Simulated traffic data-based carbon emission prediction method and apparatus, and electronic device

Abstract

The invention provides a carbon emission prediction method and device based on simulated traffic data and electronic equipment, and the method comprises the steps: determining the speed, acceleration and position of each vehicle in a target region at each moment based on the travel related data of each vehicle; determining acceleration energy consumption at each moment based on the acceleration, the position and the mass of the vehicle at each moment; determining rolling friction energy consumption at each moment based on the mass of the vehicle, the position at each moment and the friction coefficient; determining the wind resistance energy consumption at each moment based on the windward area, the air density, the air resistance coefficient and the speed and the position at each moment; and predicting the carbon emission at each moment based on the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at the moment, and predicting the carbon emission of the target area based on the carbon emission of the vehicle at each moment. According to the method, the carbon emission is accurately generated at the time and the spatial position, the prediction of the carbon emission of the space-time fine granularity is realized, and the accuracy of the prediction of the carbon emission in the target area is improved.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to a carbon emission prediction method, device and electronic equipment based on simulated traffic data. Background Art

[0002] As global climate change becomes increasingly serious, reducing carbon emissions has become a focus of attention. As one of the main sources of carbon emissions in cities, the transportation system's carbon emissions will influence the formulation of various policies.

[0003] In the related art, the gases emitted by vehicles are usually collected by various monitoring devices installed on the road, and the carbon emissions on the road are predicted based on the gases collected by the various monitoring devices.

[0004] However, the above-mentioned related technologies rely on monitoring equipment installed on the roads, and the monitoring equipment cannot cover all roads, resulting in incomplete collected gas data, thereby reducing the accuracy of carbon emission predictions. Summary of the Invention

[0005] The present invention provides a carbon emission prediction method, device and electronic equipment based on simulated traffic data, which are used to solve the defect of reducing the accuracy of carbon emission prediction in the prior art.

[0006] The present invention provides a carbon emission prediction method based on simulated traffic data, comprising the following steps.

[0007] Obtaining travel-related data for each of the vehicles in the target area, the travel-related data including the starting and ending locations of the vehicles; Determining simulated traffic data of each vehicle in the target area at each time based on the travel-related data of each vehicle, the simulated traffic data including the speed, acceleration and position of the vehicle; For each of the vehicles, determining the acceleration energy consumption of the vehicle at each of the moments based on the acceleration of the vehicle at each of the moments, the position of the vehicle at each of the moments, and the mass of the vehicle; determining a rolling friction energy consumption of the vehicle at each of the moments based on the mass of the vehicle, the position of the vehicle at each of the moments, and a friction coefficient of a road in the target area; determining the wind resistance energy consumption of the vehicle at each of the moments based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, and the speed and position of the vehicle at each of the moments; For each of the moments, based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment, predicting the carbon emissions of the vehicle at the moment; The carbon emissions of the target area are predicted based on the carbon emissions of all the vehicles at each of the moments.

[0008] According to a carbon emission prediction method based on simulated traffic data provided by the present invention, the simulated traffic data of each vehicle in the target area at each time is determined based on the travel-related data of each vehicle, including: Inputting the travel-related data of each vehicle into a path planning model to obtain a planned path for each vehicle output by the path planning model; For each vehicle, inputting the current speed of the vehicle, the current speed of the vehicle preceding the vehicle, a first relative distance between the vehicle and the preceding vehicle, and road network information of the target area into a vehicle-following model to obtain a current acceleration of the vehicle output by the vehicle-following model; Determining the next speed and next position of the vehicle at a next moment based on the planned path of each vehicle and the current acceleration of the vehicle; Iteration is performed based on the next moment speed and the next position of each vehicle to obtain simulated traffic data of each vehicle in the target area at each moment.

[0009] According to a carbon emission prediction method based on simulated traffic data provided by the present invention, determining the next speed and next position of the vehicle at a next moment based on the planned path of each vehicle and the current acceleration of the vehicle includes: Inputting each of the planned paths, the first relative speed and the second relative distance between the vehicle and the preceding vehicle in the adjacent lane, the second relative speed and the third relative distance between the vehicle and the following vehicle in the adjacent lane, and the road network information into a vehicle lane change model, and obtaining a current lane change result of the vehicle output by the vehicle lane change model; Based on the current lane change result and the current acceleration of the vehicle, a next-moment speed and a next-moment position of the vehicle are determined.

[0010] According to a carbon emission prediction method based on simulated traffic data provided by the present invention, the travel-related data further includes the vehicle type of each of the vehicles; The predicting of the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment includes: determining an energy consumption ratio of the vehicle based on the vehicle type; determining the electronic energy consumption of the vehicle at the moment based on the energy consumption ratio of the vehicle, the acceleration energy consumption of the vehicle at the moment, the rolling friction energy consumption of the vehicle at the moment, and the wind resistance energy consumption of the vehicle at the moment; The carbon emissions of the vehicle at the moment are predicted based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption and the electronic energy consumption of the vehicle at the moment.

[0011] According to a carbon emission prediction method based on simulated traffic data provided by the present invention, the travel-related data further includes the fuel type and engine type of each of the vehicles; The predicting of the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption, and the electronic energy consumption of the vehicle at the moment includes: determining a total energy consumption of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption, and the electronic energy consumption of the vehicle at the moment; determining an engine efficiency of the vehicle based on the engine type; determining a fuel energy of the vehicle based on the fuel type; determining a fuel consumption of the vehicle at the time based on the engine efficiency, the fuel energy, and the total energy consumption; The carbon emissions of the vehicle at the moment are predicted based on the carbon emission factor corresponding to the fuel type and the fuel consumption.

[0012] According to a carbon emission prediction method based on simulated traffic data provided by the present invention, the carbon emission of the target area is predicted based on the carbon emission of all the vehicles at each time, including: In a case where the fuel type is a fuel oil type, determining a duration during which the speed of the vehicle is zero based on the speed of the vehicle at all times; Determining the amount of fuel consumed by idling the vehicle based on the duration and the fuel conversion factor; Determining the idling carbon emissions of the vehicle in the target area based on the idling fuel consumption of the vehicle and the carbon emission factor corresponding to the fuel type; The carbon emissions of the target area are predicted based on the idling carbon emissions of each of the vehicles and the carbon emissions of each of the vehicles at each of the moments.

[0013] According to a carbon emission prediction method based on simulated traffic data provided by the present invention, the method further includes: In the event that target information of the target area changes, re-determining new simulated traffic data for each vehicle in the target area at each time based on the changed target information, wherein the target information includes at least one of the following: road network information, travel-related data, vehicle type, and fuel type; Based on the new simulated traffic data of each vehicle at each time, new carbon emissions of the target area are predicted.

[0014] The present invention also provides a carbon emission prediction device based on simulated traffic data, comprising: an acquisition unit, configured to acquire travel-related data of all vehicles in the target area, wherein the travel-related data includes a starting position and an ending position of the vehicle; a first determining unit, configured to determine simulated traffic data of each of the vehicles in the target area at each time based on the travel-related data of each of the vehicles, the simulated traffic data including the speed, acceleration, and position of the vehicle; a second determining unit, configured to determine, for each of the vehicles, an acceleration energy consumption of the vehicle at each of the moments based on the acceleration of the vehicle at each of the moments, the position of the vehicle at each of the moments, and the mass of the vehicle; a third determining unit, configured to determine the rolling friction energy consumption of the vehicle at each of the moments based on the mass of the vehicle, the position of the vehicle at each of the moments, and the friction coefficient of the road in the target area; a fourth determining unit, configured to determine the wind resistance energy consumption of the vehicle at each of the moments based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, and the speed and position of the vehicle at each of the moments; a first prediction unit, configured to predict, for each of the moments, the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment; The second prediction unit is used to predict the carbon emissions of the target area based on the carbon emissions of all the vehicles at each time.

[0015] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for predicting carbon emissions based on simulated traffic data as described above is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described carbon emission prediction methods based on simulated traffic data.

[0017] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described carbon emission prediction methods based on simulated traffic data.

[0018] The carbon emission prediction method, device and electronic device based on simulated traffic data provided by the present invention obtain travel-related data of all vehicles in the target area, and determine the speed, acceleration and position of each vehicle in the target area at each moment based on the travel-related data of each vehicle; for each vehicle, the acceleration energy consumption of the vehicle at each moment is determined based on the acceleration of the vehicle at each moment, the position of the vehicle at each moment, and the mass of the vehicle; the rolling friction energy consumption of the vehicle at each moment is determined based on the mass of the vehicle, the position of the vehicle at each moment and the friction coefficient of the road in the target area; the wind resistance energy consumption of the vehicle at each moment is determined based on the windward area of ​​the vehicle, the air density of the target area, the air resistance coefficient, the speed and position of the vehicle at each moment; for each moment, the carbon emissions of the vehicle at that moment are predicted based on the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at that moment; and finally, the carbon emissions of the target area are predicted based on the carbon emissions of all vehicles at each moment. It can be seen that the present invention calculates the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at each moment based on the dynamic data of the vehicle's speed, acceleration and position at each moment, and then predicts the carbon emissions of the vehicle at each moment based on the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at each moment, and accurately predicts the carbon emissions at the time and spatial location of occurrence, realizing the prediction of carbon emissions of the transportation system with fine-grained time and space, and can completely cover all vehicles in the target area, thereby improving the accuracy of carbon emission prediction in the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is one of the flow charts of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention.

[0021] Figure 2 This is the second flow chart of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention.

[0022] Figure 3 This is the third flow chart of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention.

[0023] Figure 4 This is an overall architecture diagram of a carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention.

[0024] Figure 5It is an architectural diagram of a microscopic traffic simulation system provided by an embodiment of the present invention.

[0025] Figure 6 Schematic diagram of the carbon emission conversion method provided in an embodiment of the present invention.

[0026] Figure 7 This is the fourth flow chart of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention.

[0027] Figure 8 It is a schematic diagram of a carbon emission prediction method under the influence of new regulations or new technologies provided in an embodiment of the present invention.

[0028] Figure 9 This is one of the example diagrams of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention.

[0029] Figure 10 This is the second example diagram of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention.

[0030] Figure 11 3 is a schematic diagram of the structure of a carbon emission prediction device based on simulated traffic data provided by an embodiment of the present invention.

[0031] Figure 12 It is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0033] The following combination Figures 1-10 The present invention describes a carbon emission prediction method based on simulated traffic data. This method can be implemented by an electronic device such as a terminal, tablet computer, computer, or server, or by a carbon emission prediction device based on simulated traffic data within the electronic device. The device can be implemented using software, hardware, or a combination of both.

[0034] Figure 1 This is one of the flow charts of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention. Figure 1 As shown, the carbon emission prediction method based on simulated traffic data includes the following steps: Step 101: Acquire travel-related data of all vehicles in a target area, wherein the travel-related data includes a starting position and an ending position of the vehicle.

[0035] For example, if the target area is the area for which the user needs to predict carbon emissions, the road monitoring data collected by various cameras installed in the target area during a preset time period can be analyzed to obtain the starting point location, end point location, departure time, intermediate point location along the route, and vehicle type of each vehicle in the target area. This information is then used as the vehicle's travel-related data. Alternatively, data such as the number of vehicles, the ratio of vehicle types, and the ratio of energy types can be input into a travel demand generation algorithm, which then generates travel-related data for all vehicles in the target area based on this data.

[0036] Step 102: Determine simulated traffic data of each vehicle in the target area at each time based on the travel-related data of each vehicle, wherein the simulated traffic data includes the speed, acceleration and position of the vehicle.

[0037] For example, when obtaining travel-related data of each vehicle, the planned path of each vehicle can be calculated based on the path planning model, and the current acceleration of each vehicle can be calculated based on the vehicle following model. Then, based on the planned path of each vehicle and the current acceleration of each vehicle, the simulated traffic data of each vehicle in the target area at each time can be calculated.

[0038] Step 103 : For each of the vehicles, determine the acceleration energy consumption of the vehicle at each moment based on the acceleration of the vehicle at each moment, the position of the vehicle at each moment, and the mass of the vehicle.

[0039] For example, for each vehicle, the acceleration energy consumption of the vehicle at each moment can be calculated based on the following formula (1): Where m represents the mass of the vehicle, a represents the acceleration of the vehicle at that moment, Δd represents the displacement between the vehicle's position at that moment and its position at the previous moment, and T1 represents the acceleration energy consumption of the vehicle at that moment. Using the same method, the acceleration energy consumption of each vehicle in the target area at each moment can be obtained.

[0040] Step 104 : Determine the rolling friction energy consumption of the vehicle at each moment based on the mass of the vehicle, the position of the vehicle at each moment, and the friction coefficient of the road in the target area.

[0041] For example, for each vehicle, the rolling friction energy consumption of the vehicle at each moment can be calculated based on the following formula (2): in, represents the acceleration due to gravity, Indicates the rolling friction coefficient, which is related to the characteristics of the tire and the road surface. It represents the friction coefficient of the road in the target area, reflecting the roughness and friction characteristics of the road surface. T2 represents the rolling friction energy consumption of the vehicle at that moment. Using the same method, the rolling friction energy consumption of each vehicle in the target area at each moment can be obtained.

[0042] Step 105: Determine the wind resistance energy consumption of the vehicle at each moment based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, and the speed and position of the vehicle at each moment.

[0043] For example, for each vehicle, the wind resistance energy consumption of each vehicle at each time can be calculated based on the following formula (3): Among them, A f represents the frontal area of ​​the vehicle, represents the air resistance coefficient, represents the speed of the vehicle at that moment, Represents the air density in the target area, T3 represents the wind resistance energy consumption of the vehicle at that moment, and the wind resistance energy consumption of each vehicle in the target area at each moment can be obtained using the same method.

[0044] Step 106: For each of the moments, based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment, predict the carbon emissions of the vehicle at the moment.

[0045] For example, for each vehicle at each moment, the acceleration energy consumption, rolling friction energy consumption, and windage energy consumption of the vehicle at that moment are added together to obtain the vehicle's dynamic energy consumption at that moment. This dynamic energy consumption is then used to convert the vehicle's carbon emissions at that moment. Using the same method, the carbon emissions of each vehicle in the target area at each moment can be calculated.

[0046] Step 107: Predict the carbon emissions of the target area based on the carbon emissions of all the vehicles at each time.

[0047] For example, when the carbon emissions of each vehicle in the target area at each time are obtained, the carbon emissions of all vehicles at each time are added together to predict the carbon emissions of the target area.

[0048] The carbon emission prediction method based on simulated traffic data provided by the present invention obtains travel-related data of all vehicles in the target area, and determines the speed, acceleration and position of each vehicle in the target area at each moment based on the travel-related data of each vehicle; for each vehicle, the acceleration energy consumption of the vehicle at each moment is determined based on the acceleration of the vehicle at each moment, the position of the vehicle at each moment, and the mass of the vehicle; the rolling friction energy consumption of the vehicle at each moment is determined based on the mass of the vehicle, the position of the vehicle at each moment and the friction coefficient of the road in the target area; the wind resistance energy consumption of the vehicle at each moment is determined based on the windward area of ​​the vehicle, the air density of the target area, the air resistance coefficient, the speed and position of the vehicle at each moment; for each moment, the carbon emissions of the vehicle at that moment are predicted based on the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at that moment; and finally, the carbon emissions of the target area are predicted based on the carbon emissions of all vehicles at each moment. It can be seen that the present invention calculates the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at each moment based on the dynamic data of the vehicle's speed, acceleration and position at each moment, and then predicts the carbon emissions of the vehicle at each moment based on the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at each moment, and accurately predicts the carbon emissions at the time and spatial location of occurrence, realizing the prediction of carbon emissions of the transportation system with fine-grained time and space, and can completely cover all vehicles in the target area, thereby improving the accuracy of carbon emission prediction in the target area.

[0049] In one embodiment, Figure 2 This is a second flow chart of a carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention. Figure 2 As shown, the above step 102 determines the simulated traffic data of each vehicle in the target area at each time based on the travel-related data of each vehicle, which can be specifically achieved by the following steps: Step 201: Input the travel-related data of each vehicle into a path planning model to obtain a planned path for each vehicle output by the path planning model.

[0050] For example, after obtaining travel data for all vehicles within the target area, each vehicle's travel data is input into a path planning model. The path planning model then uses a time-cost-based shortest path algorithm, a balanced static traffic assignment algorithm, or a dynamic traffic assignment algorithm to determine the planned path for each vehicle. Of course, the planned path for each vehicle can also be obtained using navigation application service results provided by a map service provider, but this is not a limitation of the present invention.

[0051] Step 202: For each of the vehicles, the current speed of the vehicle, the current speed of the vehicle preceding the vehicle, the first relative distance between the vehicle and the preceding vehicle, and the road network information of the target area are input into a vehicle following model to obtain the current acceleration of the vehicle output by the vehicle following model, and based on the planned path of each vehicle and the current acceleration of the vehicle, the speed and next position of the vehicle at the next moment are determined.

[0052] Among them, the vehicle following model is mainly used to control the vehicle longitudinally, that is, control the driving direction and determine the speed of the vehicle.

[0053] For example, for each vehicle in the target area, in the initial stage, the current speed of the vehicle and the current speed of the vehicle preceding the vehicle can be pre-set to zero or other preset values, and the first relative distance between the vehicle and the previous vehicle is calculated based on the starting position of the vehicle and the starting position of the previous vehicle. The current speed of the vehicle, the current speed of the vehicle preceding the vehicle, the first relative distance between the vehicle and the previous vehicle, and the road network information of the target area are input into the vehicle following model. The vehicle following model can calculate the current acceleration of the vehicle based on the road network information of the target area, taking into account the dynamic traffic conditions and static environmental constraints; and then, based on the current acceleration of the vehicle, the current speed of the vehicle, and the time difference between two adjacent moments, the kinematic formula is used to deduce the speed and next position of the vehicle at the next moment. According to the same method, the speed and next position of each vehicle at the next moment in the target area can be obtained. In addition, the road network information of the target area mentioned here may include road information of the target area and traffic light control information of the target area.

[0054] Step 203 : Iterate based on the next moment speed and the next position of each vehicle to obtain simulated traffic data of each vehicle in the target area at each moment.

[0055] For example, when the next moment speed and next position of each vehicle in the target area are obtained for the first time, for each vehicle, the next moment speed of the vehicle is used as the new current speed, and the next position of the vehicle is used as the new position. The new current speed of the vehicle, the new current speed of the vehicle before the vehicle, and the new first relative distance between the vehicle and the previous vehicle are input into the vehicle following model to obtain the new current acceleration of the vehicle at the next moment output by the vehicle following model, and based on the new current acceleration of the vehicle, the new next moment speed and new next position of the vehicle are determined. The process is iterated continuously until the calculation of the speed, acceleration and position of all vehicles at all moments in the preset time period is completed, thereby obtaining the simulated traffic data of each vehicle in the target area at all moments. The simulated traffic data includes the speed, acceleration and position of the vehicle, and of course, can also include the relative displacement of the vehicle at two adjacent moments in time, etc. The relative displacement is also the relative distance. When simulating the traffic data of each vehicle in the target area at each moment, a carbon emission prediction model can be constructed based on the speed, acceleration and position of each vehicle in the target area at each moment by analyzing the dynamic characteristics of the vehicle in different motion states. The speed is used to reflect the energy consumption trend of the vehicle, the acceleration is used to reflect the instantaneous power demand of the vehicle, and the position is used to determine the distribution and driving path of the vehicle in the target area. The carbon emission prediction model can dynamically calculate the instantaneous carbon emissions of each vehicle and accumulate the instantaneous carbon emissions of all vehicles at each moment to obtain the carbon emissions of the target area within the preset time period.

[0056] It should be noted that the time difference between two moments in the present invention can be a sub-second time difference or a second time difference, and the sub-second or second speed, acceleration and position can be obtained, thereby restoring the vehicle's fine-grained real driving behavior.

[0057] In this embodiment, the speed, acceleration and position of all vehicles in the target area at each moment are simulated through the path planning model and the vehicle following model, and the fine-grained real driving behavior of the vehicles is restored. Then, the carbon emissions of the target area are predicted based on the speed, acceleration and position of each vehicle in the target area at each moment, so that the predicted carbon emissions take into account the driving behavior of all vehicles in the target area at each moment, thereby improving the accuracy of carbon emission prediction.

[0058] In one embodiment, Figure 3 This is a flow chart of the third method for predicting carbon emissions based on simulated traffic data provided by an embodiment of the present invention. Figure 3 As shown, in the above step 202, the next speed and next position of the vehicle are determined based on the planned path of each vehicle and the current acceleration of the vehicle, which can be specifically achieved by the following steps: Step 301: Input each of the planned paths, the first relative speed and second relative distance between the vehicle and the preceding vehicle in the adjacent lane, the second relative speed and third relative distance between the vehicle and the following vehicle in the adjacent lane, and the road network information into a vehicle lane change model to obtain a current lane change result of the vehicle output by the vehicle lane change model.

[0059] Among them, the role of the vehicle lane change model is to control the vehicle laterally and determine the vehicle's lane change behavior. The vehicle's overall driving behavior at a given moment is jointly controlled by the vehicle following module and the vehicle lane change model.

[0060] For example, in the initial stage, the vehicle's current speed, the speed of the preceding vehicle in the adjacent lane, and the speed of the following vehicle in the adjacent lane can be preset to zero or other preset values. Then, the first relative speed between the vehicle and the preceding vehicle in the adjacent lane and the second relative speed between the vehicle and the following vehicle in the adjacent lane are both zero. The second relative distance between the vehicle and the preceding vehicle in the adjacent lane is calculated based on the vehicle's starting position and the starting position of the preceding vehicle in the adjacent lane. The third relative distance between the vehicle and the following vehicle in the adjacent lane is calculated based on the vehicle's starting position and the starting position of the following vehicle in the adjacent lane. Each planned path, the first relative speed and second relative distance between the vehicle and the preceding vehicle in the adjacent lane, the second relative speed and third relative distance between the vehicle and the following vehicle in the adjacent lane, and the road network information are input into the vehicle lane change model. The vehicle lane change model can calculate the vehicle's current lane change result based on the road network information of the target area, taking into account dynamic traffic conditions and static environmental constraints. The vehicle's current lane change result is used to indicate whether the vehicle is changing lanes to the left or right. Using the same method, the current lane change result of each vehicle in the target area can be obtained.

[0061] Step 302: Determine the next speed and next position of the vehicle based on the current lane change result and the current acceleration of the vehicle.

[0062] For example, the system determines whether a vehicle is in the process of changing lanes or has completed the change based on the current lane change result. Kinematic formulas are then used to calculate the vehicle's speed at the next moment, combining the vehicle's current acceleration and speed. Simultaneously, the system uses displacement formulas to derive the vehicle's next position at the next moment based on the vehicle's current speed, next-moment speed, and current position. This process comprehensively considers the impact of lane changes on the vehicle's motion state, accurately predicting the vehicle's dynamic changes at the next moment and providing reliable data support for traffic flow simulation and vehicle control.

[0063] It should be noted that after obtaining the vehicle's current lane change result, the new first relative speed and new second relative distance between the vehicle and the previous vehicle in the adjacent lane, and the new second relative speed and new third relative distance between the vehicle and the next vehicle in the adjacent lane need to be input into the vehicle lane change model to obtain the vehicle's new current lane change result output by the vehicle lane change model. Based on the new current lane change result, the vehicle's new current acceleration, and the vehicle's new speed, the vehicle's new next-moment speed and new next position are calculated, and the process is continuously iterated until the calculation of the speed, acceleration, and position of all vehicles at all times within the preset time period is completed, thereby obtaining the simulated traffic data of each vehicle in the target area at each time.

[0064] It should be noted that, in actual application, the path planning model, the vehicle following model and the vehicle lane changing model can be integrated into a microscopic traffic simulation system, and the travel-related data of all vehicles in the target area can be used as the input of the microscopic traffic simulation system. The present invention does not limit this.

[0065] Figure 4 This is the overall architecture diagram of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention. Figure 4 As shown, the travel data of all vehicles in the target area is used as the simulation input of the microscopic traffic simulation system. Through the simulation calculations of the path planning model, vehicle following model, and vehicle lane change model in the microscopic traffic simulation system, the speed, acceleration, and position of each vehicle in the target area at each moment are obtained. Based on the speed, acceleration, and position of each vehicle at each moment, the acceleration energy consumption, rolling friction energy consumption, wind resistance energy consumption, and electronic energy consumption are calculated. The acceleration energy consumption, rolling friction energy consumption, wind resistance energy consumption, and electronic energy consumption are then converted into fuel consumption. The fuel consumption is then converted into carbon emissions based on the carbon emission factor. The final result is a transportation system carbon emission calculation result with fine temporal and spatial granularity and covering all vehicle types. In addition, the carbon emissions of the transportation system can be recalculated based on the impact of new regulations or new technologies.

[0066] Figure 5 is an architecture diagram of a microscopic traffic simulation system provided by an embodiment of the present invention, such as Figure 5As shown, the travel-related data of all vehicles in the target area is used as the simulation input of the microscopic traffic simulation system. The path planning model in the microscopic traffic simulation system determines the planned path of each vehicle based on the travel-related data of each vehicle using a time-cost shortest path algorithm, a balanced static or dynamic traffic assignment algorithm, or a navigation application service. The vehicle following model in the microscopic traffic simulation system obtains the vehicle's acceleration based on the vehicle's speed, the speed of the previous vehicle, the first relative distance between the vehicle and the previous vehicle, and the road network information of the target area. Based on the vehicle's acceleration, the vehicle's next speed and next position are determined, thus achieving longitudinal control of the vehicle. The vehicle lane change model in the microscopic traffic simulation system obtains the vehicle's lane change result based on the planned path, the road conditions of the adjacent lanes (the first relative speed and second relative distance between the vehicle and the previous vehicle in the adjacent lane, the second relative speed and third relative distance between the vehicle and the next vehicle in the adjacent lane), and the road network information (road speed limit, signal control strategy), thus achieving lateral control of the vehicle. After multiple rounds of simulation iterations, the speed, acceleration, and position of each vehicle in the target area at each moment are obtained, and the displacement can be calculated based on the position at each moment.

[0067] In this embodiment, the vehicle lane change model is used to simulate the current lane change result of the vehicle based on the planned path, the first relative speed and second relative distance between the vehicle and the previous vehicle in the adjacent lane, the second relative speed and third relative distance between the vehicle and the next vehicle in the adjacent lane, and the road network information. The current lane change result is then combined with the current acceleration of the vehicle to determine the vehicle's speed and next position at the next moment, thereby improving the calculation accuracy of the speed and next position at the next moment, and further improving the accuracy of carbon emission prediction.

[0068] In one embodiment, the travel-related data further includes the vehicle type of each vehicle; step 106 predicts the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment, which can be specifically achieved by the following steps: Determine the energy consumption ratio of the vehicle based on the vehicle type; determine the electronic energy consumption of the vehicle at the moment based on the energy consumption ratio of the vehicle, the acceleration energy consumption of the vehicle at the moment, the rolling friction energy consumption of the vehicle at the moment, and the wind resistance energy consumption of the vehicle at the moment; predict the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption and the electronic energy consumption of the vehicle at the moment.

[0069] Among them, vehicle types include heavy trucks, light trucks, SUVs, sedans, buses or coaches, etc.

[0070] For example, a correspondence between vehicle types and energy consumption ratios is pre-stored, so when the vehicle type is identified based on the vehicle image, the energy consumption ratio of the vehicle corresponding to the vehicle type can be found, and the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at that moment are added together to obtain the dynamic energy consumption of the vehicle at that moment, and then the mechanical energy consumption of the vehicle at that moment is multiplied by the energy consumption ratio of the vehicle to obtain the electronic energy consumption of the vehicle at that moment. The electronic energy consumption here can also be referred to as the energy consumption of the vehicle's electronic equipment and air-conditioning system; further, the acceleration energy consumption, rolling friction energy consumption, wind resistance energy consumption and electronic energy consumption of the vehicle at that moment are added together to obtain the total energy consumption of the vehicle at that moment, and then based on the total energy consumption of the vehicle at that moment, the carbon emissions of the vehicle at that moment are predicted.

[0071] Figure 6 is a schematic diagram of the carbon emission conversion method provided by an embodiment of the present invention, such as Figure 6 As shown, based on the acceleration of the vehicle at each moment, the position of the vehicle at each moment, and the mass of the vehicle, the acceleration energy consumption of the vehicle at each moment is determined; based on the mass of the vehicle, the position of the vehicle at each moment and the friction coefficient of the road in the target area, the rolling friction energy consumption of the vehicle at each moment is determined; based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, the speed and position of the vehicle at each moment, the wind resistance energy consumption of the vehicle at each moment is determined; based on the energy consumption ratio of the vehicle, the acceleration energy consumption of the vehicle at each moment, the rolling friction energy consumption of the vehicle at each moment, and the wind resistance energy consumption of the vehicle at each moment, the electronic energy consumption of the vehicle at each moment is determined; and then the acceleration energy consumption, rolling friction energy consumption, wind resistance energy consumption and electronic energy consumption are converted into fuel consumption, and then the fuel consumption is converted into carbon emissions based on the carbon emission factor, and finally a carbon emission measurement result of the transportation system with fine spatiotemporal granularity and covering all vehicle types is obtained.

[0072] In this embodiment, when calculating the carbon emissions of the vehicle at each moment, the electronic energy consumption of the vehicle at each moment is taken into account, which further improves the prediction accuracy of the carbon emissions of the vehicle at each moment.

[0073] In one embodiment, the travel-related data further includes the fuel type and engine type of each vehicle. The above-mentioned prediction of the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, rolling friction energy consumption, windage energy consumption, and electronic energy consumption of the vehicle at the moment can be specifically achieved by the following method: Based on the acceleration energy consumption, rolling friction energy consumption, wind resistance energy consumption and electronic energy consumption of the vehicle at that moment, the total energy consumption of the vehicle at that moment is determined; based on the engine type, the engine efficiency of the vehicle is determined; based on the fuel type, the fuel energy of the vehicle is determined; based on the engine efficiency, the fuel energy and the total energy consumption, the fuel consumption of the vehicle at that moment is determined; based on the carbon emission factor corresponding to the fuel type and the fuel consumption, the carbon emissions of the vehicle at that moment are predicted.

[0074] Fuel types include oil and electricity. The fuel energy of oil, including diesel and gasoline, can be referred to as calorific value. The fuel energy of electricity can be expressed as the energy value of electricity, typically measured in kilowatt-hours (kWh). One kWh represents the energy consumed by a 1-kilowatt device operating for one hour. Furthermore, the carbon emission factor can be an empirical value, which is not detailed here.

[0075] For example, the acceleration energy consumption, rolling friction energy consumption, wind resistance energy consumption and electronic energy consumption of the vehicle at that moment are added together to calculate the total energy consumption of the vehicle at that moment. Here, for fuel-type vehicles, the total energy consumption is the fuel energy consumption, and for electric-type vehicles, the total energy consumption is the electricity consumption. The engine efficiency of the vehicle is found based on the engine type, and the fuel energy of the vehicle is found based on the fuel type. Then, based on the engine efficiency and fuel energy, the total energy consumption of the vehicle at that moment is converted into the fuel consumption of the vehicle at that moment. Specifically, the engine efficiency and the fuel energy are multiplied, and the total energy consumption is divided by the product to obtain the fuel consumption of the vehicle at that moment.

[0076] In this embodiment, based on the vehicle's engine efficiency and the vehicle's fuel energy, the total energy consumption of the vehicle at each moment is converted to obtain the vehicle's carbon emissions at each moment, and the carbon emissions are accurately located at the time and space of occurrence, thereby realizing a fine-grained temporal and spatial prediction of the transportation system's carbon emissions, and being able to fully cover all vehicles in the target area, thereby improving the accuracy of carbon emission predictions within the target area.

[0077] In one embodiment, the prediction of the carbon emissions of the target area based on the carbon emissions of all the vehicles at each time can be achieved in the following manner: In the case where the fuel type is a fuel oil type, based on the speed of the vehicle at all times, the duration during which the speed of the vehicle is zero is determined; based on the duration and the fuel conversion factor, the amount of fuel consumed by the vehicle at idle speed is determined; based on the amount of fuel consumed by the vehicle at idle speed and the carbon emission factor corresponding to the fuel type, the idling carbon emissions of the vehicle in the target area are determined; based on the idling carbon emissions of each of the vehicles and the carbon emissions of each of the vehicles at each time, the carbon emissions of the target area are predicted.

[0078] The fuel conversion factor may adopt an empirical value, etc., which will not be described in detail in the present invention.

[0079] For example, when the vehicle's fuel type is fuel type, first, by analyzing the vehicle's speed at all times, the duration of the zero speed, that is, the idle time, is determined; then, the idle time is multiplied by a preset fuel conversion factor to obtain the amount of idle fuel consumed by the vehicle in the idle state; the amount of idle fuel consumed by the vehicle is multiplied by the carbon emission factor corresponding to the fuel type to obtain the idle carbon emissions of the vehicle in the target area; and then the idle carbon emissions of each vehicle and the carbon emissions of each vehicle at each time are added together to finally obtain the carbon emissions of the target area.

[0080] In this embodiment, when calculating the carbon emissions of the target area, the idling carbon emissions of vehicles in the target area are taken into account, making the calculation of carbon emissions more comprehensive, thereby further improving the accuracy of the carbon emissions prediction of the target area.

[0081] In one embodiment, Figure 7 This is a fourth flow chart of a carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention. Figure 7 As shown, after the above step 107, the carbon emission prediction method based on simulated traffic data further includes the following steps: Step 108: When the target information of the target area changes, new simulated traffic data of each vehicle in the target area at each moment is re-determined based on the changed target information, and the target information includes at least one of the following: road network information, travel-related data, vehicle type, and fuel type.

[0082] For example, since the carbon emission prediction method of the present invention adopts a calculation process based on simulation, the impact of new vehicle-related regulations or new technologies can be directly realized by changing the input of the micro-traffic simulation system. The input of the specific micro-traffic simulation system may include road network information, travel-related data, vehicle type and fuel type, etc. In addition, when generating travel-related data based on the travel demand generation algorithm, the target information also includes the total number of vehicles, the proportion of vehicle types and the proportion of fuel types, etc. In the case that the target information of the target area changes, the changed target information is input into the micro-traffic simulation system, and the above steps are re-executed to finally determine the new simulated traffic data of each vehicle in the target area at each time.

[0083] Step 109: Predict new carbon emissions of the target area based on the new simulated traffic data of each vehicle at each time.

[0084] For example, when the new simulated traffic data of each vehicle in the target area at each time is obtained, the new carbon emissions of the target area are predicted based on the new simulated traffic data of each vehicle in the target area at each time, using similar steps as above, thereby realizing the carbon emission prediction under the influence of new vehicle-related regulations or new technologies, and the carbon emissions predicted before the new regulations or new technologies and the carbon emissions predicted after the new regulations or new technologies can be compared and analyzed, so as to analyze the actual impact of the new regulations or new technologies on carbon emissions.

[0085] Figure 8 Schematic diagram of a carbon emission prediction method under the influence of new regulations or new technologies provided by an embodiment of the present invention. Figure 8 As shown, the changed target information may include adjustments to travel-related data (e.g., starting location, end location, departure time), driving style (all for autonomous vehicles), road network information (e.g., adjustments to intersection signal control strategies), path planning, vehicle type, and fuel type, etc. Based on the changed target information, the new simulated traffic data for each vehicle in the target area at each time is re-determined. Based on the new simulated traffic data for each vehicle at each time, the new carbon emissions of the target area are predicted, thereby analyzing the actual impact of new regulations or new technologies on carbon emissions.

[0086] In this embodiment, when the target information of the target area changes, the new simulated traffic data of each vehicle in the target area at each time is re-determined based on the changed target information, and the new carbon emissions of the target area are predicted based on the new simulated traffic data of each vehicle at each time, thereby realizing the carbon emission prediction under the influence of new vehicle-related regulations or new technologies, and further verifying the impact of various regulations and technologies on the carbon emissions of the transportation system.

[0087] The carbon emission prediction method based on simulated traffic data of the present invention is described below based on specific embodiments: Example 1 Suppose a user needs to use this carbon emission prediction method based on simulated traffic data to calculate the carbon emissions of a certain highway in spring, and hopes to evaluate the impact of the modified highway speed limit in the road network information on carbon emissions.

[0088] Figure 9 This is one of the example diagrams of the carbon emission prediction method based on simulated traffic data provided by an embodiment of the present invention. Figure 9 As shown, the system first constructs travel data for all vehicles on the highway from midnight to midnight on weekdays based on information such as license plates, time, and vehicle type recorded at each entrance and exit. This data is stored in a comma-separated values ​​(CSV) file format. The user then uses the Dijkstra algorithm as a path planning algorithm to find the shortest route from entrance to exit for each vehicle, which serves as the planned path. In the microscopic traffic simulation system, the user selects the Krauss model as the vehicle following model, using the speed of the preceding vehicle as the criterion for determining the lane change behavior of the vehicle (if the speed of the preceding vehicle in the adjacent lane exceeds that of the vehicle in that lane, the vehicle changes lanes). Finally, by combining the path planning model, the vehicle following model, and the lane change model, the microscopic traffic conditions on the highway from midnight to midnight are simulated, resulting in the position, velocity, and acceleration of all vehicles on the highway at one-second intervals. Based on the position, speed, and acceleration of all vehicles on the highway at one-second intervals, the vehicle type is obtained from the corresponding relationship between the recognized license plate and vehicle type in the vehicle data. The mass, frontal area, drag coefficient, and engine efficiency of each vehicle are retrieved. Given the favorable spring weather, electronic energy consumption is negligible. The friction coefficient of the highway is retrieved from highway construction data, and carbon emission factors for fuels and energy sources such as gasoline, diesel, and thermal power are obtained from relevant databases. Finally, all the data used in the above steps are obtained to determine the vehicle carbon emissions of the highway.

[0089] In addition, to calculate the carbon emission impact of changing highway speed limits, users can modify the assumed speed limits of all lanes in the micro-traffic simulation system and re-execute the above steps to obtain the vehicle carbon emissions of highways under the modified speed limits, thereby analyzing the impact of the change in highway speed limits on the carbon emissions of the transportation system.

[0090] Example 2 Suppose a user needs to use this carbon emission prediction method based on simulated traffic data to calculate a city's carbon emissions in summer, and wants to evaluate the impact on carbon emissions after replacing all vehicles with self-driving vehicles.

[0091] Figure 10 This is the second example of the carbon emission prediction method based on simulated traffic data provided by the embodiment of the present invention. Figure 10 As shown in the figure, first obtain all vehicle images captured by cameras installed in the city on a certain day, use image matching technology to analyze all vehicle images to restore the trajectory of all vehicles passing through each camera, and use image recognition technology to identify vehicle license plates, vehicle types and other information. Based on the vehicle license plates, further obtain the vehicle's fuel type, and finally obtain the travel-related data of all vehicles in the city on a certain day, which is stored in JSON file format. Then, users can use The algorithm, acting as a path planning algorithm, finds the shortest route for each vehicle from the previous camera intersection to the next camera intersection, which is used as the planned path. In the microscopic traffic simulation system, the user selects the Intelligent Driver Model (IDM) as the vehicle following model and the Minimizing Overall Braking Induced by Lane Changes (MOBIL) model as the vehicle lane change model. Ultimately, by combining the path planning, vehicle following, and lane change models, a simulation of the city's microscopic traffic conditions from midnight to midnight is generated, capturing the position, velocity, and acceleration of all vehicles in the city at 0.2-second intervals throughout the day. Based on the position, speed, and acceleration of all vehicles in the city on that day at 0.2-second intervals, the vehicle type is obtained from the corresponding relationship between the recognized license plate and vehicle type in the city's own vehicle data. The mass, frontal area, air resistance coefficient, engine efficiency, and other information of each vehicle are retrieved. Considering the hot summer weather, electronic energy consumption is set to 10% of the total vehicle energy consumption. The friction coefficient of urban roads is set based on reference to public literature. The carbon emission factors of fuels such as gasoline, diesel, and thermal power are obtained from relevant databases, and the carbon emission factor of electricity is obtained from reports of local power generation companies. Finally, all the data used in the above steps are obtained to determine the carbon emissions of vehicles in the city.

[0092] Furthermore, to calculate the impact of replacing all vehicles with autonomous vehicles, we first need to obtain the control logic for acceleration and lane changes from autonomous vehicle companies, incorporate the corresponding controls into the micro-traffic simulation system, and then re-run the above steps, modifying the vehicle type of all vehicles to autonomous. This will yield the carbon emissions of the city's vehicles if all vehicles are replaced with autonomous vehicles, thereby analyzing the impact of this vehicle type change on the carbon emissions of the city's transportation system. Secondly, since autonomous vehicles include intelligent hardware such as artificial intelligence (AI) hardware and lidar, we need to obtain the typical energy consumption of typical AI hardware and autonomous vehicle perception devices from public sources and incorporate them into the energy consumption calculation. Finally, since all autonomous vehicles are electric vehicles, the carbon emissions conversion process replaces the fuel type of all vehicles with electricity, using the carbon emission factor of electricity to calculate the carbon emissions of the city's vehicles after replacing all vehicles with autonomous vehicles.

[0093] The carbon emission prediction device based on simulated traffic data provided by the present invention is described below. The carbon emission prediction device based on simulated traffic data described below and the carbon emission prediction method based on simulated traffic data described above can be referenced to each other.

[0094] Figure 11 FIG. 1 is a schematic diagram of the structure of a carbon emission prediction device based on simulated traffic data provided by an embodiment of the present invention. Figure 11 As shown, the carbon emission prediction device 1100 based on simulated traffic data includes an acquisition unit 1101, a first determination unit 1102, a second determination unit 1103, a third determination unit 1104, a fourth determination unit 1105, a first prediction unit 1106 and a second prediction unit 1107; wherein: An acquisition unit 1101 is configured to acquire travel-related data of all vehicles in a target area, wherein the travel-related data includes a starting position and an ending position of the vehicle; A first determining unit 1102 is configured to determine simulated traffic data of each vehicle in the target area at each time based on the travel-related data of each vehicle, wherein the simulated traffic data includes a speed, an acceleration, and a position of the vehicle; The second determining unit 1103 is configured to determine, for each vehicle, an acceleration energy consumption of the vehicle at each moment based on the acceleration of the vehicle at each moment, the position of the vehicle at each moment, and the mass of the vehicle; a third determining unit 1104, configured to determine a rolling friction energy consumption of the vehicle at each of the moments based on the mass of the vehicle, the position of the vehicle at each of the moments, and the friction coefficient of the road in the target area; a fourth determining unit 1105, configured to determine the wind resistance energy consumption of the vehicle at each moment based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, and the speed and position of the vehicle at each moment; A first prediction unit 1106 is configured to predict, for each moment, the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment; The second prediction unit 1107 is configured to predict the carbon emissions of the target area based on the carbon emissions of all the vehicles at each time.

[0095] The carbon emission prediction device based on simulated traffic data provided by the present invention obtains travel-related data of all vehicles in the target area, and determines the speed, acceleration and position of each vehicle in the target area at each moment based on the travel-related data of each vehicle; for each vehicle, the acceleration energy consumption of the vehicle at each moment is determined based on the acceleration of the vehicle at each moment, the position of the vehicle at each moment, and the mass of the vehicle; the rolling friction energy consumption of the vehicle at each moment is determined based on the mass of the vehicle, the position of the vehicle at each moment and the friction coefficient of the road in the target area; the wind resistance energy consumption of the vehicle at each moment is determined based on the windward area of ​​the vehicle, the air density of the target area, the air resistance coefficient, the speed and position of the vehicle at each moment; for each moment, the carbon emissions of the vehicle at that moment are predicted based on the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at that moment; and finally the carbon emissions of the target area are predicted based on the carbon emissions of all vehicles at each moment. It can be seen that the present invention calculates the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at each moment based on the dynamic data of the vehicle's speed, acceleration and position at each moment, and then predicts the carbon emissions of the vehicle at each moment based on the acceleration energy consumption, rolling friction energy consumption and wind resistance energy consumption of the vehicle at each moment, and accurately predicts the carbon emissions at the time and spatial location of occurrence, realizing the prediction of carbon emissions of the transportation system with fine-grained time and space, and can completely cover all vehicles in the target area, thereby improving the accuracy of carbon emission prediction in the target area.

[0096] Based on any of the foregoing embodiments, the first determining unit 1102 is specifically configured to: Inputting the travel-related data of each vehicle into a path planning model to obtain a planned path for each vehicle output by the path planning model; For each vehicle, inputting the current speed of the vehicle, the current speed of the vehicle preceding the vehicle, a first relative distance between the vehicle and the preceding vehicle, and road network information of the target area into a vehicle-following model to obtain a current acceleration of the vehicle output by the vehicle-following model; Determining the next speed and next position of the vehicle at a next moment based on the planned path of each vehicle and the current acceleration of the vehicle; Iteration is performed based on the next moment speed and the next position of each vehicle to obtain simulated traffic data of each vehicle in the target area at each moment.

[0097] Based on any of the foregoing embodiments, the first determining unit 1102 is further specifically configured to: Inputting each of the planned paths, the first relative speed and the second relative distance between the vehicle and the preceding vehicle in the adjacent lane, the second relative speed and the third relative distance between the vehicle and the following vehicle in the adjacent lane, and the road network information into a vehicle lane change model, and obtaining a current lane change result of the vehicle output by the vehicle lane change model; Based on the current lane change result and the current acceleration of the vehicle, a next-moment speed and a next-moment position of the vehicle are determined.

[0098] Based on any of the above embodiments, the travel-related data further includes the vehicle type of each of the vehicles; and the first prediction unit 1106 is specifically configured to: determining an energy consumption ratio of the vehicle based on the vehicle type; determining the electronic energy consumption of the vehicle at the moment based on the energy consumption ratio of the vehicle, the acceleration energy consumption of the vehicle at the moment, the rolling friction energy consumption of the vehicle at the moment, and the wind resistance energy consumption of the vehicle at the moment; The carbon emissions of the vehicle at the moment are predicted based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption and the electronic energy consumption of the vehicle at the moment.

[0099] Based on any of the above embodiments, the travel-related data further includes the fuel type and engine type of each vehicle; and the first prediction unit 1106 is further specifically configured to: determining a total energy consumption of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption, and the electronic energy consumption of the vehicle at the moment; determining an engine efficiency of the vehicle based on the engine type; determining a fuel energy of the vehicle based on the fuel type; determining a fuel consumption of the vehicle at the time based on the engine efficiency, the fuel energy, and the total energy consumption; The carbon emissions of the vehicle at the moment are predicted based on the carbon emission factor corresponding to the fuel type and the fuel consumption.

[0100] Based on any of the foregoing embodiments, the second prediction unit 1107 is specifically configured to: In a case where the fuel type is a fuel oil type, determining a duration during which the speed of the vehicle is zero based on the speed of the vehicle at all times; Determining the amount of fuel consumed by idling the vehicle based on the duration and the fuel conversion factor; Determining the idling carbon emissions of the vehicle in the target area based on the idling fuel consumption of the vehicle and the carbon emission factor corresponding to the fuel type; The carbon emissions of the target area are predicted based on the idling carbon emissions of each of the vehicles and the carbon emissions of each of the vehicles at each of the moments.

[0101] Based on any of the above embodiments, the carbon emission prediction device 1100 based on simulated traffic data further includes: a fifth determining unit, configured to, when target information of the target area changes, re-determine new simulated traffic data for each vehicle in the target area at each time based on the changed target information, wherein the target information includes at least one of the following: road network information, travel-related data, vehicle type, and fuel type; The third prediction unit is configured to predict new carbon emissions of the target area based on new simulated traffic data of each vehicle at each time.

[0102] Figure 12 FIG is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention, such as Figure 12As shown, the electronic device may include: a processor 1210, a communications interface 1220, a memory 1230 and a communication bus 1240, wherein the processor 1210, the communications interface 1220 and the memory 1230 communicate with each other via the communications bus 1240. The processor 1210 may call the logic instructions in the memory 1230 to execute a carbon emission prediction method based on simulated traffic data, the method comprising: obtaining travel-related data of all vehicles in a target area, the travel-related data including the starting position and the ending position of the vehicle; determining the simulated traffic data of each vehicle in the target area at each time based on the travel-related data of each vehicle, the simulated traffic data including the speed, acceleration and position of the vehicle; for each vehicle, determining the vehicle's speed at each time based on the acceleration of the vehicle at each time, the position of the vehicle at each time, and the mass of the vehicle. acceleration energy consumption; determining the rolling friction energy consumption of the vehicle at each of the moments described above based on the mass of the vehicle, the position of the vehicle at each of the moments described above and the friction coefficient of the road in the target area; determining the wind resistance energy consumption of the vehicle at each of the moments described above based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, the speed and position of the vehicle at each of the moments described above; for each of the moments described above, predicting the carbon emissions of the vehicle at that moment based on the acceleration energy consumption of the vehicle at that moment, the rolling friction energy consumption and the wind resistance energy consumption; predicting the carbon emissions of the target area based on the carbon emissions of all the vehicles at each of the moments described above.

[0103] Furthermore, the logic instructions in the aforementioned memory 1230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0104] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the carbon emission prediction method based on simulated traffic data provided by the above methods, the method comprising: obtaining travel-related data of all vehicles in the target area, the travel-related data including the starting position and the end position of the vehicle; based on the travel-related data of each vehicle, determining the simulated traffic data of each vehicle in the target area at each time, the simulated traffic data including the speed, acceleration and position of the vehicle; for each vehicle, based on the acceleration, speed and position of the vehicle at each time, Determine the acceleration energy consumption of the vehicle at each moment based on the position of the vehicle at each moment and the mass of the vehicle; determine the rolling friction energy consumption of the vehicle at each moment based on the mass of the vehicle, the position of the vehicle at each moment and the friction coefficient of the road in the target area; determine the wind resistance energy consumption of the vehicle at each moment based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, the speed and position of the vehicle at each moment; for each moment, predict the carbon emissions of the vehicle at that moment based on the acceleration energy consumption of the vehicle at that moment, the rolling friction energy consumption and the wind resistance energy consumption; predict the carbon emissions of the target area based on the carbon emissions of all the vehicles at each moment.

[0105] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the carbon emission prediction method based on simulated traffic data provided by the above methods, the method comprising: obtaining travel-related data of all vehicles in a target area, the travel-related data including the starting position and the ending position of the vehicle; based on the travel-related data of each of the vehicles, determining the simulated traffic data of each of the vehicles in the target area at each time, the simulated traffic data including the speed, acceleration and position of the vehicle; for each of the vehicles, based on the acceleration of the vehicle at each time, the position of the vehicle at each time, and the speed of the vehicle Based on the mass of the vehicle, the acceleration energy consumption of the vehicle at each of the moments is determined; based on the mass of the vehicle, the position of the vehicle at each of the moments and the friction coefficient of the road in the target area, the rolling friction energy consumption of the vehicle at each of the moments is determined; based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, the speed and position of the vehicle at each of the moments, the wind resistance energy consumption of the vehicle at each of the moments is determined; for each of the moments, based on the acceleration energy consumption of the vehicle at that moment, the rolling friction energy consumption and the wind resistance energy consumption, the carbon emissions of the vehicle at that moment are predicted; based on the carbon emissions of all the vehicles at each of the moments, the carbon emissions of the target area are predicted.

[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0107] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0108] 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 make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A carbon emission prediction method based on simulated traffic data, characterized in that: include: Obtaining travel-related data for each of the vehicles in the target area, the travel-related data including the starting and ending locations of the vehicles; Determining simulated traffic data of each vehicle in the target area at each time based on the travel-related data of each vehicle, the simulated traffic data including the speed, acceleration and position of the vehicle; For each of the vehicles, determining the acceleration energy consumption of the vehicle at each of the moments based on the acceleration of the vehicle at each of the moments, the position of the vehicle at each of the moments, and the mass of the vehicle; determining a rolling friction energy consumption of the vehicle at each of the moments based on the mass of the vehicle, the position of the vehicle at each of the moments, and a friction coefficient of a road in the target area; determining the wind resistance energy consumption of the vehicle at each of the moments based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, and the speed and position of the vehicle at each of the moments; For each of the moments, based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment, predicting the carbon emissions of the vehicle at the moment; The carbon emissions of the target area are predicted based on the carbon emissions of all the vehicles at each of the moments.

2. The carbon emission prediction method based on simulated traffic data according to claim 1 is characterized in that: Determining the simulated traffic data of each vehicle in the target area at each time based on the travel-related data of each vehicle includes: Inputting the travel-related data of each vehicle into a path planning model to obtain a planned path for each vehicle output by the path planning model; For each vehicle, inputting the current speed of the vehicle, the current speed of the vehicle preceding the vehicle, a first relative distance between the vehicle and the preceding vehicle, and road network information of the target area into a vehicle-following model to obtain a current acceleration of the vehicle output by the vehicle-following model; Determining the next speed and next position of the vehicle at a next moment based on the planned path of each vehicle and the current acceleration of the vehicle; Iteration is performed based on the next moment speed and the next position of each vehicle to obtain simulated traffic data of each vehicle in the target area at each moment.

3. The carbon emission prediction method based on simulated traffic data according to claim 2 is characterized in that: The determining of the next moment speed and next position of the vehicle based on the planned path of each vehicle and the current acceleration of the vehicle includes: Inputting each of the planned paths, the first relative speed and the second relative distance between the vehicle and the preceding vehicle in the adjacent lane, the second relative speed and the third relative distance between the vehicle and the following vehicle in the adjacent lane, and the road network information into a vehicle lane change model, and obtaining a current lane change result of the vehicle output by the vehicle lane change model; Based on the current lane change result and the current acceleration of the vehicle, a next-moment speed and a next-moment position of the vehicle are determined.

4. The carbon emission prediction method based on simulated traffic data according to claim 1, characterized in that: The travel-related data further includes a vehicle type of each of the vehicles; The predicting of the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment includes: determining an energy consumption ratio of the vehicle based on the vehicle type; determining the electronic energy consumption of the vehicle at the moment based on the energy consumption ratio of the vehicle, the acceleration energy consumption of the vehicle at the moment, the rolling friction energy consumption of the vehicle at the moment, and the wind resistance energy consumption of the vehicle at the moment; The carbon emissions of the vehicle at the moment are predicted based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption and the electronic energy consumption of the vehicle at the moment.

5. The carbon emission prediction method based on simulated traffic data according to claim 4 is characterized in that: The travel-related data also includes the fuel type and engine type of each of the vehicles; The predicting of the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption, and the electronic energy consumption of the vehicle at the moment includes: determining a total energy consumption of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, the wind resistance energy consumption, and the electronic energy consumption of the vehicle at the moment; determining an engine efficiency of the vehicle based on the engine type; determining a fuel energy of the vehicle based on the fuel type; determining a fuel consumption of the vehicle at the time based on the engine efficiency, the fuel energy, and the total energy consumption; The carbon emissions of the vehicle at the moment are predicted based on the carbon emission factor corresponding to the fuel type and the fuel consumption.

6. The carbon emission prediction method based on simulated traffic data according to claim 5 is characterized in that: The predicting of the carbon emissions of the target area based on the carbon emissions of all the vehicles at each time comprises: In a case where the fuel type is a fuel oil type, determining a duration during which the speed of the vehicle is zero based on the speed of the vehicle at all times; Determining the amount of fuel consumed by idling the vehicle based on the duration and the fuel conversion factor; Determining the idling carbon emissions of the vehicle in the target area based on the idling fuel consumption of the vehicle and the carbon emission factor corresponding to the fuel type; The carbon emissions of the target area are predicted based on the idling carbon emissions of each of the vehicles and the carbon emissions of each of the vehicles at each of the moments.

7. The carbon emission prediction method based on simulated traffic data according to any one of claims 1 to 6, characterized in that: The method further comprises: In the event that target information of the target area changes, re-determining new simulated traffic data for each vehicle in the target area at each time based on the changed target information, wherein the target information includes at least one of the following: road network information, travel-related data, vehicle type, and fuel type; Based on the new simulated traffic data of each vehicle at each time, new carbon emissions of the target area are predicted.

8. A carbon emission prediction device based on simulated traffic data, characterized in that: include: an acquisition unit, configured to acquire travel-related data of all vehicles in the target area, wherein the travel-related data includes a starting position and an ending position of the vehicle; a first determining unit, configured to determine simulated traffic data of each of the vehicles in the target area at each time based on the travel-related data of each of the vehicles, the simulated traffic data including the speed, acceleration, and position of the vehicle; a second determining unit, configured to determine, for each of the vehicles, an acceleration energy consumption of the vehicle at each of the moments based on the acceleration of the vehicle at each of the moments, the position of the vehicle at each of the moments, and the mass of the vehicle; a third determining unit, configured to determine the rolling friction energy consumption of the vehicle at each of the moments based on the mass of the vehicle, the position of the vehicle at each of the moments, and the friction coefficient of the road in the target area; a fourth determining unit, configured to determine the wind resistance energy consumption of the vehicle at each of the moments based on the frontal area of ​​the vehicle, the air density of the target area, the air resistance coefficient, and the speed and position of the vehicle at each of the moments; a first prediction unit, configured to predict, for each of the moments, the carbon emissions of the vehicle at the moment based on the acceleration energy consumption, the rolling friction energy consumption, and the wind resistance energy consumption of the vehicle at the moment; The second prediction unit is used to predict the carbon emissions of the target area based on the carbon emissions of all the vehicles at each time.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the carbon emission prediction method based on simulated traffic data as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the carbon emission prediction method based on simulated traffic data as claimed in any one of claims 1 to 7 is implemented.

Images