;

[0068] Figure 2A genetic algorithm solution flow chart of a double-level programming model for traffic flow control considering the herd effect under the induced path provided by the present invention;

[0069] Figure 3 This is a schematic diagram of a 9-node road network model in Example 1 of the present invention;

[0070] Figure 4 Schematic diagram of the iterative process of the optimal objective function value of the genetic algorithm;

[0071] Figure 5 Schematic diagram of the relationship between the total travel time cost of the system, the path subsidy cost and λ. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only for illustration and are not intended to limit the present invention.

[0073] The present invention provides a path traffic flow induction method considering the herd effect of travelers, such as Figure 1 As shown, the following steps are included:

[0074] The present invention assumes that the government only provides path subsidy information to some travelers in the transportation network. The government first selects several paths that have the potential to achieve system optimization as induced paths, and then releases path subsidy information to some travelers through information platforms and other means. Under the incentive of the path subsidy information, a considerable number of these travelers will choose induced paths. Under the influence of the herd effect, the remaining travelers will follow suit and choose induced paths, thereby achieving the purpose of optimizing the traffic distribution on the transportation network. It is worth pointing out that only travelers who have received path subsidy information and choose to travel on induced paths will receive corresponding subsidies; travelers who have not received path subsidy information will not receive subsidies even if they choose induced paths. In this case, the government does not need to provide subsidies to all travelers who choose induced paths, and can achieve a better traffic flow control effect, thereby effectively reducing the government's path subsidy costs.

[0075] Given a transportation network G(N,A), where N is defined as the set of nodes in the network, A is the set of directed arcs (i.e., road sections) in the network, and W represents the set of OD pairs (Origin and Destination, the origin and destination are referred to as OD points) generated by travel demand.

[0076] Step 1: Define the generalized travel impedance function considering the herd effect under the induced path.

[0077] According to the relevant theories of the classic Logit type random user equilibrium model, when travelers are affected by both the herd effect and the path subsidy information, the present invention defines the generalized travel impedance function as:

[0078]

[0079] For travelers who do not receive path subsidy information, the generalized travel impedance function is:

[0080]

[0081] in is the k+1th stage path r(r∈R w ,w∈W), is defined as the "attractiveness" of path r to travelers in the k+1th stage, β is an important parameter that relates the "attractiveness" of the path and the travel time of the path, θ is a parameter greater than 0, which is used to describe the familiarity of travelers in the traffic network with the road network, and is inversely proportional to the familiarity of travelers with the road network, φ is an important parameter for converting the path subsidy into the generalized travel impedance of the path, and ρ rw is a relation variable. If the path r between OD pairs w∈W is an induced path, then ρ rw is 1, otherwise it is 0, χ is the unit subsidy standard of the induced path, L rw is the path length of path r between OD pair w∈W, ε rw is a random term in the utility function.

[0082] It is easy to know that this problem is a convex programming problem. According to the relevant theory of the Logit model, the optimal solution of the problem (f 1,rw ,f 2,rw ) is unique. Therefore, if the travelers in the k+1th stage choose the path with the minimum generalized travel impedance according to the travel impedance function defined above, the traffic flow solution when the system reaches a stable state at the end of this stage is the minimum point of the model.

[0083] Step 2: Determine the traffic flow dynamic evolution model considering the herd effect under the induced path.

[0084] According to the travel impedance function determined in step 1, the dynamic evolution process and dynamic evolution model of traffic flow can be further derived. The specific expression of the dynamic evolution process framework of traffic flow considering the herd effect under the induced path defined by the present invention can be simplified as follows:

[0085]

[0086] Where Ω is:

[0087]

[0088] in, and They represent the path flow of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the system reaches a steady state at the end of the k+1th stage, respectively. f is the path flow, f∈Ω, and f 1,rw and f 2,rw They represent the path flows of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the system reaches a steady state at the end of the k+1th stage, respectively. A is the set of road segments, a∈A, W is the set of OD pairs generated by travel demand between OD pairs, w∈W, R w is the set of feasible paths between OD pairs w, r∈R w , x a is the traffic flow on road section a, t a is the travel impedance of section a, δ arw is a relation variable. If the road segment a is in the path r between the OD pair w, then δ arw is 1, otherwise it is 0, ψ is the proportion of travelers who receive path subsidy information, q w is the travel demand between OD pairs w∈W.

[0089] Step 3: Construct a two-level planning model for traffic flow control under the induced path considering the herd effect.

[0090] The present invention constructs a two-level planning model for traffic flow regulation under an induced path taking into account the herd effect. By optimizing the proportion of travelers who receive path subsidy information (referred to as the subsidy proportion) and the path subsidy standard, a better traffic flow regulation effect is achieved while the path subsidy cost is relatively small.

[0091] The lower model of the two-level programming model for traffic flow control is an evolution model of traffic flow that follows the dynamic evolution law of traffic flow under the herd effect (i.e., the model constructed in step 2). The upper model of the two-level programming model for traffic flow control takes minimizing the total travel time cost and path subsidy cost of the system as the objective function, and the cost is calculated based on the dynamic evolution results of traffic flow in the lower model. At the same time, the government's path subsidy cost budget constraint must be considered to ensure that the path subsidy cost is lower than the government's path subsidy cost budget.

[0092] Upper model:

[0093] The objective function of the upper model consists of two items. The first item is the total travel time cost of the system, which is defined here as the sum of all path flows multiplied by path impedance; the second item is the path subsidy cost, and the government's path subsidy cost budget constraint must be considered to ensure that the path subsidy cost is lower than the government's path subsidy cost budget. In summary, the following formula can be used to represent the upper model of the two-level planning model for traffic flow control:

[0094]

[0095] The constraints are:

[0096]

[0097] where f 1,rw and f 2,rw are the path flows of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the lower model, i.e., the dynamic evolution model of traffic flow considering the herd effect under the induced path, finally reaches a steady state under the traffic flow control strategy of the upper model. λ is an important parameter for converting path subsidy cost into time cost. ρ rw is a relation variable. If the path r between OD pairs w∈W is an induced path, then ρ rw =1, otherwise 0, χ is the path unit subsidy standard, its unit is χ yuan / km, and D is the upper limit of the path subsidy cost. Formula (6) represents the constraint of the path subsidy cost, ensuring that the path subsidy cost is lower than the government's path subsidy cost budget; Formula (7) indicates that the time equivalent cost of the path subsidy on any path is not greater than the travel time of the path.

[0098] Lower model:

[0099] The lower model is a traffic flow dynamic evolution model considering the herd effect under a given induced path. Where (f1, f2) is the path flow obtained after the following traffic flow dynamic evolution model evolves over a period of time:

[0100]

[0101] in

[0102]

[0103] Where ψ is the proportion of travelers who receive path subsidy information, i.e., the subsidy ratio, q w is the traffic demand between OD pairs w∈W.

[0104] In summary, the traffic flow control strategy considering the herd effect under the induction path in the present invention is to solve the two-level programming problem that satisfies the upper-level goal as formula (6) and the lower-level model is the traffic flow dynamic evolution model considering the herd effect under the induction path.

[0105] Step 4: Solve the two-level programming model of traffic flow control considering the herd effect under the induced path.

[0106] The present invention proposes a genetic algorithm for solving a two-level programming model of traffic flow control under an inductive path taking into account the herd effect. The algorithm can improve the convergence efficiency of algorithm iteration and solve the above problem quickly and accurately.

[0107] The genetic algorithm solution flow chart adopted by the present invention is as follows: Figure 2 As shown, the specific steps are as follows:

[0108] Step 4-1: Perform double-layer encoding on the traffic flow control problem considering the herd effect under the induced path, and generate an initial population to form an initial traffic flow induction scheme candidate set;

[0109] The genetic algorithm maps the solution of the optimization problem into individuals for iterative search through gene encoding. The present invention combines the specific situation of the constructed two-level planning model for traffic flow control (i.e., the model constructed in step 2) and adopts a two-level encoding method for chromosomes. Each layer represents the path unit subsidy standard and the proportion of travelers who receive path subsidy information. In order to facilitate the design of genetic operations such as crossover and mutation, binary sequences are used to represent related real numbers, and all genotypes are composed of a binary symbol set {0,1}. Taking the path unit subsidy standard as an example, the solution space of the actual problem is denoted as [a,b], and the chromosome length is set to N, then the search space is {0,1,2L,2 N -1}. If the binary gene sequence is converted to a decimal number of n, then the path unit subsidy standard represented by it after decoding is The same is true for the proportion of travelers who receive route subsidy information.

[0110] Take the population size as g, generate the initial population according to the range of the path unit subsidy standard and the proportion of travelers who receive path subsidy information (i.e., subsidy ratio), and set the selection crossover probability P of the genetic algorithm. c and genetic variation probability P m , set the maximum number of iterations Maxgen.

[0111] Step 4-2: Pass the chromosome to the lower model;

[0112] The path unit subsidy standard represented by the chromosomes in the population and the proportion of travelers who receive path subsidy information (i.e., subsidy ratio) are extracted, and the solution of the traffic flow dynamic evolution model considering the herd effect under the current path unit subsidy standard and subsidy ratio is obtained when it finally reaches a steady state, that is, the path flow solution for the two types of travelers.

[0113] Step 4-3: The lower model feeds back the traffic flow evolution results to the upper model;

[0114] Step 4-4: Calculate the fitness function value according to the traffic flow dynamic evolution result of the lower model;

[0115] The objective function of the upper model of the two-level planning model for traffic flow control under the induced path considering the herd effect is to minimize the sum of the total travel time cost of the system and the total path subsidy cost. It is easy to know that the value of the objective function is always positive, so the inverse of the objective function value can be mapped to the fitness value. The smaller the objective function value, the greater the fitness value. At the same time, in order to ensure that the path subsidy cost is lower than a certain value, when the path subsidy cost is higher than a certain value, a penalty function is added to reduce its fitness value. According to the principle of genetic algorithm, the greater the fitness value of an individual, the greater the probability of being selected as a parent to be inherited to the next generation. On the contrary, individuals with smaller fitness values ​​will be gradually eliminated as the population continues to evolve and develop. The fitness function Fit is shown in formula (10):

[0116]

[0117] Among them, F is the penalty function, flag is the relationship variable, if the path subsidy cost is greater than D, then flag is 1, otherwise flag is 0.

[0118] Step 4-5: Roulette wheel selection strategy.

[0119] The present invention adopts the roulette selection strategy to screen out relatively excellent individuals in a population to form a new population.

[0120] According to the individual fitness value calculated in the above formula, the selection probability of individuals in a population of size g is further determined, and the calculation formula is:

[0121]

[0122] Among them, P i is the selection probability of individuals in the population, Fit i is the fitness value of individuals in the population.

[0123] Calculate the cumulative probability Q of individuals in the population i , and its calculation formula is:

[0124]

[0125] Generate g uniformly distributed random numbers ζ in [0,1], if Q i ≤ζ≤Q i+1 , then select individual i.

[0126] Steps 4-6: Crossover operation.

[0127] Using the two-point crossover method, with a certain crossover probability P c , two crossover points are randomly set in the coding strings of two chromosomes that are paired with each other in the parent population, and part of the chromosomes of the two chromosomes between the two set crossover points are exchanged.

[0128] Steps 4-7: Genetic variation operation.

[0129] Using the basic bit mutation method, with a certain mutation probability P m , generate a random number r∈[0,1] in the chromosome of each offspring in the crossover offspring set, if r≤P m , then randomly mutate the value of the first or some bits on the chromosome. For the individuals represented by binary coded symbol strings in this chapter, if the value of a gene to be mutated is 0, the mutation operation will change it to 1; conversely, if the original gene value is 1, the mutation operation will change it to 0.

[0130] Repeat steps 4-2 to 4-7 until the number of iterations of the genetic algorithm reaches the set maximum number of iterations Maxgen, then stop running and output the optimal path unit subsidy standard and optimal subsidy ratio.

[0131] Embodiment 1:

[0132] The present invention adopts Figure 3 A 9-node road network is given as the research object to verify the traffic flow control double-layer programming model considering the herd effect under a given induced path constructed by the present invention and the designed genetic algorithm. Figure 3 It is a small transportation network, which consists of 9 nodes, 18 road sections and 96 paths. The traffic capacity between each road section is shown in the figure.

[0133] There are 4 OD pairs in the 9-node traffic network, namely (1,3), (1,4), (2,3), (2,4), and the traffic demand of each OD pair is 10, 30, 20, and 40 respectively. There are 96 valid paths in the network, of which each OD pair has 24 paths. The BPR form followed by the road segment travel time function.

[0134] The present invention runs a genetic algorithm solution code written based on Matlab on a personal computer with Intel Core i5 CPU @ 3.6 GHz and 16 GB of RAM to obtain the optimal path unit subsidy standard corresponding to the optimal solution and the proportion of travelers who receive path subsidy information (i.e., subsidy ratio). Set the number of categories to 20 and the maximum number of iterations to 200. Set the penalty coefficient F = 10 -10 , the crossover probability is 0.8, and the mutation probability is 0.05. Take λ=0.001, φ=0.1 as an example to solve the solution of the bi-level programming model.

[0135] The relationship between the objective function value and the number of iterations is as follows: Figure 4As shown in Table 1, the objective function value decreases with the increase of the number of iterations, and converges to the optimal solution in about 65 generations. In general, the genetic algorithm converges quickly and has a good optimization effect. The optimal path unit subsidy standard is 2.252 yuan / km, and the subsidy ratio is 81.89%. The subsidy cost of each induced path is shown in Table 1. The relationship between the total travel time cost of the system, the path subsidy cost and λ is shown in Table 1. Figure 5 shown.

[0136] Table 1 Subsidy costs of the induced path

[0137]

[0138]

[0139] The results of Example 1 show that under the traffic flow control model that considers the herd effect under a given induction path, when the traffic network reaches a steady state, its system total travel time cost is reduced by 15.18% compared with the system total travel time cost under the random user equilibrium state under the same conditions, indicating that the model can effectively reduce the system total travel time cost of the entire traffic network and alleviate traffic congestion to a certain extent. At the same time, compared with the traffic flow control model that does not consider the herd effect under a given induction path, under the traffic flow control model that considers the herd effect under a given induction path, when the traffic network reaches a steady state, its system total travel time cost is reduced by 0.79%, and the path subsidy cost is reduced by 5.22%. This shows that the existence of the herd effect can reduce the total travel time cost of the road network system while reducing the path subsidy cost, further enhancing the effect of the traffic flow control strategy based on path subsidy.

[0140] In summary, the traffic flow control method considering the herd effect under a given induction path proposed in the present invention has been verified through example verification and analysis, and the feasibility and operability of the method have been verified, which is of great significance.

[0141] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A path traffic flow induction method considering the herd effect of travelers, characterized in that: The steps include: Assume that the government only provides path subsidy information to some travelers in the transportation network; the government first selects several paths that have the potential to achieve system optimization as induced paths, and then releases path subsidy information to some travelers through information platforms and other means; under the incentive of path subsidy information, a considerable number of these travelers will choose induced paths; under the influence of the herd effect, the remaining travelers will follow suit and choose induced paths, thus achieving the goal of optimizing traffic distribution on the transportation network; Under the above assumptions, step 1, based on the relevant theories of the Logit type random user equilibrium model, considering that travelers are simultaneously affected by the bandwagon effect and path subsidy information, a generalized travel impedance function considering the bandwagon effect under the induced path is defined; step 2, based on the travel impedance function, a dynamic evolution model of traffic flow considering the bandwagon effect under the induced path is determined; step 3, taking the dynamic evolution model as the lower model, and taking minimizing the total travel time cost of the system and the path subsidy cost as the objective function as the upper model, a two-level programming model for traffic flow control is established; step 4, a genetic algorithm is designed to solve the two-level programming model to quickly and accurately solve the above problem.

2. A path traffic flow induction method considering the herd effect of travelers according to claim 1, characterized in that: In step 1, according to the relevant theories of the Logit type stochastic user equilibrium model, when travelers are affected by both the herd effect and the path subsidy information, the generalized travel impedance function considering the herd effect under the induced path is defined as: For travelers who do not receive path subsidy information, the generalized travel impedance function is: in is the actual travel time of path r in the k+1th stage, r∈R w ,w∈W, is defined as the "attractiveness" of path r to travelers in the k+1th stage, β is an important parameter that relates the "attractiveness" of the path and the travel time of the path, θ is a parameter greater than 0, which is used to describe the familiarity of travelers in the traffic network with the road network, and is inversely proportional to the familiarity of travelers with the road network, φ is an important parameter for converting the path subsidy into the generalized travel impedance of the path, and ρ rw is a relation variable. If the path r between OD pairs w∈W is an induced path, then ρ rw is 1, otherwise it is 0, χ is the unit subsidy standard of the induced path, L rw is the path length of path r between OD pair w∈W, ε rw is a random term in the utility function; It is easy to know that this problem is a convex programming problem. According to the relevant theory of the Logit model, the optimal solution of the problem (f 1,rw ,f 2,rw ) is unique; therefore, if the travelers in the k+1th stage choose the path with the smallest generalized travel impedance according to the travel impedance function defined above, the traffic flow solution when the system reaches a stable state at the end of this stage is the minimum point of the model.

3. A path traffic flow induction method considering the herd effect of travelers according to claim 2, characterized in that: In step 2, specifically: determine the traffic flow dynamic evolution model considering the herd effect under the induced path; The travel impedance function can further derive the dynamic evolution process and dynamic evolution model of traffic flow; The specific expression of the dynamic evolution process framework of traffic flow considering the herd effect under the induced path can be simplified as follows: Where Ω is: in, and They represent the path flow of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the system reaches a steady state at the end of the k+1th stage, respectively. f is the path flow, f∈Ω, and f 1,rw and f 2,rw They represent the path flows of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the system reaches a steady state at the end of the k+1th stage, respectively. A is the set of road segments, a∈A, W is the set of OD pairs generated by travel demand between OD pairs, w∈W, R w is the set of feasible paths between OD pairs w, r∈R w , x a is the traffic flow on section a, t a is the travel impedance of section a, δ arw is a relation variable. If the road segment a is in the path r between the OD pair w, then δ arw is 1, otherwise it is 0, ψ is the proportion of travelers who receive path subsidy information, q w is the travel demand between OD pairs w∈W.

4. A method for inducing path traffic flow considering the herd effect of travelers according to claim 3, characterized in that: Step 3 is as follows: construct a two-level planning model for traffic flow control under the induced path considering the herd effect; A two-level planning model for traffic flow regulation considering the herd effect under the induced path is constructed. By optimizing the proportion of travelers who receive path subsidy information (referred to as the subsidy ratio) and the path subsidy standard, a better traffic flow regulation effect is achieved while the path subsidy cost is relatively small. The lower model of the two-level planning model for traffic flow control is an evolution model of traffic flow that follows the dynamic evolution law of traffic flow under the herd effect; the upper model of the two-level planning model for traffic flow control takes minimizing the total travel time cost and path subsidy cost of the system as the objective function, and the cost is calculated based on the dynamic evolution result of traffic flow in the lower model. At the same time, the government's path subsidy cost budget constraint must be considered to ensure that the path subsidy cost is lower than the government's path subsidy cost budget; Upper model: The objective function of the upper model consists of two items. The first item is the total travel time cost of the system, which is defined here as the sum of all path flows multiplied by path impedance. The second item is the path subsidy cost. At the same time, the government's path subsidy cost budget constraint must be considered to ensure that the path subsidy cost is lower than the government's path subsidy cost budget. In summary, the upper model of the two-level planning model for traffic flow control can be expressed by the following formula: The constraints are: where f 1,rw and f 2,rw are the path flows of travelers who receive path subsidy information and travelers who do not receive path subsidy information when the lower model, i.e., the dynamic evolution model of traffic flow considering the herd effect under the induced path, finally reaches a steady state under the traffic flow control strategy of the upper model. λ is an important parameter for converting path subsidy cost into time cost. ρ rw is a relation variable. If the path r between OD pairs w∈W is an induced path, then ρ rw =1, otherwise 0, χ is the path unit subsidy standard, its unit is χ yuan / km, D is the upper limit of the path subsidy cost; Formula (6) represents the constraint of the path subsidy cost, ensuring that the path subsidy cost is lower than the government's path subsidy cost budget; Formula (7) represents that the time equivalent cost of the path subsidy on any path is not greater than the travel time of the path; Lower model: The lower model is a traffic flow dynamic evolution model considering the herd effect under a given induced path; where (f1, f2) is the path flow obtained after the following traffic flow dynamic evolution model has evolved over a period of time: in Where ψ is the proportion of travelers who receive path subsidy information, i.e., the subsidy ratio, q w is the traffic demand between OD pairs w∈W; In summary, the traffic flow control strategy considering the herd effect under the induction path in the present invention is to solve the two-level programming problem that satisfies the upper-level goal as formula (6) and the lower-level model is the traffic flow dynamic evolution model considering the herd effect under the induction path.

5. A path traffic flow induction method considering the herd effect of travelers according to claim 4, characterized in that: Step 4 is specifically as follows: solving the two-level programming model of traffic flow control considering the herd effect under the induced path; A genetic algorithm is used to solve the bi-level programming model of traffic flow control under the induced path considering the herd effect; the algorithm can improve the convergence efficiency of the algorithm iteration and solve the above problem quickly and accurately. The specific steps are as follows: Step 4-1: Perform double-layer encoding on the traffic flow control problem considering the herd effect under the induced path, and generate an initial population to form an initial traffic flow induction scheme candidate set; The genetic algorithm maps the solution of the optimization problem into individuals for iterative search through gene coding. Combined with the specific situation of the constructed two-level planning model for traffic flow control, a two-level coding method is used for chromosomes, and each level represents the path unit subsidy standard and the proportion of travelers who receive path subsidy information. To facilitate the design of genetic operations such as crossover and mutation, binary sequences are used to represent related real numbers, and all genotypes are composed of a binary symbol set {0,1}. Taking the path unit subsidy standard as an example, the solution space of the actual problem is recorded as [a,b], and the chromosome length is set to N, then the search space is {0,1,2L,2 N -1}; If the binary gene sequence is converted into a decimal number n, then the path unit subsidy standard represented by it after decoding is The same is true for the proportion of travelers who received path subsidy information; Take the population size as g, generate the initial population according to the range of the path unit subsidy standard and the proportion of travelers who receive path subsidy information (i.e., subsidy ratio), and set the selection crossover probability P of the genetic algorithm. c and genetic variation probability P m , set the maximum number of iterations Maxgen; Step 4-2: Pass the chromosome to the lower model; Extract the path unit subsidy standard represented by the chromosome in the population and the proportion of travelers who receive path subsidy information (i.e., subsidy ratio), and solve the traffic flow dynamic evolution model considering the herd effect under the current path unit subsidy standard and subsidy ratio until it finally reaches the steady state solution, i.e., the path flow solution for the two types of travelers; Step 4-3: The lower model feeds back the traffic flow evolution results to the upper model; Step 4-4: Calculate the fitness function value according to the traffic flow dynamic evolution result of the lower model; The objective function of the upper model of the two-level programming model for traffic flow control under the induced path considering the herd effect is to minimize the sum of the total travel time cost of the system and the total path subsidy cost. It is easy to know that the value of the objective function is always positive, so the inverse of the objective function value can be mapped to the fitness value. The smaller the objective function value, the greater the fitness value. At the same time, in order to ensure that the path subsidy cost is lower than a certain value, when the path subsidy cost is higher than a certain value, a penalty function is added to make its fitness value smaller. According to the principle of genetic algorithm, the individual with a larger fitness value is selected as the parent and inherited to the next generation. On the contrary, the individual with a smaller fitness value will be gradually eliminated as the population continues to evolve and develop. The fitness function Fit is shown in formula (10): Among them, F is the penalty function, flag is the relationship variable, if the path subsidy cost is greater than D, then flag is 1, otherwise flag is 0; Step 4-5: Roulette wheel selection strategy; The roulette wheel selection strategy is used to select the better individuals in the population to form a new population; According to the individual fitness value calculated in the above formula, the selection probability of individuals in a population of size g is further determined, and the calculation formula is: Among them, P i is the selection probability of individuals in the population, Fit i is the fitness value of individuals in the population; Calculate the cumulative probability Q of individuals in the population i , and its calculation formula is: Generate g uniformly distributed random numbers ζ in [0,1], if Q i ≤ζ≤Q i+1 , then select individual i; Step 4-6: Crossover operation; Using the two-point crossover method, with a certain crossover probability P c , randomly set two crossover points in the coding strings of two chromosomes that are paired with each other in the parent population, and exchange the parts of the two chromosomes between the two set crossover points; Steps 4-7: genetic variation operations; Using the basic bit mutation method, with a certain mutation probability P m , generate a random number r∈[0,1] in the chromosome of each offspring in the crossover offspring set, if r≤P m , then randomly mutate the value of the number or some bits on the chromosome. For the individuals represented by binary coded symbol strings in this chapter, if the value of a gene to be mutated is 0, the mutation operation will change it to 1; conversely, if the original gene value is 1, the mutation operation will change it to 0; Repeat steps 4-2 to 4-7 until the number of iterations of the genetic algorithm reaches the set maximum number of iterations Maxgen, then stop running and output the optimal path unit subsidy standard and optimal subsidy ratio.

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