Highway maintenance construction area vehicle operation carbon emission metering method based on multi-agent simulation, storage medium and equipment

Abstract

The invention discloses an expressway maintenance construction area vehicle operation carbon emission metering method based on multi-agent simulation, a storage medium and equipment. In the method, a simulation model based on a multi-agent system is constructed, and the method comprises a vehicle agent for realizing generation and position updating of a vehicle in a road network; the road section intelligent agent is used for updating road section flow and traffic capacity; and the construction intelligent agent realizes adjustment of the construction scheme of the construction road section. And according to the division of the vehicle types and the vehicle energy types, carbon emission generated by vehicle operation during construction is measured and calculated, and carbon emission generated by construction activities in the construction process is measured and calculated. According to the method, through quantitative analysis, the influence of different construction schemes on the average speed, the vehicle delay time and the carbon emission of the highway section can be evaluated. The rule that the traffic carbon emission changes along with the length of a construction area of a construction road section, the speed limit value and the lane closing form can be revealed and can be used as a reference basis for construction engineering design and implementation.

Description

Technical Field

[0001] The invention relates to a carbon emission measurement method, storage medium and equipment for vehicles running in a highway maintenance construction area based on multi-agent simulation, and belongs to the technical field of intelligent transportation. Background Art

[0002] Low carbon and environmental protection have always been a topic of great concern in the world today. The growth of traffic demand has also promoted the demand for highway construction. Due to their long service life, some highways have also suffered varying degrees of damage and aging. In order to ensure the safety of highways and improve their service levels, necessary maintenance projects are required. The impact of highway construction during highway maintenance on the environment only accounts for a small part of the entire life cycle. Most of the environmental pressure comes from the operation of highways, vehicle driving and transportation activities. Construction activities will lead to reduced traffic capacity of highway construction sections, causing traffic delays, and thus generating considerable additional carbon emissions.

[0003] At present, most of the technologies for calculating carbon emissions from highway maintenance projects only focus on the material production and infrastructure consumption during the construction process, and rarely consider the traffic delay module caused by the construction section. However, on large highways with hundreds of thousands of vehicles passing through every day and a high rate of large vehicles, the additional fuel consumption and carbon emissions caused by delays cannot be ignored. The research on multi-agent systems in traffic simulation is widely used in traffic flow modeling, travel driving analysis, and traffic policy evaluation in urban traffic. However, the simulation research on the energy consumption, emission behavior, and corresponding traffic organization methods of vehicles on highways, especially in construction sections, is still relatively scarce. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies in the prior art and to provide a method, storage medium and device for measuring carbon emissions from vehicle operation in highway maintenance construction areas based on multi-agent simulation. In this method, a multi-agent system simulation model is constructed for the highway during the construction phase to simulate vehicle operation. This method can study the traffic organization mode of the highway construction section and specifically examine the carbon emission characteristics of different types of vehicles in the highway system under different construction schemes.

[0005] The technical solution adopted by the present invention is as follows:

[0006] The carbon emission measurement method of vehicle operation in highway maintenance construction area based on multi-agent simulation includes the following steps:

[0007] S1. Obtain the interchange flow and travel distribution of the expressway to be constructed and the basic information of the road network, conduct data statistics, determine the spatiotemporal characteristics of expressway vehicle travel demand and calibrate the parameters required for modeling;

[0008] S2. Construct a vehicle operation simulation model during the highway construction phase based on a multi-agent system. Use a microscopic traffic model to simulate the individual characteristics of vehicles on the highway under different construction schemes, as well as the vehicle-road interaction and vehicle-vehicle interaction effects of traffic flow;

[0009] S3. Based on the bottom-up inventory method, the carbon dioxide emissions generated by simulated vehicle operation on the highway during the construction period are calculated, and combined with the staged life cycle theory to calculate the carbon dioxide generated during the construction process. Based on this method, a reference basis can be provided for the design and implementation of construction projects;

[0010] In the above technical solution, further, S1 specifically includes:

[0011] S11 first determines the scope of the research area, collects the interchange flow data, travel distribution and basic information of the road network of the highways involved in the construction area, and cleans the data;

[0012] S12 performs descriptive statistical analysis on the collected data to determine the temporal and spatial distribution characteristics of vehicle flow, identify peak hours, vehicle type distribution, and travel patterns, analyze the flow change patterns at key nodes and sections, and obtain model input data.

[0013] Furthermore, the multi-agent simulation model constructed in S2 includes:

[0014] The data input layer is the basis of the simulation process and provides various initial data required for modeling, including road network information and vehicle information, such as: road network nodes, topology diagrams, basic attributes of road sections, OD distribution of highway traffic, road section capacity and free flow speed and other road network parameters, vehicle types, OD matrix, road section flow data, the proportion of different types of vehicles in different time periods and other vehicle parameters;

[0015] The entity layer defines the properties and characteristics of different agents involved in the simulation process, including vehicle agents: representing vehicles traveling on the highway; road segment agents: representing various sections or segments of the road; and construction agents: representing the implementation of traffic management strategies for construction activities.

[0016] The operation layer, which performs operations through key modules to simulate the operation of a highway construction site;

[0017] The data output layer is used to record the simulation results and output various statistical data, among which the key indicators include: the overall carbon dioxide emissions of highway vehicles, the mileage of all vehicles, vehicle delay time, and the average speed of the road section.

[0018] Furthermore, the operation layer of the multi-agent simulation model constructed in S2 specifically includes:

[0019] S21 Initialization Module: It is used to initialize all agents according to the input data and give them various attributes and values ​​required for simulation. This includes setting the initial position, speed, destination, etc. of the vehicle, as well as determining the initial state of the road section and the setting of the construction section.

[0020] S22 Vehicle generation module: Generate corresponding vehicles on the highway section and arrange vehicle travel plans according to the preset vehicle type, departure time, starting and ending locations.

[0021] S23 Driving Module: The driving module controls the vehicle to drive on the road at a set average speed, reflecting the real-time traffic flow status. The vehicle adjusts the driving speed according to the traffic capacity and traffic volume of the road section to simulate the vehicle driving conditions on the real road.

[0022] S24 Planning Module: The planning module is responsible for planning the optimal driving route for the vehicle. Based on the vehicle's starting and ending points and the current road conditions of each section, the module uses a path planning algorithm to determine the shortest path the vehicle should take to avoid congestion and delays.

[0023] S25 Section Flow and Capacity Update Module: monitors the traffic flow of vehicles on each section and dynamically updates the section's capacity based on the section's attributes.

[0024] S26 Construction Plan Module: The Construction Plan Module simulates different traffic management strategies, adjusts speed limits and construction section parameters, and evaluates the impact of different construction plans on traffic flow.

[0025] S27 Energy Consumption Statistics Module: Tracks and estimates the energy consumption of each vehicle during the simulation process, and simulates and reflects the dynamic changes of energy consumption with speed during vehicle operation.

[0026] Furthermore, the driving module includes:

[0027] When a vehicle is traveling on a road segment, it will go to the next highway interchange node according to the planned route. The speed of the vehicle on the road segment is determined by the traffic flow of the road network, that is, the average speed on the road segment is used to control the speed of the vehicle at each iteration; the vehicle's driving speed is as follows:

[0028]

[0029] in, is the driving speed of the vehicle during this iteration; is the average speed of the road section during this iteration; The speed limit for the road section;

[0030] The distance travelled by the vehicle each time is:

[0031]

[0032] Among them, D T (m) is the distance traveled in this iteration process; Indicates the distance between the vehicle and the next interconnected node in the previous iteration; Δt is the simulation duration of each iteration;

[0033] According to the vehicle's travel distance D T (m), update the vehicle's mileage and the distance to the next node. If the vehicle is traveling on a construction section, the distance from the construction area and the distance required to leave the construction area must also be updated accordingly.

[0034] Furthermore, the method for updating the section flow in the section flow and traffic capacity update module is as follows:

[0035] On an ordinary road section that is not under construction, when a vehicle is at an interchange node in the mth iteration, that is, it has just arrived at the section, the flow rate of the section is increased by 1. When the distance from the vehicle to the next node in the mth iteration is less than or equal to 0, the vehicle has left the section, and the flow rate of the section is reduced by 1.

[0036] If the vehicle label attribute of the vehicle passing through the road section is a medium or large vehicle, the number of large vehicles on the road section needs to be updated synchronously;

[0037] If a road section is under construction, it will be divided into three sections: pre-entry into the construction area, construction area, and exit from the construction area. Similar to the sections not under construction, the traffic flow and number of large vehicles in the three sections will be updated.

[0038] Furthermore, the method for updating the section traffic capacity in the section traffic and traffic capacity update module is as follows: the section traffic capacity is the maximum traffic flow that the section can accommodate, which changes due to the characteristics of the section and the traffic flow state, specifically:

[0039] C p =C B ×N lane ×f HV ×f w ×f p

[0040] f HV =1 / 1+P HV (E HV -1)

[0041] P HV =N HV / Q

[0042] Among them, C p is the actual traffic capacity of the basic section of the expressway; CB It is the basic traffic capacity of the basic section of the expressway; N lane is the number of lanes in the highway section; f HV is the correction factor for large vehicles; f w is the correction factor for the lane width and lateral clear width of the road section; f P P is the driver influence correction coefficient; HV N is the proportion of medium and large vehicles in the total traffic volume of the road section; HV is the number of large and medium-sized vehicles on the road section; E HV is the coefficient for converting large vehicles into small passenger cars; Q is the flow rate of the road section.

[0043] Furthermore, the bottom-up inventory method in S3 is a calculation method based on micro data and detailed vehicle information. The bottom-up inventory method is used to evaluate the carbon emissions generated by the operation of each individual vehicle in the transportation system. The overall carbon emissions of highway vehicles are:

[0044]

[0045] Where CE represents the total carbon emissions from transportation; i represents the type of vehicle (such as small passenger cars, large trucks, special operation vehicles, etc.); j represents the type of energy (such as gasoline, diesel, new energy, etc.); N ij Indicates the number of vehicles of a specific type traveling; M ij Indicates the mileage of a specific type of vehicle within a certain period of time; FC ij Indicates the average fuel consumption per unit mileage of a specific type of vehicle; F j Indicates the carbon emission factor for a vehicle using a specific fuel type.

[0046] Furthermore, the staged life cycle theory is used in S3 to calculate the carbon emissions of the two stages of material production and construction during the construction process. Taking the final product of the material as the object, the usage of various building materials during the construction process is determined, and the formula for determining the total emissions generated in this stage based on the unit carbon emissions of the material is as follows:

[0047]

[0048] Among them, A i represents the usage of the i-th material; Represents the carbon emission factor of the ith material.

[0049] During the maintenance construction process, various types of construction machinery will be used to complete various construction processes. These machines consume energy during operation, mainly diesel or electricity. Energy consumption is the source of carbon emissions in this stage. The formula for determining the total carbon emissions based on the energy consumed by all construction machinery during the construction cycle is as follows:

[0050]

[0051] Among them, H i is the usage time of the i-th construction equipment; is the carbon emission factor of the i-th device.

[0052] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the carbon emission measurement of vehicle operation in a highway maintenance construction area based on multi-agent simulation as described in any one of the above items is implemented.

[0053] The present invention also provides a computer electronic device, comprising a memory and a processor;

[0054] The memory is used to store computer programs;

[0055] The processor is used to implement the carbon emission measurement of vehicles operating in highway maintenance construction areas based on multi-agent simulation as described in any of the above items when executing the computer program.

[0056] The beneficial effects of the present invention are:

[0057] The present invention accurately calculates the carbon emissions generated by different vehicles during operation through detailed classification of vehicle types, energy types and driving speeds; conducts carbon assessment on vehicle operation on the expressway network under different traffic organization schemes based on a multi-agent model; analyzes the impact of different construction organization schemes on traffic flow through quantitative analysis of simulation models; reveals the law of changes in traffic carbon emissions with the speed limit value of the construction section length and the construction closure mode, selects a better construction scheme, and reduces the negative impact of construction on traffic and the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of a method in a specific embodiment of the present invention;

[0059] Figure 2 A hierarchical framework diagram of multi-agent simulation of a highway operation system in a specific embodiment of the present invention;

[0060] Figure 3 Run flow charts for multi-agent simulation systems;

[0061] Figure 4 This is a schematic diagram of the construction section area division of the simulation model;

[0062] Figure 5 A diagram showing lane closures for construction sections;

[0063] Figure 6 Speed-energy consumption regression fitting curves for different types of vehicles;

[0064] Figure 7 This is the base map of Hangzhou Ring Expressway;

[0065] Figure 8 This is the topology diagram of the Hangzhou Ring Expressway network;

[0066] Fig. 9 It is the traffic proportion chart of vehicle types throughout the year;

[0067] Fig.10 It is the chord diagram of OD distribution of highway vehicles throughout the day;

[0068] Fig.11 It is the highway traffic distribution map for different time periods throughout the day;

[0069] Fig.12 Model verification diagram for multi-agent simulation system;

[0070] Fig.13 Schematic diagram for example analysis;

[0071] Fig.14 This is a graph showing the average speed change of the road section under different construction closure modes;

[0072] Fig.15 This is the sensitivity analysis diagram of the construction operation area length;

[0073] Fig.16 This is a graph showing the changes in additional carbon emissions under different construction closure methods; DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 are within the scope of protection of the present invention.

[0075] According to a specific embodiment of the present invention, a method for measuring carbon emissions from vehicle operation in a highway maintenance construction area based on multi-agent simulation is provided. Figure 1 As shown, the following steps are included:

[0076] S1. Analysis of spatiotemporal characteristics of highway travel demand and calibration of model parameters

[0077] S11. Road network information calibration

[0078] S111. Determination of road network nodes

[0079] Define all nodes in the road network, including interchanges, entrances and exits, transportation hubs and other key nodes.

[0080] S112. Construction of road network topology

[0081] Draw a topological diagram of the road network to clarify the connection relationship and flow direction between nodes.

[0082] S113, road section attribute calibration

[0083] The basic properties of each road section, such as length, number of lanes, and design speed, are measured and recorded as input data for model initialization.

[0084] S12. Vehicle information calibration

[0085] S121. Collect vehicle flow data for each section and time period of the expressway, clean the data, and process missing values ​​and outliers. Extract key features from the original data, including vehicle type, axle ratio, and interchange and section flow size.

[0086] S122. Analyze the origin and destination (OD) matrix to understand the distribution of the vehicle's travel origin and destination points.

[0087] S123. Serialize the traffic data according to the time sequence to identify time characteristics such as peak travel time and off-peak travel time.

[0088] S2. Simulation mechanism of vehicle operation in highway construction area based on multi-agent system

[0089] In order to simulate the operation and emission of vehicles on the highway to be constructed, a multi-agent system simulation method is used. Figure 2 The simulation model is constructed using a hierarchical framework diagram; the simulation iteration step is 1 minute, and the maximum number of simulation iterations is 1440, that is, the total simulation duration is 24 hours a day, and the carbon emissions of the highways within the research scope are counted for a whole day. The simulation process is as follows: Figure 3 .

[0090] S22. Vehicle Generation

[0091] According to the actual traffic flow and travel mode, vehicles are dynamically introduced into the simulated road network. The starting point and destination of the vehicle are determined according to the OD matrix of the highway, and vehicles are generated according to the established time intervals and based on specific time distribution. Each vehicle is given a series of attributes when it is created, including vehicle model, energy type, and initial driving speed.

[0092] S221. Classification of vehicle axle type and energy type

[0093] According to the standard of "Vehicle Type Classification for Toll Road Vehicle Tolls" (JT 2019-02), vehicles are divided into passenger cars, trucks and special-purpose vehicles. This standard applies to all types of vehicles traveling on toll roads.

[0094] Small and medium-sized buses usually use gasoline engines, while large buses (such as buses and long-distance coaches) may use diesel engines. Trucks usually use diesel as fuel, and diesel engines are more suitable for heavy loads and long-distance transportation. The fuel type of special operation vehicles varies depending on the type of vehicle and its purpose. On highways, Class 1 buses are more common, and most long-distance special operation vehicles with heavy load transportation needs use diesel as fuel. Therefore, in the process of vehicle generation, only Class 1 buses are considered as electric vehicles, and all special operation vehicles are diesel vehicles. As shown in Table 1.

[0095] Table 1 Generated vehicle types

[0096]

[0097] S23: Vehicle position update

[0098] When a vehicle is traveling on a road segment, it will follow the planned route to the next highway interchange node. The speed of the vehicle on the road segment is determined by the traffic volume of the road network, that is, the average speed on the road segment is used to control the speed of the vehicle at each iteration. The vehicle's driving speed is as follows:

[0099]

[0100] in, is the driving speed of the vehicle during this iteration; is the average speed of the road section during this iteration; The speed limit for the road section.

[0101] The distance traveled by the vehicle each time is calculated as follows:

[0102]

[0103] Among them, D T (m) is the distance traveled in this iteration process; It represents the distance between the vehicle and the next interconnected node in the previous iteration; Δt is the simulation duration of each iteration.

[0104] According to the vehicle's travel distance D T (m), the vehicle’s mileage and distance to the next node can be updated as shown in the following formula. If the vehicle is driving on a construction section, the distance from the vehicle to the construction area must also be updated accordingly. And the distance required to leave the construction area

[0105] Mile C (m) = Mile C (m-1)+DT (m)

[0106]

[0107] Among them, Mile C (m) is the cumulative mileage of the vehicle in this iteration.

[0108] S24. Vehicle route selection

[0109] When a vehicle has just entered the road network or has reached an interchange node while traveling on a road section and is preparing to drive to the next interchange node, the Dijkstra shortest path algorithm will be used to plan a path with the shortest travel time based on the road resistance function on the road network, thereby updating the next node and the road section where the vehicle is located at the next iteration.

[0110] If the vehicle reaches the destination node, the vehicle leaves the road network and reaches the highway interchange exit. At this time, the vehicle leaves the system and completes the settlement.

[0111] S25, road traffic update

[0112] S251, Vehicle traffic update when entering normal road section

[0113] On an ordinary road section that has not been constructed, when a vehicle is at an interchange node in the mth iteration, that is, it has just arrived at the section, the flow rate of the section is Q(m) = Q(m-1) + 1. When the distance from the vehicle to the next node in the mth iteration is less than or equal to 0, the vehicle has left the section, and the flow rate is Q(m) = Q(m-1)-1.

[0114] If the vehicle label attribute of the vehicle passing through the road section is identified as a medium or large vehicle, the number of large vehicles N on the road section needs to be updated. HV (m) = N HV (m-1)+1.

[0115] S252, Vehicle flow update for entering construction section

[0116] If a road section is under construction, the road section is divided into three sections: pre-entry construction area, construction area, and exit construction area. The flow rates of the three sections are Q′, Q″, and Q″′ respectively. Figure 4 shown.

[0117] When a vehicle's label attribute identifies that the vehicle has just arrived at the construction section in the mth iteration, the flow rate before entering the construction area is Q′(m)=Q′(m-1)+1.

[0118] When a vehicle's label attributes identify that the vehicle has entered a construction section in the mth iteration, the flow rate in the construction area is Q″(m)=Q″(m-1)+1, and the flow rate pre-entering the construction area is Q′(m)=Q′(m-1)-1.

[0119] When the label attribute of a vehicle identifies that the vehicle has left the construction area in the mth iteration, the flow rate of this section Q″(m)=Q″(m-1)-1, and the flow rate leaving the construction area Q″′(m)=Q′(m-1)+1. After the vehicle leaves this section and arrives at the next section, the flow rate Q″′ leaving the construction area is updated accordingly.

[0120] If the vehicle label attribute of the vehicle passing through the road section identifies the vehicle as a medium or large vehicle, the large vehicle flow N′ of the three sections will be updated accordingly HV , N″ HV , N″′ HV , the entire road section N HV =N′ HV +N″ HV +N″′ HV .

[0121] S26. Calculation of road section capacity

[0122] The capacity of a road section refers to the maximum traffic flow that the road section can accommodate under specific conditions. It is a particularly important variable for solving the driving speed of the road section. According to the "Road Capacity Manual", the specific formula can be obtained as follows:

[0123] C p =C B ×N lane ×f HV ×f w ×f p

[0124] f HV =1 / 1+P HV (E HV -1)

[0125] P HV =N HV / Q

[0126] Among them, C p is the actual traffic capacity of the basic section of the expressway; C B It is the basic traffic capacity of the basic section of the expressway; N lane is the number of lanes in the highway section; f HV is the correction factor for large vehicles; f w is the correction factor for the lane width and lateral clear width of the road section; f P P is the driver influence correction factor;HV N is the proportion of medium and large vehicles in the total traffic volume of the road section; HV is the number of large and medium-sized vehicles on the road section; E HV is the coefficient for converting large vehicles into small passenger cars; Q is the flow rate of the road section.

[0127] The capacity of a highway section is affected by the geometric characteristics of the section and the traffic flow state. If the highway is under maintenance and the lanes are closed, the number of one-way lanes will decrease, and f w The coefficient will also decrease with lane width and lateral clear width restrictions.

[0128] S261. Determine the basic traffic capacity of each lane

[0129] If the driver is skilled and abides by traffic rules, the capacity of a single lane on a highway is as follows:

[0130] Table 2 Single lane capacity of expressways under different speed limit conditions

[0131]

[0132] S262. Determine lateral clearance correction factor

[0133] The lateral clearance correction factor is determined based on the impact of lane width on vehicle driving conditions. The lateral clearance correction factor is as follows:

[0134] Table 3. Highway lateral clearance correction coefficient

[0135]

[0136] S263, determine vehicle conversion coefficient

[0137] In construction areas or other specific sections, the impact coefficient for large vehicles needs to be revised according to the latest route design specifications. HV The value is 3.

[0138] S264, Driver influence correction factor

[0139] Whether the driver is familiar with the driving environment will directly affect the driving status of the vehicle, especially in the renovation, expansion and maintenance construction areas, where changes in environmental conditions will affect traffic efficiency and driving speed. In view of the special environment and external conditions of the maintenance construction area, the correction coefficient should be appropriately adjusted, and the minimum can be reduced to 0.85.

[0140] S27. Calculation of average speed of road section

[0141] The calculation of the travel speed of the highway section is converted based on the BPR (Bureau of Public Roads) road resistance function model. The formula is as follows:

[0142]

[0143] Among them, V A is the travel speed of the road section; V0 is the free flow speed of the road section; α and β are related parameters of the function, and in the present invention, α takes a value of 1 and β takes a value of 5.

[0144] S28. Implementation of construction plan

[0145] When construction activities begin, the construction agent will act on the road agent and vehicle agent with information about the specific construction methods, affecting them.

[0146] S281, Construction closure form

[0147] The design and construction plan mainly determines the number of closed lanes N closelane , which will directly affect the traffic capacity calculation of the road section. For the construction form of closing a one-way lane, the number of lanes of the road section will be directly changed. For construction forms that close half of the lane, the number of lanes that are actually accessible in both directions will be updated. Different construction closure methods such as Figure 5 .

[0148] S282, length of construction section

[0149] The length of the construction zone will directly affect the speed change of vehicles on the construction road section. During the modeling process, the vehicle will be considered to be slowing down when it enters the warning zone and is considered to be affected by the construction work. When the vehicle leaves the downstream transition zone, the speed The update returns to the normal way of calculating road sections.

[0150] When setting the length of each area of ​​the construction section, safety factors must be considered comprehensively. According to the "Highway Maintenance Safety Operation Regulations" (JTGH-2015), the length of the downstream transition zone and the termination zone should not be less than 30m. The specific provisions of the warning zone, upstream transition zone and buffer zone are shown in Tables 4, 5, and 6.

[0151] Table 4 Minimum length of highway warning area

[0152]

[0153] Table 5 Minimum length of upstream transition zone

[0154]

[0155] Table 6 Minimum buffer length

[0156]

[0157] S283, construction speed control

[0158] The vehicle travel speed in the construction section is mainly affected by the construction speed limit V lim The vehicle speed is always kept below the safe speed. When the traffic capacity is high, the vehicle speed will not be lower than the minimum speed limit.

[0159] S29, Vehicle Energy Consumption Update

[0160] The vehicle's energy consumption level will be dynamically updated as the vehicle's speed changes. The vehicle's energy consumption is calculated using the following formula:

[0161]

[0162] in, is the vehicle's speed The energy consumption per 100 kilometers is calculated from the function of the speed-energy consumption regression relationship in S3. Decision: CS C (m) and CS C (m-1) are the total energy consumption of the vehicle after this and the previous iteration respectively; D T (m) is the distance traveled by the vehicle in this iteration.

[0163] S3. Carbon emission calculation of highways

[0164] S31. Calculation of highway vehicle traffic carbon emissions using the bottom-up approach

[0165] When calculating the carbon emissions of vehicles during highway construction, the present invention adopts a bottom-up inventory method, starting from specific vehicles and driving conditions, taking into account data such as the type and number of running vehicles, mileage, and fuel consumption per unit mileage, and calculates the fuel consumption and carbon emissions of each vehicle. The specific formula is:

[0166]

[0167] Where CE represents the total carbon emissions from transportation; i represents the type of vehicle (such as small passenger cars, large trucks, special operation vehicles, etc.); j represents the type of energy (such as gasoline, diesel, new energy, etc.); N ij Indicates the number of vehicles of a specific type traveling; M ij Indicates the mileage of a specific type of vehicle within a certain period of time; FC ij Indicates the average fuel consumption per unit mileage of a specific type of vehicle; F jIndicates the carbon emission factor for a vehicle using a specific fuel type.

[0168] S311、Speed-energy consumption regression relationship model fitting for different types of vehicles

[0169] Fuel and electric energy efficiency will change with different driving speeds during vehicle driving. Multiple regression analysis method provides high accuracy for energy consumption model based on field measurements and a large amount of experimental data.

[0170] Based on the actual field survey data in the relevant literature, the present invention uses SPSS software to perform multiple regression analysis, and can fit different types of vehicle speed-energy consumption regression relationship models to obtain the fuel consumption per 100 kilometers of each vehicle. Figure 6 , the fitting relationship is as follows:

[0171] The “vehicle speed-fuel consumption” regression relationship model for Class 1 and Class 2 fuel-powered passenger vehicles:

[0172] FC=0.00156V 2 -0.282V+18.773

[0173] The “vehicle speed-power consumption” regression relationship model for Class 1 and Class 2 electric buses:

[0174] FC=0.004V 2 -0.0.6597V+38.362

[0175] The “vehicle speed-fuel consumption” regression relationship model for Class 3 and Class 4 fuel-powered passenger vehicles:

[0176] FC=0.0059V 2 -1.2397V+84.83

[0177] 1. “Speed-fuel consumption” regression relationship model for fuel trucks and special operation vehicles:

[0178] FC=0.0094V 2 -1.372V+61.533

[0179] 2. “Speed-fuel consumption” regression relationship model for fuel trucks and special operation vehicles:

[0180] FC=0.0215V 2 -3.5334V+163.42

[0181] The “vehicle speed-fuel consumption” regression relationship model for 3 types of fuel trucks and special operation vehicles:

[0182] FC=0.0294V 2 -4.6605V+202.24

[0183] The “vehicle speed-fuel consumption” regression relationship model for 4 types of fuel trucks and special operation vehicles:

[0184] FC=0.0353V 2 -5.6114V+238.79

[0185] The “vehicle speed-fuel consumption” regression relationship model for 5 types of fuel trucks and special operation vehicles:

[0186] FC=0.0259V 2 -4.1614V+189.37

[0187] The “vehicle speed-fuel consumption” regression relationship model for 6 types of fuel trucks and special operation vehicles:

[0188] FC=0.014V 2 -2.1786V+115.82

[0189] The power consumption of various trucks in the present invention refers to the average energy consumption parameters of electric trucks on the current market:

[0190] The power consumption of Class 1 electric trucks and special operation vehicles is 19kwh / 100km;

[0191] The power consumption of Class 2 electric trucks and special operation vehicles is 32.5kwh / 100km;

[0192] The power consumption of Class 3 and Class 4 electric trucks and special operation vehicles is 80kwh / 100km;

[0193] The power consumption of Class 5 and Class 6 electric trucks and special-purpose vehicles is 140kwh / 100km.

[0194] S312. Definition of carbon emission factors

[0195] According to the baseline emission factor of China's power grid and the calculation and correction coefficient of the IPCC CO2 emission coefficient table of various fuels, the carbon emission reference coefficients of different energy types can be obtained as shown in Table 7:

[0196] Table 7 Carbon emission factor parameter values

[0197]

[0198] S32. Calculate the cumulative carbon emissions during the construction process through phased life cycle assessment

[0199] During the highway maintenance and repair stage, material production and mechanical construction are the main sources of carbon emissions. Material production involves the mining, processing, and transportation of raw materials, while mechanical construction includes the energy consumption of mechanical equipment and emissions during the construction process. Based on the basic theory of the life cycle approach, this paper mainly calculates the carbon emissions of the two stages of material production and construction during the construction process.

[0200] S321, Material production stage

[0201] The present invention takes the final product of the material as the object, determines the usage of various building materials during the construction process, and determines the total emissions generated in this stage according to the unit carbon emissions of the materials. The specific formula is:

[0202]

[0203] Among them, A i represents the usage of the i-th material; Represents the carbon emission factor of the ith material.

[0204] The carbon emissions of construction materials are mainly attributed to two core links: the carbon dioxide directly produced by combustion during the acquisition and use of energy; and the carbon dioxide indirectly released due to chemical reactions during the production of building materials. The carbon emission factors of road construction materials are shown in the table.

[0205] Table 8 Carbon emission factors of construction materials

[0206]

[0207] S322, Construction Phase

[0208] During the maintenance construction process, various types of construction machinery will be used to complete various construction processes. These machines consume energy during operation, mainly diesel or electricity. Energy consumption is the source of carbon emissions at this stage. The total carbon emissions are usually determined based on the energy consumed by all construction machinery during the construction cycle. The specific formula is:

[0209]

[0210] Among them, H i is the usage time of the i-th construction equipment; is the carbon emission factor of the i-th device.

[0211] The carbon emission factor library in the table is calculated by combining the energy consumption per unit time of construction machinery and the carbon emission factor of terminal energy:

[0212] Table 9 Carbon emission factors of construction machinery

[0213]

[0214] by Figure 7 Taking the construction of the Hangzhou Ring Expressway as an example, the present invention is used to implement the vehicle operation carbon emission measurement method in the highway maintenance construction area based on multi-agent simulation, as follows:

[0215] S1. Analysis of spatiotemporal characteristics of highway travel demand and calibration of model parameters

[0216] S11. Road section attribute calibration

[0217] Each interchange hub node is equipped with an exit and an entrance for vehicles to enter and exit the ring expressway network. The hubs and interchanges are set as the section nodes inside the ring line as shown in Table 10. The nodes are connected to each other to form 46 sections, which are set with two-way four-lane, two-way six-lane and two-way eight-lane, with different corresponding traffic capacities. The topological structure of the road network is shown in Figure 8 shown.

[0218] Table 10 Hangzhou Ring Expressway Interchange Hubs

[0219]

[0220]

[0221] S12. Vehicle attribute calibration and spatiotemporal analysis

[0222] S121, Traffic Statistics

[0223] The analysis was conducted using the "Highway Toll Station Entrance and Exit Flow Table" compiled by the Zhejiang Provincial Department of Transportation in 2021. The data covers the total monthly flow of various types of vehicles recorded at each interchange hub toll station on the 32 expressway lines in Zhejiang Province from January to December 2021.

[0224] Use the pandas library to read the data file, match the "entry station" and "exit station" columns, extract the relevant toll station records containing the words "Hangzhou Ring Expressway", and obtain the inbound and outbound traffic data of the entire Hangzhou Ring Expressway. Group the data of the Hangzhou Ring Expressway according to the vehicle type and obtain the total traffic of each vehicle type.

[0225] By counting the total number of vehicles of various types at the entrances and exits of Hangzhou Ring Expressway throughout the year, we can get the proportion of each type of vehicle traffic, such as Fig. 9 .

[0226] Since the number of special operation vehicles on the highway is almost negligible, only buses and trucks are considered in the simulation. The generation ratio of buses to trucks is about 3:1, and the generation of vehicle subtypes in the simulation is randomly assigned according to the proportion of various types of vehicles.

[0227] S122, all-day highway vehicle OD distribution

[0228] The "Highway OD Distribution Table" compiled by the Zhejiang Provincial Department of Transportation in 2021 is used, which records the OD distribution of all interchange hub nodes on all expressways in Zhejiang Province from January to December 2021. "O" indicates that the vehicle enters the interchange hub node, and "D" indicates that the vehicle exits the interchange hub node.

[0229] Use the pandas library to read the original data file and perform data cleaning, including processing missing values, outliers, and duplicate values. In the original data, use the string matching method to extract the data of all nodes of Hangzhou Ring Expressway and obtain the traffic data between the 23 nodes. Divide the sum of the traffic data for 12 months by the number of days in a year to get the average daily OD volume between hub interchanges. Distribute the OD matrix according to the ratio of 3:1 between passenger cars and trucks to get Fig.10 The OD matrix.

[0230] S123, all-day traffic distribution

[0231] In the survey report of the ring expressway, the traffic distribution data of the expressway at different time periods throughout the day observed at the observation point were obtained, such as Fig.11 During the simulation process, the OD distribution of each period of the day is calculated based on the traffic distribution data of the whole day and the proportion of vehicles in each period, which is used as the input for vehicle generation in the simulation model.

[0232] S2. Simulation analysis of highway vehicle operation based on multi-agent system

[0233] S21. Model initialization

[0234] The model parameters are initialized according to the actual operation standards of the expressway. The design speed of the ring expressway is 120km / h, and the speed limit of the entire line is 100km / h. When there are no vehicles on the road, the free flow speed of the eight lanes is 120km / h, and the free flow speed of the four and six lanes is 100km / h. According to the provisions of the Highway Capacity Manual, the theoretical single-lane capacity of the basic section of the ring expressway is determined to be 2200pcu / h. Referring to the "Design Specifications for Highway Traffic Safety Facilities" (JTGD81-2017), the lane width is calibrated to a standard 3.75 meters and the lateral clearance is 1.25 meters.

[0235] S22. Simulation operation and analysis

[0236] (1) Simulation of standard situation without construction

[0237] In order to simulate the vehicle operation of the highway for 24 hours a day, the present invention sets the simulation time step to 1 minute to simulate the vehicle operation of the highway for a whole day. Through the simulation, the traffic flow of each section can be counted every minute, and the traffic data of the whole day can be summarized and analyzed.

[0238] The verification data of the simulation results of this invention comes from the "Highway Section Flow Table" compiled by the Zhejiang Provincial Department of Transportation in 2021. This data table covers the traffic flow data of different types of vehicles on each section of each road every month from January to December 2021. By integrating these data and converting the number of natural vehicles into the number of converted vehicles, the daily average section flow data of each section throughout the year can be obtained.

[0239] In order to verify the accuracy of the simulation model for different types of vehicle generation and path planning, the present invention selected the first 8 sections as samples and compared the simulation results with the actual data. Fig.12 The comparison results show that the errors of various types of vehicle generation and path planning in the simulation model are all within 10%, indicating that the model has high accuracy and reliability.

[0240] (2) Analysis of the impact of different construction methods on road traffic under construction scenarios

[0241] like Fig.13 As shown, in order to specifically analyze the impact of different construction methods on the road capacity and traffic flow of a two-way six-lane and two-way four-lane section, the present invention sets a construction section, sets the speed limit to 60km / h, and the construction area length to 1000m, simulates the vehicle driving conditions on the highway for 24 hours, and statistics the changes in the average speed of the construction section throughout the day under different construction methods.

[0242] When the construction section is a two-way four-lane road, the changes in the average speed of the section throughout the day under different organization plans are as follows: Fig.14 As shown in (a).

[0243] The baseline speed when there was no construction was maintained at around 96 km / h almost all day, indicating that vehicles were able to travel smoothly at a higher speed without construction interference. When a single lane was closed, the speed dropped, but the drop was limited. Before 5 a.m., the speed was relatively stable, and after 5 a.m., it began to drop sharply, with the average speed mostly maintained between 60 and 72 km / h. This shows that although one lane was reduced, the remaining lanes can still support relatively smooth traffic flow.

[0244] When half of the lanes were closed, the average speed of the road section began to reach the bottleneck of traffic capacity at around 4 a.m., and the speed began to drop continuously, even to a minimum of less than 48 km / h, reflecting the great interference of the construction on the two-way traffic flow. This shows that the significant reduction in the number of lanes has greatly increased the travel time.

[0245] When the construction section is a two-way six-lane road, the changes in the average speed of the section throughout the day under different organization plans are as follows: Fig.14 As shown in (b).

[0246] The average speed of the original road section has always remained at around 96km / h, indicating that vehicles can maintain a high driving speed without construction interference. However, after the construction began, the closure of the inner and outer single lanes and the closure of the adjacent two lanes will reduce the capacity of the remaining lanes, causing the traffic flow to slow down.

[0247] The number of closed lanes directly affects the traffic capacity of the construction area. The more lanes are closed, the more obvious the average speed drops, and the more severe the congestion during the construction period. In the construction section, when the inner and outer single lanes are closed, the average speed basically drops to about 60km / h. When the adjacent two lanes are closed, the average speed drops more significantly, and the lowest drops to about 36km / h. Compared with the original section, the average speed has been reduced by 48-60km / h.

[0248] During the all-day road closure period due to construction, after 8 p.m., vehicle traffic volume was relatively low and traffic pressure in the construction area was reduced. When a single lane was closed, the average speed of the road section gradually increased. However, when two lanes were closed, there were insufficient lanes left, and there were still many vehicles in queues, so speed recovery was still slow.

[0249] (3) Sensitivity analysis of construction influencing factors

[0250] This study experimentally evaluated the sensitivity of the construction zone length and speed limit to carbon emissions. Take a two-way four-lane closed one-way lane as an example. First, Python was used to process the data of the full-day simulation, and the traffic volume data per minute was accumulated to identify the specific time periods when the traffic volume in the construction area was within the range of 1250, 1500, 2000, and 2250pcu / h. Then, the carbon emissions and large vehicle rate in the corresponding time were counted. The large vehicle rate was controlled at about 16% to ensure the consistency and comparability of the data. Fig.15 (a) studied the effect of the length of the construction area on carbon emissions when the speed limit is 80 km / h; Fig.15 (b) studies the impact of speed limit on carbon emissions when the construction area is 1 km long.

[0251] For all construction zone lengths and speed limits, carbon emissions increase with traffic volume, which is expected since more vehicles mean more fuel consumption and emissions.

[0252] from Fig.15 It can be seen that the increase in the length of the construction area will lead to an increase in carbon emissions. However, within the range of lower traffic volume, the difference in carbon emissions between different construction area lengths is not significant. Properly increasing the length of the construction area can reduce the construction period without causing a particularly large impact on carbon emissions. As the length of the construction area increases, the rate at which carbon emissions increase with the increase in traffic volume accelerates.

[0253] from Fig.15 It can be seen that carbon emissions increased sharply at a speed limit of 30km / h, which is because the low speed limit increases the idling time and congestion of vehicles. In the speed limit range of 50km / h to 70km / h, carbon emissions change relatively smoothly, which will not cause serious congestion. In addition, there will be a slight increase in carbon emissions as the speed limit increases, because excessive speed may also lead to increased fuel consumption efficiency.

[0254] (4) Construction analysis of two-way four-lane road section

[0255] This paper takes the section from Wuchang Interchange to Liuxia Hub Interchange as a construction case, simulates the vehicle operation of the entire ring highway for 24 hours through the Agent model, and measures carbon emissions, and evaluates the additional carbon emissions caused by the two-way four-lane under different construction schemes, speed limits and construction area lengths compared with the case without construction. The conclusion can provide a scientific basis for the subsequent formulation of the best construction plan. The simulation results are shown in Fig.16 (a), (b).

[0256] Carbon emissions generally show an increasing trend as the speed limit increases. In the case of a closed single lane, carbon emissions reach a relative peak at a speed limit of 60-70 km / h, and will rise slightly as the speed limit increases further. For a closed half-lane, this peak is between 50-60 km / h.

[0257] When the speed limit is high, the congestion of vehicles is not serious, and the rate of change of carbon emissions with the increase of the length of the construction zone is relatively slow, and there is a significant fluctuation. When the length of the construction zone exceeds 1,000 meters, the increase will be more obvious. This is because when the length of the construction zone is small, vehicles can still pass through the section in a short time, and the delay time is small. Changing the length of the construction zone will not have a great impact on traffic emissions. When half of the lane is closed, the low speed limit will make the impact of the length of the construction zone on carbon emissions more significant, and traffic stagnation and delay will be more serious.

[0258] Closing half a lane causes more severe traffic congestion, so it contributes more to carbon emissions than closing a single lane. In order to mitigate the impact on carbon emissions, it is usually necessary to set a higher speed limit, or control the speed limit to around 50km / h.

[0259] (5) Construction analysis of two-way six-lane road section

[0260] In order to explore the difference in additional carbon emissions caused by different construction organization schemes for two-way six-lane road sections, this paper selected the one-way section from Dajing Hub to Banshan Interchange as the construction section for simulation analysis. The results simulated by the Agent model are shown in Figure 2. Fig.16 (c), (d).

[0261] When closing a single lane, raising the speed limit can effectively reduce carbon emissions, and there is an optimal speed range between 60-70km / h, which can reduce carbon emissions while maintaining safety. When closing two adjacent lanes, carbon emissions are relatively low when the speed limit is 40-50km / h, and there is no significant effect of raising the speed limit on carbon emissions at 70-80km / h.

[0262] For the construction method of closing a single lane, since there are still relatively sufficient lanes, when the speed limit is high, the construction length does not have a significant impact on carbon emissions. There may even be a phenomenon where the length of the construction section increases and carbon emissions decrease. When the construction length is greater than 1 km, carbon emissions increase faster. For the method of closing two adjacent lanes, the traffic capacity of the section is greatly restricted, and the traffic flow is concentrated in one lane. At this time, the impact of the construction length on carbon emissions is relatively large, especially when the length exceeds 1.5 km. Longer construction areas will lead to more serious vehicle congestion and need to be carefully controlled.

[0263] In contrast, six-lane two-way roads, whether closing a single lane or two adjacent lanes, show a significant increase in carbon emissions when there are high speed limits and long construction zones.

[0264] S3. Carbon emission calculation of highways

[0265] According to the engineering background and construction requirements of Hangzhou Ring Expressway, the construction length of the two sections of the main line from Xiasha Hub Interchange to Hongken Interchange of the east line of Hangzhou Ring Expressway is 2,000 meters and 1,750 meters respectively, with a width of 3.75 meters, and the disease treatment and overlay construction are carried out. Both sections of the main line are two-way four-lane.

[0266] The construction will first conduct a detailed disease survey of the construction area to confirm the type and specific location of the disease. Then the disease treatment will be carried out, using emulsified asphalt to fill cracks and modified asphalt to fill potholes. The specific process includes:

[0267] (1) Milling: Mill the old road surface to a depth of 5 cm to remove the damaged parts.

[0268] (2) Spreading tack coat oil: Spread tack coat oil on the road surface after milling to enhance the adhesion between the new and old road surfaces.

[0269] (3) Spreading asphalt mixture: Spread modified asphalt mixture with a paving thickness of 5 cm.

[0270] (4) Rolling: Use a roller to roll the asphalt mixture to ensure it is dense and smooth.

[0271] S31. Calculation of vehicle traffic emissions during construction using the bottom-up approach

[0272] The construction process of this case involves maintenance work on the road section from Xiasha Interchange to Hongken Interchange. In order to maximize construction efficiency, reduce the interference of construction on traffic flow, and ensure the safety of construction workers and passing vehicles, two different traffic diversion plans were implemented.

[0273] Plan A: Close one lane of the road section, and design a safe speed limit of 60km / h in the construction area. According to the construction work volume and facility maintenance quota, the construction period is estimated to be 38.5 days.

[0274] Plan B: Close half of the road lane, and design a safe speed limit of 50km / h in the construction area. According to the construction workload and facility maintenance quota, the construction period is 24.2 days.

[0275] The additional carbon emissions and delays generated by the vehicle are calculated through Agent model simulation as shown in the following table:

[0276] Table 11 Traffic carbon emissions of different construction schemes

[0277]

[0278] S32. Calculation of carbon emissions from construction projects through phased life cycle assessment

[0279] With reference to the relevant regulations and standards of Highway Engineering Budget Quotas (JTG / T3832-2018), we first conducted a comprehensive and detailed analysis and calculation of the various materials involved in the construction project and the carbon emissions generated by each construction link.

[0280] S321. Material physical and chemical emission analysis

[0281] In this construction case, three key materials were selected, namely modified asphalt, emulsified asphalt and crushed stone. In the method of calculating carbon emissions, the amount of materials used and the comprehensive carbon emission factor were mainly considered. The calculation results are shown in Table 12:

[0282] Table 12 Material emission

[0283]

[0284]

[0285] S322, Construction Emission Analysis

[0286] During the construction process, a variety of mechanical equipment is involved, and these equipment play a key role in different construction links. The types and quantities of mechanical equipment used in each construction link are different. These equipment will consume fuel or electricity during operation, thereby generating corresponding carbon emissions. The energy consumption and equipment operation time vary with different construction cycles. Based on the amount of equipment used in each construction process and the energy carbon emission factor of the equipment, the carbon emission results can be calculated as shown in Table 13:

[0287] Table 13 Emissions from construction

[0288]

[0289] S33. Total carbon emissions from road maintenance activities

[0290] This paper comprehensively considers the maintenance construction activities themselves and the additional carbon emissions from vehicle operation caused by construction. The carbon emissions generated by construction during the entire construction period are the sum of the carbon emissions from construction activities and the additional carbon emissions from vehicle operation in the construction area. The results are shown in Tables 14 and 15:

[0291] Table 14 Carbon emission analysis of Scheme A

[0292]

[0293] Table 15 Carbon emission analysis of Scheme B

[0294]

[0295] It should be noted that the carbon emission measurement of vehicles running in highway maintenance construction areas based on multi-agent simulation provided in the above embodiment can essentially exist in the form of a program processing flow. Therefore, similarly, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer electronic device corresponding to the carbon emission measurement of vehicles running in highway maintenance construction areas based on multi-agent simulation provided in the above embodiment, which includes a memory and a processor;

[0296] The memory is used to store computer programs;

[0297] The processor is used to implement the carbon emission measurement of vehicles operating in highway maintenance construction areas based on multi-agent simulation as described above when executing the computer program.

[0298] Similarly, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer-readable storage medium corresponding to the carbon emission measurement of vehicles operating in highway maintenance and construction areas based on multi-agent simulation provided in the above embodiment, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the carbon emission measurement of vehicles operating in highway maintenance and construction areas based on multi-agent simulation as mentioned above is implemented.

[0299] The above-described embodiments are only some of the preferred solutions of the present invention, but they are not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for measuring carbon emissions from vehicle operation in highway maintenance construction areas based on multi-agent simulation, characterized in that: The following steps are involved: S1. Obtain the interchange flow and travel distribution of the expressway to be constructed and the basic information of the road network, conduct data statistics, determine the spatiotemporal characteristics of expressway vehicle travel demand and calibrate the parameters required for modeling; S2. Construct a vehicle operation simulation model during the highway construction phase based on a multi-agent system, and use the model to simulate the individual characteristics of vehicles on the highway under different construction schemes and the vehicle-road interaction and vehicle-vehicle interaction effects of traffic flow; S3. The carbon emissions generated by simulated vehicle operation on the highway during construction are calculated based on the bottom-up inventory method, and the carbon emissions generated during the construction process are calculated in combination with the staged life cycle theory.

2. According to claim 1, the method for measuring carbon emissions from vehicle operation in highway maintenance construction areas based on multi-agent simulation is characterized in that: The S1 specifically includes: S11 first determines the scope of the research area, collects the interchange flow data, travel distribution and basic information of the road network of the highways involved in the construction area, and cleans the data; S12 conducts descriptive statistical analysis on the collected data to determine the temporal and spatial distribution characteristics of vehicle flow, identify peak hours, vehicle type distribution, and travel patterns, and analyze the flow change patterns at key nodes and sections.

3. According to claim 1, the method for measuring carbon emissions from vehicle operation in highway maintenance construction areas based on multi-agent simulation is characterized in that: The simulation model constructed in S2 includes: Data input layer, used to provide initial simulation data; The entity layer is used to define the properties and characteristics of different agents involved in the simulation process, including: vehicle agents to represent vehicles traveling on the highway; road segment agents to represent various sections or segments of the road; construction agents to represent the implementation of traffic management strategies for construction activities; The operation layer, which performs operations through key modules to simulate the operation of a highway construction site; The data output layer is used to record simulation results and output various statistical data.

4. According to claim 3, the method for measuring carbon emissions from vehicle operation in highway maintenance construction areas based on multi-agent simulation is characterized in that: The simulation model operation layer in S2 includes: S21 initialization module: used to initialize all agents according to the input data and give them various attributes and values ​​required for simulation; S22 vehicle generation module: generates corresponding vehicles on the highway section according to the preset vehicle type, departure time, start and end locations, and arranges the vehicle travel plan; S23 driving module: The driving module controls the vehicle to drive on the road at a set speed, reflecting the real-time traffic flow status. The vehicle adjusts the driving speed according to the traffic capacity and traffic volume of the road section, simulating the vehicle driving conditions on the real road; S24 Planning Module: The planning module is responsible for planning the optimal driving route for the vehicle. Based on the starting and ending points of the vehicle and the current road conditions of each section, the module uses a path planning algorithm to determine the shortest path that the vehicle should travel to avoid congestion and delays; S25 Section flow and capacity update module: monitors the traffic flow of vehicles on each section and dynamically updates the section's capacity based on the section's attributes; S26 Construction Plan Module: The construction plan module simulates different traffic management strategies, adjusts speed limits and construction section parameters, and evaluates the impact of different construction plans on traffic flow; S27 Energy Consumption Statistics Module: Tracks and estimates the energy consumption of each vehicle during the simulation process, and simulates and reflects the dynamic changes of energy consumption with speed during vehicle operation.

5. According to claim 4, the method for measuring carbon emissions from vehicle operation in highway maintenance construction areas based on multi-agent simulation is characterized in that: The driving module includes: When a vehicle is traveling on a road segment, it will go to the next highway interchange node according to the planned route. The speed of the vehicle on the road segment is determined by the traffic flow of the road network, that is, the average speed on the road segment is used to control the speed of the vehicle at each iteration; the vehicle's driving speed is as follows: in, is the driving speed of the vehicle during this iteration; is the average speed of the road section during this iteration; The speed limit for the road section; The distance travelled by the vehicle each time is: Among them, D T (m) is the distance traveled in this iteration process; Indicates the distance between the vehicle and the next interconnected node in the previous iteration; Δt is the simulation duration of each iteration; According to the vehicle's travel distance D T (m), update the vehicle's mileage and the distance to the next node. If the vehicle is traveling on a construction section, the distance from the construction area and the distance required to leave the construction area must also be updated accordingly.

6. According to claim 4, the method for measuring carbon emissions from vehicle operation in highway maintenance construction areas based on multi-agent simulation is characterized in that: The method for updating the section flow in the section flow and traffic capacity update module is as follows: On an ordinary road section that is not under construction, when a vehicle is at an interchange node in the mth iteration, that is, it has just arrived at the section, the flow rate of the section is increased by 1. When the distance from the vehicle to the next node in the mth iteration is less than or equal to 0, the vehicle has left the section, and the flow rate of the section is reduced by 1. If the vehicle label attribute of the vehicle passing through the road section is a medium or large vehicle, the number of large vehicles on the road section needs to be updated synchronously; If a road section is under construction, it will be divided into three sections: pre-entry into the construction area, construction area, and exit from the construction area. Similar to the sections not under construction, the traffic flow and number of large vehicles in the three sections will be updated.

7. The method for measuring carbon emissions from vehicle operation in highway maintenance construction areas based on multi-agent simulation according to claim 4 is characterized in that: The method for updating the section traffic capacity in the section traffic and traffic capacity update module is as follows: the section traffic capacity is the maximum traffic flow that the section can accommodate, which changes due to the characteristics of the section and the traffic flow status, specifically: C p =C B ×N lane ×f HV ×f w ×f p f HV =1 / 1+P HV (E HV -1) P HV =N HV Q Among them, C p is the actual traffic capacity of the basic section of the expressway; C B It is the basic traffic capacity of the basic section of the expressway; N lane is the number of lanes in the highway section; f HV is the large vehicle correction factor; f w is the correction factor for the lane width and lateral clear width of the road section; f P P is the driver influence correction factor; HV N is the proportion of medium and large vehicles in the total traffic volume of the road section; HV is the number of large and medium-sized vehicles on the road section; E HV is the coefficient for converting large vehicles into small passenger cars; Q is the flow rate of the road section.

8. According to claim 1, the carbon emission measurement of vehicles in highway maintenance construction areas based on multi-agent simulation is characterized by: In S3, a bottom-up inventory method is used to evaluate the carbon emissions generated by the operation of each individual vehicle in the transportation system. The overall carbon emissions of highway vehicles are: Where CE represents the total carbon emissions from transportation; i represents the type of transportation; j represents the type of energy; N ij Indicates the number of vehicles of a specific type traveling; M ij Indicates the mileage of a specific type of vehicle within a certain period of time; FC ij Indicates the average fuel consumption per unit mileage of a specific type of vehicle; F j Indicates the carbon emission factor of a vehicle with a specific fuel type; The staged life cycle theory is used to calculate the carbon emissions of the two stages of material production and construction during the construction process: taking the final product of the material as the object, determining the usage of various building materials during the construction process, and determining the total emissions generated in this stage based on the unit carbon emissions of the material, as follows: Among them, A i represents the usage of the i-th material; represents the carbon emission factor of the ith material; The total carbon emissions are determined based on the energy consumed by all construction machinery during the construction cycle, as follows: Among them, H i is the usage time of the i-th construction equipment; is the carbon emission factor of the i-th device.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for measuring carbon emissions from vehicle operation in a highway maintenance construction area based on multi-agent simulation as described in any one of claims 1 to 8 is implemented.

10. A computer electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the method for measuring carbon emissions from vehicle operation in a highway maintenance construction area based on multi-agent simulation as described in any one of claims 1 to 8 when executing the computer program.

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