Highway dynamic rate setting method and system considering carbon reduction benefit and new energy automobile excitation, medium and program product

Abstract

A highway dynamic rate setting method and system considering carbon reduction benefit and new energy vehicle excitation, a medium and a program product are characterized in that a multi-objective optimization model is constructed to integrate carbon emission, driving cost and operation income objectives, path impedance is analyzed in combination with real-time traffic data and a road network selection model, dynamic adjustment parameters are generated, and the dynamic rate setting efficiency is improved. According to the method, low-carbon path selection is guided through differential charging of fuel vehicle types, the truck rate is corrected based on the total cargo weight so as to excite full-load transportation, meanwhile, preferential strategies are set for new energy vehicle types, and a dynamic rate system covering the vehicle types, road sections and time periods is formed. A Pareto optimal solution set is generated based on an optimization algorithm, road network efficiency improvement, carbon emission reduction and income increase are taken into consideration, a rate strategy is continuously optimized through a closed-loop monitoring feedback mechanism, collaborative optimization is finally realized in environmental protection, economical efficiency and high efficiency, and a systematic low-carbon solution is provided for highway charging management.

Description

Technical Field

[0001] The present invention relates to highway toll management technology in the field of transportation, and particularly to a method, system, medium and program product for setting dynamic highway toll rates considering carbon reduction benefits and incentives for new energy vehicles. Background Art

[0002] In the highway system, traditional toll models are usually based on fixed rates or simple time-of-day tolls, making it difficult to adapt to real-time traffic flow changes and environmental target requirements. Current dynamic toll methods mostly focus on improving traffic efficiency and lack comprehensive consideration of carbon emissions, incentives for new energy vehicles, and operating revenues, making it difficult to achieve a balance among carbon reduction benefits, cost control, and economic returns. Therefore, there is an urgent need for a highway dynamic toll rate setting scheme based on multi-objective optimization that comprehensively considers the carbon emissions, driving costs, and operator revenues of all vehicle types, and achieves the coordinated optimization of low-carbon guidance, incentives for new energy vehicles, and network efficiency through path impedance analysis and dynamic toll rate adjustment.

[0003] It should be noted that the information disclosed in the above background art section is only for understanding the background of the present application, and thus may include information that does not constitute prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] The main object of the present invention is to overcome the defects existing in the above background art, and to provide a method, system, device and medium for setting dynamic highway toll rates considering carbon reduction benefits and incentives for new energy vehicles.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A method for setting dynamic highway toll rates considering carbon reduction benefits and incentives for new energy vehicles, comprising the following steps: S1. Based on carbon emission indicators, combined with speed correction coefficients and road network traffic data, construct a multi-objective optimization model, the objective function of the multi-objective optimization model includes minimizing the carbon emissions of all vehicle types, minimizing the driving costs of all vehicle types, and maximizing the toll revenue of the operator, and set a benchmark toll rate; S2. Real-time monitor the road network operation status through the ETC system, obtain the traffic flow, vehicle speed and carbon emission data of trucks and passenger cars (including new energy vehicles) in different sections, and evaluate the impact of the current toll rate scheme on carbon reduction effect, driving cost and toll revenue; S3. According to the traffic flow and carbon emission data of trucks and passenger cars in different sections, combined with the road network selection model, analyze the path impedance of each section in the road network, and generate section dynamic adjustment parameters, the path impedance includes tolls, fuel consumption costs and time value; S4. Dynamically adjust the parameters according to the road sections and the toll rates according to the real-time monitoring data: Implement differential tolls for fuel vehicle types to guide them to choose low-carbon routes and reduce costs. For fuel trucks, further modify the toll rate adjustment parameters according to their total load weight to encourage an increase in the full-load rate of trucks. Implement significant preferential toll rates for new energy vehicle types (including buses and trucks) to increase their usage proportion; S5. Use an optimization algorithm to solve the multi-objective optimization model, generate toll rate plans for different road sections and vehicle types, and ensure the coordinated realization of the carbon reduction benefit, driving cost reduction, and toll revenue objectives; S6. Implement the new toll rate plan, and continuously monitor the road network efficiency, carbon emissions, and toll revenue. Optimize the toll rate strategy in a loop according to the monitoring results.

[0006] A highway dynamic toll rate setting system considering carbon reduction benefits and new energy vehicle incentives, including: A data collection module for obtaining the traffic flow, vehicle speed, energy consumption per unit mileage, and carbon emission data of trucks and buses in different road sections in real time through the ETC system; An optimization model module for constructing a multi-objective optimization model, with the objective functions of minimizing the carbon emissions of all vehicle types, minimizing the driving cost, and maximizing the toll revenue of the operator, and setting the benchmark toll rate in combination with the speed correction coefficient and road network traffic flow data; A dynamic adjustment module for performing the following operations: Analyze the path impedance of each road section according to the road network selection model to generate dynamic adjustment parameters for the road sections, where the path impedance includes tolls, fuel consumption costs, and time value; Dynamically adjust the toll rates according to the dynamic adjustment parameters of the road sections and the real-time monitoring data: Implement differential tolls for fuel vehicle types to guide them to choose low-carbon routes and reduce costs. For fuel trucks, further modify the toll rate adjustment parameters according to their total load weight to encourage an increase in the full-load rate of trucks. Implement significant preferential toll rates for new energy vehicle types to increase their usage proportion; A monitoring and feedback module for continuously monitoring the road network efficiency, carbon emissions, and toll revenue data after implementing the toll rate plan for different road sections and vehicle types, and feeding back the monitoring results to the optimization model module to optimize the toll rate strategy in a loop; An algorithm solving module that uses the non-dominated sorting genetic algorithm NSGA-II to solve the multi-objective optimization model, generates a Pareto optimal solution set, and screens the toll rate plan that takes into account the carbon reduction benefit, cost control, and revenue objectives according to the preset weights.

[0007] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the highway dynamic toll rate setting method described above.

[0008] A computer program product includes a computer program which, when executed by a processor, implements the highway dynamic toll rate setting method described above.

[0009] The present invention has the following beneficial effects: The present invention provides a highway dynamic toll rate setting method considering carbon emission reduction benefits and new energy vehicle incentives. By analyzing real-time traffic data and a road network selection model, this method dynamically adjusts the highway toll rates to achieve the comprehensive goals of low-carbon guidance for all vehicle types, an increase in the proportion of new energy vehicle usage, and optimization of operating revenues, thereby ensuring the environmental friendliness, economy, and efficiency of the transportation system.

[0010] The main innovative contributions and technical advantages of the present invention are as follows. First, a multi-objective optimization model based on carbon emission indicators, speed correction coefficients, and road network traffic flow data is constructed. This model comprehensively considers the carbon emissions, driving costs, road network traffic flow balance, and toll revenues of the operating party for all vehicle types (including new energy buses and trucks) to set the benchmark toll rates and achieve the comprehensive goals of low-carbon guidance, new energy vehicle incentives, traffic flow balance, and revenue optimization. Second, through the road network selection model, combined with the traffic flow and carbon emission data of different sections, the path impedance of each section is analyzed to generate dynamic adjustment parameters, providing an accurate basis for toll rate adjustment. Third, according to the section dynamic adjustment parameters and real-time monitoring data, the toll rates are dynamically adjusted: differential tolls are implemented for fuel vehicle types to guide the selection of low-carbon paths; for the problem of uneven traffic flow during peak hours, toll rate adjustments are used to guide vehicles to divert to low-traffic sections to achieve road network traffic flow balance; for fuel trucks with a relatively high total cargo weight, the toll rate adjustment parameters are corrected to encourage an increase in the full load rate; preferential toll rates are implemented for new energy vehicle types (including buses and trucks) to increase their usage proportion, while ensuring that the toll rates for new energy trucks are not lower than those for small cars. In addition, the usage proportion of new energy buses and trucks is significantly increased through a differential preferential toll rate strategy, specifically including setting fixed preferential coefficients for new energy trucks and new energy small cars and meeting the constraint that the toll rates for new energy trucks are not lower than those for small cars to ensure reasonable incentives for new energy vehicle types. At the same time, for fuel trucks with a relatively high total cargo weight, through full load rate calculation and toll rate adjustment parameter correction, trucks are encouraged to increase their full load rate, reduce inefficient transportation, and lower the carbon emissions and costs per unit of cargo. Finally, after implementing the new toll rate plan, the road network efficiency, carbon emissions (including the contributions of new energy vehicle types), traffic flow distribution, and toll revenues are continuously monitored, and circular optimization is carried out to ensure the long-term achievement of the carbon emission reduction, traffic flow balance, and revenue goals.

[0011] The present invention demonstrates significant advantages in the aspect of dynamic toll rate setting on expressways. It can effectively reduce the overall carbon emissions and driving costs, guide fuel vehicle models to choose low-carbon paths through dynamic toll rates, optimize the traffic flow distribution on the road network during peak hours, relieve congestion and improve the overall road network efficiency. In addition, while achieving carbon reduction and traffic flow balance, it comprehensively considers the toll revenue of the operator to ensure the stability of economic benefits, achieving a multi-objective balance of environmental protection, economy and efficiency. Compared with known solutions, the present invention realizes a more comprehensive optimization effect through a multi-objective optimization model, comprehensively considering carbon emissions, driving costs, traffic flow balance and revenue. At the same time, by introducing path impedance analysis and dynamically adjusting parameters, it can divert traffic more accurately, filling the gap in the neglect of the inefficient operation problem of fuel trucks in existing solutions, and significantly improving the carbon reduction and efficiency effects.

[0012] The present invention achieves these advantages mainly due to its innovative design: the combination of multi-objective optimization and real-time data analysis makes the toll rate adjustment more targeted and adaptable; the full-load rate incentive mechanism directly affects the freight transport behavior through toll rate adjustment, solving the problem of inefficient operation; the path impedance analysis ensures the accuracy of traffic flow balance. The reason why it was not thought of before may be that its implementation requires the integration of multi-source real-time data (such as ETC, traffic flow, carbon emission data) and complex optimization algorithms (such as NSGA-II), which requires relatively high technical capabilities. In addition, there has been less attention paid to the full-load rate of trucks and the traffic flow balance of the road network in the past, mainly focusing on new energy passenger cars and ignoring trucks, lacking a perspective of comprehensive optimization, while the present invention fills this gap, combining the latest technological progress of big data and multi-objective optimization.

[0013] In summary, the present invention realizes multiple technical effects such as carbon reduction, cost reduction, new energy promotion and revenue guarantee through multi-objective optimization, dynamic adjustment and innovative incentive mechanisms, and has significant practical value and promotion potential.

[0014] Other beneficial effects in the embodiments of the present invention will be further described below. Brief Description of the Drawings

[0015] Figure 1 It is a flowchart of the method for setting dynamic toll rates on expressways considering carbon reduction benefits and new energy vehicle incentives of the present invention.

[0016] Figure 2 It is a mechanism diagram of the method for setting dynamic toll rates on expressways considering carbon reduction benefits and new energy vehicle incentives of the present invention.

[0017] Figure 3 It is a flowchart of the algorithm for solving the multi-objective model by the optimization algorithm in the embodiment of the present invention. Detailed Description of the Invention

[0018] The following provides a detailed description of the embodiments of the present invention. It should be emphasized that the following description is merely exemplary and not intended to limit the scope of the present invention and its applications.

[0019] The present invention aims to solve the following technical problems in highway toll management: 1. Unbalanced network traffic flow during peak hours: During morning and evening peak hours, the traffic flow on some sections of the highway (such as around cities or main arterial roads) surges, leading to severe congestion, while the traffic flow on other sections (such as suburbs or branch lines) is relatively low, resulting in unbalanced utilization of road network resources. This uneven traffic distribution not only increases the carbon emissions and time costs of congested sections but also reduces the overall operating efficiency of the road network, affecting the smoothness and economic benefits of the transportation system. 2. High carbon emissions problem: The traditional fixed rate or simple time-of-day tolling model cannot effectively guide fuel vehicle types to choose low-carbon paths, resulting in relatively high carbon emissions in the mixed traffic flow of trucks and buses and making it difficult to meet the carbon reduction goals of green transportation. 3. Insufficient incentives for new energy vehicles: The existing tolling methods lack sufficient incentives for new energy vehicle types (including buses and trucks). 4. Balance between revenue and efficiency problem: The existing tolling methods mainly focus on traffic efficiency and lack comprehensive consideration of carbon emissions, driving costs, and operator revenue, making it difficult to achieve a balance between carbon reduction goals and economic benefits. 5. Inefficient operation of fuel trucks: Fuel trucks often operate with empty or low loads, resulting in relatively high carbon emissions and transportation costs per unit of goods, and lacking an effective full-load rate incentive mechanism.

[0020] The present invention proposes a method for setting dynamic toll rates on highways considering carbon reduction benefits and incentives for new energy vehicles, which realizes dynamic toll rate adjustment on highways through a series of steps, comprehensively considering the goals of carbon reduction, incentives for new energy vehicles, and operating revenue. Figure 1 The overall process of the method for setting dynamic toll rates on highways considering carbon reduction benefits and incentives for new energy vehicles of the present invention is shown. Figure 2The mechanism for setting dynamic toll rates on highways considering carbon emission reduction benefits and incentives for new energy vehicles in the present invention is shown. First, based on information such as the total weight of the vehicle, carbon emission characteristics, driving speed, and road network traffic flow, the system establishes a comprehensive optimization model to set an initial toll standard for each vehicle type as the basis for subsequent adjustments. Then, through the ETC system, it monitors in real time the traffic flow, speed, and carbon emissions of trucks and buses (including new energy vehicle models) on the highway, and evaluates whether the current toll plan has achieved the expected carbon emission reduction effect, cost control, and revenue goals. In the analysis stage, based on the traffic flow and carbon emission data of different sections, combined with the road network selection model, the system evaluates the "traffic difficulty" of each section (such as congestion level and carbon emission level) to generate a dynamic adjustment parameter to reflect the actual condition of the section. Subsequently, the system adjusts the toll standard according to these parameters and real-time data: for fuel vehicle models, by increasing or decreasing the toll rate to guide them to choose a more environmentally friendly route while reducing the driving cost; for fuel trucks with a heavy load, if the full load rate is low, the toll standard will be further adjusted to encourage the truck to increase the full load rate and reduce inefficient transportation; for new energy vehicle models (including buses and trucks), significant preferential toll rates are given to increase their usage proportion, while ensuring that the toll rate for new energy trucks is not lower than that of small passenger cars. Finally, the system uses an optimization algorithm to generate the final toll plan for different sections and vehicle types to ensure the best balance among carbon emission reduction, cost reduction, and revenue guarantee. After implementing the new plan, the system continuously monitors the operation efficiency of the road network, carbon emissions, and the revenue of the operator. If the expected goals are not achieved, it returns to the adjustment step to cycle and optimize the toll strategy to achieve long-term comprehensive benefits.

[0021] Refer to Figure 1 , an embodiment of the present invention provides a method for setting dynamic toll rates on highways considering carbon emission reduction benefits and incentives for new energy vehicles, including the following steps: Step S1, based on carbon emission indicators, combined with a speed correction coefficient and road network traffic flow data, construct a multi-objective optimization model. The objective function of the multi-objective optimization model includes minimizing the carbon emissions of all vehicle types, minimizing the driving costs of all vehicle types, and maximizing the toll revenue of the operator, and set a benchmark toll rate.

[0022] In some embodiments, when constructing the multi-objective optimization model, the constraint conditions include: the carbon emissions of fuel vehicle models on the recommended route do not exceed their carbon emissions on the original congested section; the driving time cost of fuel vehicle models on the recommended route is significantly reduced compared to the original route; the adjustment range of the toll rate is limited between a preset minimum toll rate and a maximum toll rate; the toll rate for new energy trucks is not lower than the toll rate for new energy buses; and traffic flow conservation and section passing capacity limitations are satisfied.

[0023] Step S2: Monitor the operation status of the road network in real time through the ETC system, obtain the traffic flow, vehicle speed, and carbon emission data of trucks and passenger cars (including new energy vehicles) on different sections, and evaluate the impact of the current toll rate plan on the carbon reduction effect, driving cost, and toll revenue.

[0024] In some embodiments, the data collected in real time through the ETC system includes: the real-time traffic flow, average vehicle speed, and energy consumption per unit mileage of each section by vehicle type; the carbon emissions of each section dynamically calculated based on the carbon emission factor and energy consumption data; and the comprehensive impact of the current toll rate plan on the road network travel time, total carbon emissions, and toll revenue evaluated through the path allocation model.

[0025] Step S3: According to the traffic flow and carbon emission data of trucks and passenger cars on different sections, combined with the road network selection model, analyze the path impedance of each section in the road network, and generate dynamic adjustment parameters for the section. The path impedance includes tolls, fuel consumption costs, and time value.

[0026] In some embodiments, the calculation method of the path impedance specifically includes: using a generalized cost function to comprehensively quantify tolls, fuel consumption costs, and time value, where the time cost is dynamically calculated based on the BPR function; updating the section travel time according to the real-time traffic flow, and predicting the vehicle path selection probability through the road network selection model; generating dynamic adjustment parameters by combining carbon emissions and path impedance to quantify the congestion degree and environmental protection benefits of the section.

[0027] Step S4: Dynamically adjust the toll rate according to the section dynamic adjustment parameters and real-time monitoring data: Implement differential tolls for fuel vehicle types to guide them to choose low-carbon paths and reduce costs. For fuel trucks, further modify the toll rate adjustment parameters according to their total load weight to encourage an increase in the full load rate of trucks. Implement significant preferential toll rates for new energy vehicle types (including passenger cars and trucks) to increase their usage ratio.

[0028] Specifically, implement differential tolls for fuel vehicle types, increase the toll rate for high-carbon emission or congested sections to guide vehicles to divert to low-carbon paths, and reduce the toll rate for low-carbon emission or unobstructed sections to reduce driving costs; for fuel trucks, modify the toll rate adjustment parameters according to the ratio of their total load weight to the registered load weight to encourage trucks to increase their full load rate; implement preferential toll rates for new energy vehicle types, where the preferential toll rate for new energy trucks is not lower than that for new energy passenger cars, to increase the usage ratio of new energy vehicles.

[0029] In some embodiments, in step S4, the mechanism for dynamically adjusting rates includes: increasing rates during peak hours or on congested roads to discourage vehicle entry, and reducing rates during non-peak hours or on roads with smooth traffic to guide vehicle diversion; adjusting path selection behavior through differentiated rates to achieve dynamic balance of road network traffic flow; in step S6, further synchronously optimizing traffic carbon emission indicators, traffic efficiency and operating income through a closed-loop feedback mechanism.

[0030] In some embodiments, the rate adjustment for fuel trucks specifically includes: dynamically correcting the rate adjustment coefficient based on the ratio of the total weight of the vehicle and cargo to the registered load weight, the higher the full load rate, the greater the rate discount; setting additional rate penalty items for low-load or empty trucks to encourage improved transportation efficiency; obtaining load data in real time through the ETC system and automatically triggering the rate correction mechanism.

[0031] In some embodiments, preferential strategies for new energy vehicle models include: setting fixed preferential coefficients for new energy buses and trucks, and the preferential coefficient of new energy trucks is no lower than that of buses; implementing superimposed discounts for new energy vehicle models on congested or high-carbon emission sections to ensure that their comprehensive rate is significantly lower than that of similar fuel vehicles; dynamically monitoring the proportion of new energy vehicle traffic, and automatically increasing the discount range if it does not reach the preset threshold.

[0032] Step S5: using an optimization algorithm to solve the multi-objective optimization model, generate rate plans for different road sections and vehicle types, and ensure the coordinated realization of carbon reduction benefits, driving cost reduction and toll revenue goals.

[0033] See also Figure 3 In some embodiments, a non-dominated sorting genetic algorithm (NSGA-II) is used to solve a multi-objective optimization model. The specific process includes: a) Initialize population parameters and OD traffic flow allocation scheme; b) The decision variables are iteratively transferred to the path allocation layer, the path travel time is calculated based on the BPR function, and the path flow is updated using the moving average method (MSA); c) Perform non-dominated sorting and crowding calculation on individuals in the population according to the objective function value, and select the elite solution set; d) Perform crossover and mutation operations on the selected individuals to generate a new generation of population; e) The flow distribution and population evolution process is executed cyclically until the convergence condition is met or the preset number of iterations is reached, and then the Pareto optimal solution set is output.

[0034] In some embodiments, when generating a rate plan, the Pareto optimal solution is selected in the following manner: Prioritize comprehensive optimization solutions that simultaneously meet the goals of shortening road network travel time, reducing carbon emissions, and increasing toll revenue; For the solution set that cannot fully balance multiple objectives, select a recommended solution that emphasizes carbon reduction benefits or revenue guarantee according to the preset weight allocation scheme; Verify the feasibility and stability of the solution set by combining historical traffic data.

[0035] Step S6: Implement the new toll rate plan and continuously monitor the road network efficiency, carbon emissions, and toll revenue. Optimize the toll rate strategy in a loop according to the monitoring results.

[0036] In some embodiments, the loop optimization mechanism specifically includes: Regularly update the input parameters of the multi-objective optimization model based on real-time monitoring data; When the carbon emissions or congestion index of the road network exceeds the preset threshold, trigger the dynamic toll rate emergency adjustment process; Analyze the historical optimization results through machine learning algorithms, and adaptively adjust the model weight parameters to improve the long-term optimization effect.

[0037] The embodiment of the present invention also provides a highway dynamic toll rate setting system considering carbon reduction benefits and new energy vehicle incentives, including: A data collection module for real-time obtaining traffic flow, vehicle speed, energy consumption per unit mileage, and carbon emission data of trucks and buses (including new energy vehicles) in different sections through the ETC system; An optimization model module for constructing a multi-objective optimization model, where the model takes minimizing the carbon emissions of all vehicle types, minimizing the driving cost, and maximizing the toll revenue of the operator as the objective function, and sets the benchmark toll rate by combining the speed correction coefficient and road network traffic data; A dynamic adjustment module for performing the following operations: Analyze the path impedance of each section according to the road network selection model to generate section dynamic adjustment parameters, where the path impedance includes tolls, fuel consumption costs, and time values; Dynamically adjust the toll rate according to the section dynamic adjustment parameters and real-time monitoring data: Implement differential tolls for fuel vehicle types to guide them to choose low-carbon paths and reduce costs. For fuel trucks, further correct their toll rate adjustment parameters according to their total load weight to encourage an increase in the full load rate of trucks. Implement a significant preferential toll rate strategy for new energy vehicle types (including buses and trucks) to increase their usage ratio; A monitoring and feedback module for continuously monitoring the road network efficiency, carbon emissions, and toll revenue data after implementing the toll rate plan by section and by vehicle type, and feeding back the monitoring results to the optimization model module to optimize the toll rate strategy in a loop; An algorithm solving module that uses the non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model, generates a Pareto optimal solution set, and screens the toll rate plan that takes into account carbon reduction benefits, cost control, and revenue objectives according to the preset weight.

[0038] The following further describes specific embodiments of the present invention and examples of its algorithm implementation.

[0039] To solve the technical problems of high carbon emissions, insufficient incentives, and revenue balance in highway toll management, the present invention provides a method for setting dynamic highway toll rates considering carbon reduction benefits and incentives for new energy vehicles. By analyzing real-time traffic data, total vehicle and cargo weight, and vehicle carbon emission characteristics, a multi-objective optimization model is used to dynamically adjust highway toll rates. The carbon emissions, driving costs, and toll revenue of the operator for all vehicle types (trucks and buses) are comprehensively considered to generate a differentiated toll plan and determine the toll rate standards for each section and each vehicle type. The method includes the following steps: S1. Based on carbon emission indicators, combined with speed correction coefficients and road network traffic data, construct a multi-objective optimization model that includes the carbon emissions, driving costs, and toll revenue of the operator for all vehicle types, and set a benchmark toll rate; S2. Real-time monitor the operation status of the road network through the ETC system, obtain the traffic flow and carbon emission data of trucks and buses (including new energy vehicles) in different sections, and evaluate the carbon reduction, cost, and revenue effects of the current toll rate plan; S3. According to the traffic flow and carbon emission data of trucks and buses in different sections, combined with the road network selection model, analyze the path impedance of each section in the road network to form section dynamic adjustment parameters; S4: Dynamically adjust the toll rate according to the section dynamic adjustment parameters and monitoring data, implement differentiated tolls for fuel vehicle types to guide them to choose low-carbon paths and reduce costs. For fuel trucks, further correct the toll rate adjustment parameters according to their total cargo weight to encourage an increase in the full-load rate of trucks. Implement a significant preferential toll rate strategy for new energy vehicle types (including buses and trucks) to increase their usage ratio; S5. Use an optimization algorithm to solve the multi-objective model, generate a toll rate plan for each section and each vehicle type, and ensure the realization of carbon reduction benefits, cost reduction, and revenue goals; S6. Implement the new toll rate plan and continuously monitor the road network efficiency, carbon emissions, and toll revenue, and cycle to optimize the toll rate strategy.

[0040] Furthermore, in the step S1, the process of constructing a multi-objective optimization model that includes the carbon emissions, driving costs, and toll revenue of the operator for all vehicle types and setting a benchmark toll rate is as follows: Furthermore, the objective functions of the multi-objective optimization model include minimizing the carbon emissions of all vehicle types (trucks and buses) 、minimizing the driving costs of all vehicle types and maximizing the toll revenue of the operator , respectively, following the following formulas: Objective function 1, minimizing the road network travel time :

[0041] is the dynamic toll rate After adjustment, the number of vehicles of each vehicle type on the road section m , and the travel time of the road section ; where "1~4" represents passenger cars of categories 1 to 4, and "5~10" represents freight cars of categories 1 to 6 Objective function 2, minimizing carbon emissions :

[0042] is the length of the road section ; is the basic energy consumption per unit mileage of vehicle type m ; is the energy consumption speed correction coefficient of vehicle type m ; is the unit energy consumption emission factor of vehicle type m ;

[0043] Objective function 3, maximizing the toll revenue of the operator :

[0044] is the number of vehicles of each vehicle type r on the toll road section m ; is the length of the toll road section r ; is the toll rate after adjustment for vehicle type m on the toll road section r .

[0045] Furthermore, in order to generate different tolling schemes, a benchmark toll rate adjustment coefficient is set according to the road section and vehicle type , and a process assumes a formula for the actual toll rate to express the tolling schemes of different vehicle types on different road sections , which is defined as follows

[0046] is the basic toll rate of the toll road section is the set of toll road sections within the research scope

[0047] ​Furthermore, the constraint conditions of the multi-objective optimization model include: the carbon emissions of fuel vehicles on the recommended route are not higher than those on the original congested section; the travel time cost of fuel vehicles on the recommended route is reduced compared to the original road; the toll plan shall not be lower than or exceed the minimum or maximum toll limit constraints of the highway section; the rate of new energy trucks is not lower than that of new energy passenger cars; the rate adjustment range constraint; the traffic flow conservation constraint; the non-zero traffic volume constraint.

[0048]

[0049] Among them, is the estimated carbon emissions of fuel vehicles on the recommended section, is the estimated carbon emissions of fuel vehicles on the congested section, is the travel time cost of fuel vehicles on the recommended section, is the travel time cost of fuel vehicles on the congested section; and are the lower and upper limits of the rate adjustment respectively, is the new energy passenger car rate, is the new energy truck rate, is the OD pair (origin-destination pair) i.e., the departure node r to the arrival node s traffic volume, is the OD pair between the k th path traffic volume, is the section traffic volume, is a variable that takes 0 or 1. If the section belongs to the kth path from r to s in the OD pair, then otherwise , is the section traffic capacity, W is the set of sections within the research scope, is the set of all feasible paths between the OD pair .

[0050] Furthermore, in step S2, the ETC system is used to monitor the real-time operation status of the road network, obtain the traffic flow, vehicle speed, and carbon emission data of trucks and passenger cars, and evaluate the carbon reduction, cost, and revenue effects of the current toll rate plan.

[0051] Furthermore, the ETC system is used to monitor the real-time operation status of the road network, obtain the traffic flow, vehicle speed, and carbon emission data of trucks and passenger cars, and collect the toll data of each highway toll section in real time through the ETC system The traffic volume of trucks , the traffic volume of passenger cars , and the carbon emissions . Among them, for a certain highway toll section The calculation definition of carbon emissions for different vehicle types is as follows:

[0052] Furthermore, calculate the estimated road network travel time, estimated carbon emissions, and toll revenue for all vehicle types under the initial toll rate plan.

[0053] The estimated road network travel time under the initial toll rate is defined as follows:

[0054] The estimated carbon emissions under the initial toll rate are defined as follows

[0055] The estimated toll revenue under the initial toll rate is defined as follows

[0056] Among them, is the travel time of the section after toll adjustment; ; is the initial toll rate for vehicle type m passing through the toll section .

[0057] Furthermore, in step S3, according to the traffic volume and carbon emission data of trucks and passenger cars in different sections, combined with the road network selection model, analyze the path impedance of each section in the road network to form the section dynamic adjustment parameters.

[0058] Furthermore, the path selection model is defined as that the travel path of users will change with different toll standards, and the probability of path selection is strongly correlated with the cost. The calculation formula for the travel vehicle path selection probability is defined as follows in this article:

[0059] In the formula, is the probability that the k-th path between the departure node r and the arrival node s is selected; is the path impedance of the k-th path between the departure node r and the arrival node s, is the path impedance of the j-th path between the departure node r and the arrival node s, is the set of all feasible paths between the OD pair ( r , s ); is the scale parameter of the Gumbel distribution.

[0060] Furthermore, combined with the road network selection model, the path impedance of each section in the road network is analyzed. Path impedance is an important indicator for traffic flow distribution and an important influencing factor for the path selection of travel vehicles. In model calculations, path impedance can be specifically described by a road impedance function. When traveling, users mainly consider factors such as tolls, fuel consumption costs, and time. Therefore, to fully consider the impact of differential tolls, this paper comprehensively considers tolls, fuel consumption costs, and time value, and constructs a road network traffic volume distribution model based on the generalized cost function. The generalized cost function in the traffic network is also called the generalized path impedance function.

[0061] Furthermore, the generalized path impedance consists of two parts: travel time cost and travel cost (such as fuel consumption and tolls). The travel time of the vehicle section is expressed by the function of the Bureau of Public Road (BPR) of the United States. The calculation formula of the generalized path impedance is defined as follows:

[0062] is the generalized path impedance, is the toll, is the fuel cost of the fuel vehicle, is the energy consumption cost of the new energy vehicle, is the time cost.

[0063] Toll is defined as the following formula:

[0064] is when the section r is the toll for an independent toll bridge or tunnel.

[0065] Fuel cost of fuel vehicle is defined as the following formula:

[0066] is the energy consumption per unit mileage, is the price per unit of fuel.

[0067] Energy consumption cost of new energy vehicle is defined as the following formula: =

[0068] is the price per unit of electricity.

[0069] Toll is defined as the following formula

[0070] is the unit time value of the j type of vehicle, is the travel time of the road section .

[0071] Among them, the definitions of the intermediate parameters are as follows:

[0072]

[0073]

[0074]

[0075] is the free flow time of the road section , and are the model parameters of the BPR function, is the free flow speed of the vehicle, is the unit time value of the type of passenger car, is the per capita GDP of the region where the highway is located, is the average number of passengers carried by the passenger car, is the unit time, is the unit time value of the type of freight truck, is the average income of the drivers of the

[0076] type of freight truck.

[0077] In the formula, is the actual driving speed of the vehicle on the road section ; is the free flow driving speed of the vehicle on the road section .

[0078] Furthermore, based on the BPR function, the calculation relationship between vehicle speed and traffic flow is constructed and defined as follows: According to the path impedance of the road section , and the initial carbon emission prediction, the road section rate adjustment factor

[0079] is defined as follows: is the dynamic adjustment parameter of the road section ; is the road section impedance of the road section . is the maximum impedance of a road section in the road network; is the road section carbon emission; is the maximum carbon emission of a road section in the road network; and are weight coefficients.

[0080] Furthermore, in step S4, according to the dynamic adjustment parameters of the road section and the monitoring data, the toll rate is dynamically adjusted. For fuel vehicle models, differential tolls are implemented to guide them to choose low-carbon paths and reduce costs. For new energy vehicle models, preferential toll rates are implemented to increase their usage ratio.

[0081] Furthermore, according to the dynamic adjustment parameters of the road section , for road sections with high carbon emissions or congestion ( relatively high), the toll rate for fuel vehicle models is increased. For road sections with low carbon emissions or smooth traffic ( relatively low), the toll rate for fuel vehicle models is decreased, guiding them to choose low-carbon paths and reduce costs. Define the critical value of the dynamic adjustment parameters of the road section , as the decision boundary for toll rate adjustment, to distinguish the congestion or carbon emission status of the road section: When the dynamic adjustment parameter of the road section , it indicates that the carbon emission or congestion degree of the road section is relatively high, and vehicle diversion needs to be guided by increasing the toll rate.

[0082] When the dynamic adjustment parameter of the road section , it indicates that the carbon emission or congestion degree of the road section is relatively low, and vehicles can be attracted by reducing the toll rate to optimize the path selection.

[0083] Furthermore, for fuel vehicle models (trucks and buses), the toll rate adjustment for different road sections is defined as follows: For fuel vehicles on the road section, the toll rate is increased to inhibit the traffic flow and guide the vehicles to choose low-carbon paths. The toll rate adjustment for this type of vehicle model is defined as follows:

[0084] For fuel vehicles on the road section, the toll rate is decreased to attract the traffic flow and reduce the overall driving cost:

[0085] where is the amplification factor, controlling the upward amplitude of the toll rate.

[0086] Furthermore, for fuel trucks, the rate adjustment parameters are further corrected according to their total load weight to encourage an increase in the full-load rate. The correction factor for fuel trucks is set based on the total vehicle and cargo weight recorded in each ETC toll data and the registered load weight of the vehicle, and is defined as follows: For fuel trucks on the section of, the rate adjustment parameters are further corrected to encourage an increase in the full-load rate. The rate adjustment for this type of vehicle is defined as follows:

[0087]

[0088] For fuel trucks on the section of, the rate adjustment parameters are further corrected to encourage an increase in the full-load rate. The rate adjustment for this type of vehicle is defined as follows:

[0089]

[0090] Among them, is the rate of the fuel truck before dynamic adjustment on section r, is the corrected dynamic adjustment rate of the fuel truck ; is the dynamic adjustment rate correction factor of the fuel truck ; , , , , , are set coefficient values; is the total vehicle and cargo weight recorded in the ETC data; is the registered load weight of the vehicle.

[0091] Furthermore, for new energy vehicle models (trucks and buses), the rate adjustments for different sections are defined as follows: For new energy trucks on the section of, the rate is increased by a certain amount to suppress traffic flow. To encourage low-carbon travel, a certain preferential coefficient is provided, but for traffic guidance, its preferential coefficient is larger than that of buses. The rate adjustment for this type of vehicle is defined as follows:

[0092] Among them, is the dynamic adjustment rate of new energy trucks on congested sections or sections with high carbon emissions; is the preferential coefficient of the dynamic adjustment rate of new energy trucks on congested sections or sections with high carbon emissions; is the amplification factor, which controls the rate increase range.

[0093] For new energy vehicle buses on sections of roads, a certain rate increase is imposed to curb traffic flow, but a certain preferential coefficient is provided to encourage low-carbon travel. The rate adjustment for this type of vehicle is defined as follows:

[0094] Among them, is the dynamically adjusted rate for new energy buses on congested sections or sections with high carbon emissions; is the preferential coefficient for the dynamically adjusted rate of new energy buses on congested sections or sections with high carbon emissions.

[0095] This preferential coefficient and the rate satisfy the following constraints:

[0096] is the basic toll rate for toll roads, is the upper limit of rate adjustment.

[0097] For new energy passenger and freight vehicles on sections of roads, the rate is reduced to attract traffic flow and reduce the overall driving cost:

[0098] At the same time, the following constraints are satisfied:

[0099] Among them, is the dynamically adjusted rate for new energy passenger and freight vehicles on non-congested sections or sections with low carbon emissions, is the preferential coefficient for the dynamically adjusted rate of new energy passenger and freight vehicles on non-congested sections or sections with low carbon emissions.

[0100] Furthermore, in step S5, an optimization algorithm is used to solve the multi-objective model to generate a rate plan for different road sections and vehicle types, ensuring the realization of carbon reduction benefits, cost reduction, and revenue goals. The process is as follows: Furthermore, for the multi-objective optimization model solving algorithm, the non-dominated sorting genetic algorithm (NSGA-II) is adopted to generate a Pareto optimal solution set; Furthermore, a rate plan that satisfies objective function 1 (minimizing the travel time of the road network ), objective function 2 (minimizing carbon emissions ), and objective function 3 (maximizing the toll revenue of the operator) is selected from the optimal solution set to ensure that the carbon emissions of fuel vehicles are not higher than those in the original congested sections, the driving cost is reduced by a certain proportion, and the toll revenue is increased.

[0101] Further, in step S6, implement the new toll rate plan and continuously monitor the road network efficiency, carbon emissions, and toll revenue, and cyclically optimize the toll rate strategy.

[0102] Experimental example In this example, differential tolls are to be levied on a certain toll section of a highway. In the study of this example, the parameters to be set for solving differential tolls include time value, BPR function, basic toll rate, etc. Among them, the time value (VOT) parameter is initially calibrated based on basic data such as the per capita GDP of a certain province and the income of truck drivers, and then a certain toll section of the highway is selected for calibration. According to the influence degree of adjusting relevant VOT parameters on the model value and the fitting degree with the calibration data, the relevant VOT parameters are adjusted so that the setting of the VOT parameter better adapts to the actual parameter situation of a certain toll section of the highway; the BPR function relies on the highway model of a certain province and is calibrated and verified based on the operation of the highways in a certain province. The basic toll rate can be implemented according to relevant regulations.

[0103] Table 1 Calculation parameter values

[0104] In this example, based on Python, a program is designed to output the optimization results of the two-layer model through iterative calculation. The Pareto solution space distribution of the differential toll model is as Figure 3 shown.

[0105] From the Pareto optimal solution set, three optimization strategy recommended solutions with the least travel time (optimise-Ftime, OP-FT), the highest toll revenue (optimise-Fincome, OP-FI), and the lowest carbon emissions (optimise-Femi, OP-FE) are selected based on different strategy inclinations and compared with the non-differential toll plan (Base). Table 2 shows the recommended solutions under each optimization strategy. The recommended solutions obtained by the time-optimal strategy and the carbon emission lowest strategy are very close, indicating that improving the road network operation efficiency, reducing congestion and delay time will also reduce the carbon emissions of traffic. Under these two optimization strategies, the road network travel time is reduced by about 13.71%, the road network traffic carbon emissions are reduced by about 12.24%, and the road network toll revenue increases by 0.19%. Under the toll-optimal strategy, the toll increases by 1.26%, and at the same time, both the road network travel time and carbon emissions are reduced by more than 10%. The comparison results of each plan prove that the differential toll model for different road sections and vehicle types solved by the genetic algorithm realizes the multi-objective optimization of road network operation efficiency, toll revenue, and low carbon.

[0106] Table 2 Optimization strategy results under the mode combination of different road sections and vehicle types

[0107] In this case study, 100 Pareto solutions were calculated iteratively through Python. By comparing all Pareto solutions with the baseline scenario, low-carbon differential tolling schemes were screened according to the scenarios considering road network toll revenue and road network travel time. In this case, all multi-objectives in the Pareto solution set are superior to the baseline scenario, so the three-dimensional spatial point set can be reduced to two dimensions for visualization.

[0108] The Pareto solution with the least carbon emissions in the solution set was selected as the optimization scheme considering carbon reduction benefits. The situation of each optimization objective is shown in Table 3. Under the condition that the total OD demand of the road network remains unchanged, the adjusted coefficients of the toll rates for different vehicle types on a certain highway toll section are shown in Table 4. The optimization scheme considering carbon reduction benefits achieved a 13% increase in road network efficiency, a 12% reduction in carbon emissions, and a 0.2% increase in total toll revenue. With the growth of travel demand, the optimization strategy considering carbon reduction benefits has great potential for emissions reduction.

[0109] Table 3 Differential tolling optimization scheme considering carbon reduction benefits

[0110] Table 4 Adjusted coefficients of toll rates for different vehicle types on a certain highway toll section

[0111] As an alternative embodiment, in the toll rate adjustment strategy of the present invention, it can be further refined according to time and sections. For example, higher toll rates are set for high-flow sections during peak hours to guide vehicle diversion. At the same time, different preferential margins are set for new energy vehicles according to energy types (such as pure electric, hydrogen fuel). The full-load rate incentive for fuel trucks can also be extended to a multi-level mechanism, and different toll rate adjustment ranges are set according to the high or low full-load rate to more precisely encourage full-load transportation. In terms of data monitoring and optimization methods, the present invention can introduce more data sources, such as drone monitoring or in-vehicle GPS, to obtain more comprehensive traffic flow and carbon emission information, or add environmental factors such as air quality and weather as the basis for toll rate adjustment. In addition, machine learning can be used to predict the traffic flow during peak hours and adjust the toll rates in advance, or replace the existing optimization algorithm (such as replacing NSGA-II with particle swarm optimization) to improve the calculation efficiency.

[0112] The present invention can be applied to implement dynamic toll rate adjustment in toll tunnels: According to real-time traffic data, monitor the tunnel traffic during peak hours in the morning and evening. When the traffic volume approaches the upper limit of the passing capacity, uniformly increase the passing toll rate for all vehicles, and decrease it during off-peak hours, so as to guide vehicles to avoid peak hours or choose other routes, balance the tunnel traffic distribution, and reduce the inefficient operation and carbon emissions of congested sections. Since the adjustment strategy does not target specific vehicle types or user groups, it ensures the fairness of urban traffic, and at the same time improves the tunnel passing efficiency and environmental benefits. Another similar scenario is at the toll points of urban expressways, where the toll rate is adjusted according to the congestion situation of the toll stations to guide vehicles to divert to other routes or travel at off-peak times, relieve the congestion of the toll stations, and reduce carbon emissions.

[0113] The main innovative contributions and technical advantages of the present invention: 1. Construction of a multi-objective optimization model: Based on carbon emission indicators, speed correction coefficients, and road network traffic data, construct a multi-objective optimization model that includes the carbon emissions, driving costs, road network traffic balance, and toll revenue of the operator for all vehicle types (including new energy buses and trucks), and is used to set the benchmark toll rate and achieve the comprehensive goals of low-carbon guidance, new energy vehicle incentive, traffic balance, and revenue optimization.

[0114] 2. Path impedance analysis and generation of dynamic adjustment parameters: Through the road network selection model, combined with the traffic volume and carbon emission data of different sections, analyze the path impedance of each section, and generate dynamic adjustment parameters to reflect the congestion and carbon emission status of the section, providing an accurate basis for toll rate adjustment.

[0115] 3. Dynamic toll rate adjustment mechanism: Dynamically adjust the toll rate according to the section dynamic adjustment parameters and real-time monitoring data: Implement differential tolls for fuel vehicle types to guide the selection of low-carbon paths, and through toll rate adjustment to guide vehicles to divert to low-traffic sections to achieve road network traffic balance for the problem of uneven traffic volume during peak hours, correct the toll rate adjustment parameters for fuel trucks with a higher total load weight to encourage an increase in the full load rate, implement preferential toll rates for new energy vehicle types (including buses and trucks) to increase their usage ratio, and at the same time ensure that the toll rate for new energy trucks is not lower than that of small cars.

[0116] 4. Incentive mechanism for new energy vehicle types (buses and trucks): Significantly increase the usage ratio of new energy buses and trucks through differential preferential toll rate strategies, specifically including setting fixed preferential coefficients for new energy trucks and new energy small cars, and meeting the constraint that the toll rate for new energy trucks is not lower than that of small cars to ensure reasonable incentives for new energy vehicle types.

[0117] 5. Incentive mechanism for the full load rate of fuel trucks: For fuel trucks with a higher total load weight, through full load rate calculation and correction of toll rate adjustment parameters, encourage trucks to increase their full load rate, reduce inefficient transportation, and reduce the carbon emissions and costs per unit of goods.

[0118] 6. Continuous Monitoring and Cyclic Optimization Strategy: After implementing the new toll rate plan, continuously monitor the road network efficiency, carbon emissions (including the contribution of new energy vehicle models), traffic flow distribution, and toll revenue. Ensure the long-term realization of carbon reduction, traffic flow balance, and revenue goals through cyclic optimization (S2 - S6).

[0119] The present invention demonstrates significant advantages in the dynamic toll rate setting for highways. First of all, it can effectively reduce the overall carbon emissions and driving costs. By guiding fuel vehicle models to choose low-carbon paths through dynamic toll rates, while optimizing the traffic flow distribution on the road network during peak hours, alleviating congestion and improving the overall road network efficiency. Secondly, the present invention encourages fuel trucks to increase their load factor through the load factor incentive mechanism, reducing inefficient transportation and lowering the carbon emissions and costs per unit of goods. In addition, while achieving carbon reduction and traffic flow balance, the present invention comprehensively considers the toll revenue of the operator, ensuring the stability of economic benefits and achieving the multi-objective balance of environmental protection, economy, and efficiency.

[0120] Compared with the known solutions, which mainly adjust toll rates through traffic flow monitoring to alleviate congestion but do not consider carbon emission optimization, load factor incentives, or the increase in the proportion of new energy vehicles, the present invention achieves a more comprehensive optimization effect through a multi-objective optimization model that comprehensively considers carbon emissions, driving costs, traffic flow balance, and revenue. In addition, the present invention introduces path impedance analysis and dynamic adjustment parameters, which can divert traffic more accurately. At the same time, the load factor incentive mechanism fills the gap in the existing solutions' neglect of the inefficient operation problem of fuel trucks, significantly enhancing the carbon reduction and efficiency effects.

[0121] The present invention achieves these advantages mainly due to its innovative design: the combination of multi-objective optimization and real-time data analysis makes the toll rate adjustment more targeted and adaptable; the load factor incentive mechanism directly affects the truck transportation behavior through toll rate adjustment, solving the problem of inefficient operation; and the path impedance analysis ensures the accuracy of traffic flow balance. The present invention integrates multi-source real-time data (such as ETC, traffic flow, carbon emission data) and complex optimization algorithms (such as NSGA-II). In addition, there has been less attention paid to the load factor of trucks and the traffic flow balance of the road network in the past, with more focus on new energy passenger cars and neglect of trucks, lacking a comprehensive optimization perspective, while the present invention fills this gap, combining the latest technological progress in big data and multi-objective optimization.

[0122] The present invention has achieved remarkable technical effects in the aspect of dynamic toll rate setting on expressways. Compared with the traditional mode mainly based on fixed toll rates or simple time-of-day tolling in the background art, it has the following advantages: Firstly, the present invention can effectively reduce the overall carbon emissions and driving costs, while significantly increasing the usage ratio of new energy buses and trucks, contributing to the development of green transportation; Secondly, through dynamic toll rate adjustment and full-load rate incentive mechanisms, the distribution of road network traffic flow is optimized, and the operation efficiency of expressways is improved; Finally, while ensuring the carbon reduction target and new energy incentives, the present invention takes into account the stability of the toll revenue of the operator, achieving an overall balance of environmental protection, economy and efficiency.

[0123] The realization of these technical effects benefits from multiple innovative points of the present invention: Firstly, by constructing a multi-objective optimization model including carbon emissions, driving costs and operation revenues, and combining real-time traffic data and path impedance analysis, the dynamic adjustment of toll rates is achieved. Compared with the traditional fixed toll rates, it can better adapt to traffic flow changes, accurately guide fuel vehicle types to choose low-carbon paths, thereby reducing carbon emissions and costs; Secondly, a significant preferential toll rate strategy is implemented for new energy vehicle types (including buses and trucks), and it is ensured that the toll rate of new energy trucks is not lower than that of passenger cars, motivating the increase in the usage ratio of new energy vehicles; Thirdly, an innovative full-load rate incentive mechanism for fuel trucks is proposed. By further correcting the toll rate adjustment parameters for fuel trucks with a higher total cargo weight, trucks are encouraged to increase their full-load rate, reduce empty or low-load driving, and thus reduce the carbon emissions and transportation costs per unit of goods; Fourthly, based on the path impedance analysis of the road network selection model and the introduction of dynamic adjustment parameters, the toll rate adjustment is more targeted, which can effectively divert traffic on congested sections and improve the overall efficiency of the road network.

[0124] Combining the above innovative points, the present invention has achieved multiple technical effects of carbon reduction, cost reduction, new energy promotion and revenue guarantee through multi-objective optimization, dynamic adjustment and innovative incentive mechanisms, and has significant practical value and promotion potential.

[0125] The embodiment of the present invention also provides a storage medium for storing a computer program, which when executed, at least executes the method as described above.

[0126] The embodiment of the present invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein, the processor is used to execute the computer program to at least execute the method as described above.

[0127] The embodiment of the present invention also provides a processor, which executes a computer program and at least executes the method as described above.

[0128] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but not limited to, these and any other suitable types of memories.

[0129] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the displayed or discussed components can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0130] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated unit can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0132] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the aforementioned storage medium includes: various media such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0133] Alternatively, if the above integrated unit is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the aforementioned storage medium includes: various media such as removable storage devices, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0134] The methods disclosed in several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0135] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0136] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0137] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those skilled in the technical field to which the present invention pertains, without departing from the concept of the present invention, several equivalent substitutions or obvious variations can be made, and as long as the performance or use is the same, they should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for setting dynamic toll rates for highways taking into account carbon reduction benefits and new energy vehicle incentives, characterized in that: The following steps are involved: S1. Based on the carbon emission index, combined with the speed correction coefficient and the road network flow data, a multi-objective optimization model is constructed. The objective function of the multi-objective optimization model includes minimizing the carbon emissions of all vehicle models, minimizing the driving costs of all vehicle models, and maximizing the toll revenue of the operator, and setting a base rate; S2. Use the ETC system to monitor the operation status of the road network in real time, obtain the flow, speed and carbon emission data of trucks and buses in different sections, and evaluate the impact of the current rate plan on carbon reduction, driving costs and toll revenue; S3. Analyze the path impedance of each section in the road network based on the traffic and carbon emission data of trucks and buses in different sections and combine it with the road network selection model to generate dynamic adjustment parameters of the section. The path impedance includes tolls, fuel consumption costs and time value. S4. Dynamically adjust the rates according to the dynamic adjustment parameters of the road section and the real-time monitoring data: implement differentiated charges for fuel vehicles to guide them to choose low-carbon paths and reduce costs. For fuel trucks, further revise their rate adjustment parameters according to their gross cargo weight to encourage trucks to increase their full load rate, and implement preferential rates for new energy vehicles to encourage an increase in their usage ratio; S5. Solve the multi-objective optimization model using an optimization algorithm to generate a rate plan for each road section and vehicle type to ensure the coordinated realization of carbon reduction benefits, driving cost reduction, and toll revenue goals; S6. Implement the new rate plan and continuously monitor road network efficiency, carbon emissions and toll revenue, and optimize the rate strategy based on the monitoring results.

2. The method according to claim 1, characterized in that In step S1, when constructing a multi-objective optimization model, the constraints include: The carbon emissions of fuel vehicles on the recommended route should not exceed their carbon emissions on the original congested section; The travel time cost of fuel vehicles on the recommended route is significantly lower than that of the original route; The adjustment range of charging rates is limited to between the preset minimum rate and the maximum rate; The rate for new energy trucks shall not be lower than the rate for new energy buses; Meet the traffic flow conservation and road section capacity restrictions.

3. The method according to claim 1, characterized in that In step S2, the data collected in real time by the ETC system includes: Real-time traffic flow, average vehicle speed and energy consumption per unit mileage for each road section and vehicle type; Dynamically calculated carbon emissions of road sections based on carbon emission factors and energy consumption data; The comprehensive impact of the current toll scheme on road network travel time, total carbon emissions and toll revenue is evaluated through the path allocation model.

4. The method according to claim 1, characterized in that In step S3, the method for calculating the path impedance includes: A generalized cost function is used to comprehensively quantify tolls, fuel costs and time value, where the time cost is dynamically calculated based on the BPR function; Update the travel time of road sections according to real-time traffic flow, and predict the probability of vehicle path selection through the road network selection model; Carbon emissions and path impedance are combined to generate dynamic adjustment parameters to quantify the congestion level and environmental benefits of the road section.

5. The method according to claim 1, characterized in that In step S4, the mechanism for dynamically adjusting the rate includes: Increase the toll rate during peak hours or on congested roads to discourage vehicles from entering, and reduce the toll rate during non-peak hours or on smooth traffic sections to guide vehicle diversion; adjust the path selection behavior through differentiated toll rates to achieve dynamic balance of road network traffic flow; In step S6, the traffic carbon emission index, traffic efficiency and operating income are further optimized synchronously through a closed-loop feedback mechanism.

6. The method according to claim 1, characterized in that In step S4, the rate adjustment for fuel trucks includes: The rate adjustment coefficient is dynamically modified according to the ratio of the total weight of the vehicle and cargo to the registered load weight. The higher the full load ratio, the greater the rate discount; Set additional rate penalties for low-loaded or empty trucks to encourage transportation efficiency improvement; The ETC system obtains load data in real time and automatically triggers the rate correction mechanism.

7. The method according to claim 1, characterized in that In step S4, the preferential policies for new energy vehicles include: Set fixed preferential coefficients for new energy buses and trucks, and the preferential coefficient for new energy trucks shall not be lower than that for buses; Implement additional discounts for new energy vehicles on congested or high-carbon emission roads to ensure that their comprehensive rates are significantly lower than those of similar fuel vehicles; Dynamically monitor the proportion of new energy vehicle traffic and automatically increase the discount if it does not reach the preset threshold.

8. The method according to any one of claims 1 to 7, characterized in that: In step S5, a non-dominated sorting genetic algorithm NSGA-II is used to solve the multi-objective optimization model. The specific process includes: a) Initialize population parameters and OD traffic flow allocation scheme; b) The decision variables are iteratively transferred to the path allocation layer, the path travel time is calculated based on the BPR function, and the path flow is updated using the moving average method MSA; c) Perform non-dominated sorting and crowding calculation on individuals in the population according to the objective function value, and select the elite solution set; d) Perform crossover and mutation operations on the selected individuals to generate a new generation of population; e) The flow distribution and population evolution process is executed cyclically until the convergence condition is met or the preset number of iterations is reached, and then the Pareto optimal solution set is output.

9. The method according to claim 8, characterized in that In step S5, when generating a rate plan, the Pareto optimal solution is selected in the following manner: Prioritize comprehensive optimization solutions that simultaneously meet the goals of shortening road network travel time, reducing carbon emissions, and increasing toll revenue; For the solution set that cannot fully take into account multiple objectives, the recommended solution that focuses on carbon reduction benefits or revenue guarantee is selected according to the preset weight allocation scheme; The feasibility and stability of the solution set are verified by combining historical traffic data.

10. The method according to any one of claims 1 to 7, characterized in that: In step S6, the loop optimization mechanism includes: Regularly update the input parameters of the multi-objective optimization model based on real-time monitoring data; When the road network carbon emissions or congestion index exceeds the preset threshold, the dynamic rate emergency adjustment process is triggered; Analyze historical optimization results through machine learning algorithms and adaptively adjust model weight parameters to improve long-term optimization effects.

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