WO2023071564A1 - 交通仿真转换方法、装置、计算机设备及存储介质 - Google Patents

交通仿真转换方法、装置、计算机设备及存储介质 Download PDF

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Publication number
WO2023071564A1
WO2023071564A1 PCT/CN2022/118436 CN2022118436W WO2023071564A1 WO 2023071564 A1 WO2023071564 A1 WO 2023071564A1 CN 2022118436 W CN2022118436 W CN 2022118436W WO 2023071564 A1 WO2023071564 A1 WO 2023071564A1
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vehicle
traffic
road
traffic simulation
distance
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PCT/CN2022/118436
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English (en)
French (fr)
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张祥琦
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腾讯科技（深圳）有限公司
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Publication of WO2023071564A1 publication Critical patent/WO2023071564A1/zh
Priority to US18/342,485 priority Critical patent/US20230342520A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/02—Registering or indicating driving, working, idle, or waiting time only

Definitions

  • the present application relates to the technical field of traffic simulation, in particular to a traffic simulation conversion method, device, computer equipment and storage medium.
  • Traffic simulation is a technology that uses simulation technology to study traffic behavior. By establishing a mathematical model of the real-time movement of the transportation system within a certain period of time, it can track and describe the changes in traffic movement over time and space. According to the degree of detailed description of traffic simulation, it can be divided into macro traffic simulation, meso traffic simulation and micro traffic simulation.
  • further microscopic traffic simulation is realized based on the simulation results of mesoscopic traffic simulation operation.
  • the results of mesoscopic traffic simulation provide basic OD (Origin-Destination, traffic volume) data and road network models for microscopic traffic simulation.
  • the micro-traffic simulation is realized by further adding detailed features to the model.
  • the meso-micro traffic simulation conversion method in the related art is for the conversion of the whole road network, and the difference of the traffic state before and after the conversion must be relatively large. How to maintain the consistency of the traffic state before and after the meso-micro traffic simulation conversion is a problem that needs to be solved.
  • the embodiment of the present application provides a traffic simulation conversion method, device, computer equipment and storage medium, which can realize flexible conversion between mesoscopic traffic simulation and microscopic traffic simulation. Described technical scheme is as follows:
  • a traffic simulation conversion method is provided, the method is executed by a computer device, and the method includes:
  • the first traffic simulation and the second traffic simulation respectively include one of micro-traffic simulation and meso-traffic simulation, and the first traffic simulation is different from the second traffic simulation.
  • a traffic simulation conversion device comprising:
  • An acquisition module configured to acquire the first vehicle traffic data of the vehicle in the first traffic simulation
  • a conversion module configured to convert the second vehicle traffic data of the vehicle in the second traffic simulation according to the first vehicle traffic data
  • a running module configured to run the second traffic simulation according to the second vehicle traffic data of the vehicle
  • the first traffic simulation and the second traffic simulation respectively include one of micro-traffic simulation and meso-traffic simulation, and the first traffic simulation is different from the second traffic simulation.
  • a computer device in another aspect, includes a processor and a memory, at least one program is stored in the memory, and the at least one program is loaded and executed by the processor to implement the above-mentioned The traffic simulation conversion method described in any one of the embodiments.
  • a computer-readable storage medium wherein computer instructions are stored in the computer-readable storage medium, and the computer instructions are loaded and executed by a processor to implement the traffic simulation conversion method provided in various aspects of the present application .
  • a computer program product comprising computer instructions stored on a computer readable storage medium.
  • the processor of the computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above traffic simulation conversion method.
  • the vehicle-level traffic simulation conversion method is realized, so that the traffic conditions before and after the conversion can be kept basically the same, and the accuracy and flexibility of the traffic simulation conversion are improved.
  • Fig. 1 shows a schematic diagram of an interface for simultaneously running mesoscopic traffic simulation and microscopic traffic simulation in different areas provided by an exemplary embodiment of the present application;
  • Fig. 2 shows a schematic diagram of an interface for traffic state prediction provided by an exemplary embodiment of the present application
  • Fig. 3 shows a structural block diagram of a traffic simulation system provided by an exemplary embodiment of the present application
  • Fig. 4 shows the flowchart of the traffic simulation conversion method provided by an exemplary embodiment of the present application
  • Fig. 5 shows the flow chart of transitioning from mesoscopic traffic simulation to microscopic traffic simulation provided by an exemplary embodiment of the present application
  • Fig. 6 shows the flow chart of converting vehicle traffic data of vehicles in mesoscopic traffic simulation to obtain vehicle traffic data in microscopic traffic simulation provided by an exemplary embodiment of the present application
  • Fig. 7 shows the flow chart of converting mesoscopic traffic simulation into microscopic traffic simulation provided by an exemplary embodiment of the present application
  • FIG. 8 shows a flow chart of a traffic simulation conversion method provided by an exemplary embodiment of the present application
  • Fig. 9 shows a flow chart of converting the vehicle traffic data of the vehicle in the micro-traffic simulation to obtain the distance between the current position of the vehicle and the starting point of the road in the meso-traffic simulation provided by an exemplary embodiment of the present application;
  • Fig. 10 shows the flow chart of converting the vehicle traffic data of the vehicle in the microscopic traffic simulation to obtain the time when the vehicle in the mesoscopic traffic simulation lands on the next road provided by an exemplary embodiment of the present application;
  • Fig. 11 shows the flow chart of converting micro traffic simulation into meso traffic simulation provided by an exemplary embodiment of the present application
  • Fig. 12 shows a structural block diagram of a traffic simulation conversion device provided by an exemplary embodiment of the present application
  • Fig. 13 shows a structural block diagram of a computer device provided by an exemplary embodiment of the present application.
  • Micro-traffic simulation The most detailed description of the elements and behavior of the traffic system.
  • the microscopic traffic simulation model describes the traffic flow with a single vehicle as the basic unit, and the microcosmic behaviors of vehicles on the road, such as following, overtaking, and lane changing, can be more realistically reflected.
  • the mesoscopic traffic simulation model often describes traffic flow with a queue composed of several vehicles as a unit. It can describe the inflow and outflow behavior of queues on road sections and nodes, and it can also describe the behavior of vehicles such as lane changes. can be approximated in a simple way. It can be considered that each vehicle in the mesoscopic traffic simulation belongs to a queue.
  • Micro-map A map that meets the operating requirements of micro-traffic simulation, consisting of "roads” and “connecting sections” between roads.
  • “connecting sections” can be understood as intersection sections between two roads.
  • Both the "road” and “connecting road segment” in the micro-map are composed of multiple discrete points, and the information of each discrete point includes the longitude and latitude of the discrete point.
  • Mesoscopic map A map that meets the operational requirements of mesoscopic traffic simulation can be considered as a simplified version of a microscopic map. It consists of a "road” and a “connection segment” between roads, for example, a “connection segment” can be understood as an intersection segment between two roads. "Roads” and “Links” in meso maps only contain length information.
  • Micro-traffic simulation needs to describe the traffic behavior of vehicles in detail, such as vehicle lane changes, vehicle behavior at intersections, interactions between vehicles, and so on. Therefore, the vehicle traffic data possessed by microscopic vehicles is more specific, such as the longitude and latitude of the vehicle (ie, the global coordinates of the vehicle), the heading of the vehicle, the speed of the vehicle, and so on. Specifically, the vehicle traffic data of the micro vehicle includes at least one of the vehicle traffic data shown in Table 1:
  • the vehicle traffic data of mesoscopic vehicles is not as fine as that of microscopic vehicles; the data of mesoscopic vehicles mainly describes the vehicle traffic on road sections. Relative position, such as the distance from the vehicle's current position to the start of the road segment, etc.
  • the vehicle traffic data of the mesoscopic vehicle includes at least one of the vehicle traffic data shown in Table 2:
  • Fig. 1 shows a schematic diagram of an interface for simultaneously running mesoscopic traffic simulation and microscopic traffic simulation in different areas provided by an exemplary embodiment of the present application.
  • users can customize the areas of mesoscopic traffic simulation and microscopic traffic simulation. For example, run mesoscopic traffic simulation on common areas to obtain general traffic status information in common areas; run microscopic traffic simulation on key areas to obtain more detailed information on traffic conditions. That is, use mesoscopic traffic simulation for most areas of the entire map to reduce the amount of calculation and ensure operational efficiency; use microscopic traffic simulation for a small number of areas that you want to focus on to improve the precision of the simulation.
  • the vehicle traffic data in the mesoscopic traffic simulation of the vehicle is determined.
  • the vehicle traffic data of vehicles in the microscopic traffic simulation realizes the transformation from the mesoscopic traffic simulation to the microscopic traffic simulation;
  • the vehicle traffic data determines the vehicle traffic data of the vehicle in the mesoscopic traffic simulation, and realizes the conversion from the microscopic traffic simulation to the mesoscopic traffic simulation.
  • the microscopic vehicle traffic data refers to the vehicle traffic data of microscopic vehicles, please refer to Table 1, the microscopic vehicle traffic data includes at least one of vehicle number, vehicle speed, road section number, longitude, latitude, and vehicle head orientation.
  • Mesoscopic vehicle traffic data refers to the vehicle traffic data of mesoscopic vehicles. Please refer to Table 2.
  • Mesoscopic vehicle traffic data includes at least one of vehicle number, vehicle speed, road section number, location in the road section, and registration time.
  • the user defines a microscopic traffic simulation area 211 in the overall area 210 , and the remaining peripheral area is a mesoscopic traffic simulation area 212 .
  • the road sections in the microscopic traffic simulation area 211 and the mesoscopic traffic simulation area 212 are composed of roads represented by straight lines and connecting road sections represented by squares, such as road 213 represented by straight lines and connecting road sections 214 represented by squares.
  • the vehicle traffic data of each vehicle is displayed, for example, the vehicle 215 is currently traveling at a speed of 16 meters per second; while in the mesoscopic traffic simulation area 212, the attention to the specific vehicle traffic data is ignored, Instead, describe the road conditions.
  • the straight line 213 in FIG. 2 represents the current road congestion
  • the dotted line 216 represents the current road flow is moderate
  • the dotted line 217 represents the current road flow, and so on.
  • Fig. 2 shows a schematic diagram of an interface for traffic state prediction provided by an exemplary embodiment of the present application.
  • the user chooses to enable the function of "forecasting the future traffic state".
  • Quantitative methods can efficiently and quickly obtain forecast results.
  • the mesoscopic vehicle traffic data in the mesoscopic traffic simulation is determined based on the microscopic vehicle traffic data in the microscopic traffic simulation; The vehicle realizes the conversion from the micro-traffic simulation to the meso-traffic simulation; predicts the traffic status based on the meso-traffic simulation display.
  • the traffic state is used to represent the smoothness of the road, and a higher value indicates smoother traffic.
  • the traffic state is used to represent the degree of congestion of the road, and a higher value indicates more congested traffic.
  • the traffic state includes at least one of congestion, traffic, and accident.
  • the user clicks on the forecast control 231 on the interface 230 of the traffic simulation platform to start the function of "forecasting the future traffic state", and sets the future time 232 to be 30 minutes later.
  • Save the vehicle traffic data of all vehicles in the micro-traffic simulation operation at the current moment determine the vehicle traffic data in the meso-traffic simulation based on the vehicle traffic data in the micro-traffic simulation, and realize the conversion from micro-traffic simulation to meso-traffic simulation for all vehicles; Run the mesoscopic traffic simulation for a certain period of time after conversion to obtain the predicted traffic state after 30 minutes.
  • the predicted traffic state after 30 minutes is displayed on the traffic simulation platform interface 230 , including road section name 234 , traffic flow 235 , average vehicle speed 236 and traffic index 237 .
  • road section name 234 comprises the direction of road;
  • the unit of traffic flow 235 is car/hour, and promptly traffic flow 235 refers to the number of vehicles passing through this road in one hour;
  • the traffic index 237 is a conceptual index that comprehensively reflects whether the road is smooth or congested. The higher the value, the more congested the traffic is.
  • Fig. 1 and Fig. 2 respectively show the traffic simulation transformation of some areas involved in mesoscopic traffic simulation and microscopic traffic simulation for different areas at the same time through the traffic simulation conversion method of the present application, and, at the same time At different times in the region, the overall transformation involved in the mesoscopic traffic simulation and microscopic traffic simulation is carried out.
  • the following examples will describe in detail the implementation of the traffic simulation conversion method.
  • Fig. 3 shows a structural block diagram of a traffic simulation system provided by an exemplary embodiment of the present application.
  • the traffic simulation system 100 includes: a sensor 110 , a road data server 120 and a traffic simulation server 130 .
  • the sensor 110 can be a camera in the road network system, a geomagnetic coil arranged under the road surface, and the like.
  • the sensor 110 is used to acquire road data, for example, the number of passing vehicles, the speed of the vehicles, the traffic behavior of the vehicles, and so on.
  • the sensor 110 is connected to the road data server 120 through a wireless network or a wired network.
  • the road data server 120 is used to summarize the data collected by the sensors 110 and perform corresponding processing, and collect and process the road data obtained by the sensors 110 into vehicle traffic data that can be used for traffic simulation.
  • the road data server 120 includes at least one of a server, multiple servers, a cloud computing platform and a virtualization center.
  • the road data server 120 is connected to the traffic simulation server 130 through a wired network or a wireless network.
  • the traffic simulation server 130 is used for running traffic simulation according to the aggregated and processed vehicle traffic data obtained by the road data server 120 , and realizing conversion between different traffic simulation modes.
  • the traffic simulation server 130 includes at least one of a server, multiple servers, a cloud computing platform and a virtualization center.
  • the number of the above-mentioned sensors 110, road data servers 120, and traffic simulation servers 130 can be more or less.
  • the above-mentioned sensor 110, road data server 120, and traffic simulation server 130 can be only one, or the number of the above-mentioned sensor 110, road data server 120, and traffic simulation server 130 is dozens or hundreds, or more.
  • the example does not limit the number of sensors or servers and the type of equipment.
  • FIG. 4 shows a flowchart of a traffic simulation conversion method provided by an exemplary embodiment of the present application, and the method is applied to the terminal 120 or the server 140 shown in FIG. 3 .
  • the method includes:
  • Step 320 Obtain the first vehicle traffic data of the vehicle in the first traffic simulation
  • the first traffic simulation includes one of micro-traffic simulation and meso-traffic simulation.
  • Micro-traffic simulation is a more detailed traffic simulation method than meso-traffic simulation. Therefore, the vehicle traffic data of micro-vehicles in micro-traffic simulation is more specific and accurate than that in meso-traffic simulation.
  • the vehicle traffic data in the micro-traffic simulation is shown in Table 1 above, and the vehicle traffic data in the medium-scale traffic simulation is shown in Table 2 above.
  • the first vehicle traffic data includes: the number of the road segment where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road segment.
  • the first vehicle traffic data includes: the number of the road section where the vehicle is located, the latitude and longitude of the vehicle, and the speed of the vehicle.
  • Step 340 According to the first vehicle traffic data, convert to obtain the second vehicle traffic data of the vehicle in the second traffic simulation;
  • the second traffic simulation includes one of micro-traffic simulation and meso-traffic simulation, and the first traffic simulation is different from the second traffic simulation.
  • the first traffic simulation includes mesoscopic traffic simulation
  • the second traffic simulation includes microscopic traffic simulation
  • the distance between the current position of the vehicle in the mesoscopic traffic simulation and the starting point of the road section is converted into the latitude and longitude of the vehicle and the orientation of the vehicle in the microscopic traffic simulation.
  • the second vehicle traffic data includes: the latitude and longitude of the vehicle and the heading of the vehicle.
  • the second vehicle traffic data of the vehicle in the micro-traffic simulation is obtained through conversion.
  • the second vehicle traffic data includes: the latitude and longitude of the vehicle and the head orientation of the vehicle .
  • the road segments on the meso map and the micro map include at least one of roads and connecting road segments.
  • the longitude and latitude of the vehicle are determined according to the number of the road where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road; and according to the number of the road where the vehicle is located and the distance between the current position of the vehicle The distance from the beginning of the road determines the heading of the vehicle.
  • the longitude and latitude of the vehicle are determined according to the number of the connecting road section where the vehicle is located and the distance between the current position of the vehicle and the starting point of the connecting road section; and according to the number of the road where the vehicle is located and the distance of the vehicle The distance from the current position to the starting point of the road determines the heading of the vehicle.
  • the first traffic simulation includes microscopic traffic simulation
  • the second traffic simulation includes mesoscopic traffic simulation
  • the latitude and longitude of the vehicle in the micro-traffic simulation is converted to obtain the distance between the current position of the vehicle and the starting point of the road section and the time when the vehicle lands on the next road in the meso-traffic simulation.
  • the second vehicle traffic data includes: the distance between the current position of the vehicle and the starting point of the road section and the time when the vehicle lands on the next road.
  • the time when the vehicle lands on the next road is a parameter set for the vehicle on the connecting road segment. If the parameter is greater than 0, it means that the current position of the vehicle is in the connecting road segment.
  • step 340 includes at least one of the following steps: according to the numbering of the road section where the vehicle is located and the latitude and longitude of the vehicle, convert the distance between the current position of the vehicle in the mesoscopic traffic simulation and the starting point of the road section; according to the numbering of the road section where the vehicle is located, The longitude and latitude of the vehicle and the speed of the vehicle are converted to obtain the time when the vehicle lands on the next road in the mesoscopic traffic simulation.
  • the road segments on the meso map and the micro map are composed of roads and connecting road segments.
  • the distance between the current position of the vehicle and the starting point of the road section in the mesoscopic traffic simulation is obtained through conversion.
  • the road section where the vehicle is located is a connecting road section, according to the number of the road section where the vehicle is located, the latitude and longitude of the vehicle, and the speed of the vehicle, the time when the vehicle lands on the next road in the mesoscopic traffic simulation is obtained through conversion.
  • the distance between the current position of the vehicle in the mesoscopic traffic simulation and the starting point of the connecting road section is converted to distance.
  • Step 360 Run a second traffic simulation according to the second vehicle traffic data of the vehicle.
  • the microscopic traffic simulation is run according to the latitude and longitude of the vehicle and the heading of the vehicle.
  • the mesoscopic traffic simulation is run according to the distance between the current position of the vehicle and the starting point of the road section and the time when the vehicle lands on the next road.
  • the second vehicle traffic data of the vehicles in the second traffic simulation is obtained through conversion, and then the second traffic simulation is performed.
  • This method converts the vehicle traffic data of each vehicle in turn, and realizes the mutual conversion between the mesoscopic traffic simulation and the microscopic traffic simulation at the vehicle level, so that the vehicles before and after the traffic simulation conversion can maintain consistency, avoiding traffic simulation problems caused by traffic simulation.
  • the problem of the excessive difference in traffic status before and after the conversion caused by the change of the mode improves the accuracy of the traffic simulation conversion.
  • the following embodiments specifically illustrate the implementation process of transitioning from mesoscopic traffic simulation to microscopic traffic simulation.
  • FIG. 5 shows a flow chart of converting mesoscopic traffic simulation to microscopic traffic simulation provided by an exemplary embodiment of the present application. This method is applied to the terminal 120 or server 140 shown in FIG. 3 . As shown in Figure 5, the method includes:
  • Step 422 Obtain the first vehicle traffic data of the vehicle in the mesoscopic traffic simulation
  • the first traffic simulation is mesoscopic traffic simulation
  • the data of mesoscopic vehicles is acquired to realize the transformation from mesoscopic vehicles to microscopic vehicles.
  • the first vehicle traffic data includes: the number of the road segment where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road segment.
  • Step 442 According to the number of the road section where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road section, convert and obtain the second vehicle traffic data of the vehicle in the micro-traffic simulation;
  • the second traffic simulation is a microscopic traffic simulation, that is, based on the first vehicle traffic data in the mesoscopic traffic simulation, it is necessary to obtain Arrived second vehicle traffic data.
  • the second vehicle traffic data includes: the latitude and longitude of the vehicle and the heading of the vehicle.
  • road segments consist of roads and the links between roads. Since the descriptions of vehicles on the road and in the connecting roads are quite different, the roads and connecting roads are distinguished, and the conversion of vehicle traffic data is implemented separately.
  • the longitude and latitude of the vehicle are determined according to the number of the road where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road; and according to the number of the road where the vehicle is located and the distance between the current position of the vehicle The distance from the beginning of the road determines the heading of the vehicle.
  • the longitude and latitude of the vehicle are determined according to the number of the connecting road section where the vehicle is located and the distance between the current position of the vehicle and the starting point of the connecting road section; and according to the number of the road where the vehicle is located and the distance of the vehicle The distance from the current position to the starting point of the road determines the heading of the vehicle.
  • Fig. 6 shows a flow chart of converting vehicle traffic data of vehicles in mesoscopic traffic simulation to vehicle traffic data in microscopic traffic simulation provided by an exemplary embodiment of the present application.
  • the road in the micro-traffic simulation includes N discrete points arranged in sequence, where N is a positive integer; the connecting road section in the micro-traffic simulation includes M discrete points arranged in sequence, where , M is a positive integer.
  • Step 4421 When the type of the road segment where the vehicle is located is a road, according to the number of the road where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road, the N discrete points on the road that are closest to the current position of the vehicle The latitude and longitude of i discrete points are determined as the latitude and longitude of the vehicle;
  • N is a positive integer
  • i is a positive integer smaller than N.
  • step 4421 can be divided into the following steps:
  • the road where the vehicle is located is searched in the micro map.
  • the road is composed of N discrete points P 1 , P 2 , . . . , P N arranged in sequence, where P 1 is the starting point of the road. Get the distance between adjacent discrete points among N discrete points.
  • each road has orientation information, for example, for a north-south two-way two-lane lane, the southbound road and the northbound road correspond to different road numbers. That is, the starting point P 1 of each road is uniquely determined.
  • dis_to_start j represents the distance from the j-th discrete point to the starting point of the road
  • distance(p j-1 , p j ) represents the distance from the j-1th discrete point P j-1 to the j-th discrete point P j
  • j is a positive integer less than N.
  • the distance between the first discrete point and the starting point of the road is 0, the distance between the second discrete point and the starting point of the road is the distance between the first discrete point and the second discrete point, and the distance between the third discrete point and the road
  • the starting point distance is the sum of the distance from the second discrete point to the starting point of the road and the distance from the third discrete point to the second discrete point, and so on, adding up in turn to determine the distance between the N discrete points and the starting point of the road. distance.
  • the distances from the N discrete points to the starting point of the road are respectively different from the distance from the current position of the vehicle to the starting point of the road, that is, dis_to_start j -road_pos meso , to obtain the distances of the N discrete points from the current position of the vehicle.
  • the latitude and longitude of the ith discrete point with the smallest difference is determined as the latitude and longitude of the vehicle; that is, the latitude and longitude of the ith discrete point closest to the current position of the vehicle is approximated as the latitude and longitude of the vehicle; that is, The j that makes the following formula obtain the minimum value is assigned to i:
  • the position of the ith discrete point is taken as the current position of the vehicle, and the latitude and longitude of the ith discrete point is taken as the latitude and longitude of the vehicle.
  • the road section in the micro-map can be described by a function, that is, a step of sampling the function describing the road section to obtain discrete points is added before step (1).
  • Step 4422 When the type of the road segment where the vehicle is located is a connecting road segment, according to the number of the connecting road segment where the vehicle is located and the distance between the current position of the vehicle and the starting point of the connecting road segment, among the M discrete points in the connecting road segment that are closest to the current position of the vehicle The latitude and longitude of the close i-th discrete point is determined as the latitude and longitude of the vehicle.
  • step 4422 can be divided into the following steps:
  • the connected road segment where the vehicle is located is searched in the micro map.
  • the connecting road section is composed of M discrete points P 1 , P 2 , . . . , PM arranged in sequence, where P 1 is the starting point of the connecting road section. Get the distance between adjacent discrete points among the M discrete points.
  • each connecting road segment is similar to a road, and each connecting road segment has direction information; that is, the starting point P 1 of each connecting road segment is uniquely determined.
  • the distances between the M discrete points in the connecting road segment and the starting point of the connecting road segment are sequentially calculated by accumulating the distances between adjacent discrete points.
  • the specific calculation formula is as follows:
  • dis_to_start j represents the distance between the j-th discrete point and the starting point of the connecting road section
  • distance(p j-1 , p j ) represents the distance from the j-1th discrete point P j-1 to the j-th discrete point P j
  • j is a positive integer smaller than M.
  • the distance between the first discrete point and the starting point of the connecting road section is 0, the distance between the second discrete point and the starting point of the connecting road section is the distance between the first discrete point and the second discrete point, and the third discrete point
  • the distance from the starting point of the connecting road section is the sum of the distance from the second discrete point to the starting point of the connecting road section and the distance from the third discrete point to the second discrete point, and so on, adding up in turn to determine the M discrete points respectively The distance from the start point of the link segment.
  • the distances between the M discrete points and the starting point of the connecting road section are respectively compared with the distance between the current position of the vehicle and the starting point of the connecting road section, that is, dis_to_start j ⁇ road_pos meso , get the distances of M discrete points from the current position of the vehicle.
  • the latitude and longitude of the ith discrete point with the smallest difference is determined as the latitude and longitude of the vehicle; that is, the latitude and longitude of the ith discrete point closest to the current position of the vehicle is approximated as the latitude and longitude of the vehicle; that is, The j that makes the following formula obtain the minimum value is assigned to i:
  • the position of the ith discrete point is taken as the current position of the vehicle, and the latitude and longitude of the ith discrete point is taken as the latitude and longitude of the vehicle.
  • the road section in the micro-map can be described by a function, that is, a step of sampling the function describing the road section to obtain discrete points is added before step (1).
  • step 4421 and step 4422 only need to be executed according to the type of the road section where the vehicle is currently located, but not all of them. If the type of the road section where the vehicle is located is a road, execute step 4421; if the type of the road section where the vehicle is located is a connecting road section, execute step 4422.
  • Step 4423 Determine the head direction of the vehicle according to the i-th discrete point and the i-1th discrete point.
  • the head orientation of the vehicle is used to indicate the driving direction of the vehicle, which can be represented by the direction of a segment of the vehicle trajectory with a smaller value near the current position of the vehicle.
  • the i-th discrete point closest to the current vehicle position can be determined by the aforementioned steps, that is, the position of the i-th discrete point is approximately expressed as the current position of the vehicle. Take a segment of vehicle trajectory in a small range near the position, and use the direction of the trajectory as the vehicle's driving direction.
  • the head orientation of the vehicle is determined according to the ith discrete point and the i-1th discrete point; specifically, the connection line between the i-th discrete point and the i-1th discrete point
  • the angle in the world coordinate system is used as the head orientation of the vehicle.
  • the head orientation of the vehicle is determined according to the i-th discrete point and the i+1-th discrete point; specifically, the connection line between the i-th discrete point and the i+1-th discrete point
  • the angle in the world coordinate system is used as the head orientation of the vehicle.
  • the unit of the heading of the vehicle is radians (rad), that is, the heading of the vehicle is represented by an angle, which is the angle of the heading of the vehicle relative to the world coordinate system (global coordinate system), not relative to the vehicle The angle of the current segment.
  • Step 462 Run micro-traffic simulation according to the second vehicle traffic data of the vehicle.
  • micro-traffic simulation is run.
  • Micro-traffic simulation focuses on the behavior of each vehicle, such as vehicle lane changes, vehicle steering, vehicle traffic behavior at intersections, vehicle interactions with other vehicles, and so on.
  • Fig. 7 shows a flow chart of converting mesoscopic traffic simulation into microscopic traffic simulation provided by an exemplary embodiment of the present application.
  • the input information flow is mesoscopic vehicle traffic data, specifically, the number of the road section where the mesoscopic vehicle is located and the distance between the current location of the mesoscopic vehicle and the starting point of the road section.
  • step 501 it is judged whether the vehicle is at the intersection, if the vehicle is not at the intersection, step 502 is performed; if the vehicle is at the intersection, then step 503 is performed;
  • step 502 according to the method described in step 4421 of the foregoing embodiment, the longitude and latitude of the vehicle on the road are obtained;
  • step 503 according to the method described in step 4422 of the foregoing embodiment, the latitude and longitude of the vehicle at the intersection is acquired;
  • step 504 based on the position of the i-th discrete point closest to the vehicle's current position determined in step 502 or step 503, according to the method described in step 4423 of the foregoing embodiment, the head orientation of the vehicle is obtained.
  • the output information flow of the traffic simulation conversion method described in the embodiment of the present application is microscopic vehicle traffic data, specifically, the latitude and longitude of the vehicle and the heading of the vehicle.
  • the information of the discrete points in the road segment is obtained by obtaining the serial number of the road segment where the vehicle is located, and the discrete point closest to the current position of the vehicle is determined by combining the discrete point information and the distance between the current position of the vehicle and the starting point of the road segment. point, and combine the latitude and longitude of the discrete point with the information of the discrete point near the discrete point to determine the latitude and longitude of the vehicle and the direction of the vehicle's head, so as to realize the transformation of mesoscopic traffic simulation into microscopic traffic simulation.
  • This method provides a method for converting mesoscopic traffic simulation into microscopic traffic simulation, traverses all vehicles in the world, and performs conversion according to the vehicle traffic data of each vehicle, which ensures the data consistency before and after conversion, and avoids errors before and after conversion.
  • the problem of major deviations in traffic conditions improves the accuracy of traffic simulation conversions.
  • the road section is represented by discrete points, it is possible to realize the conversion of traffic simulation at any position of the road section, which realizes the conversion of the whole area and the operation of different traffic simulation methods in different areas at the same time, with high flexibility .
  • the vehicle traffic data of mesoscopic vehicles, the vehicle traffic data of microscopic vehicles and the representation method of microscopic maps that this method relies on are relatively simple, and can be easily applied to the conversion of various microscopic simulations and mesoscopic simulations.
  • the following embodiments specifically illustrate the process of converting micro-traffic simulation to meso-traffic simulation.
  • FIG. 8 shows a flowchart of a traffic simulation conversion method provided by an exemplary embodiment of the present application, and the method is applied to the terminal 120 or the server 140 shown in FIG. 3 .
  • the method includes:
  • Step 622 Obtain the first vehicle traffic data of the vehicle in the micro-traffic simulation
  • the first traffic simulation is microscopic traffic simulation, that is, if the initially running traffic simulation mode is microscopic traffic simulation, the data of microscopic vehicles is acquired to realize the transformation from microscopic vehicles to mesoscopic vehicles.
  • the first vehicle traffic data includes: the number of the road section where the vehicle is located, the latitude and longitude of the vehicle, and the speed of the vehicle.
  • the second traffic simulation is mesoscopic traffic simulation, that is, it is necessary to convert the first vehicle traffic data based on the vehicle in microscopic traffic simulation to be used in mesoscopic traffic simulation. Arrived second vehicle traffic data.
  • Step 642 According to the number of the road section where the vehicle is located and the latitude and longitude of the vehicle, convert the distance between the current position of the vehicle and the starting point of the road section in the mesoscopic traffic simulation;
  • the information of the road section where the vehicle is located is obtained through the number of the road section where the vehicle is located, including the information of the discrete points on the road section where the vehicle is located; the discrete point closest to the current position of the vehicle is determined according to the latitude and longitude of the vehicle and the latitude and longitude of the discrete points, and the calculated The distance between the discrete point and the starting point of the road section; according to the distance between the discrete point and the starting point of the road section and the distance between the discrete point and the current position of the vehicle, the distance between the current position of the vehicle and the starting point of the road section is approximated.
  • step 642 is described more specifically by taking the road section as an example.
  • the situation that the road section is a connecting road section is similar to this, and will not be repeated here.
  • the road in the micro-traffic simulation includes N discrete points arranged in sequence, where N is a positive integer.
  • Fig. 9 shows a flow chart of converting the vehicle traffic data of the vehicle in the micro-traffic simulation to obtain the distance between the current position of the vehicle and the starting point of the road in the meso-traffic simulation provided by an exemplary embodiment of the present application.
  • Step 6421 Determine the ith discrete point closest to the current position of the vehicle according to the latitude and longitude of the N discrete points and the latitude and longitude of the vehicle;
  • the information of the road where the vehicle is located is acquired according to the number of the road where the vehicle is located, including N discrete points P 1 , P 2 , . . . , P N on the road where the vehicle is located and their latitude and longitude. Traverse the N discrete points, and determine the i-th discrete point P i closest to the vehicle's current position P vehicle . Wherein, i is a positive integer smaller than N.
  • distance(P vehicle , P j ) represents the distance between the vehicle's current position P vehicle and the jth discrete point P j , and j is a positive integer smaller than N.
  • Step 6422 Determine the distance between the current position of the vehicle and the starting point of the road according to the distance between the i-th discrete point and the starting point of the road and the distance between the i-th discrete point and the current position of the vehicle.
  • the i-th discrete point is similar to the calculation process in step 4421 or step 4422 from the starting point of the road, by accumulating the first discrete point P 1 to the i-th discrete point P between adjacent discrete points Distance, to determine the distance between the i-th discrete point and the starting point of the road, which will not be repeated here.
  • the sum of the obtained distance between the i-th discrete point and the starting point of the road and the distance between the i-th discrete point and the current position of the vehicle is determined as the distance between the current position of the vehicle and the starting point of the road.
  • the specific calculation formula is as follows:
  • road_pos meso dis_to_start i + distance(P vehicle , P i );
  • road_pos meso is the distance from the current position of the vehicle in the mesoscopic traffic simulation to the starting point of the road
  • dis_to_start i is the distance from the i-th discrete point to the starting point of the road
  • distance(P vehicle , P i ) is the i-th discrete point The distance from the vehicle's current position. It should be noted that distance(P vehicle , P i ) may be a positive number or a negative number.
  • distance(P vehicle , P i ) is a positive number; when the line connecting the i-th discrete point and the current position of the vehicle and When the direction of the road is reversed, distance(P vehicle , P i ) is a negative number.
  • Step 644 According to the number of the road section where the vehicle is located, the latitude and longitude of the vehicle and the speed of the vehicle, convert the time when the vehicle lands on the next road in the mesoscopic traffic simulation;
  • the time when the vehicle lands on the next road is 0, the current position of the vehicle is in the road; when the time when the vehicle lands on the next road is greater than 0, the current position of the vehicle is in the connecting road segment. Therefore, the time when the vehicle lands on the next road is usually the vehicle traffic data that needs to be converted for the vehicles in the connecting road segment.
  • the information of the connected road section where the vehicle is located is obtained through the number of the connected road section where the vehicle is located, including the information of the discrete point on the connected road section where the vehicle is located; the discrete point closest to the current position of the vehicle is determined according to the latitude and longitude of the vehicle and the latitude and longitude of the discrete point, And calculate the distance between the discrete point and the starting point of the road section, which is the distance between the current position of the vehicle and the starting point of the connecting road section; and determine the total length of the connecting road section according to the latitude and longitude of the discrete point; finally, according to the total length of the connecting road section, the The distance of the location from the start of the connected road segment and the speed of the vehicle determine when the vehicle lands on the next road.
  • the connecting link in the micro-traffic simulation includes M discrete points arranged in sequence, where M is a positive integer.
  • Fig. 10 shows a flow chart of converting the vehicle traffic data of the vehicle in the micro-traffic simulation into the time when the vehicle lands on the next road in the meso-traffic simulation provided by an exemplary embodiment of the present application.
  • Step 6441 Determine the ith discrete point closest to the current position of the vehicle according to the latitude and longitude of the M discrete points and the latitude and longitude of the vehicle;
  • the information of the connected road section where the vehicle is located is acquired according to the number of the connected road section where the vehicle is located, including M discrete points P 1 , P 2 , ..., PM and their longitudes and latitudes on the connected road section where the vehicle is located. Traverse the M discrete points, and determine the i-th discrete point P i closest to the vehicle's current position P vehicle . Wherein, i is a positive integer smaller than N.
  • (P vehicle , P j ) represents the distance between the vehicle's current position P vehicle and the jth discrete point P j , and j is a positive integer smaller than M.
  • Step 6442 According to the distance between the i-th discrete point and the starting point of the connecting road segment, the distance between the i-th discrete point and the current position of the vehicle, the speed of the vehicle and the total length of the connecting road segment, determine the time when the vehicle lands on the next road.
  • step 6442 can be divided into the following steps:
  • the connected road segment where the vehicle is located is searched in the micro map.
  • the connecting road section is composed of M discrete points P 1 , P 2 , . . . , PM arranged in sequence, where P 1 is the starting point of the connecting road section. Get the distance between adjacent discrete points among the M discrete points.
  • each connecting road segment is similar to a road, and each connecting road segment has direction information; that is, the starting point P 1 of each connecting road segment is uniquely determined.
  • the distance dis_to_start i from the i-th discrete point to the starting point of the connecting road segment and the total length length of the connecting road segment are calculated, wherein the connecting The total length of the road section is the distance between the Nth discrete point P N and the starting point of the connected road section.
  • the distance dis_to_start i of the i-th discrete point from the starting point of the connecting road section is determined; by Accumulate the distance between the first discrete point P 1 and the adjacent discrete points between the Nth discrete point P N , and determine the distance dis_to_start N between the Nth discrete point and the starting point of the connected road section, that is, the total number of connected road sections length length.
  • the specific calculation formula is as follows:
  • dis_to_start i dis_to_start i-1 +distance(p i-1 , p i );
  • dis_to_start N dis_to_start N-1 +distance(p N-1 , p N );
  • dis_to_start i-1 is the distance between the i-1th discrete point P i-1 and the starting point of the connecting road section, and distance(p i-1 , p i ) represents the i-1th discrete point P i-1 to the first
  • the distance of the i discrete point P i , i is a positive integer smaller than N
  • dis_to_start N-1 indicates the distance between the N-1th discrete point P N-1 and the starting point of the connecting road section
  • distance(p N-1 , p N ) represents the distance from the N-1th discrete point P N- 1 to the Nth discrete point P N.
  • dis_to_next length - dis_to_start i - distance (p vehicle , p i ).
  • distance(P vehicle , P i ) may be a positive number or a negative number. If the line connecting the i-th discrete point and the current position of the vehicle is in the same direction as the road, distance(P vehicle , P i ) is a positive number; the line connecting the i-th discrete point and the current position of the vehicle to the road In the case of the reverse direction, distance(P vehicle , P i ) is a negative number.
  • the time when the vehicle lands on the next road is obtained by dividing the distance dis_to_next of the vehicle from the next road by the speed speed mirco of the vehicle.
  • the specific formula is as follows:
  • the total length length of the connected road section is a part of the map data, and the data of the total length of the connected road section can be directly obtained by searching according to the number of the road section where the vehicle is located, without the need of accumulating in step (3).
  • the distance between adjacent discrete points obtains the calculation process of the total length of connected road segments.
  • the road section in the micro-map may be described by a function, that is, a step of sampling the function describing the road section to obtain discrete points is added before step (1).
  • the road section where the vehicle is located is a road
  • the time to land on the next road in the mesoscopic vehicle traffic data is 0. Therefore, the distance between the current position of the vehicle and the starting point of the road is usually used to indicate the mesoscopic traffic simulation
  • the relative position of the vehicle in the road that is, execute step 642; when the type of the road segment where the vehicle is located is a connecting road segment, usually more concerned about the time when the vehicle lands on the next road, that is, execute step 644.
  • step 642 when the type of the road segment where the vehicle is located is a road, execute step 642; when the type of the road segment where the vehicle is located is a connecting road segment, execute steps 642 and 644.
  • step 642 is executed regardless of whether the type of the road section where the vehicle is located is a road or a connecting road section.
  • Step 662 Run the mesoscopic traffic simulation according to the second vehicle traffic data of the vehicle.
  • the mesoscopic traffic simulation is run.
  • Mesoscopic traffic simulation does not pay attention to the traffic behavior of each specific mesoscopic vehicle, but focuses on the traffic conditions at the road section level, such as the traffic flow on the road section, average vehicle speed, traffic index, and so on.
  • Each mesoscopic vehicle in a mesoscopic traffic simulation belongs to a queue.
  • the vehicle is added to the corresponding position in the queue to which the vehicle belongs.
  • Fig. 11 shows a flow chart of converting a micro-traffic simulation into a meso-traffic simulation provided by an exemplary embodiment of the present application.
  • the input information flow is microscopic vehicle traffic data, specifically, the number of the road section where the microscopic vehicle is located and the longitude and latitude of the microscopic vehicle.
  • step 701 it is judged whether the vehicle is at the intersection, if the vehicle is not at the intersection, step 702 is executed; if the vehicle is at the intersection, step 703 is executed;
  • step 702 according to the method described in step 642 of the foregoing embodiment, the distance between the current position of the vehicle and the starting point of the road when the vehicle is on the road is acquired;
  • step 703 according to the method described in step 644 of the foregoing embodiment, the time when the vehicle lands on the next road at the intersection is acquired;
  • the output information flow of the traffic simulation conversion method described in the embodiment of the present application is mesoscopic vehicle traffic data, specifically, the distance between the current position of the vehicle and the starting point of the road and the time when the vehicle lands on the next road at the intersection.
  • the vehicle traffic data of the microscopic vehicle is converted into the vehicle traffic data of the mesoscopic vehicle.
  • the micro-traffic simulation is converted to the meso-traffic simulation.
  • This method provides a method to convert micro-traffic simulation into meso-traffic simulation. It traverses all vehicles in the world and converts them according to the vehicle traffic data of each vehicle, which ensures the data consistency before and after the conversion and avoids errors before and after the conversion. The problem of deviation in traffic conditions improves the accuracy of traffic simulation conversion.
  • the road section is represented by discrete points, it is possible to realize the conversion of traffic simulation at any position of the road section, which realizes the conversion of the whole area and the operation of different traffic simulation methods in different areas at the same time, with high flexibility .
  • the vehicle traffic data of mesoscopic vehicles, the vehicle traffic data of microscopic vehicles and the representation method of microscopic maps that this method relies on are relatively simple, and can be easily applied to the conversion of various microscopic traffic simulations and mesoscopic traffic simulations.
  • the vehicle traffic data of mesoscopic vehicles that this method relies on is suitable for the flow density model and queue model in mesoscopic traffic simulation; for another example, the vehicle traffic data of microscopic vehicles that this method relies on is compatible with Vehicle traffic data in software such as sumo and vissim can be exchanged.
  • Fig. 12 is a structural block diagram of a traffic simulation conversion device provided by an exemplary embodiment of the present application. As shown in Fig. 12, the device includes:
  • An acquisition module 820 configured to acquire the first vehicle traffic data of the vehicle in the first traffic simulation
  • a conversion module 840 configured to convert the second vehicle traffic data of the vehicle in the second traffic simulation according to the first vehicle traffic data
  • a running module 860 configured to run the second traffic simulation according to the second vehicle traffic data of the vehicle
  • the first traffic simulation and the second traffic simulation respectively include one of micro-traffic simulation and meso-traffic simulation, and the first traffic simulation is different from the second traffic simulation.
  • the first traffic simulation includes the mesoscopic traffic simulation
  • the second traffic simulation includes the microscopic traffic simulation
  • the acquiring module 820 is configured to acquire the The first vehicle traffic data in the mesoscopic traffic simulation
  • the first vehicle traffic data includes: the numbering of the road section where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road section
  • the conversion module 840 uses Based on the number of the road section where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road section, the second vehicle traffic data of the vehicle in the micro-traffic simulation is converted, and the second vehicle traffic data Including: the latitude and longitude of the vehicle and the head orientation of the vehicle.
  • the conversion module 840 includes a determination submodule 842, and the determination submodule 842 is configured to, when the type of the road section where the vehicle is located is a road, according to the number of the road where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road to determine the latitude and longitude of the vehicle; Head orientation; when the type of road section where the vehicle is located is a connecting road section, determine the latitude and longitude of the vehicle according to the number of the connecting road section where the vehicle is located and the distance between the current position of the vehicle and the starting point of the connecting road section; And determining the head orientation of the vehicle according to the number of the road where the vehicle is located and the distance between the current position of the vehicle and the starting point of the road.
  • the road in the micro-traffic simulation includes N discrete points arranged in sequence, where N is a positive integer;
  • N is a positive integer
  • the type of is a road
  • the N discrete points in the road are compared with the current position of the vehicle
  • the latitude and longitude of the i-th discrete point with the closest position is determined as the latitude and longitude of the vehicle, and i is a positive integer smaller than N.
  • the conversion module 840 includes an acquisition submodule 844 and a difference submodule 846, the acquisition submodule 844 is used to acquire the N discrete points according to the number of the road where the vehicle is located The distance between the adjacent discrete points in the road; the determination submodule 842 is used to determine the distance between the N discrete points in the road and the road by accumulating the distance between the adjacent discrete points.
  • the difference sub-module 846 is used to make a difference between the distances of the N discrete points from the starting point of the road and the distance of the current position of the vehicle from the starting point of the road; the The determination sub-module 842 is configured to determine the latitude and longitude of the i-th discrete point with the smallest difference as the latitude and longitude of the vehicle, where i is a positive integer less than or equal to N.
  • the connecting section in the micro-traffic simulation includes M discrete points arranged in sequence, where M is a positive integer;
  • the determining submodule 842 is used for the When the type of the road section where the vehicle is located is a connecting road section, according to the number of the connecting road section where the vehicle is located and the distance between the current position of the vehicle and the starting point of the connecting road section, determine the latitude and longitude of the vehicle, including: the determiner Module 842, for when the type of the road section where the vehicle is located is a connecting road section, according to the number of the connecting road section where the vehicle is located and the distance between the current position of the vehicle and the starting point of the connecting road section, the connecting road section The latitude and longitude of the i-th discrete point closest to the current position of the vehicle among the M discrete points in , is determined as the latitude and longitude of the vehicle, and i is a positive integer less than N.
  • the obtaining submodule 844 is configured to obtain the distance between adjacent discrete points among the M discrete points according to the number of the connecting road section where the vehicle is located; the determining submodule 842, configured to determine the distances between the M discrete points in the connecting road section and the starting point of the connecting road section by accumulating the distances between the adjacent discrete points; the making difference sub-module 846, using The difference between the distances between the M discrete points and the starting point of the connecting road section and the distance between the current position of the vehicle and the starting point of the connecting road section is made; The latitude and longitude of the i-th discrete point is determined as the latitude and longitude of the vehicle, and i is a positive integer less than or equal to M.
  • the determination submodule 842 is configured to determine the head orientation of the vehicle according to the i-th discrete point and the i-1th discrete point.
  • the first traffic simulation includes the micro-traffic simulation
  • the second traffic simulation includes the meso-traffic simulation
  • the acquisition module 820 is configured to acquire the The first vehicle traffic data in the micro-traffic simulation
  • the first vehicle traffic data includes: the number of the road section where the vehicle is located, the latitude and longitude of the vehicle and the speed of the vehicle
  • the conversion module 840 is used for the following At least one of: according to the number of the road section where the vehicle is located and the latitude and longitude of the vehicle, convert the distance between the current position of the vehicle in the mesoscopic traffic simulation and the starting point of the road section; according to the road section where the vehicle is located
  • the number of the vehicle, the latitude and longitude of the vehicle and the speed of the vehicle are converted to obtain the time when the vehicle lands on the next road in the mesoscopic traffic simulation.
  • the road in the micro-traffic simulation includes N discrete points arranged in sequence, where N is a positive integer; the determining submodule 842 is configured to The latitude and longitude of the point and the latitude and longitude of the vehicle determine the i-th discrete point closest to the current position of the vehicle, and i is a positive integer less than N; The distance between the ith discrete point and the current position of the vehicle determines the distance between the current position of the vehicle and the starting point of the road.
  • the connecting road section in the micro-traffic simulation includes M discrete points arranged in sequence, where M is a positive integer; the determining submodule 842 is configured to The latitude and longitude of the discrete point and the latitude and longitude of the vehicle determine the ith discrete point closest to the current position of the vehicle, where i is a positive integer less than M; The distance between the point and the starting point of the connecting road section, the distance between the i-th discrete point and the current position of the vehicle, the speed of the vehicle and the total length of the connecting road section determine the time for the vehicle to land on the next road time.
  • the obtaining submodule 844 is configured to obtain the distance between adjacent discrete points among the M discrete points according to the number of the connecting road section where the vehicle is located; the determining submodule 842, configured to determine the distance between the i-th discrete point and the starting point of the connected road section and the total length of the connected road section by accumulating the distances between the adjacent discrete points; the determining submodule 842, is used to determine the distance between the vehicle and The distance of the next road; the determining submodule 842 is configured to determine the time when the vehicle lands on the next road according to the distance from the vehicle to the next road and the speed of the vehicle.
  • the obtaining module 820 is used to obtain the microscopic traffic simulation area; the running module 860 is used to display the microscopic traffic simulation in the microscopic traffic area; The mesoscopic traffic simulation area displays the mesoscopic traffic simulation area; the vehicle traffic data is displayed in the microscopic traffic simulation area.
  • the obtaining module 820 is configured to determine the vehicle traffic data in the mesoscopic traffic simulation based on the vehicle traffic data in the microscopic traffic simulation in response to the traffic prediction operation; the conversion module 840 is used to Realize the transformation from the micro-traffic simulation to the meso-traffic simulation for the vehicles in the prediction area; run module 860, used to display the predicted traffic state.
  • the traffic simulation conversion device provided by the above-mentioned embodiment is only illustrated by the division of the above-mentioned functional modules. The structure is divided into different functional modules to complete all or part of the functions described above.
  • Fig. 13 is a schematic structural diagram of a computer device according to an exemplary embodiment.
  • the computer device 1300 includes a central processing unit (Central Processing Unit, CPU) 1301, a system memory 1304 including a random access memory (Random Access Memory, RAM) 1302 and a read-only memory (Read-Only Memory, ROM) 1303, and A system bus 1305 that connects the system memory 1304 and the central processing unit 1301 .
  • the computer device 1300 also includes a basic input/output system (Input/Output, I/O system) 1306 that helps to transmit information between various devices in the computer device, and is used to store an operating system 1313, an application program 1314 and other programs The mass storage device 1307 of the module 1315.
  • I/O system Basic input/output system
  • the basic input/output system 1306 includes a display 1308 for displaying information and input devices 1309 such as a mouse and a keyboard for users to input information. Both the display 1308 and the input device 1309 are connected to the central processing unit 1301 through the input and output controller 1310 connected to the system bus 1305 .
  • the basic input/output system 1306 may also include an input-output controller 1310 for receiving and processing input from a keyboard, mouse, or electronic stylus and other devices. Similarly, input output controller 1310 also provides output to a display screen, printer, or other type of output device.
  • the mass storage device 1307 is connected to the central processing unit 1301 through a mass storage controller (not shown) connected to the system bus 1305 .
  • the mass storage device 1307 and its associated computer device readable media provide non-volatile storage for the computer device 1300 . That is to say, the mass storage device 1307 may include a computer-readable medium (not shown) such as a hard disk or a Compact Disc Read-Only Memory (CD-ROM) drive.
  • a computer-readable medium such as a hard disk or a Compact Disc Read-Only Memory (CD-ROM) drive.
  • the computer device readable media may include computer device storage media and communication media.
  • Computer device storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer device readable instructions, data structures, program modules or other data.
  • Computer equipment storage media include RAM, ROM, Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), CD-ROM , Digital Video Disc (DVD) or other optical storage, cassette, tape, magnetic disk storage or other magnetic storage device.
  • EPROM Erasable Programmable Read Only Memory
  • EEPROM Electrically Erasable Programmable Read-Only Memory
  • CD-ROM Compact Disc
  • DVD Digital Video Disc
  • the storage medium of the computer device is not limited to the above-mentioned ones.
  • the above-mentioned system memory 1304 and mass storage device 1307 may be collectively referred to as memory.
  • the computer device 1300 may also operate on a remote computer device connected to a network through a network such as the Internet. That is, the computer equipment 1300 can be connected to the network 1311 through the network interface unit 1312 connected to the system bus 1305, or in other words, the network interface unit 1312 can also be used to connect to other types of networks or remote computer equipment systems (not shown). out).
  • the memory also includes one or more programs, the one or more programs are stored in the memory, and the central processing unit 1301 implements all or part of the steps of the above-mentioned traffic simulation conversion method by executing the one or more programs.
  • Embodiments of the present application also provide a computer-readable storage medium, on which at least one instruction, at least one program, code set or instruction set is stored, at least one instruction, at least one program, code set or The instruction set is loaded and executed by the processor, so as to realize the traffic simulation conversion method provided by the above method embodiments.
  • Embodiments of the present application also provide a computer program product or computer program, where the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium.
  • the processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the traffic simulation conversion method described in any one of the above embodiments.
  • the computer-readable storage medium may include: a read-only memory (ROM, Read Only Memory), a random access memory (RAM, Random Access Memory), a solid-state hard drive (SSD, Solid State Drives) or an optical disc, etc.
  • the random access memory may include resistive random access memory (ReRAM, Resistance Random Access Memory) and dynamic random access memory (DRAM, Dynamic Random Access Memory).
  • ReRAM resistive random access memory
  • DRAM Dynamic Random Access Memory

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Abstract

一种交通仿真转换方法、装置、计算机设备及存储介质，涉及交通仿真技术领域。该方法包括：获取车辆在第一交通仿真中的第一车辆交通数据（320）；根据所述第一车辆交通数据，转换得到所述车辆在第二交通仿真中的第二车辆交通数据（340）；根据所述车辆的所述第二车辆交通数据，运行所述第二交通仿真（360）；其中，所述第一交通仿真和所述第二交通仿真分别包括微观交通仿真和中观交通仿真中的一种，且所述第一交通仿真不同于所述第二交通仿真。该方法提供了一种通过对交通仿真中每辆车的车辆交通数据进行转换，从而实现车辆级别的交通仿真转换，使得转换前后的交通状况的一致性良好，提升了交通仿真的准确性。

Description

交通仿真转换方法、装置、计算机设备及存储介质
本申请要求于2021年10月28日提交的申请号为202111260948.7、发明名称为“交通仿真转换方法、装置、计算机设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及交通仿真技术领域，特别涉及一种交通仿真转换方法、装置、计算机设备及存储介质。
背景技术
交通仿真是一门使用仿真技术来研究交通行为的技术，通过建立交通运输系统在一定期间内实时运动的数学模型，来对交通运动随时间和空间的变化进行跟踪描述。根据交通仿真的细节描述程度可以将其划分为宏观交通仿真、中观交通仿真和微观交通仿真。
相关技术中，基于中观交通仿真运行的仿真结果来实现进一步的微观交通仿真，例如，中观交通仿真的结果为微观交通仿真提供基础OD(Origin-Destination，交通量)数据和路网模型，而后通过进一步向加入细节特征来实现微观交通仿真。
然而相关技术中的中观微观交通仿真转换方法是对于整体路网的转换，转换前后的交通状态差别必然是较大的。如何实现中观微观交通仿真转换前后能够维持交通状态的一致性，是需要解决的问题。
发明内容
本申请实施例提供了一种交通仿真转换方法、装置、计算机设备及存储介质，能够使得中观交通仿真和微观交通仿真之间实现灵活的转换。所述技术方案如下：
一方面，提供了一种交通仿真转换方法，所述方法由计算机设备执行，所述方法包括：
获取车辆在第一交通仿真中的第一车辆交通数据；
根据所述第一车辆交通数据，转换得到所述车辆在第二交通仿真中的第二车辆交通数据；
根据所述车辆的所述第二车辆交通数据，运行所述第二交通仿真；
其中，所述第一交通仿真和所述第二交通仿真分别包括微观交通仿真和中观交通仿真中的一种，且所述第一交通仿真不同于所述第二交通仿真。
另一方面，提供了一种交通仿真转换装置，所述装置包括：
获取模块，用于获取车辆在第一交通仿真中的第一车辆交通数据；
转换模块，用于根据所述第一车辆交通数据，转换得到所述车辆在第二交通仿真中的第二车辆交通数据；
运行模块，用于根据所述车辆的所述第二车辆交通数据，运行所述第二交通仿真；
其中，所述第一交通仿真和所述第二交通仿真分别包括微观交通仿真和中观交通仿真中的一种，且所述第一交通仿真不同于所述第二交通仿真。
另一方面，提供了一种计算机设备，所述计算机设备包括处理器和存储器，所述存储器中存储有至少一段程序，所述至少一段程序由所述处理器加载并执行以实现如上述本申请实施例中任一所述的交通仿真转换方法。
另一方面，提供了一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机指令，所述计算机指令由处理器加载并执行以实现如本申请各个方面提供的交通仿真转换方法。
另一方面，提供了一种计算机程序产品，该计算机程序产品包括计算机指令，该计算机指令存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机指令，处理器执行该计算机指令，使得该计算机设备执行上述交通仿真转换方法。
本申请实施例提供的技术方案带来的有益效果至少包括：
通过对具体车辆在交通仿真中的车辆交通数据进行转换，实现了车辆级别的交通仿真转换方法，使得转换前后的交通状况得以保持基本一致，提升了交通仿真转换的精确性和灵活性。
附图说明
图1示出了本申请一个示例性实施例提供的在不同区域同时运行中观交通仿真和微观交通仿真的界面示意图；
图2示出了本申请一个示例性实施例提供的进行交通状态预测的界面示意图；
图3示出了本申请一个示例性实施例提供的一种交通仿真系统的结构框图；
图4示出了本申请一个示例性实施例提供的交通仿真转换方法的流程图；
图5示出了本申请一个示例性实施例提供的中观交通仿真转换到微观交通仿真的流程图；
图6示出了本申请一个示例性实施例提供的将车辆在中观交通仿真中的车辆交通数据转换得到在微观交通仿真中的车辆交通数据的流程图；
图7示出了本申请一个示例性实施例提供的中观交通仿真转换为微观交通仿真的流程图；
图8示出了本申请一个示例性实施例提供的交通仿真转换方法的流程图；
图9示出了本申请一个示例性实施例提供的将车辆在微观交通仿真中的车辆交通数据转换得到在中观交通仿真中的车辆当前位置距道路的起点的距离的流程图；
图10示出了本申请一个示例性实施例提供的将车辆在微观交通仿真中的车辆交通数据转换得到在中观交通仿真中的车辆登陆下一条道路的时间的流程图；
图11示出了本申请一个示例性实施例提供的微观交通仿真转换为中观交通仿真的流程图；
图12示出了本申请一个示例性实施例提供的交通仿真转换装置的结构框图；
图13示出了本申请一个示例性实施例提供的计算机设备的结构框图。
具体实施方式
首先，对本申请实施例中所涉及的术语进行介绍：
微观交通仿真：对交通系统的要素及行为的细节描述程度最高。例如，微观交通仿真模型对交通流的描述是以单个车辆为基本单元的，车辆在道路上的跟车、超车及车道变换等微观行为都能得到较真实的反映。
中观交通仿真：中观交通仿真模型对交通流的描述往往是以若干辆车构成的队列为单元的，能够描述队列在路段和节点的流入流出行为，对车辆的车道变换之类的行为也可以用简单的方式近似描述。可以认为，中观交通仿真中的每辆车都属于一个队列。
微观地图：满足微观交通仿真的运行需求的地图，由“道路”和道路之间的“连接路段”组成，例如，“连接路段”可以理解为两条道路之间的路口路段。微观地图中的“道路”和“连接路段”都由多个离散点组成，每个离散点的信息包括该离散点的经度和纬度。
中观地图：满足中观交通仿真的运行需求的地图，可以认为是微观地图的简化版本。由“道路”和道路之间的“连接路段”组成，例如，“连接路段”可以理解为两条道路之间的路口路段。中观地图中的“道路”和“连接路段”只包含长度信息。
微观车辆：微观交通仿真需要精细的描述车辆的交通行为，例如车辆的变道、车辆在路口处的行为、车辆之间的相互作用，等等。因此，微观车辆所具有的车辆交通数据更具体，例如车辆的经纬度(即车辆的全局坐标)、车辆的车头朝向、车辆的速度，等等。具体地，微观车辆的车辆交通数据包括表1所示的车辆交通数据中的至少一个：
表1
Figure PCTCN2022118436-appb-000001
Figure PCTCN2022118436-appb-000002
中观车辆：中观交通仿真可以看作是简化版的微观交通仿真，因此，中观车辆所具有的车辆交通数据也没有微观车辆的精细；中观车辆的数据主要描述了车辆在路段上的相对位置，例如车辆当前位置距路段起点的距离，等等。具体地，中观车辆的车辆交通数据包括表2所示的车辆交通数据中的至少一个：
表2
Figure PCTCN2022118436-appb-000003
图1示出了本申请一个示例性实施例提供的在不同区域同时运行中观交通仿真和微观交通仿真的界面示意图。
在用户使用交通仿真中的“实时仿真”的功能时，可以自定义中观交通仿真和微观交通仿真的区域。例如，对普通区域运行中观交通仿真，获得普通区域的大致交通状况信息；对于重点关注的区域运行微观交通仿真，获得交通状态的更多细节信息。也即，对于整个地图中的大部分区域使用中观交通仿真来降低计算量，保证运行效率；对于小部分希望重点关注的区域使用微观交通仿真，提升仿真的精细度。
在“实时仿真”功能的运行期间，每隔固定时间遍历所有车辆，当中观交通仿真区域中的车辆运行到微观交通仿真区域中时，基于该车辆的中观交通仿真中的车辆交通数据确定该车辆在微观交通仿真中的车辆交通数据，实现中观交通仿真到微观交通仿真的转换；当微观交通仿真区域中的车辆运行到中观交通仿真区域中时，基于该车辆的微观交通仿真中的车辆交通数据确定该车辆在中观交通仿真中的车辆交通数据，实现微观交通仿真到中观交通仿真的转换。可选地，获取微观交通仿真区域；在微观交通区域内显示微观交通仿真，在除微观交通区域以外的中观交通仿真区域显示中观交通仿真；在微观交通仿真区域中显示微观车辆交通数据，在中观交通仿真区域中显示中观车辆交通数据。其中，微观车辆交通数据指微观车辆的车辆交通数据，请参考表1，微观车辆交通数据包括车辆编号、车辆速度、路段编号、经度、纬度、车头朝向中的至少一种。中观车辆交通数据指中观车辆的车辆交通数据，请参考表2，中观车辆交通数据包括车辆编号、车辆速度、路段编号、路段中位置、登录时间中的至少一种。
示例性的，如图1所示，用户在整体区域210中划定微观交通仿真区域211，则剩余的外围区域为中观交通仿真区域212。可以看到，微观交通仿真区域211和中观交通仿真区域212中的路段都由直线表示的道路和方块表示的连接路段组成，例如直线表示的道路213和方块表示的连接路段214。在微观交通仿真区域211中，显示有每辆车的车辆交通数据，例如 车辆215当前以16米每秒的速度行驶；而在中观交通仿真区域212中，忽略对具体车辆交通数据的关注，而是对道路状况做一个描述，例如图2中的直线213代表当前路段拥堵，点划线216表示当前道路流量适中，虚线217表示当前道路通畅，等等。
图2示出了本申请一个示例性实施例提供的进行交通状态预测的界面示意图。
在运行微观交通仿真的过程中，用户选择启用“预测未来交通状态”的功能，为了快速的对未来交通状态进行仿真获得预测结果，将微观交通仿真整体转换为中观交通仿真运行，以低运算量的方式高效快速获得预测结果。可选地，响应于交通预测操作，基于微观交通仿真中的微观车辆交通数据确定中观交通仿真中的中观车辆交通数据；根据微观车辆交通数据和中观车辆交通数据，对预测区域内的车辆实现微观交通仿真到中观交通仿真的转换；根据中观交通仿真显示预测交通状态。可选地，交通状态用于表示道路的畅通程度，数值越高表明交通越畅通。可选地，交通状态用于表示道路的拥挤程度，数值越高表明交通越拥挤。可选地，交通状态包括拥挤、畅通、事故中的至少一种。
示例性的，用户在交通仿真平台界面230，通过点击预测控件231启动“预测未来交通状态”的功能，并设置未来时间232为30分钟后。保存当前时刻微观交通仿真运行中所有车辆的车辆交通数据，基于微观交通仿真中的车辆交通数据确定中观交通仿真中的车辆交通数据，对所有车辆实现微观交通仿真到中观交通仿真的转换；在转换后运行中观交通仿真一定时间，获得30分钟后的预测交通状态。交通仿真平台界面230上显示有30分钟后的预测交通状态，包括路段名称234、交通流量235、平均车速236和交通指数237。其中，路段名称234中包括道路的方向；交通流量235的单位为辆/时，即交通流量235指一小时内通过该道路的车辆数；平均车速236的单位为千米/时，即通过该道路的车辆的平均时速；交通指数237是综合反应道路畅通或拥堵的概念性指数，数值越高表明交通越拥堵。
如上，图1、图2分别示出了通过本申请的交通仿真转换方法，在同一时刻对不同区域分别进行中观交通仿真和微观交通仿真所涉及的部分区域的交通仿真转换，以及，在同一区域的不同时刻分别进行中观交通仿真和微观交通仿真所涉及的对于整体的转换。以下实施例将对该交通仿真转换方法的实施方式进行详细的说明。
图3示出了本申请一个示例性实施例提供的交通仿真系统的结构框图。该交通仿真系统100包括：传感器110、道路数据服务器120和交通仿真服务器130。
传感器110能够是路网系统中的摄像头、布置在路面下方的地磁线圈，等等。传感器110用于获取道路数据，例如，通过的车辆数、车辆时速、车辆的交通行为，等等。
传感器110通过无线网络或有线网络与道路数据服务器120相连。
道路数据服务器120用于汇总传感器110采集得到的数据并进行相应处理，将传感器110获取到的道路数据汇总处理为可用于交通仿真的车辆交通数据。道路数据服务器120包括一台服务器、多台服务器、云计算平台和虚拟化中心中的至少一种。
道路数据服务器120通过有线网络或无线网络与交通仿真服务器130相连。
交通仿真服务器130用于根据道路数据服务器120汇总处理后得到的车辆交通数据，运行交通仿真，并且实现不同交通仿真方式间的转换。交通仿真服务器130包括一台服务器、多台服务器、云计算平台和虚拟化中心中的至少一种。
本领域技术人员能够知晓，上述传感器110、道路数据服务器120、交通仿真服务器130的数量能够更多或更少。比如上述传感器110、道路数据服务器120、交通仿真服务器130能够仅为一个，或者上述传感器110、道路数据服务器120、交通仿真服务器130为几十个或几百个，或者更多数量，本申请实施例对传感器或服务器的数量和设备类型不加以限定。
图4示出了本申请一个示例性实施例提供的交通仿真转换方法的流程图，该方法应用于图3所示的终端120或服务器140中。如图4所示，该方法包括：
步骤320：获取车辆在第一交通仿真中的第一车辆交通数据；
第一交通仿真包括微观交通仿真和中观交通仿真中的一种。
微观交通仿真为比中观交通仿真更为精细的交通仿真方式，因此，微观交通仿真中的微观车辆的车辆交通数据比中观交通仿真中的车辆交通数据更为具体，精度更高。微观交通仿真中的车辆交通数据如前述表1所示，中观车量仿真中的车辆交通数据如前述表2所示。
示例性的，在第一交通仿真为中观交通仿真的情况下，第一车辆交通数据包括：车辆所在路段的编号和车辆当前位置距路段的起点的距离。
示例性的，在第一交通仿真为微观交通仿真的情况下，第一车辆交通数据包括：车辆所在路段的编号、车辆的经纬度和车辆的速度。
步骤340：根据第一车辆交通数据，转换得到车辆在第二交通仿真中的第二车辆交通数据；
第二交通仿真包括微观交通仿真和中观交通仿真中的一种，且第一交通仿真不同于第二交通仿真。
示例性的，在第一交通仿真包括中观交通仿真，第二交通仿真包括微观交通仿真的情况下，根据微观车辆的车辆交通数据和中观车辆的车辆交通数据分别包含的数据内容可知，需要将中观交通仿真中的车辆当前位置距路段的起点的距离，转换得到微观交通仿真中的车辆的经纬度和车辆的车头朝向。
也即，第二车辆交通数据包括：车辆的经纬度和车辆的车头朝向。
也即，根据车辆所在路段的编号和车辆当前位置距路段的起点的距离，转换得到车辆在微观交通仿真中的第二车辆交通数据，第二车辆交通数据包括：车辆的经纬度和车辆的车头朝向。
进一步的，中观地图和微观地图上的路段包括道路和连接路段中的至少一种。
示例性的，在车辆所在路段的类型为道路的情况下，根据车辆所在道路的编号和车辆当前位置距道路的起点的距离，确定车辆的经纬度；以及根据车辆所在道路的编号和车辆当前位置距道路的起点的距离确定车辆的车头朝向。
示例性的，在车辆所在路段的类型为连接路段的情况下，根据车辆所在连接路段的编号和车辆当前位置距连接路段的起点的距离，确定车辆的经纬度；以及根据车辆所在道路的编号和车辆当前位置距道路的起点的距离确定车辆的车头朝向。
示例性的，在第一交通仿真包括微观交通仿真，第二交通仿真包括中观交通仿真的情况下，根据微观车辆的车辆交通数据和中观车辆的车辆交通数据分别包含的数据内容可知，需要将微观交通仿真中的车辆的经纬度，转换得到中观交通仿真中的车辆当前位置距路段的起点的距离和车辆登陆下一条道路的时间。
也即，第二车辆交通数据包括：车辆当前位置距路段的起点的距离和车辆登陆下一条道路的时间。其中，车辆登陆下一条道路的时间是针对在连接路段的车辆设置的参数，在该参数大于0的情况下，代表车辆当前位置处于连接路段中。
也即，步骤340包括如下步骤中的至少一种：根据车辆所在路段的编号和车辆的经纬度，转换得到中观交通仿真中的车辆当前位置距路段的起点的距离；根据车辆所在路段的编号、车辆的经纬度和车辆的速度，转换得到中观交通仿真中的车辆登陆下一条道路的时间。
进一步的，中观地图和微观地图上的路段都由道路和连接路段组成。
示例性的，在车辆所在路段的类型为道路的情况下，根据车辆所在路段的编号和车辆的经纬度，转换得到中观交通仿真中的车辆当前位置距路段的起点的距离。
示例性的，在车辆所在路段的类型为连接路段的情况下，根据车辆所在路段的编号、车辆的经纬度和车辆的速度，转换得到中观交通仿真中的车辆登陆下一条道路的时间。
在一种可能的实施方式中，在车辆所在路段的类型为连接路段的情况下，根据车辆所在路段的编号和车辆的经纬度，转换得到中观交通仿真中的车辆当前位置距连接路段的起点的距离。
步骤360：根据车辆的第二车辆交通数据，运行第二交通仿真。
基于上述获得的车辆的第二车辆交通数据，运行第二交通仿真。
在第二交通仿真为微观交通仿真的情况下，根据车辆的经纬度和车辆的车头朝向，运行微观交通仿真。
在第二交通仿真为中观交通仿真的情况下，根据车辆当前位置距路段的起点的距离和车辆登陆下一条道路的时间，运行中观交通仿真。
综上所述，本申请实施例中基于获取到的第一交通仿真中的第一车辆交通数据，转换得到车辆在第二交通仿真中的第二车辆交通数据，而后进行第二交通仿真。该方法通过依次对每辆车辆的车辆交通数据进行转换，实现了精细到车辆级别的中观交通仿真和微观交通仿真的相互转换，使得交通仿真转换前后车辆能够保持一致性，避免了因交通仿真方式的转变而导致的转换前后的交通状态差别过大的问题，提升了交通仿真转换的精确性。
上述实施例中介绍了第一交通仿真转换到第二交通仿真的实施过程。
以下实施例针对中观交通仿真转换到微观交通仿真的实施过程进行具体说明。
图5示出了本申请一个示例性实施例提供的中观交通仿真转换到微观交通仿真的流程图，该方法应用于图3所示的终端120或服务器140中。如图5所示，该方法包括：
步骤422：获取车辆在中观交通仿真中的第一车辆交通数据；
在第一交通仿真为中观交通仿真的情况下，即，在初始运行的交通仿真方式为中观交通仿真的情况下，获取中观车辆的数据以实现中观车辆到微观车辆的转换。
示例性的，第一车辆交通数据包括：车辆所在路段的编号和车辆当前位置距路段的起点的距离。
步骤442：根据车辆所在路段的编号和车辆当前位置距路段的起点的距离，转换得到车辆在微观交通仿真中的第二车辆交通数据；
在第一交通仿真为中观交通仿真的情况下，第二交通仿真为微观交通仿真，即，需要基于车辆在中观交通仿真中的第一车辆交通数据，转换得到在微观交通仿真中需要用到的第二车辆交通数据。
示例性的，第二车辆交通数据包括：车辆的经纬度和车辆的车头朝向。
在微观地图和中观地图中，路段都由道路和道路之间的连接路段组成。由于车辆在道路中和在连接路段中的描述差别较大，因此将道路和连接路段区分开，分别实施车辆交通数据的转换。
示例性的，在车辆所在路段的类型为道路的情况下，根据车辆所在道路的编号和车辆当前位置距道路的起点的距离，确定车辆的经纬度；以及根据车辆所在道路的编号和车辆当前位置距道路的起点的距离确定车辆的车头朝向。
示例性的，在车辆所在路段的类型为连接路段的情况下，根据车辆所在连接路段的编号和车辆当前位置距连接路段的起点的距离，确定车辆的经纬度；以及根据车辆所在道路的编号和车辆当前位置距道路的起点的距离确定车辆的车头朝向。
以下对步骤442进行更具体的说明。图6示出了本申请一个示例性实施例提供的将车辆在中观交通仿真中的车辆交通数据转换得到在微观交通仿真中的车辆交通数据的流程图。
由前述对微观地图的说明可知，微观交通仿真中的道路包括按序排列的N个离散点，其中，N为正整数；微观交通仿真中的连接路段包括按序排列的M个离散点，其中，M为正整数。
步骤4421：在车辆所在路段的类型为道路的情况下，根据车辆所在的道路的编号和车辆当前位置距道路的起点的距离，将道路中的N个离散点中与车辆当前位置最相近的第i个离散点的经纬度，确定为车辆的经纬度；
其中，N为正整数，i为小于N的正整数。
示例性的，步骤4421可拆分为以下几个步骤：
(1)根据车辆所在道路的编号，获取N个离散点中的相邻离散点之间的距离；
示例性的，根据第一车辆交通数据中的车辆所在道路的编号road_id meso，在微观地图中查找车辆所在道路。该道路由按序排列的N个离散点P 1，P 2，…，P N组成，其中，P 1为道路的起点。获取N个离散点中的相邻离散点之间的距离。
需要注意的是，每条道路是具有走向信息的，例如，南北走向的双向两车道，南向道路和北向道路对应的为不同的道路编号。也即，每条道路的起点P 1是唯一确定的。
(2)通过累加相邻离散点之间的距离，确定道路中的N个离散点分别距道路的起点的距离；
示例性的，从道路起点P 1开始，通过累加相邻离散点之间的距离，依次计算道路中的N个离散点距道路的起点的距离。具体计算如下公式：
Figure PCTCN2022118436-appb-000004
其中，dis_to_start j代表第j个离散点距道路起点的距离，distance(p j-1，p j)代表第j-1个离散点P j-1到第j个离散点P j的距离，j为小于N的正整数。
也即，第1个离散点距道路的起点的距离为0，第2个离散点距道路的起点的距离为第1个离散点距第2个离散点的距离，第3个离散点距道路的起点的距离为第2个离散点距道路的起点的距离与第3个离散点距第2个离散点的距离之和，以此类推，依次累加，确定N个离散点分别距道路的起点的距离。
需要注意的是，由于相邻离散点之间的距离较近，可忽略道路弯曲等因素，用直线长度近似表示相邻离散点之间的距离。
(3)将N个离散点距道路的起点的距离分别与车辆当前位置距道路的起点的距离作差；
在上一步骤中分别获得N个离散点距道路的起点的距离后，将N个离散点距道路的起点的距离分别与车辆当前位置距道路的起点的距离作差，即dis_to_start j-road_pos meso，获取N个离散点分别距车辆当前位置的距离。
(4)将差值最小的第i个离散点的经纬度，确定为车辆的经纬度，i为小于或等于N的正整数。
示例性的，将差值最小的第i个离散点的经纬度确定为车辆的经纬度；也即，将距车辆当前位置最相近的第i个离散点的经纬度近似为车辆的经纬度；也即，将使得下式取得最小值的j赋值给i：
min 1<j<N(dis_to_start j-road_pos meso)。
示例性的，将第i个离散点的位置作为车辆当前位置，将第i个离散点的经纬度作为车辆的经纬度。
可选地，可以通过函数来描述微观地图中路段，即，在步骤(1)之前增加对描述路段的函数进行采样获取离散点的步骤。
步骤4422：在车辆所在路段的类型为连接路段的情况下，根据车辆所在连接路段的编号和车辆当前位置距连接路段的起点的距离，将连接路段中的M个离散点中与车辆当前位置最相近的第i个离散点的经纬度，确定为车辆的经纬度。
示例性的，与步骤4421类似，步骤4422可拆分为以下几个步骤：
(1)根据车辆所在连接路段的编号，获取M个离散点中的相邻离散点之间的距离；
示例性的，根据第一车辆交通数据中的车辆所在连接路段的编号road_id meso，在微观地图中查找车辆所在连接路段。该连接路段由按序排列的M个离散点P 1，P 2，…，P M组成，其中，P 1为连接路段的起点。获取M个离散点中的相邻离散点之间的距离。
需要注意的是，连接路段与道路类似，每条连接路段是具有走向信息的；也即，每条连接路段的起点P 1是唯一确定的。
(2)通过累加相邻离散点之间的距离，确定连接路段中的M个离散点分别距连接路段的起点的距离；
示例性的，从连接路段起点P 1开始，通过累加相邻离散点之间的距离，依次计算连接路段中的M个离散点距连接路段起点的距离。具体计算如下公式：
Figure PCTCN2022118436-appb-000005
其中，dis_to_start j代表第j个离散点距连接路段起点的距离，distance(p j-1，p j)代表第j-1个离散点P j-1到第j个离散点P j的距离，j为小于M的正整数。
也即，第1个离散点距连接路段的起点的距离为0，第2个离散点距连接路段的起点的距离为第1个离散点距第2个离散点的距离，第3个离散点距连接路段的起点的距离为第2个离散点距连接路段的起点的距离与第3个离散点距第2个离散点的距离之和，以此类推，依次累加，确定M个离散点分别距连接路段的起点的距离。
需要注意的是，由于相邻离散点之间的距离较近，可忽略道路弯曲等因素，用直线长度近似表示相邻离散点之间的距离。
(3)将M个离散点距连接路段的起点的距离分别与车辆当前位置距连接路段的起点的距离作差；
在上一步骤中分别获得M个离散点距连接路段起点的距离后，将M个离散点距连接路段的起点的距离分别与车辆当前位置距连接路段的起点的距离作差，即dis_to_start j-road_pos meso，获取M个离散点分别距车辆当前位置的距离。
(4)将差值最小的第i个离散点的经纬度，确定为车辆的经纬度，i为小于或等于M的正整数。
示例性的，将差值最小的第i个离散点的经纬度确定为车辆的经纬度；也即，将距车辆当前位置最相近的第i个离散点的经纬度近似为车辆的经纬度；也即，将使得下式取得最小值的j赋值给i：
min 1<j<M(dis_to_start j-road_pos meso)。
示例性的，将第i个离散点的位置作为车辆当前位置，将第i个离散点的经纬度作为车辆的经纬度。
可选地，可以通过函数来描述微观地图中路段，即，在步骤(1)之前增加对描述路段的函数进行采样获取离散点的步骤。
需要注意的是，上述步骤4421和步骤4422只需根据车辆当前所在路段的类型择一执行，而非全部执行。在车辆所在路段的类型为道路的情况下，执行步骤4421；在车辆所在路段的类型为连接路段的情况下，执行步骤4422。
步骤4423：根据第i个离散点和第i-1个离散点确定车辆的车头朝向。
车辆的车头朝向用于指示车辆行驶的方向，可以通过车辆当前位置附近的取值较小的一段车辆行驶轨迹的方向来表示。
由前述步骤可以确定与车辆当前位置最近的第i个离散点，也即，将第i个离散点的位置近似表示为车辆当前位置。在该位置的附近小范围的取一段车辆行驶轨迹，将该行驶轨迹的方向作为车辆的行驶方向。
在一种可能的实施方式中，根据第i个离散点和第i-1个离散点确定为车辆的车头朝向；具体地，将第i个离散点和第i-1个离散点的连线在世界坐标系(全局坐标系)中的角度作为车辆的车头朝向。
在一种可能的实施方式中，根据第i个离散点和第i+1个离散点确定为车辆的车头朝向；具体地，将第i个离散点和第i+1个离散点的连线在世界坐标系(全局坐标系)中的角度作为车辆的车头朝向。
需要注意的是，车头朝向的单位为弧度(rad)，也即，车头朝向通过角度的方式进行表示，该角度为车头朝向相对于世界坐标系(全局坐标系)的角度，而非相对于车辆当前路段的角度。
步骤462：根据车辆的第二车辆交通数据，运行微观交通仿真。
示例性的，基于上述获得的车辆的经纬度和车辆的车头朝向，运行微观交通仿真。
微观交通仿真中关注每一辆车辆的行为，例如，车辆的变道、车辆的转向、车辆在路口的交通行为、车辆与其他车辆的相互作用，等等。
此外，可以通过输入信息流和输出信息流的方式更直观的看到本申请所述的交通仿真转换方法中的中观交通仿真转换为微观交通仿真的过程。
图7示出了本申请一个示例性实施例提供的中观交通仿真转换为微观交通仿真的流程图。
由图7可见，输入信息流为中观车辆交通数据，具体的，为中观车辆所在路段的编号和中观车辆当前位置距路段的起点的距离。
在步骤501中，判断车辆是否在路口，若车辆不在路口，执行步骤502；若车辆在路口，则执行步骤503；
在步骤502中，根据前述实施例的步骤4421所述的方法，获取车辆在道路中的经纬度；
在步骤503中，根据前述实施例的步骤4422所述的方法，获取车辆在路口中的经纬度；
在步骤504中，基于步骤502或步骤503确定的与车辆当前位置最相近的第i个离散点的位置，根据前述实施例的步骤4423所述的方法，获取车辆的车头朝向。
最终，本申请实施例中所述的交通仿真转换方法的输出信息流为微观车辆交通数据，具体地，为车辆的经纬度和车辆的车头朝向。
综上所述，本申请实施例中通过获取到的车辆所在路段的编号，获取路段中离散点的信息，结合离散点信息和车辆当前位置距路段的起点的距离确定与车辆当前位置最近的离散点，并通过该离散点的经纬度结合该离散点附近的离散点信息，确定车辆的经纬度和车辆的车头朝向，实现中观交通仿真转换为微观交通仿真。该方法提供了一种将中观交通仿真转换为微观交通仿真的方法，遍历全局所有车辆，根据每一辆车辆的车辆交通数据进行转换，保证了转换前后的数据一致性，避免了转换前后的交通状况出现重大偏差的问题，提升了交通仿真转换的精确性。
此外，由于路段通过离散点表示，因此可以在路段的任意位置实现交通仿真的转换，实现了既能对整体区域进行转换，又能同一时刻在不同区域运行不同的交通仿真方式，灵活性很高。
此外，本方法依赖的中观车辆的车辆交通数据、微观车辆的车辆交通数据以及微观地图的表示方法都较为简单，可以容易的应用到各种微观仿真和中观仿真的转换中去。
以下实施例针对微观交通仿真转换为中观交通仿真的过程进行具体说明。
图8示出了本申请一个示例性实施例提供的交通仿真转换方法的流程图，该方法应用于图3所示的终端120或服务器140中。如图8所示，该方法包括：
步骤622：获取车辆在微观交通仿真中的第一车辆交通数据；
在第一交通仿真为微观交通仿真的情况下，即，在初始运行的交通仿真方式为微观交通仿真的情况下，获取微观车辆的数据以实现微观车辆到中观车辆的转换。
示例性的，第一车辆交通数据包括：车辆所在路段的编号、车辆的经纬度和车辆的速度。
在第一交通仿真为微观交通仿真的情况下，第二交通仿真为中观交通仿真，即，需要基于车辆在微观交通仿真中的第一车辆交通数据，转换得到在中观交通仿真中需要用到的第二车辆交通数据。
步骤642：根据车辆所在路段的编号和车辆的经纬度，转换得到中观交通仿真中的车辆当前位置距路段的起点的距离；
示例性的，通过车辆所在路段的编号获取车辆所在路段的信息，包括车辆所在路段上的离散点的信息；根据车辆的经纬度和离散点的经纬度确定与车辆当前位置最近的离散点，并计算该离散点距路段的起点的距离；根据该离散点距路段的起点的距离和该离散点距车辆当前位置的距离，近似得到车辆当前位置距路段的起点的距离。
示例性的，以路段为道路为例，对步骤642进行更具体的说明。路段为连接路段的情况与此类似，就不再赘述。微观交通仿真中的道路包括按序排列的N个离散点，其中，N为正整数。
图9示出了本申请一个示例性实施例提供的将车辆在微观交通仿真中的车辆交通数据转换得到在中观交通仿真中的车辆当前位置距道路的起点的距离的流程图。
步骤6421：根据N个离散点的经纬度与车辆的经纬度确定与车辆当前位置最接近的第i个离散点；
示例性的，根据车辆所在道路的编号获取车辆所在道路的信息，包括车辆所在道路上的N个离散点P 1，P 2，…，P N及其经纬度。遍历N个离散点，确定与车辆当前位置P vehicle最接近的第i个离散点P i。其中，i为小于N的正整数。
也即，将取得下式的最小值的j赋值给i：
min 1<j<N distance(P vehicle，P j)；
其中，distance(P vehicle，P j)代表车辆当前位置P vehicle和第j个离散点P j之间的距离，j为小于N的正整数。
步骤6422：根据第i个离散点距道路的起点的距离和第i个离散点与车辆当前位置的距离确定车辆当前位置距道路的起点的距离。
第i个离散点距道路的起点的距离与步骤4421或步骤4422中的计算过程类似，通过累加第一个离散点P 1到第i个离散点P i之间的相邻离散点之间的距离，确定第i个离散点距道路的起点的距离，这里就不再赘述。
将获得的第i个离散点距道路的起点的距离与第i个离散点与车辆当前位置的距离之和，确定为车辆当前位置距道路的起点的距离。具体计算公式如下：
road_pos meso＝dis_to_start i+distance(P vehicle，P i)；
其中，road_pos meso为中观交通仿真中的车辆当前位置距道路的起点的距离，dis_to_start i为第i个离散点距道路的起点的距离，distance(P vehicle，P i)为第i个离散点与车辆当前位置的距离。需要注意的是，distance(P vehicle，P i)可能为一个正数，也可能为一个负数。在第i个离散点与车辆当前位置的连线与道路的走向为同向的情况下，distance(P vehicle，P i)为正数；在第i个离散点与车辆当前位置的连线与道路的走向为反向的情况下，distance(P vehicle，P i)为负数。
步骤644：根据车辆所在路段的编号、车辆的经纬度和车辆的速度，转换得到中观交通仿真中的车辆登陆下一条道路的时间；
在车辆登陆下一条道路的时间为0的情况下，车辆当前位置处于道路中；在车辆登陆下一条道路的时间大于0的情况下，车辆当前位置处于连接路段中。因此，车辆登陆下一条道路的时间通常是针对处在连接路段中的车辆而言需要转换得到的车辆交通数据。
示例性的，通过车辆所在连接路段的编号获取车辆所在连接路段的信息，包括车辆所在连接路段上的离散点的信息；根据车辆的经纬度和离散点的经纬度确定与车辆当前位置最近的离散点，并计算该离散点距路段的起点的距离，即为车辆当前位置距连接路段的起点的距离；以及，根据离散点的经纬度确定连接路段的总长度；最后，根据连接路段的总长度、车辆当前位置距连接路段的起点的距离和车辆的速度，确定车辆登陆下一条道路的时间。
示例性的，微观交通仿真中的连接路段包括按序排列的M个离散点，其中，M为正整数。
以下对步骤644进行更具体的说明。图10示出了本申请一个示例性实施例提供的将车辆在微观交通仿真中的车辆交通数据转换得到在中观交通仿真中的车辆登陆下一条道路的时间的流程图。
步骤6441：根据M个离散点的经纬度与车辆的经纬度确定与车辆当前位置最接近的第i个离散点；
示例性的，根据车辆所在连接路段的编号获取车辆所在连接路段的信息，包括车辆所在连接路段上的M个离散点P 1，P 2，…，P M及其经纬度。遍历M个离散点，确定与车辆当前位置P vehicle最接近的第i个离散点P i。其中，i为小于N的正整数。
也即，将下式取得最小值的j赋值给i：
min 1<j<M(P vehicle，P j)；
其中，(P vehicle，P j)代表车辆当前位置P vehicle和第j个离散点P j之间的距离，j为小于M的正整数。
步骤6442：根据第i个离散点距连接路段的起点的距离、第i个离散点与车辆当前位置的距离、车辆的速度和连接路段的总长度，确定车辆登陆下一条道路的时间。
示例性的，步骤6442可拆分为以下几个步骤：
(1)根据车辆所在连接路段的编号，获取M个离散点中的相邻离散点之间的距离；
示例性的，根据第一车辆交通数据中的车辆所在连接路段的编号road_id micro，在微观地图中查找车辆所在连接路段。该连接路段由按序排列的M个离散点P 1，P 2，…，P M组成，其中，P 1为连接路段的起点。获取M个离散点中的相邻离散点之间的距离。
需要注意的是，连接路段与道路类似，每条连接路段是具有走向信息的；也即，每条连接路段的起点P 1是唯一确定的。
(2)通过累加相邻离散点之间的距离，确定第i个离散点距连接路段的起点的距离和连接路段的总长度；
示例性的，从连接路段起点P 1开始，通过累加相邻离散点之间的距离，计算得到第i个离散点距连接路段的起点的距离dis_to_start i和连接路段的总长度length，其中，连接路段的总长度即为第N个离散点P N距连接路段的起点的距离。
示例性的，通过累加第1个离散点P 1到第i个离散点P i之间的相邻离散点之间的距离，确定第i个离散点距连接路段的起点的距离dis_to_start i；通过累加第1个离散点P 1到第N个离散点P N之间的相邻离散点之间的距离，确定第N个离散点距连接路段的起点的距离dis_to_start N，也即连接路段的总长度length。具体计算如下公式：
dis_to_start i＝dis_to_start i-1+distance(p i-1，p i)；
length＝dis_to_start N＝dis_to_start N-1+distance(p N-1，p N)；
其中，dis_to_start i-1为第i-1个离散点P i-1距连接路段的起点的距离，distance(p i-1，p i)代表第i-1个离散点P i-1到第i个离散点P i的距离，i为小于N的正整数；dis_to_start N-1表示第N-1个离散点P N-1距连接路段的起点的距离，distance(p N-1，p N)代表第N-1个离散点P N- 1到第N个离散点P N的距离。
需要注意的是，由于相邻离散点之间的距离较近，可忽略道路弯曲等因素，用直线长度近似表示相邻离散点之间的距离。
(3)根据连接路段的总长度、第i个离散点距连接路段的起点的距离和第i个离散点与车辆当前位置的距离，确定车辆距下一条道路的距离；
示例性的，通过将连接路段的总长度length减去第i个离散点距连接路段的起点的距离dis_to_start i，再减去第i个离散点与车辆当前位置的距离，可以获得车辆距下一条道路的距离dis_to_next。具体公式如下：
dis_to_next＝length-dis_to_start i-distance(p vehicle，p i)。
需要注意的是，distance(P vehicle，P i)可能为一个正数，也可能为一个负数。在第i个离散点与车辆当前位置的连线与道路的走向同向的情况下，distance(P vehicle，P i)为正数；在第i个离散点与车辆当前位置的连线与道路的走向反向的情况下，distance(P vehicle，P i)为负数。
(4)根据车辆距下一条道路的距离和车辆的速度，确定车辆登陆下一条道路的时间。
示例性的，通过将车辆距下一条道路的距离dis_to_next除以车辆的速度speed mirco，获得 车辆登陆下一条道路的时间。具体公式如下：
Figure PCTCN2022118436-appb-000006
在一种可能的实施方式中，连接路段的总长度length为地图数据的一部分，即可根据车辆所在路段的编号通过查找直接获取连接路段的总长度的数据，而无需通过步骤(3)中累计相邻离散点之间的距离获取连接路段的总长度的计算过程。
在一种可能的实施方式中，可以通过函数来描述微观地图中路段，即，在步骤(1)之前增加对描述路段的函数进行采样获取离散点的步骤。
需要注意的是，在车辆所在路段的类型为道路时，中观车辆交通数据中的登陆下一条道路的时间为0，因此，通常采用车辆当前位置距道路的起点的距离来指示中观交通仿真中车辆在道路中的相对位置，即执行步骤642；在车辆所在路段的类型为连接路段时，通常更关心该车辆登陆下一条道路的时间，即执行步骤644。
在另一种可能的实施方式中，在车辆所在路段的类型为道路时，执行步骤642；在车辆所在路段的类型为连接路段时，执行步骤642和步骤644。
在另一种可能的实施方式中，无论车辆所在路段的类型为道路还是连接路段，都执行步骤642。
步骤662：根据车辆的第二车辆交通数据，运行中观交通仿真。
示例性的，基于上述获得的车辆当前位置距道路的起点的距离、车辆登陆下一条道路的时间，运行中观交通仿真。
中观交通仿真中不关注具体每辆中观车辆的交通行为，而是关注路段级别的交通状况，例如，路段上的车流量、平均车速、交通指数，等等。中观交通仿真中的每一辆中观车辆都属于一个队列。
示例性的，根据车辆当前位置距道路的起点的距离，将车辆加入车辆所属队列中对应的位置。基于队列模型运行中观交通仿真。
此外，可以通过输入信息流和输出信息流的方式更直观的看到本申请所述的交通仿真转换方法中的微观交通仿真转换为中观交通仿真的过程。
图11示出了本申请一个示例性实施例提供的微观交通仿真转换为中观交通仿真的流程图。
由图11可见，输入信息流为微观车辆交通数据，具体的，为微观车辆所在路段的编号和微观车辆的经纬度。
在步骤701中，判断车辆是否在路口，若车辆不在路口，执行步骤702；若车辆在路口，则执行步骤703；
在步骤702中，根据前述实施例的步骤642所述的方法，获取车辆在道路中时车辆当前位置距道路起点的距离；
在步骤703中，根据前述实施例的步骤644所述的方法，获取车辆在路口时登陆下一条道路的时间；
最终，本申请实施例中所述的交通仿真转换方法的输出信息流为中观车辆交通数据，具体地，为车辆当前位置距道路起点的距离和车辆在路口时登陆下一条道路的时间。
综上所述，本申请实施例中通过获取车辆所在路段的离散点的信息，结合车辆的经纬度和车辆运动速度，将微观车辆的车辆交通数据转换得到中观车辆的车辆交通数据，实现了将微观交通仿真转换为中观交通仿真。该方法提供了一种将微观交通仿真转换为中观交通仿真的方法，遍历全局所有车辆，根据每一辆车辆的车辆交通数据进行转换，保证了转换前后的数据一致性，避免了转换前后的交通状况出现偏差的问题，提升了交通仿真转换的精确性。
此外，由于路段通过离散点表示，因此可以在路段的任意位置实现交通仿真的转换，实现了既能对整体区域进行转换，又能同一时刻在不同区域运行不同的交通仿真方式，灵活性 很高。
此外，本方法依赖的中观车辆的车辆交通数据、微观车辆的车辆交通数据以及微观地图的表示方法都较为简单，可以容易的应用到各种微观交通仿真和中观交通仿真的转换中去。例如，本方法依赖的中观车辆的车辆交通数据适用于中观交通仿真中的流密速模型和队列模型；再例如，本方法依赖的微观车辆的车辆交通数据，与交通仿真技术领域中常用的sumo、vissim等软件中的车辆交通数据可以互通。
图12是本申请一个示例性实施例提供的交通仿真转换装置的结构框图，如图12所示，该装置包括：
获取模块820，用于获取车辆在第一交通仿真中的第一车辆交通数据；
转换模块840，用于根据所述第一车辆交通数据，转换得到所述车辆在第二交通仿真中的第二车辆交通数据；
运行模块860，用于根据所述车辆的所述第二车辆交通数据，运行所述第二交通仿真；
其中，所述第一交通仿真和所述第二交通仿真分别包括微观交通仿真和中观交通仿真中的一种，且所述第一交通仿真不同于所述第二交通仿真。
在一个可能的实施例中，所述第一交通仿真包括所述中观交通仿真，所述第二交通仿真包括所述微观交通仿真；所述获取模块820，用于获取所述车辆在所述中观交通仿真中的第一车辆交通数据，所述第一车辆交通数据包括：所述车辆所在路段的编号和所述车辆当前位置距所述路段的起点的距离；所述转换模块840，用于根据所述车辆所在路段的编号和所述车辆当前位置距所述路段的起点的距离，转换得到所述车辆在所述微观交通仿真中的第二车辆交通数据，所述第二车辆交通数据包括：所述车辆的经纬度和所述车辆的车头朝向。
在一个可能的实施例中，所述转换模块840包括确定子模块842，所述确定子模块842，用于在所述车辆所在路段的类型为道路的情况下，根据所述车辆所在道路的编号和所述车辆当前位置距所述道路的起点的距离，确定所述车辆的经纬度；以及根据所述车辆所在道路的编号和所述车辆当前位置距所述道路的起点的距离确定所述车辆的车头朝向；在所述车辆所在路段的类型为连接路段的情况下，根据所述车辆所在连接路段的编号和所述车辆当前位置距所述连接路段的起点的距离，确定所述车辆的经纬度；以及根据所述车辆所在道路的编号和所述车辆当前位置距所述道路的起点的距离确定所述车辆的车头朝向。
在一个可能的实施例中，所述微观交通仿真中的所述道路包括按序排列的N个离散点，其中，N为正整数；所述确定子模块842，用于在所述车辆所在路段的类型为道路的情况下，根据所述车辆所在的道路的编号和所述车辆当前位置距所述道路的起点的距离，将所述道路中的所述N个离散点中与所述车辆当前位置最相近的第i个离散点的经纬度，确定为所述车辆的经纬度，i为小于N的正整数。
在一个可能的实施例中，所述转换模块840包括获取子模块844和作差子模块846，所述获取子模块844，用于根据所述车辆所在道路的编号，获取所述N个离散点中的相邻离散点之间的距离；所述确定子模块842，用于通过累加所述相邻离散点之间的距离，确定所述道路中的所述N个离散点分别距所述道路的起点的距离；所述作差子模块846，用于将所述N个离散点距所述道路的起点的距离分别与所述车辆当前位置距所述道路的起点的距离作差；所述确定子模块842，用于将差值最小的第i个离散点的经纬度，确定为所述车辆的经纬度，i为小于或等于N的正整数。
在一个可能的实施例中，所述微观交通仿真中的所述连接路段包括按序排列的M个离散点，其中，M为正整数；所述确定子模块842，用于所述在所述车辆所在路段的类型为连接路段的情况下，根据所述车辆所在连接路段的编号和所述车辆当前位置距所述连接路段的起点的距离，确定所述车辆的经纬度，包括：所述确定子模块842，用于在所述车辆所在路段的类型为连接路段的情况下，根据所述车辆所在连接路段的编号和所述车辆当前位置距所述连接路段的起点的距离，将所述连接路段中的所述M个离散点中与所述车辆当前位置最相近的 第i个离散点的经纬度，确定为所述车辆的经纬度，i为小于N的正整数。
在一个可能的实施例中，所述获取子模块844，用于根据所述车辆所在连接路段的编号，获取所述M个离散点中的相邻离散点之间的距离；所述确定子模块842，用于通过累加所述相邻离散点之间的距离，确定所述连接路段中的所述M个离散点分别距所述连接路段的起点的距离；所述作差子模块846，用于将所述M个离散点距所述连接路段的起点的距离分别与所述车辆当前位置距所述连接路段的起点的距离作差；所述确定子模块842，用于将差值最小的第i个离散点的经纬度，确定为所述车辆的经纬度，i为小于或等于M的正整数。
在一个可能的实施例中，所述确定子模块842，用于根据所述第i个离散点与第i-1个离散点确定所述车辆的车头朝向。
在一个可能的实施例中，所述第一交通仿真包括所述微观交通仿真，所述第二交通仿真包括所述中观交通仿真；所述获取模块820，用于获取所述车辆在所述微观交通仿真中的第一车辆交通数据，所述第一车辆交通数据包括：所述车辆所在路段的编号、所述车辆的经纬度和所述车辆的速度；所述转换模块840，用于如下中的至少一种：根据所述车辆所在路段的编号和所述车辆的经纬度，转换得到所述中观交通仿真中的所述车辆当前位置距所述路段的起点的距离；根据所述车辆所在路段的编号、所述车辆的经纬度和所述车辆的速度，转换得到所述中观交通仿真中的所述车辆登陆下一条道路的时间。
在一个可能的实施例中，所述微观交通仿真中的所述道路包括按序排列的N个离散点，其中，N为正整数；所述确定子模块842，用于根据所述N个离散点的经纬度与所述车辆的经纬度确定与所述车辆当前位置最接近的第i个离散点，i为小于N的正整数；根据所述第i个离散点距所述道路的起点的距离和所述第i个离散点与所述车辆当前位置的距离确定所述车辆当前位置距所述道路的起点的距离。
在一个可能的实施例中，所述微观交通仿真中的所述连接路段包括按序排列的M个离散点，其中，M为正整数；所述确定子模块842，用于根据所述M个离散点的经纬度与所述车辆的经纬度确定与所述车辆当前位置最接近的第i个离散点，i为小于M的正整数；所述确定子模块842，用于根据所述第i个离散点距所述连接路段的起点的距离、所述第i个离散点与所述车辆当前位置的距离、所述车辆的速度和所述连接路段的总长度，确定所述车辆登陆下一条道路的时间。
在一个可能的实施例中，所述获取子模块844，用于根据所述车辆所在连接路段的编号，获取所述M个离散点中的相邻离散点之间的距离；所述确定子模块842，用于通过累加所述相邻离散点之间的距离，确定所述第i个离散点距所述连接路段的起点的距离和所述连接路段的总长度；所述确定子模块842，用于根据所述连接路段的总长度、所述第i个离散点距所述连接路段的起点的距离和所述第i个离散点与所述车辆当前位置的距离，确定所述车辆距所述下一条道路的距离；所述确定子模块842，用于根据所述车辆距所述下一条道路的距离和所述车辆的速度，确定所述车辆登陆下一条道路的时间。
在一个可能的实施例中，获取模块820，用于获取微观交通仿真区域；运行模块860，用于在所述微观交通区域内显示所述微观交通仿真，在除所述微观交通区域以外的中观交通仿真区域显示所述中观交通仿真；在所述微观交通仿真区域中显示车辆交通数据。
在一个可能的实施例中，获取模块820，用于响应于交通预测操作，基于所述微观交通仿真中的车辆交通数据确定所述中观交通仿真中的车辆交通数据；转换模块840，用于对预测区域内的车辆实现所述微观交通仿真到所述中观交通仿真的转换；运行模块860，用于显示预测交通状态。
需要说明的是：上述实施例提供的交通仿真转换装置，仅以上述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成，即将设备的内部结构划分成不同的功能模块，以完成以上描述的全部或者部分功能。
图13是根据一示例性实施例示出的一种计算机设备的结构示意图。所述计算机设备1300 包括中央处理单元(Central Processing Unit，CPU)1301、包括随机存取存储器(Random Access Memory，RAM)1302和只读存储器(Read-Only Memory，ROM)1303的系统存储器1304，以及连接系统存储器1304和中央处理单元1301的系统总线1305。所述计算机设备1300还包括帮助计算机设备内的各个器件之间传输信息的基本输入/输出系统(Input/Output，I/O系统)1306，和用于存储操作系统1313、应用程序1314和其他程序模块1315的大容量存储设备1307。
所述基本输入/输出系统1306包括有用于显示信息的显示器1308和用于用户输入信息的诸如鼠标、键盘之类的输入设备1309。其中所述显示器1308和输入设备1309都通过连接到系统总线1305的输入输出控制器1310连接到中央处理单元1301。所述基本输入/输出系统1306还可以包括输入输出控制器1310以用于接收和处理来自键盘、鼠标、或电子触控笔等多个其他设备的输入。类似地，输入输出控制器1310还提供输出到显示屏、打印机或其他类型的输出设备。
所述大容量存储设备1307通过连接到系统总线1305的大容量存储控制器(未示出)连接到中央处理单元1301。所述大容量存储设备1307及其相关联的计算机设备可读介质为计算机设备1300提供非易失性存储。也就是说，所述大容量存储设备1307可以包括诸如硬盘或者只读光盘(Compact Disc Read-Only Memory，CD-ROM)驱动器之类的计算机设备可读介质(未示出)。
不失一般性，所述计算机设备可读介质可以包括计算机设备存储介质和通信介质。计算机设备存储介质包括以用于存储诸如计算机设备可读指令、数据结构、程序模块或其他数据等信息的任何方法或技术实现的易失性和非易失性、可移动和不可移动介质。计算机设备存储介质包括RAM、ROM、可擦除可编程只读存储器(Erasable Programmable Read Only Memory，EPROM)、带电可擦可编程只读存储器(Electrically Erasable Programmable Read-Only Memory，EEPROM)，CD-ROM、数字视频光盘(Digital Video Disc，DVD)或其他光学存储、磁带盒、磁带、磁盘存储或其他磁性存储设备。当然，本领域技术人员可知所述计算机设备存储介质不局限于上述几种。上述的系统存储器1304和大容量存储设备1307可以统称为存储器。
根据本公开的各种实施例，所述计算机设备1300还可以通过诸如因特网等网络连接到网络上的远程计算机设备运行。也即计算机设备1300可以通过连接在所述系统总线1305上的网络接口单元1312连接到网络1311，或者说，也可以使用网络接口单元1312来连接到其他类型的网络或远程计算机设备系统(未示出)。
所述存储器还包括一个或者一个以上的程序，所述一个或者一个以上程序存储于存储器中，中央处理器1301通过执行该一个或一个以上程序来实现上述交通仿真转换方法的全部或者部分步骤。本申请的实施例还提供了一种计算机可读存储介质，该计算机可读存储介质上存储有至少一条指令、至少一段程序、代码集或指令集，至少一条指令、至少一段程序、代码集或指令集由处理器加载并执行，以实现上述各方法实施例提供的交通仿真转换方法。
本申请的实施例还提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机指令，该计算机指令存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机指令，处理器执行该计算机指令，使得该计算机设备执行上述实施例中任一所述的交通仿真转换方法。
可选地，该计算机可读存储介质可以包括：只读存储器(ROM，Read Only Memory)、随机存取记忆体(RAM，Random Access Memory)、固态硬盘(SSD，Solid State Drives)或光盘等。其中，随机存取记忆体可以包括电阻式随机存取记忆体(ReRAM,Resistance Random Access Memory)和动态随机存取存储器(DRAM，Dynamic Random Access Memory)。上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。

Claims (20)

  1. 一种交通仿真转换方法，其中，所述方法由计算机设备执行，所述方法包括：
    获取车辆在第一交通仿真中的第一车辆交通数据；
    根据所述第一车辆交通数据，转换得到所述车辆在第二交通仿真中的第二车辆交通数据；
    根据所述车辆的所述第二车辆交通数据，运行所述第二交通仿真；
    其中，所述第一交通仿真和所述第二交通仿真分别包括微观交通仿真和中观交通仿真中的一种，且所述第一交通仿真不同于所述第二交通仿真。
  2. 根据权利要求1所述的方法，其中，所述第一交通仿真包括所述中观交通仿真，所述第二交通仿真包括所述微观交通仿真；
    所述获取车辆在第一交通仿真中的第一车辆交通数据，包括：
    获取所述车辆在所述中观交通仿真中的第一车辆交通数据，所述第一车辆交通数据包括：所述车辆所在路段的编号和所述车辆当前位置距所述路段的起点的距离；
    所述根据所述第一车辆交通数据，转换得到所述车辆在第二交通仿真中的第二车辆交通数据，包括：
    根据所述车辆所在路段的编号和所述车辆当前位置距所述路段的起点的距离，转换得到所述车辆在所述微观交通仿真中的第二车辆交通数据，所述第二车辆交通数据包括：所述车辆的经纬度和所述车辆的车头朝向。
  3. 根据权利要求2所述的方法，其中，所述根据所述车辆所在路段的编号和所述车辆当前位置距所述路段的起点的距离，转换得到所述车辆在所述微观交通仿真中的第二车辆交通数据，包括：
    在所述车辆所在路段的类型为道路的情况下，根据所述车辆所在道路的编号和所述车辆当前位置距所述道路的起点的距离，确定所述车辆的经纬度；
    在所述车辆所在路段的类型为连接路段的情况下，根据所述车辆所在连接路段的编号和所述车辆当前位置距所述连接路段的起点的距离，确定所述车辆的经纬度。
  4. 根据权利要求3所述的方法，其中，所述微观交通仿真中的所述道路包括按序排列的N个离散点，其中，N为正整数；
    所述在所述车辆所在路段的类型为道路的情况下，根据所述车辆所在的道路的编号和所述车辆当前位置距所述道路的起点的距离，确定所述车辆的经纬度，包括：
    在所述车辆所在路段的类型为道路的情况下，根据所述车辆所在的道路的编号和所述车辆当前位置距所述道路的起点的距离，将所述道路中的所述N个离散点中与所述车辆当前位置最相近的第i个离散点的经纬度，确定为所述车辆的经纬度，i为小于N的正整数。
  5. 根据权利要求4所述的方法，其中，所述在所述车辆所在路段的类型为道路的情况下，根据所述车辆所在的道路的编号和所述车辆当前位置距所述道路的起点的距离，将所述道路中的所述N个离散点中与所述车辆当前位置最相近的第i个离散点的经纬度，确定为所述车辆的经纬度，包括：
    根据所述车辆所在道路的编号，获取所述N个离散点中的相邻离散点之间的距离；
    通过累加所述相邻离散点之间的距离，确定所述道路中的所述N个离散点分别距所述道路的起点的距离；
    将所述N个离散点距所述道路的起点的距离分别与所述车辆当前位置距所述道路的起点的距离作差；
    将差值最小的第i个离散点的经纬度，确定为所述车辆的经纬度，i为小于或等于N的正整数。
  6. 根据权利要求3所述的方法，其中，所述微观交通仿真中的所述连接路段包括按序排列的M个离散点，其中，M为正整数；
    所述在所述车辆所在路段的类型为连接路段的情况下，根据所述车辆所在连接路段的编号和所述车辆当前位置距所述连接路段的起点的距离，确定所述车辆的经纬度，包括：
    在所述车辆所在路段的类型为连接路段的情况下，根据所述车辆所在连接路段的编号和所述车辆当前位置距所述连接路段的起点的距离，将所述连接路段中的所述M个离散点中与所述车辆当前位置最相近的第i个离散点的经纬度，确定为所述车辆的经纬度，i为小于N的正整数。
  7. 根据权利要求6所述的方法，其中，所述在所述车辆所在路段的类型为连接路段的情况下，根据所述车辆所在连接路段的编号和所述车辆当前位置距所述连接路段的起点的距离，将所述连接路段中的所述M个离散点中与所述车辆当前位置最相近的第i个离散点的经纬度，确定为所述车辆的经纬度，包括：
    根据所述车辆所在连接路段的编号，获取所述M个离散点中的相邻离散点之间的距离；
    通过累加所述相邻离散点之间的距离，确定所述连接路段中的所述M个离散点分别距所述连接路段的起点的距离；
    将所述M个离散点距所述连接路段的起点的距离分别与所述车辆当前位置距所述连接路段的起点的距离作差；
    将差值最小的第i个离散点的经纬度，确定为所述车辆的经纬度，i为小于或等于M的正整数。
  8. 根据权利要求2所述的方法，其中，所述根据所述车辆所在路段的编号和所述车辆当前位置距所述路段的起点的距离，转换得到所述车辆在所述微观交通仿真中的第二车辆交通数据，包括：
    根据所述车辆所在道路的编号和所述车辆当前位置距所述道路的起点的距离确定所述车辆的车头朝向。
  9. 根据权利要求8所述的方法，其中，所述根据所述车辆所在道路的编号和所述车辆当前位置距所述道路的起点的距离确定所述车辆的车头朝向，包括：
    根据所述车辆所在道路的编号和所述车辆当前位置距所述道路的起点的距离，确定与所述车辆当前位置最相近的第i个离散点；
    根据所述第i个离散点和第i-1个离散点确定所述车辆的车头朝向，i为小于N+1且大于1的整数。
  10. 根据权利要求1所述的方法，其中，所述第一交通仿真包括所述微观交通仿真，所述第二交通仿真包括所述中观交通仿真；
    所述获取车辆在第一交通仿真的第一车辆交通数据，包括：
    获取所述车辆在所述微观交通仿真中的第一车辆交通数据，所述第一车辆交通数据包括：所述车辆所在路段的编号、所述车辆的经纬度和所述车辆的速度；
    所述根据所述第一车辆交通数据，转换得到所述车辆在第二交通仿真中的第二车辆交通数据，包括如下步骤中的至少一种：
    根据所述车辆所在路段的编号和所述车辆的经纬度，转换得到所述中观交通仿真中的所述车辆当前位置距所述路段的起点的距离；
    根据所述车辆所在路段的编号、所述车辆的经纬度和所述车辆的速度，转换得到所述中观交通仿真中的所述车辆登陆下一条道路的时间。
  11. 根据权利要求10所述的方法，其中，所述微观交通仿真中的所述道路包括按序排列的N个离散点，其中，N为正整数；
    所述根据所述车辆所在道路的编号和所述车辆的经纬度，确定所述车辆当前位置距所述道路的起点的距离，包括：
    根据所述N个离散点的经纬度与所述车辆的经纬度确定与所述车辆当前位置最接近的第i个离散点，i为小于N的正整数；
    根据所述第i个离散点距所述道路的起点的距离和所述第i个离散点与所述车辆当前位置的距离确定所述车辆当前位置距所述道路的起点的距离。
  12. 根据权利要求10所述的方法，其中，所述微观交通仿真中的所述连接路段包括按序排列的M个离散点，其中，M为正整数；
    所述根据所述车辆所在连接路段的编号、所述车辆的经纬度和所述车辆的速度，确定所述车辆登陆下一条道路的时间，包括：
    根据所述M个离散点的经纬度与所述车辆的经纬度确定与所述车辆当前位置最接近的第i个离散点，i为小于M的正整数；
    根据所述第i个离散点距所述连接路段的起点的距离、所述第i个离散点与所述车辆当前位置的距离、所述车辆的速度和所述连接路段的总长度，确定所述车辆登陆下一条道路的时间。
  13. 根据权利要求1所述的方法，其中，所述方法还包括：
    获取微观交通仿真区域；
    在所述微观交通区域内显示所述微观交通仿真，在除所述微观交通区域以外的中观交通仿真区域显示所述中观交通仿真；
    在所述微观交通仿真区域中显示微观车辆交通数据，在所述中观交通仿真区域中显示中观车辆交通数据，所述微观车辆交通数据指在微观车辆的车辆交通数据，所述中观车辆交通数据指在中观车辆的车辆交通数据。
  14. 根据权利要求1所述的方法，其中，所述方法还包括：
    响应于交通预测操作，基于所述微观交通仿真中的微观车辆交通数据确定所述中观交通仿真中的中观车辆交通数据；
    根据所述微观车辆交通数据和所述中观车辆交通数据，对预测区域内的车辆实现所述微观交通仿真到所述中观交通仿真的转换；
    根据所述中观交通仿真显示预测交通状态。
  15. 一种交通仿真转换装置，其中，所述装置包括：
    获取模块，用于获取车辆在第一交通仿真中的第一车辆交通数据；
    转换模块，用于根据所述第一车辆交通数据，转换得到所述车辆在第二交通仿真中的第二车辆交通数据；
    运行模块，用于根据所述车辆的所述第二车辆交通数据，运行所述第二交通仿真；
    其中，所述第一交通仿真和所述第二交通仿真分别包括微观交通仿真和中观交通仿真中的一种，且所述第一交通仿真不同于所述第二交通仿真。
  16. 根据权利要求14所述的装置，其中，所述第一交通仿真包括所述中观交通仿真，所述 第二交通仿真包括所述微观交通仿真；
    所述获取模块，还用于获取所述车辆在所述中观交通仿真中的第一车辆交通数据，所述第一车辆交通数据包括：所述车辆所在路段的编号和所述车辆当前位置距所述路段的起点的距离；
    所述转换模块，还用于根据所述车辆所在路段的编号和所述车辆当前位置距所述路段的起点的距离，转换得到所述车辆在所述微观交通仿真中的第二车辆交通数据，所述第二车辆交通数据包括：所述车辆的经纬度和所述车辆的车头朝向。
  17. 根据权利要求14所述的装置，其中，所述第一交通仿真包括所述微观交通仿真，所述第二交通仿真包括所述中观交通仿真；
    所述获取模块，还用于获取所述车辆在所述微观交通仿真中的第一车辆交通数据，所述第一车辆交通数据包括：所述车辆所在路段的编号、所述车辆的经纬度和所述车辆的速度；所述转换模块，还用于如下中的至少一种：根据所述车辆所在路段的编号和所述车辆的经纬度，转换得到所述中观交通仿真中的所述车辆当前位置距所述路段的起点的距离；根据所述车辆所在路段的编号、所述车辆的经纬度和所述车辆的速度，转换得到所述中观交通仿真中的所述车辆登陆下一条道路的时间。
  18. 一种计算机设备，其中，所述计算机设备包括处理器，与所述处理器相连的存储器，以及存储在所述存储器上的程序指令，所述处理器执行的所述程序指令时实现如权利要求1至11任一所述的交通仿真转换方法。
  19. 一种计算机可读存储介质，所述存储介质中存储有程序指令，其中，所述程序指令被处理器执行时实现如权利要求1至11任一所述的交通仿真转换方法。
  20. 一种计算机程序产品，其中，所述计算机程序产品中存储有至少一条指令、至少一段程序、代码集或指令集，所述至少一条指令、所述至少一段程序、所述代码集或指令集由处理器加载并执行以实现如权利要求1至11任一所述的交通仿真转换方法。
PCT/CN2022/118436 2021-10-28 2022-09-13 交通仿真转换方法、装置、计算机设备及存储介质 WO2023071564A1 (zh)

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