New highway toll pricing method, device and equipment and medium

Abstract

The invention relates to the technical field of highway toll management and optimization, in particular to a new highway toll pricing method, device and equipment and a medium, and the method comprises the steps: designing a hybrid RPamp; the SP investigates questionnaires and collects and preprocesses data; a multi-user path selection model is constructed, user grading is carried out according to the diversion willingness, and a conditional Logit model is established; time values of different users are calculated, an appropriate road impedance model is selected, a user generalized travel cost model is established, and time cost and economic cost are unified; establishing a flow distribution model based on time value heterogeneity; constructing a bilevel planning-based charging optimization model by taking the minimization of the overall travel time of the road network as a target; the model is solved by adopting a simulated annealing algorithm, the optimal charging level is finally output, a method is provided for a new highway charging pricing mechanism, and the charging reasonability and the road network passing efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of highway toll management and optimization, and particularly relates to a toll pricing method, device, equipment and medium for a newly built highway. Background Art

[0002] With the continuous expansion of the highway network, the construction and maintenance costs are constantly rising. At present, the average cost per kilometer of highway projects in Jiangsu Province has increased from about 30 million yuan in the initial stage of road network construction to more than 300 million yuan, with a growth rate of 10 times. The huge investment has increased the difficulty of raising construction funds. At the same time, the financial pressure of maintenance and operation management has also gradually increased. How to improve the capital recovery efficiency through a scientific and reasonable toll standard and reduce the financial burden has become an important issue in current highway management.

[0003] After the newly built highway is opened to traffic, it will lead to the redistribution of the traffic flow of the original road network, and the toll standard has become the focus of attention from all walks of life. This not only directly affects the travel route selection of the majority of users, but also ultimately affects the traffic flow distribution of the overall road network and the toll revenue of the operator.

[0004] Currently, the formulation of highway toll standards is mainly based on the principle of compensating construction costs and empirical formulas, but these methods do not fully consider the route selection preferences of different types of users and their sensitivity to toll levels. Therefore, it is difficult to balance the economy of tolls and the traffic efficiency of the road network.

[0005] The information disclosed in this background art section is only intended to deepen the understanding of the overall background art of the present invention, and should not be regarded as an admission or any form of implication that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0006] The present invention provides a toll pricing method, device, equipment and medium for a newly built highway, thereby effectively solving the problems in the background art.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a toll pricing method for a newly built highway, including the following steps:

[0008] S10: Design a travel behavior hybrid RP&SP questionnaire, and complete the data collection and preprocessing work;

[0009] S20: According to the data, construct a multi-user route selection model, and classify users based on the diversion willingness index for the data samples to obtain the user classification result;

[0010] S30: Based on the user classification result, establish a conditional Logit model respectively to determine the utility functions of different user groups;

[0011] S40: Calculate the time value according to the utility function, select a road impedance model, establish a user's generalized travel cost model, and unify the time cost and economic cost;

[0012] S50: Based on the user's generalized travel cost model, establish a traffic flow allocation model for multiple types of users;

[0013] S60: Based on the traffic flow allocation model, with the goal of minimizing the overall travel time of the road network, construct a highway toll rate optimization model based on bilevel programming;

[0014] S70: Based on the highway toll rate optimization model, use the Hooke-Jeeves method to solve the simulated annealing algorithm to obtain the optimal toll standard and output the result.

[0015] Furthermore, in step S10, the travel behavior hybrid RP&SP questionnaire includes: designing a passenger car questionnaire and a freight car questionnaire respectively according to different user vehicle types, and introducing the RFM model in the questionnaire to classify users and design questions to obtain the frequency of using the highway per week and the toll expenses.

[0016] Furthermore, in step S30, the utility function models for different user groups include:

[0017] U in =β1T in +β2C in ;

[0018] In the formula, U in represents the utility of traveler n choosing plan i, β1 and β2 represent the parameters before the travel time variable and cost variable in the utility function, T in is the time cost of traveler n choosing plan i, and C in is the cost of traveler n choosing plan i.

[0019] Furthermore, in step S40, the model for calculating the time value includes:

[0020]

[0021] Among them, β1 and β2 represent the parameters before the travel time variable and cost variable in the utility function.

[0022] Furthermore, in step S40, according to the utility function, calculate the time value, select a road impedance model, and establish a user's generalized travel cost model. The user's generalized travel cost model includes:

[0023]

[0024] In the formula, represents the generalized travel cost of the m-th type of user on path k between D and rs, and v a represents the traffic flow on road section a, and t a (v a ) represents the impedance function of road section a, is an explanatory variable, x represents the highway toll level, and ρ a represents the length of the toll road section, and θ m represents the average time value of the m-th type of user.

[0025] Furthermore, in step S60, the highway toll rate optimization model based on bilevel programming minimizes the overall travel time of the road network in the upper layer, and optimizes and calculates the toll rate according to the generalized traffic travel cost of users in the lower layer, so as to achieve the balance between the road network traffic efficiency and economic benefits.

[0026] Furthermore, the model for minimizing the overall travel time of the road network in the upper layer includes:

[0027]

[0028] In the formula, represents the solution of the network equilibrium model under multiple types of users in the lower layer, t a represents the impedance function of road section a, and x represents the highway toll level.

[0029] Furthermore, the model for optimizing and calculating the toll rate according to the generalized traffic travel cost of users in the lower layer includes:

[0030]

[0031] The constraint conditions include:

[0032]

[0033] In the formula, t a (v a ) is the impedance function of road section a, θ m represents the average time value of the m-th type of user, x represents the highway toll level, ρ a represents the length of the toll road section, represents the traffic flow of the m-th type of user on road section a, represents the actual traffic demand of the m-th type of user from r to s during the research period, represents the traffic flow of the m-th type of user on path k between OD pair rs, is an explanatory variable.

[0034] Further, in step S70, the simulated annealing algorithm is used to solve the optimal toll standard, and the steps include:

[0035] S71: Initialization, set the feasible region of the toll range, and define the basic parameters of the simulated annealing algorithm, including the initial temperature, the temperature condition for stopping iteration, and the number of iterations at each temperature value;

[0036] S72: Set the initial temperature, and uniformly generate multiple random points within the toll range. Substitute each of the random points into the lower layer of the bilevel programming model, calculate the section flow and the actual flow of each OD pair rs, and obtain the objective function value of the upper layer programming;

[0037] S73: Conduct a Markov chain test. If a specific condition is satisfied, continue to execute step S84; otherwise, jump to step 85;

[0038] S74: According to the randomly generated value, if a predetermined condition is satisfied, use the Hooke-Jeeves method to find the local optimal solution starting from the current point; otherwise, generate a new random point within the toll range;

[0039] S75: Apply the Metropolis criterion, generate a random number and compare it with the change of the objective function. If the condition is satisfied, accept the new solution and return to step S83; otherwise, continue the iteration;

[0040] S76: Conduct a cooling process, calculate the standard deviation of the objective function value, update the system temperature, and adjust the temperature according to a predetermined formula;

[0041] S77: Determine whether the stopping iteration condition is reached, and judge whether the current temperature is lower than the lowest temperature T min , if so, end the algorithm and output the current solution as the optimal toll standard; otherwise, return to step S83 to continue the iteration.

[0042] The present invention further includes a toll pricing device for a newly built expressway, which uses the method as described above and includes:

[0043] A data collection module, which is used to design a travel behavior hybrid RP&SP questionnaire and complete the data collection and preprocessing work;

[0044] A path selection model construction module, which is used to construct a multi-user path selection model based on the data, and classify users for the data samples based on the diversion willingness index to obtain the user classification results;

[0045] A conditional Logit model establishment module, which is used to establish conditional Logit models respectively based on the user classification results, determine the utility functions of different user groups, and obtain different user characteristic classifications;

[0046] A time value calculation module, configured to calculate the time value according to the classification of different user characteristics, select a road impedance model, and establish a user generalized travel cost model;

[0047] A traffic flow allocation module, configured to unify the time cost and the economic cost based on the heterogeneity of the time value, and establish a traffic flow allocation model under multiple types of users;

[0048] A toll rate optimization module, configured to construct a highway toll rate optimization model based on bilevel programming with the goal of minimizing the overall travel time of the road network based on the traffic flow allocation model;

[0049] A toll standard solving module, configured to perform a simulated annealing algorithm using the Hooke-Jeeves method based on the highway toll rate optimization model, and use the simulated annealing algorithm to solve for the optimal toll standard and output the result.

[0050] The present invention further includes a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method as described above is implemented.

[0051] The present invention further includes a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method as described above is implemented.

[0052] The beneficial effects of the present invention are as follows:

[0053] 1. The present invention designs a highway travel user questionnaire based on a hybrid RP&SP, providing a feasible data collection method for calculating the time value of multiple types of users;

[0054] 2. By using a conditional Logit path selection model to model various user groups and obtaining the time value of different travel user types based on the utility function, the travel time value of multiple types of highway users is quantified, improving the accuracy and rationality of the toll model;

[0055] 3. A highway toll bilevel programming model based on the heterogeneity of user time value is established, evaluating the impact of toll standards on road network traffic volume and toll benefits, and providing a method for the toll pricing mechanism of newly built highways. Description of the Drawings

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments described in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0057] Figure 1 It is a schematic flowchart of the toll pricing method for a newly built expressway;

[0058] Figure 2 It is a schematic diagram of the travel behavior questionnaire for RP / SP users;

[0059] Figure 3 It is a schematic diagram of the structure of the user path selector;

[0060] Figure 4 It is a schematic diagram of the toll model calculator based on bilevel programming;

[0061] Figure 5 It is a schematic flowchart of the model annealing algorithm;

[0062] Figure 6 It is a schematic diagram of the structure of the toll pricing device for a newly built expressway;

[0063] Figure 7 It is a schematic diagram of the structure of a computer device. Specific embodiments

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0065] As Figures 1 to 5 shown: A toll pricing method for a newly built expressway includes the following steps:

[0066] S10: Design a travel behavior hybrid RP&SP questionnaire and complete the data collection and preprocessing work;

[0067] S20: According to the data, construct a multi-user path selection model, and classify users based on the diversion willingness index for the data samples to obtain the user classification results;

[0068] S30: Based on the user classification results, establish conditional Logit models respectively to determine the utility functions of different user groups;

[0069] S40: According to the utility function, calculate the value of time, select a road impedance model, establish a user generalized travel cost model, and unify the time cost and economic cost.

[0070] S50: Based on the generalized user travel cost model, establish a traffic assignment model for multiple types of users;

[0071] S60: Based on the traffic assignment model, with the goal of minimizing the overall travel time of the road network, construct an optimization model for highway toll rates based on bilevel programming;

[0072] S70: Based on the optimization model for highway toll rates, use the Hooke-Jeeves method to solve the simulated annealing algorithm to obtain the optimal toll standard and output the results.

[0073] The present invention designs a highway travel user questionnaire based on hybrid RP&SP, providing a feasible data collection method for calculating the time value of multiple types of users;

[0074] The present invention models various user groups by adopting the conditional Logit path choice model, and obtains the time value of different travel user types according to the utility function, quantifying the travel time value of multiple types of users on the highway, and improving the accuracy and rationality of the toll model;

[0075] The present invention establishes a bilevel programming model for highway tolls based on the heterogeneity of user time value, evaluates the impact of toll standards on road network traffic volume and toll benefits, and provides a method for the toll pricing mechanism of newly built highways.

[0076] By designing a hybrid RP&SP questionnaire to collect travel behavior data of different users, the path choice preferences and sensitivities to tolls of different user groups are fully considered. This scientific data collection and analysis method helps to formulate more reasonable toll standards. By combining user needs and toll economy, the problem that traditional empirical formulas fail to fully consider user differences can be avoided.

[0077] Through the multi-user path choice model and the conditional Logit model, the present invention can optimize the toll standard for different user groups by combining their time value heterogeneity, thereby balancing tolls and traffic efficiency. Through the traffic assignment model and the strategy of normalizing time cost and economic cost, the traffic distribution of the road network can be further improved, and the overall traffic efficiency of the road network can be enhanced. This optimization can better cope with the challenge of reallocating the traffic volume of the existing road network after the opening of newly built highways, and thus optimize the overall traffic benefits.

[0078] The bilevel programming model takes the minimization of the overall travel time of the road network as the optimization goal. Through the Hooke-Jeeves local minimum search and the simulated annealing algorithm, this method can quickly calculate the optimal toll standard, ensuring that the toll level can not only attract users to use the newly built highway, but also improve the travel efficiency of the overall road network.

[0079] After the opening of a newly built expressway, the redistribution of traffic flow will have an impact on the existing road network. Through the optimization of users' travel route selection, the present invention can guide users to reasonably choose toll roads, promote the reasonable distribution of traffic flow in the overall road network, and thus alleviate the traffic congestion caused by uneven traffic flow.

[0080] The existing tolling methods usually rely on the compensation principle of construction costs and empirical formulas, lacking the segmentation and customization for different user groups. Through the multi-user route selection model and the calculation of time value heterogeneity of the present invention, the limitations of traditional tolling methods can be broken through, and a more accurate tolling scheme can be provided, thus effectively avoiding the blindness and roughness existing in the existing tolling methods.

[0081] In this embodiment, a newly built expressway within a certain regional road network needs to customize the toll standard, and it is necessary to consider the impact of this toll standard on the regional traffic flow distribution and the overall travel benefit, and finally maximize the overall travel benefit of the road network. The specific implementation plan is as follows:

[0082] Step S10: Design a mixed RP&SP questionnaire for travel behavior to collect relevant data for studying the expressway route selection behavior.

[0083] As a preference for the above embodiments, in step S10, the travel behavior hybrid RP&SP questionnaire includes: designing a passenger car questionnaire and a freight car questionnaire respectively according to different user vehicle models, and introducing the RFM model into the questionnaire to classify users and design questions to obtain the frequency of using the highway per week and the toll expenses. Specifically, the design of the travel behavior hybrid RP&SP questionnaire includes two types of travel behavior questionnaire data: Revealed Preference (RP) and Stated Preference (SP). In order to calibrate the time value heterogeneity of highway users, the hybrid RP&SP questionnaire design method is adopted and divided into a passenger car questionnaire and a freight car questionnaire according to vehicle models. Considering that user classification is required for subsequent establishment of the route selection model, the RFM model is introduced in the questionnaire design to design survey questions. The RFM model is a customer classification model that measures the customer value situation from three directions: the customer's recent purchase behavior, purchase frequency, and expenses. These three directions are respectively represented as: the time difference from the most recent consumption (Recency): this value is inversely proportional to the customer value situation, and the larger the value, the smaller the customer value; the consumption frequency within a certain period of time (Frequency): this value is directly proportional to the customer value situation, and the larger the value, the greater the customer value; the total customer consumption amount within a certain period of time (Monetary): this value is directly proportional to the customer value situation, and the larger the value, the greater the customer value. Considering that the use of the highway is also a consumption process, and the product obtained from the consumption is the right to use the highway. Since the time difference from the most recent consumption in the RFM model requires multiple sets of data support, considering the rationality of the questionnaire, the latter two items are selected to compile questions. For example, asking the respondents about the frequency of using the highway per week or the toll expenses for the highway per week, which is used as one of the bases for subsequent user classification. In the scenario selection part, the RP survey is used to collect the recent travel scenarios of the respondents, and on this basis, the hybrid RP&SP scenarios are designed. The influencing factors for scenario selection include additional expenses and time savings.

[0084] The questionnaire in this study mainly includes three parts: the personal attribute part, the RP survey part, and the hybrid RP&SP scenario selection part.

[0085] (1) In the personal attribute part, different question settings exist according to whether it is a passenger car or a freight car. Among them, there are personal attribute surveys common to both passenger cars and freight cars, such as gender, age, income, etc.

[0086] (2) The main purpose of the RP survey part is to obtain the actual travel conditions of the respondents and provide data support for the subsequent SP scenario design. To avoid the respondents' incorrect scenario recall due to fuzzy memory, the respondents are asked about their most recent trip. The questions set mainly target whether the respondents used the highway, the travel time required for the journey, and the tolls required for passage.

[0087] (3) What is different about the mixed RP&SP scenario selection part from the scenario selection in a general SP survey is that the scenarios set in the scenario selection part of the SP survey are based on complete assumptions, while the mixed RP&SP scenarios will refer to the scenarios actually experienced by the respondents in the above RP survey. In the questionnaire of the present invention, each scenario mainly considers two alternative options. One is the original route, that is, to let the respondents make the most recent trip again; the second option is to reduce the travel time of this trip by increasing the toll. In the bus questionnaire, the scenario selection influencing factors include additional costs and time savings. As Figure 2 shown in the schematic diagram of the RP / SP user travel behavior questionnaire, a certain scenario hypothetical question is as follows:

[0088] The time you spent on the road during this or the last trip was approximately ________ minutes, and you spent _________ yuan. (□ If you are not clear about the travel time and cost of this or the last trip, then assume that your travel distance this time is about 50 km, the cost is 25 yuan, and the time is about 40 min.)

[0089] Assume that in each of the following situations, you have a chance to reselect your route. Your choice is:

[0090] Assume there is a new route that can save 10 minutes,

[0091] but it will cost an additional 30 yuan. Your choice is □ new route □ original route;

[0092] If it costs an additional 20 yuan, your choice is □ new route □ original route;

[0093] If it costs an additional 10 yuan, your choice is □ new route □ original route;

[0094] Field research and data result analysis of the questionnaire survey.

[0095] Field research was carried out in highway service areas, and 3,011 valid questionnaires were collected. Each questionnaire had 9 scenarios, with a total of 27,099 sample sizes. The sample data was processed to analyze the direct and indirect impacts of various factors on travel route selection and the interactions between factors, as shown in Table 1.

[0096] Statistical Results of Questionnaire Data in Table 1

[0097]

[0098] Step S20: Based on the data, construct a multi-user path selection model, and classify the data samples according to the diversion willingness index to obtain the user classification results;

[0099] Step S30: Based on the user classification results, establish conditional Logit models respectively to determine the utility functions of different user groups, as Figure 3 shown in the schematic diagram of the user path selector structure;

[0100] Construct a multi-user path selection model. Users with a greater time value are obviously more inclined to pay more tolls in exchange for less travel time. Therefore, it is considered that the user group with a stronger diversion willingness has a higher time value, and the greater the difference in diversion willingness between two user groups, the greater the difference in time value. According to the travel behavior questionnaire data, classify the data samples based on the diversion willingness index. Passenger cars and trucks are each divided into two categories. Then, classify users according to this influencing factor and establish conditional Logit models respectively, and use linear functions as the manifestation form of the utility function.

[0101] In this embodiment, in step S30, the utility function models of different user groups include:

[0102] U in =β1T in +β2C in ;

[0103] In the formula, U in represents the utility of traveler n choosing plan i, β1 and β2 represent the parameters before the travel time variable and cost variable in the utility function, T in is the time cost (unit: minute) of traveler n choosing plan i, and C in is the cost cost (unit: yuan) of traveler n choosing plan i.

[0104] Among the passenger car samples, the difference in diversion willingness between user groups classified according to the highway usage frequency is the largest. Therefore, the passenger car user group is divided into: Class I passenger car users, with a highway usage frequency less than 4 times per week; Class II passenger car users, with a highway toll greater than 4 times per week. Among the truck samples, the difference in diversion willingness between user groups classified according to the transported goods is the largest. Therefore, the truck user group is divided into: Class I truck users, transporting emergency goods; Class II truck users, transporting non-emergency goods.

[0105] Calculate the utility function through data structure conversion, parameter calibration and test results.

[0106] The utility function for the path selection of the first type of bus users is: U in = -0.0792T i - 0.0734C i ;

[0107] The utility function for the path selection of the second type of bus users is: U in = -0.0731T i - 0.0978C i ;

[0108] The utility function for the path selection of the first type of truck users is: U in = -0.0802T i - 0.0879C i ;

[0109] The utility function for the path selection of the second type of truck users is: U in = -0.0819T i - 0.1217C i ;

[0110] Step S40: According to the utility function, calculate the value of time, select the road impedance model, establish the user's generalized travel cost model, and unify the time cost and economic cost;

[0111] Calculate the value of time based on the questionnaire results and the path selection model. Different values of time may have different impacts on users' travel demands and path selections. Adopt the discrete value of time and calculate the value of time separately according to the user characteristic classification in the above steps.

[0112] Among them, in step S40, the model for calculating the value of time includes:

[0113]

[0114] Among them, β1 and β2 represent the parameters before the travel time variable and cost variable in the utility function.

[0115] The specific values of time for various users are as follows: for the first type of bus users: 64.93 yuan / h, for the second type of bus users: 45.15 yuan / h, for the first type of truck users: 54.74 yuan / h, for the second type of truck users: 40.38 yuan / h. In bus travel, the value of time for low-frequency travelers is higher than that of medium- and high-frequency travelers. In truck travel, the value of time for the owners transporting urgent goods is higher than that of non-urgent goods.

[0116] Select the road impedance model in the traffic flow assignment model. The road impedance function represents the travel time cost of a road section, and its form needs to conform to the actual traffic operation law, that is, the travel time of a road section increases with the increase of traffic flow. The present invention selects the classical BPR (Bureau of Public Road) model.

[0117] As an optimization of the above embodiment, in step S40, the road impedance model includes:

[0118]

[0119] In the formula, represents the travel time of section a in the free flow state, C a represents the actual traffic capacity of section a, Q a represents the traffic flow on section a, and α and β represent parameters. The typical values of the American BPR function are α = 0.15 and β = 4. In different scenarios, α and β may be different and should be calibrated according to the actual usage. The present invention calculates the travel time corresponding to Q / C and the free travel time using traffic survey data, and performs non-linear fitting, and finally determines the parameters as α = 0.163 and β = 4.

[0120] Step S40: Calculate the value of time according to the utility function, select the road impedance model, and establish a user's generalized travel cost model;

[0121] Considering the travel time cost and the cost of expenses, define the comprehensive travel cost of multiple types of users. Divide all travel users into M categories, θ m (θ m > 0) represents the average value of time of the m-th type of user (m = 1, 2,..., M). The VOT of each type of user is discrete and is sorted according to the magnitude of the value of time:

[0122] θ1 < θ2 < θ3 <... < θ m m = 1, 2,..., M;

[0123] As an optimization of the above embodiment, in step S40, according to the utility function, calculate the value of time, select the road impedance model, establish a user's generalized travel cost model, and convert the toll amount x into a time cost through the corresponding value of time of various travel users. Therefore, the generalized travel cost of the m-th type of user on path k between OD pair rs is: The user's generalized travel cost model includes:

[0124]

[0125] In the formula, represents the generalized travel cost of the m-th type of user on path k between OD pair rs, v a represents the traffic flow on section a, ta (v a ) represents the impedance function of section a, which is only related to the traffic flow on section a and has nothing to do with the traffic flows of other sections. is an explanatory variable. represents that section a is on path k of OD pair rs. represents that section a is not on path k of OD pair rs. x represents the highway toll level, and ρ a represents the length of the toll section, and θ m (θ m > 0) represents the average value of time of the m-th type of user (m = 1, 2,..., M).

[0126] Step 50: Based on the user's generalized travel cost function, establish a traffic flow allocation model under multiple types of users;

[0127] Comprehensively considering factors such as travel time, cross-sectional traffic volume, travel cost, and the heterogeneity of user time value, draw on the advantages of the BPR model to normalize the time cost and economic cost, and at the same time consider the user equilibrium problem expression of time value heterogeneity as follows:

[0128]

[0129] The constraint conditions include:

[0130]

[0131] In the formula, t a (v a ) is the impedance function of section a, and θ m (θ m > 0) represents the average value of time of the m-th type of user, x represents the highway toll level, and ρ a represents the length of the toll section, represents the traffic flow of the m-th type of user on section a, represents the actual traffic demand from r to s of the m-th type of user during the research period, represents the traffic flow of the m-th type of user on path k between OD pair rs, is an explanatory variable, represents that section a is on path k of OD pair rs, represents that section a is not on path k of OD pair rs. The mathematical programming problem is also called the UE programming, and all the demands q rs are allocated to the shortest path. The multi-type user network equilibrium model is the same as the single-user network equilibrium model, and its optimal solution satisfies the Kuhn-Tucker conditions:

[0132]

[0133]

[0134] In the formula, represents the total impedance of the m-th type of user on path k between OD pair rs, that is, the generalized cost; represents the minimum impedance of the m-th type of user of OD pair rs, that is, the minimum generalized cost.

[0135] In this embodiment, in step S60, the highway toll rate optimization model based on bilevel programming minimizes the overall travel time of the road network in the upper layer, and optimizes the calculation of the toll rate according to the generalized traffic travel cost of users in the lower layer, so as to achieve the balance between the road network traffic efficiency and economic benefits.

[0136] Among them, as Figure 4 shown, the schematic diagram of the toll model calculator based on bilevel programming, the model for minimizing the overall travel time of the road network in the upper layer includes:

[0137]

[0138] In the formula, represents the solution of the network equilibrium model under multiple types of users in the lower layer, t a represents the impedance function of section a, and x represents the highway toll level.

[0139] As an optimization of the above embodiment, the model for optimizing the calculation of the toll rate according to the generalized traffic travel cost of users in the lower layer includes:

[0140]

[0141] The constraint conditions include:

[0142]

[0143] In the formula, t a (v a ) is the impedance function of section a, θ m (θ m > 0) represents the average time value of the m-th type of user, x represents the highway toll level, ρ a represents the length of the toll section, represents the traffic flow of the m-th type of user on section a, represents the actual traffic demand of the m-th type of user from r to s during the research period, represents the traffic flow of the m-th type of user on path k between OD pair rs, is the explanatory variable, represents that section a is on path k of OD pair rs, It is indicated that section a is not on path k of OD pair rs. The mathematical programming problem is also called the UE programming, and all demands q rs are allocated to the shortest paths.

[0144] Input road network information and model parameters. Input the road network topology structure, define the basic attribute information of sections, including node information, section length, traffic capacity, free flow speed, and toll level. In the tolling method, the existing highway toll collection mode is adopted, and the solution is based on the linear distance - toll function, that is, tolls are collected per kilometer. The toll standard for type-I passenger cars is implemented at 0.55 yuan per vehicle-kilometer. Input the OD information of the road network. Input the parameters of the section impedance function. Input the time values of multiple types of users.

[0145] Step S70: Based on the highway toll rate optimization model, use the Hooke-Jeeves method to solve the simulated annealing algorithm for the optimal toll standard and output the results. The Hooke-Jeeves method is beneficial to accelerating the cooling rate during the annealing process.

[0146] When the highway toll rate in the lower-level model is relatively low, due to its superior driving conditions, it will attract a large number of vehicles, resulting in congestion and increasing the overall travel time of users in the upper-level function. However, when the toll rate level in the lower-level model is too high, the travel cost is too high, and the new road will lose a large number of users, causing the road network distribution to be unbalanced again and increasing the overall travel time of the upper-level function. The bilevel programming problem is a complex NP-hard (Non-deterministic Polynomial) problem. The present invention uses the simulated annealing algorithm for solution. To make the algorithm more accurate and faster in calculation speed, the Hooke-Jeeves local minimum search is introduced. For the convenience of subsequent description, define γ as the feasible region of toll x, that is, the toll in each section cannot exceed this interval:

[0147]

[0148] In the formula, γ represents the feasible region of toll x, that is, the toll in each section cannot exceed this interval, and x represents the highway toll level.

[0149] As an optimization of the above embodiment, in step S70, the simulated annealing algorithm is used to solve the optimal toll standard, and the steps include:

[0150] S71: Initialize, set the feasible region of the toll interval, and define the basic parameters of the simulated annealing algorithm, including the initial temperature, the temperature condition for stopping iteration, and the number of iterations at each temperature value;

[0151] S72: Set the initial temperature, and uniformly generate multiple random points within the toll range. Take each random point as a known condition and substitute it into the lower layer of the bi-level programming model to calculate the link flow and the actual flow of each OD pair rs, and obtain the objective function value of the upper layer planning;

[0152] S73: Conduct a Markov chain test. If the specific conditions are met, continue to execute step S84; otherwise, jump to step 85;

[0153] S74: According to the randomly generated value, if the predetermined conditions are met, use the Hooke-Jeeves method to find the local optimal solution starting from the current point; otherwise, generate a new random point within the toll range;

[0154] S75: Apply the Metropolis criterion, generate a random number and compare it with the change in the objective function. If the conditions are met, accept the new solution and return to step S83; otherwise, continue the iteration;

[0155] S76: Conduct a cooling process, calculate the standard deviation of the objective function value, and update the system temperature, adjusting the temperature according to the predetermined formula;

[0156] S77: Judge whether the stop iteration condition is reached, and judge whether the current temperature is lower than the lowest temperature T min , if so, end the algorithm and output the current solution as the optimal toll standard; otherwise, return to step S83 to continue the iteration.

[0157] Specifically, as Figure 5 shown, the steps of the simulated annealing algorithm are as follows:

[0158] Step0: Initialization. Define Ω as the feasible domain of the toll x, that is, the toll in each link cannot exceed this interval:

[0159]

[0160] Given the initial value x (0) ∈Ω, set the basic parameter values of the simulated annealing 0 ≤ χ0 ≤ 1, 0 < δ < 1, 0 < h < 1, the temperature condition T min for stopping iteration, the number of iterations L at each T value, and the counters k = k1 = 0.

[0161] Step1: Set the initial temperature. Uniformly generate n random points within the toll range Ω, denoted as x (i) (i = 1,…,n). Substitute each x (i) as a known condition into the lower layer of the bi-level programming model (the traffic assignment model based on the heterogeneity of time value) to obtain the link flow v a (x (i) ) and the actual flow q of each OD pair rsrs (x (i) ). It is easy to calculate the objective function value of the upper-level planning from the solution of the lower-level problem Let n0 represent the number of points x that satisfy (i) . represents the average value of all when . Calculate the initial temperature T (0) :

[0162]

[0163] Step 2: Markov chain test. If k < L, continue to Step 3, otherwise jump to Step 5.

[0164] Step 3: Generate a new solution x'. Generate a random number h in the interval [0, 1] random . If h random > h, then use the Hooke-Jeeves method to find the local optimal solution x' starting from the point x (k 1 ) . If h random ≤ h, then generate a random point x' within the charging range Ω.

[0165] Step 4: Metropolis acceptance criterion. Generate a random number b in the interval [0, 1]. If or when satisfies

[0166]

[0167] then let k1 = k1 + 1 and return to Step 2. Otherwise, let k1 = k1 + 1 and return to Step 2.

[0168] Step 5: Cooling process. Calculate the standard deviation σ(T ) of the objective function value. Calculate the system temperature value: (k)

[0169]

[0170] Let k = k + 1, x (0) = x (L) , k1 = 0.

[0171] Step 6: Stop iteration judgment. If T (k) < T min , end the program and output the current solution x (0) as the optimal rate value. Otherwise, return to Step 2.

[0172] The present invention further includes a toll pricing device for a newly built expressway, as Figure 6 shown, which uses the method as described above and includes:

[0173] A data collection module, which is used to design a travel behavior hybrid RP&SP questionnaire and complete the data collection and preprocessing work;

[0174] A path selection model construction module, which is used to construct a multi-user path selection model based on the data and classify users of the data samples based on the diversion willingness index to obtain a user classification result;

[0175] A conditional Logit model establishment module, which is used to establish conditional Logit models respectively based on the user classification result and determine the utility functions of different user groups;

[0176] A time value calculation module, which is used to calculate the time value according to the utility function, select a road impedance model, establish a user generalized travel cost model, and unify the time cost and economic cost;

[0177] A traffic assignment module, which is used to establish a traffic assignment model under multiple types of users based on the user generalized travel cost model;

[0178] A toll rate optimization module, which is used to construct a highway toll rate optimization model based on bilevel programming with the goal of minimizing the overall travel time of the road network based on the traffic assignment model;

[0179] A toll standard solving module, which is used to solve the optimal toll standard by using the Hooke-Jeeves method for the simulated annealing algorithm based on the highway toll rate optimization model and output the result.

[0180] Please refer to Figure 7 the structural schematic diagram of the computer device provided by the embodiment of the present application shown. A computer device 400 provided by an embodiment of the present application includes: a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it executes the method as described above.

[0181] An embodiment of the present application further provides a storage medium 430. A computer program is stored on the storage medium 430. When the computer program is run by the processor 410, it executes the method as described above.

[0182] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0183] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0184] In the present invention, unless otherwise clearly specified and defined, terms such as "installed", "connected", "coupled", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0185] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0186] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0187] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0188] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0189] Those of ordinary skill in the art can understand that all or part of the steps carried out in the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0190] The storage medium mentioned above can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for pricing tolls for newly built expressways, characterized in that: The steps include: S10: Design a hybrid RP&SP questionnaire on travel behavior and complete data collection and preprocessing; S20: constructing a multi-user route selection model based on the data, and classifying the users of the data samples based on the route change willingness index to obtain a user classification result; S30: Based on the user classification results, conditional Logit models are established to determine the utility functions of different user groups; S40: Calculate the time value according to the utility function, select a road impedance model, establish a user generalized travel cost model, and unify the time cost and the economic cost; S50: establishing a traffic allocation model for multiple types of users based on the user generalized travel cost model; S60: Based on the traffic distribution model, taking minimization of the overall travel time of the road network as the optimization goal, constructing a highway toll rate optimization model based on double-layer planning; S70: Based on the highway toll rate optimization model, the simulated annealing algorithm of the Hooke-Jeeves method is used to solve the optimal charging standard and output the result.

2. The method for pricing tolls for newly built expressways according to claim 1 is characterized in that: In step S10, the travel behavior hybrid RP&SP questionnaire includes: designing a passenger car questionnaire and a truck questionnaire respectively according to different user vehicle models, and introducing an RFM model in the questionnaire to classify users and design questions to obtain the frequency of weekly highway use and toll expenses.

3. The method for pricing tolls for newly built expressways according to claim 1 is characterized in that: In step S30, the utility function models of different user groups include: U in =β1T in +β2C in ; Where U in represents the utility of traveler n choosing option i, β1 and β2 represent the parameters before the travel time variable and cost variable in the utility function, T in The time cost of traveler n choosing option i, C in The cost of choosing option i for traveler n.

4. The method for pricing tolls for newly built expressways according to claim 1 is characterized in that: In step S40, the model for calculating the time value includes: Among them, β1 and β2 represent the parameters before the travel time variable and cost variable in the utility function.

5. The method for pricing tolls for newly built expressways according to claim 1 is characterized in that: In step S40, the time value is calculated according to the utility function, and a road impedance model is selected to establish a user generalized travel cost model, wherein the user generalized travel cost model includes: In the formula, represents the generalized travel cost of D to the mth user on the path k between rs, v a represents the traffic flow on section a, ta(va) represents the impedance function of section a, is the explanatory variable, x represents the highway toll level, ρ a represents the length of the toll road section, θ m Represents the average time value of the mth type of user.

6. The method for pricing tolls for newly built expressways according to claim 1 is characterized in that: In step S60, the highway toll rate optimization model based on double-layer planning minimizes the overall travel time of the road network by optimizing the upper layer, and optimizes the calculation of the toll rate according to the user's generalized travel cost in the lower layer to achieve a balance between the road network's travel efficiency and economic benefits.

7. The method for pricing tolls for newly built expressways according to claim 6 is characterized in that: The upper-level optimization model for minimizing the overall travel time of the road network includes: In the formula, represents the solution of the network equilibrium model under multiple types of users in the lower layer, t a represents the impedance function of road section a, and x represents the highway toll level.

8. The method for pricing tolls for newly built expressways according to claim 6 is characterized in that: The lower layer optimizes the calculation of charging rates based on the user's generalized transportation travel costs, including: The constraints include: Where ta(va) is the impedance function of section a, θ m represents the average time value of the mth user, x represents the highway toll level, ρ a Indicates the length of the toll road section, represents the traffic flow of the mth user on road segment a, represents the actual traffic demand of the mth user from r to s during the study period, represents the traffic of the mth user on the path k between OD and RS, is the explanatory variable.

9. The method for pricing tolls for newly built expressways according to claim 1, characterized in that: In step S70, the simulated annealing algorithm is used to solve the optimal charging standard, and the steps include: S71: Initialization, setting the feasible domain of the charging interval, and defining the basic parameters of the simulated annealing algorithm, including the initial temperature, the temperature condition for stopping iteration, and the number of iterations at each temperature value; S72: setting an initial temperature, and evenly generating multiple random points within the charging range, bringing each of the random points into the lower layer of the two-layer programming model, calculating the section flow and the actual flow of each OD pair rs, and obtaining the objective function value of the upper layer programming; S73: Perform a Markov chain test. If the specific conditions are met, continue to step S84. If not, jump to step 85. S74: According to the randomly generated value, if the predetermined condition is met, the Hooke-Jeeves method is used to find the local optimal solution from the current point; if not, a new random point is generated within the charging range; S75: Apply the Metropolis criterion, generate random numbers and compare them with the change of the objective function. If the conditions are met, accept the new solution and return to step S83. If not, continue to iterate; S76: performing a cooling process, calculating the standard deviation of the objective function value, and updating the system temperature, and adjusting the temperature according to a predetermined formula; S87: Determine whether the condition for stopping iteration is met, and determine whether the current temperature is lower than the minimum temperature T min If so, the algorithm ends and outputs the current solution as the optimal charging standard; otherwise, return to step S83 to continue iteration.

10. A new highway toll pricing device, characterized in that: Use of the method according to any one of claims 1 to 9, comprising: Data collection module, used to design a hybrid RP&SP questionnaire on travel behavior and complete data collection and preprocessing; A route selection model building module is used to build a multi-user route selection model based on the data, and classify the users of the data samples based on the rerouting willingness index to obtain a user classification result; The conditional Logit model building module is used to build conditional Logit models based on the user classification results to determine the utility functions of different user groups; A time value calculation module is used to calculate the time value according to the utility function, select a road impedance model, establish a user generalized travel cost model, and unify the time cost and the economic cost; A traffic allocation module, used to establish a traffic allocation model for multiple types of users based on the user generalized travel cost model; A toll rate optimization module is used to construct a highway toll rate optimization model based on double-layer planning based on the traffic distribution model and taking minimization of the overall travel time of the road network as the optimization goal; The charging standard solving module is used to solve the optimal charging standard based on the highway charging rate optimization model by using the Hooke-Jeeves method and simulated annealing algorithm, and output the result.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

12. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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