Traffic control measures optimization methods, equipment and media based on emission reduction targets

Abstract

The embodiment of the present invention discloses a method, device and medium for optimizing traffic control measures based on emission reduction targets. The method includes: obtaining a traffic network model of a target area, wherein the traffic network model covers multiple modes of transportation; using a genetic algorithm to determine the travel proportion and travel volume of each road section in the traffic network model according to the travel characteristics of each mode of transportation; allocating travel volume to the actual road network of the target area according to the travel proportion and travel volume of each road section, and identifying key control areas with overloaded travel volume according to the allocation results; calculating the total emissions of the existing road network under the allocation results, and determining the emission reduction target according to the total emissions; optimizing the traffic control measures of the key control area according to the allocation results to achieve the emission reduction target and travel efficiency target of the actual road network. This embodiment takes emission reduction targets into consideration in traffic control, and can achieve environmental benefits in traffic control.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of traffic environmental protection technology, and in particular to a method, device and medium for optimizing traffic control measures based on emission reduction targets. Background Art

[0002] In the traditional urban traffic resource allocation dominated by motorized travel, the optimization goal is to meet the needs of motorized development. Existing technologies (such as CN107331166B "A dynamic restriction method based on path analysis" and CN111127893A "Highway service area traffic control method based on vehicle-road collaboration technology") lack consideration of emission reduction targets when formulating traffic control measures, and are unable to achieve environmental benefits in traffic control. Summary of the invention

[0003] The embodiments of the present invention provide a method, device and medium for optimizing traffic control measures based on emission reduction targets to solve the above technical problems.

[0004] In a first aspect, an embodiment of the present invention provides a method for optimizing traffic control measures based on emission reduction targets, including:

[0005] Acquire a traffic network model of a target area, wherein the traffic network model covers multiple modes of transportation;

[0006] According to the travel characteristics of each mode of transportation, a genetic algorithm is used to determine the travel proportion and travel volume of each section in the traffic network model, wherein the genetic algorithm takes reducing emissions and improving travel efficiency as optimization goals;

[0007] According to the travel proportion and travel volume of each road section, the travel volume is allocated to the actual road network of the target area, and the key control areas with overloaded travel volume are identified according to the allocation results;

[0008] Calculate the total emissions of the existing road network under the allocation results, and determine the emission reduction target based on the total emissions;

[0009] The traffic control measures in the key control area are optimized according to the allocation results to achieve the emission reduction target and travel efficiency target of the actual road network.

[0010] In a second aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0011] one or more processors;

[0012] a memory for storing one or more programs,

[0013] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for optimizing traffic control measures based on emission reduction targets as described in any embodiment.

[0014] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing traffic control measures based on emission reduction targets as described in any embodiment.

[0015] An embodiment of the present invention provides a method for optimizing traffic control measures based on emission reduction targets. The method optimizes the travel proportion and travel volume of each road section according to the traffic network model of the target area, and applies the optimization result to the existing road network to identify the control area with overloaded travel volume. At the same time, the emission reduction target is determined by the total emissions of the existing road network, and the control parameters of traffic control measures and their combination schemes are optimized according to the emission reduction target to meet the emission reduction target and road network operation efficiency indicators, providing a basis for decision-making and implementation of traffic control, thereby reducing pollutant emissions and improving the traffic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a method for optimizing traffic control measures based on emission reduction targets provided in an embodiment of the present invention.

[0018] Figure 2 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0020] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0021] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0022] Figure 1 1 is a flow chart of a method for optimizing traffic control measures based on emission reduction targets provided by an embodiment of the present invention. The method is applicable to traffic control in a specific area and is executed by an electronic device. Figure 1 As shown, the method specifically includes:

[0023] S110. Obtain a traffic network model of a target area, wherein the traffic network model covers multiple modes of transportation.

[0024] The target area refers to a city or area that needs traffic control. This embodiment will optimize the traffic control measures in the area. The traffic network model refers to a model used to reflect the traffic network structure of the target area. The model covers multiple modes of transportation such as walking, cycling, cars, ground buses and rail transit. Among them, walking, cycling and rail transit have independent infrastructures, and they correspond to different network structures when modeling; ground buses and cars share road resources and can share a network structure. The network structures of various modes of transportation together constitute the traffic network model.

[0025] The constructed transportation network model includes nodes and edges. Nodes represent transportation hubs or important intersections in the target area, and edges represent roads or rail lines connecting these nodes. The connection relationship between each node reflects the travel path selection of different transportation modes.

[0026] S120. According to the travel characteristics of each mode of transportation, a genetic algorithm is used to optimize the travel proportion and travel volume of each road section in the traffic network model, wherein the genetic algorithm takes reducing emissions and improving travel efficiency as optimization goals.

[0027] This step uses the traffic network model to optimize the travel ratio and travel volume of various modes of transportation in each road section. The optimization result represents the traffic conditions of the target area under an ideal condition, or can be understood as a travel volume planning for the target area.

[0028] In a specific implementation, first, based on the historical traffic data of the target area, the travel characteristics of each mode of transportation are extracted, and the travel characteristics include travel time, travel cost, and selection preference, etc. Optionally, the travel time can be obtained through a traffic flow monitoring system; the travel cost includes fuel cost, parking fee, ticket price, etc., which can be obtained through historical price information; the selection preference can be obtained through survey data or big data analysis, reflecting the traveler's preference for different modes of transportation.

[0029] Based on the travel characteristics of each mode of transportation, a utility function of each mode of transportation can be constructed, which is used to evaluate the comprehensive attractiveness of each mode of transportation to travelers. Optionally, the utility function can be expressed as:

[0030]

[0031] in, and Indicates the mode of transportation travel time, travel cost and choice preferences; They are weight coefficients respectively. The weight coefficients can be calibrated through actual data to reflect the preferences and needs of different travelers.

[0032] Based on the utility function, a fitness function of the genetic algorithm can be constructed, which will update the parameters to be optimized in the genetic algorithm to continuously reduce the travel efficiency and total emissions of the traffic network model.

[0033] Optionally, in addition to travel efficiency and total emissions, the stability of traffic operation and road network travel demand can also be considered to construct the following fitness function:

[0034]

[0035] In the formula, the meaning of each variable is explained as follows:

[0036] Represents the fitness value.

[0037] Indicates road segment In transportation The travel volume under this condition is usually measured in vehicles / hour or pedestrians / hour, which reflects the traffic load of the road section.

[0038] The emission factor of transportation mode i is used to characterize the pollutant emissions generated by different transportation modes during driving. The main pollutants include carbon dioxide (CO 2 ), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), etc. The emission factor can be determined according to the type of transportation mode. For example, the emission factor of a car is usually greater than that of an electric bicycle or walking. Optionally, the emission factor of walking, cycling and rail transportation is 0.

[0039] Indicates road segment The length of represents the distance traveled by each mode of transport on this road section. The longer the road section, the more total emissions generated by the vehicles traveling on it.

[0040] Represents the total travel volume of each transportation mode on the road segment. Indicates road segment The carrying capacity is the maximum traffic flow (i.e., travel volume) that the road section can accommodate under normal circumstances. This value is usually determined by the road design (such as the number of lanes, width, etc.) and traffic management regulations. The unit is the same as If > , it means that the traffic flow on this road section has exceeded its design carrying capacity (i.e., travel volume is overloaded), which may cause congestion.

[0041] Indicates road segment travel needs, Indicates the mode of transportation On the road The travel demand on a given platform is expressed in vehicles / hour or pedestrians / hour. It can be obtained through historical traffic data or prediction models.

[0042] represents the number of transportation modes, Used to index all transportation modes involved in the fitness function; Indicates the number of road segments, All road segments used to index emissions calculations; Indicates the number of road sections with overloaded travel volume, Used to index road segments that are overloaded with travel.

[0043] are weight coefficients, which are used to balance different optimization objectives in the fitness function. Used to optimize travel efficiency, that is, to minimize travel time and cost; For optimizing emissions, i.e., to reduce the polluting emissions generated by traffic; It is an optimization item for traffic control, which aims to avoid overloading of road sections and improve the stability of traffic operation. It is a penalty term for flow conservation, ensuring that the traffic flow of each road section meets travel demand. ; Together we ensure that traffic is not too low while avoiding traffic overload.

[0044] Optionally, in addition to travel efficiency and total emissions, traffic stability and utilization of road facilities can also be considered to construct the following fitness function:

[0045]

[0046] The variables with the same signs as those in the fitness function (2) have the same meanings. The meanings of the remaining variables are explained as follows:

[0047] Represents the fitness value.

[0048] Indicates the number of road sections where the travel volume is not overloaded, .

[0049] Indicates the road section where the traffic volume is not overloaded The total number of trips by transportation mode, Indicates road segment carrying capacity.

[0050] It indicates the ideal ratio of the total travel volume to the carrying capacity of a road section, for example, 60%. When the travel volume is close to this ideal ratio, it can maintain the effective use of road infrastructure while avoiding congestion, which can be regarded as an ideal travel volume. represents the sensitivity parameter of the ideal ratio constraint, Formed by is the independent variable, is the mean, Gaussian distribution with standard deviation and 1 as the maximum value; hour, Take the maximum value 1.

[0051] is the weight coefficient, It is used to optimize the utilization of road infrastructure, that is, when the travel volume of a road section does not exceed its carrying capacity, it is hoped to minimize the gap between the travel volume of the road section and the ideal travel volume.

[0052] The above two fitness functions evaluate the traffic mode allocation scheme from different perspectives, and can be flexibly selected according to the actual situation in practical applications. It is suitable for situations where the road network travel demand is relatively clear, such as when there is abundant historical traffic data and the road network travel conditions are relatively stable, the historical travel demand can be used as the road network travel demand; for example, when there is a traffic demand prediction model with high accuracy, the prediction model can be used to predict the road network travel demand. Fitness function It is applicable to situations where the road network travel demand is not very clear, such as when the road network travel conditions change frequently, or when neither historical data nor prediction models can be used to make a good prediction, then the infrastructure-related traffic planning information can be introduced into the allocation of traffic modes through the utilization rate of road infrastructure. Of course, the two fitness functions can also be used in combination, such as including the five constraints involved in the two fitness functions in the same fitness function, and adjusting the emphasis through various weight coefficients, etc. This embodiment does not make specific restrictions.

[0053] By executing a genetic algorithm based on any of the above fitness functions, the travel ratio and travel volume of each road section in the traffic network model can be optimized. Optionally, the execution of the genetic algorithm may include the following steps:

[0054] Step 1: Encode chromosomes. Each chromosome represents a combination of travel proportions and travel volumes of each road section in the traffic network model. For example, there may be three modes of travel on a road: walking, cycling, and driving. The travel proportion of each mode and the road section flow are encoded as genes.

[0055] Step 2: Perform selection, crossover and mutation operations on each chromosome.

[0056] Selection: According to any of the above fitness functions, select individuals with better performance (smaller fitness value), and the optimal combination of travel proportion and road section flow of these individuals will be given priority for crossover and mutation operations.

[0057] Crossover: Exchange some genes of two chromosomes to generate new traffic mode allocation plans and test new travel ratios and flow combinations.

[0058] Mutation: Randomly adjust the gene values ​​of certain chromosomes to simulate changes in travel proportions of different transportation modes to explore potential optimization solutions.

[0059] Step 3: Repeat the above operations based on the new chromosome to continuously optimize the travel ratio and road flow. The goal is to find a reasonable traffic mode allocation plan that can reduce travel time and emissions. The optimization process will continue until the fitness value converges or the maximum number of iterations is reached.

[0060] S130. Allocate travel volume to the actual road network of the target area according to the travel proportion and travel volume of each road section, and identify key control areas with overloaded travel volume based on the allocation results.

[0061] The actual road network can also be called the current road network, which represents the current actual road network structure, including the actual road layout, road conditions and traffic restrictions. This information is not fully considered in the above-mentioned traffic network model, and the choice of travelers in the actual road network may also be different from the path choice in the traffic network model, so that the travel volume of certain sections in the actual road network may be greatly different from the optimization result of the traffic network model. In view of this, this step re-allocates the travel volume of the current road network according to the travel proportion and travel volume of each section obtained in S120 to reflect the actual situation of the current road network.

[0062] Optionally, the current road network allocation can be performed based on a mature traffic flow allocation model. Specifically, the travel proportion and flow optimized in S120 are applied to a traffic flow allocation model, such as a four-stage model, which will simulate how different modes of transportation flow in the urban transportation network through iterative calculations, and determine the actual travel proportion and travel volume of each road section. Furthermore, it is also possible to combine the time series analysis model to predict the travel volume in different time periods in the future, so as to ensure that the optimization plan of subsequent measures can cope with dynamic traffic demand, especially changes in peak hours in the morning and evening.

[0063] After obtaining the travel volume allocation results of the current road network, the sections where the actual travel volume exceeds the upper limit of the carrying capacity can be identified as travel volume overload areas, and these areas will be the focus of subsequent traffic control. Optionally, the identification standard is that the actual flow of a certain section of road exceeds 90% of its design carrying capacity. Furthermore, in order to ensure the real-time effectiveness of the control area, a dynamic monitoring mechanism can be introduced to update the control area according to the travel volume in different time periods in the future, to ensure that subsequent control measures are always implemented in the most congested areas.

[0064] S140: Calculate the total emissions of the current road network under the allocation results, and determine the emission reduction target based on the total emissions.

[0065] This step determines a reasonable emission reduction target based on the emissions of the existing road network. In order to facilitate the subsequent comparison of travel efficiency before and after traffic control, the total travel time of the existing road network can also be calculated.

[0066] Optionally, the total emissions of the existing road network can be calculated based on the travel volume, road section length and emission factors of each mode of transportation :

[0067]

[0068] Optional, total travel time on the existing network The travel volume and travel time of each road section can be multiplied and summed up to get:

[0069]

[0070] The meanings of the variables in formulas (4) and (5) are the same as those in the previous formulas, but and Determine the values ​​according to the results of the existing road network.

[0071] After the total emissions and total travel time of the current road network are calculated, a specific emission reduction target value can be determined based on the total emissions of the current road network. Optionally, the emission reduction target can be expressed as the ratio of the current total emissions to the ideal total emissions, with a value between 0 and 1. This target will serve as the core indicator for subsequent optimization of traffic control measures.

[0072] S150. Optimize the traffic control measures in the key control area according to the allocation result to achieve the emission reduction target and travel efficiency target of the actual road network.

[0073] After the emission reduction target is determined, this embodiment simulates the changes brought about by the implementation of traffic control measures in key control areas based on the road network allocation results, and achieves the goals of reducing emissions and improving traffic efficiency by optimizing the control parameters.

[0074] In a specific implementation, first, different traffic control measures in the key control areas can be combined. Optionally, traffic control measures include bus priority (such as setting up bus lanes), congestion charges (charging specific road sections during peak hours), parking fee adjustments (setting differentiated parking rates for specific areas), etc. Different measures can be used in combination. For example, the combination of bus priority and congestion charges can not only improve the efficiency of the use of the public transportation system, but also limit the frequency of use of private cars through charges. Furthermore, under the dynamic monitoring mechanism, the selection of specific combinations can be dynamically adjusted according to the actual situation of the control area in each time period in the future.

[0075] Then, through traffic flow simulation, the traffic flow changes of the existing road network under each combination are simulated; according to the simulation results of each combination, it is calculated whether each combination has achieved the emission reduction target and travel efficiency target after the set control period; if not, the control parameters under each combination are optimized, and the traffic flow simulation operation is re-executed according to the optimized control parameters; this cycle is repeated until the emission reduction target and travel efficiency target are finally achieved.

[0076] Furthermore, the core goal of this optimization is to achieve the emission reduction target by adjusting the parameters of the measures (such as toll sections and rates, the location and time of bus lanes, etc.). The optimization process can be achieved manually based on experience or automatically through genetic algorithms, where the fitness function in the genetic algorithm is centered on the balance between emission reduction and total travel time. Regardless of the implementation method, the optimization objects and specific goals of each control measure include:

[0077] 1) Congestion charging optimization: Optimize parameters such as toll sections, toll periods, and toll rates to find a combination that can minimize traffic flow without having too much negative impact on travel efficiency.

[0078] 2) Public transport priority optimization: By optimizing the location and usage time of bus lanes, the attractiveness and efficiency of public transport can be improved, thereby reducing the use of private cars.

[0079] 3) Parking fee optimization: Differentiate parking fees, especially by increasing parking fees in high-demand areas to reduce private car travel in the area.

[0080] After the control parameters are optimized, two main indicators are used to evaluate the control effect of each combination: one is the change in total travel time, and the other is the reduction ratio of total emissions. The evaluation criteria include:

[0081] 1) Time efficiency: evaluate whether the total travel time has been reduced within a reasonable range after the implementation of control measures. In the case of dynamic changes in travel volume, focus on whether traffic congestion during peak hours has been effectively improved.

[0082] 2) Emission benefits: Compare the total emissions before and after the implementation of the measures, and calculate the absolute amount and relative proportion of emission reduction.

[0083] Compare the time benefits and emission benefits of different combinations under their respective optimal control parameters, and select the combination with the best emission benefit and time benefit; use this combination and its optimal control parameters as the final control plan to provide a basis for decision-making and implementation of traffic control.

[0084] In summary, an embodiment of the present invention provides a method for optimizing traffic control measures based on emission reduction targets, which optimizes the travel proportion and travel volume of each road section according to the traffic network model of the target area, and applies the optimization result to the existing road network to identify the control area with overloaded travel volume; at the same time, the emission reduction target is determined by the total emissions of the existing road network, and the control parameters of traffic control measures and their combination schemes are optimized according to the emission reduction target to meet the emission reduction target and road network operation efficiency indicators, providing a basis for decision-making and implementation of traffic control, thereby reducing pollutant emissions and improving the traffic environment.

[0085] Figure 2 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 2 A processor 60 is taken as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0086] The memory 61 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the traffic control measures optimization method based on emission reduction targets in the embodiment of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 61, that is, realizing the above-mentioned traffic control measures optimization method based on emission reduction targets.

[0087] The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include a memory remotely arranged relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0088] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0089] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing traffic control measures based on emission reduction targets of any embodiment.

[0090] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0091] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0092] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0093] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing traffic control measures based on emission reduction targets, characterized in that: include: Acquire a traffic network model of a target area, wherein the traffic network model covers multiple modes of transportation; According to the travel characteristics of each mode of transportation, a genetic algorithm is used to determine the travel proportion and travel volume of each road section in the traffic network model, wherein the genetic algorithm takes reducing emissions and improving travel efficiency as optimization goals; According to the travel proportion and travel volume of each road section, the travel volume is allocated to the actual road network of the target area, and the key control areas with overloaded travel volume are identified according to the allocation results; Calculate the total emissions of the existing road network under the allocation results, and determine the emission reduction target based on the total emissions; Optimizing the traffic control measures in the key control area according to the allocation results to achieve the emission reduction target and travel efficiency target of the actual road network; The fitness function of the genetic algorithm is used to reduce the travel cost, total emissions, and the number of road sections that need to be regulated in the traffic network model; The fitness function is: ; ； in, represents the fitness function value, Indicates the mode of transportation The utility function of Indicates road segment j Mode of transportation i The amount of travel; Indicates the mode of transportation i The emission factor, Indicates the length of the road segment; Indicates road segment k Total trips by mode of transport, Indicates road segment k Design load-bearing capacity; Indicates road segment j travel needs; m Indicates the total number of road sections. n Indicates the number of transportation modes, l Indicates the number of road sections where the total travel volume exceeds the carrying capacity; and is the weight coefficient.

2. The method according to claim 1, characterized in that Replace the fitness function with: ; in, represents the fitness function value, represents the utility function of transportation mode i; Indicates road segment j Mode of transportation i The amount of travel; Indicates the mode of transportation i The emission factor, Indicates the length of the road segment; Indicates road sections where the traffic volume is not overloaded p The total number of trips by transportation mode, Indicates road segment p Carrying capacity; Indicates the road section with overloaded traffic k The total number of trips by transportation mode, Indicates road segment k Carrying capacity; r It represents the ideal ratio between the total travel volume and carrying capacity of the road section. a sensitivity parameter representing the ideal ratio constraint; m Indicates the total number of road sections. n represents the number of travel modes, Indicates the number of road sections where the travel volume is not overloaded. Indicates the number of road sections with overloaded travel volume; and is the weight coefficient.

3. The method according to claim 1, characterized in that The method allocates the travel volume to the actual road network of the target area according to the travel proportion and travel volume of each road section, and identifies the key control areas with overloaded travel volume according to the allocation results, including: According to the travel proportion and travel volume of each road section, the traffic flow allocation model is used to allocate the travel volume of the existing road network; Road sections where the allocated travel volume exceeds the upper limit of the carrying capacity will be identified as key control areas for travel overload.

4. The method according to claim 1, characterized in that: Optimizing the traffic control measures in the key control area according to the allocation results to achieve the emission reduction target and travel efficiency target of the actual road network includes: Combining different traffic control measures in the key control areas and optimizing the control parameters under each combination so that the traffic network achieves the emission reduction target; Compare the total emissions and travel efficiency of different combinations under their respective optimal control parameters, and select the combination with the smallest total emissions and meeting the travel efficiency target as the final traffic control measure.

5. The method according to claim 1, characterized in that: Optimizing the traffic control measures in the key control area according to the allocation result includes: Perform traffic flow simulation according to the allocation results to obtain traffic flow changes of the current road network under various combinations; Based on the simulation results, calculate whether each combination has achieved the emission reduction target and travel efficiency target after the set control period; If it is not achieved, a genetic algorithm is used to optimize the control parameters under each combination, and the traffic flow simulation operation is re-executed according to the optimized control parameters; this cycle is repeated until the emission reduction target and travel efficiency target are finally achieved.

6. The method according to claim 1, characterized in that The method allocates the travel volume of the actual road network of the target area according to the travel proportion and travel volume of each road section, and identifies the key control area with overloaded travel volume according to the allocation result, including: dynamically allocating the travel volume of the current road network in multiple future time periods according to the travel proportion and travel volume of each road section using a traffic flow allocation model; and identifying the key control area with overloaded travel volume in each future time period according to the dynamic allocation result; Correspondingly, the traffic control measures in the key control area are optimized according to the allocation results to achieve the emission reduction target and travel efficiency target of the actual road network, including: according to the travel volume allocation results in each future time period, the traffic control measures in the key control area in each future time period are optimized to achieve the emission reduction target and travel efficiency target of the actual road network.

7. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method for optimizing traffic control measures based on emission reduction targets as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, which, when executed by a processor, implements the method for optimizing traffic control measures based on emission reduction targets as described in any one of claims 1-6.

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