CN115952692A - Road traffic simulation method and device, storage medium and electronic equipment - Google Patents
Road traffic simulation method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a road traffic simulation method and device, a storage medium and electronic equipment. Wherein, the method comprises the following steps: the method comprises the steps that a target time period needing simulation deduction is preset in a vehicle simulation system, the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, initial simulation data of a target simulation road at the starting time are obtained under the condition that the current time reaches the starting time, the initial simulation data are determined by real-time driving data of the starting time, the real-time driving data represent vehicle driving data collected on the target road in real time, simulation deduction is conducted by means of the initial simulation data and historical driving data, target index data are generated, and the historical driving data are used for adding simulation vehicles driving on the target simulation road in the continuous process of the simulation deduction. The method and the device solve the technical problem that the utilization rate of real-time twin traffic simulation in road traffic simulation is low.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a road traffic simulation method and apparatus, a storage medium, and an electronic device.
Background
In the existing microscopic simulation software, a real-time twin traffic simulation system can reproduce the vehicle state of the real world in a high-precision and low-delay mode, but the existing real-time twin traffic simulation system lacks the capability of predicting road traffic after a certain time period, so that the public can only utilize the system to complete the visualization of road simulation, and can not exert the advantage of calculation, thereby causing the technical problem of low utilization rate of real-time twin traffic simulation.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a road traffic simulation method and device, a storage medium and electronic equipment, and aims to at least solve the technical problem of low utilization rate of real-time twin traffic simulation in road traffic simulation.
According to an aspect of an embodiment of the present application, there is provided a simulation method of road traffic, including: presetting a target time period required to be subjected to simulation deduction in a vehicle simulation system, wherein the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, and the target time period corresponds to a target simulation road required to be subjected to the simulation deduction; under the condition that the current time reaches the starting time, acquiring initial simulation data of a target simulation road at the starting time, wherein the initial simulation data is determined by real-time running data of the starting time, the real-time running data represents vehicle running data acquired in real time on the target road, and the target road corresponds to the target simulation road; and performing simulation deduction by using the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulation vehicles which drive on the target simulation road in the continuous process of the simulation deduction, and the historical driving data represents vehicle driving data collected on the target road in the target time period of a historical period.
According to another aspect of the embodiments of the present application, there is also provided a road traffic simulation apparatus, including: the system comprises a setting module, a simulation module and a simulation module, wherein the setting module is used for presetting a target time period required to be subjected to simulation deduction in a vehicle simulation system, the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, and the target time period corresponds to a target simulation road required to be subjected to simulation deduction; the acquisition module is used for acquiring initial simulation data of a target simulation road at the starting time under the condition that the current time reaches the starting time, wherein the initial simulation data is determined by real-time running data of the starting time, the real-time running data represents vehicle running data acquired in real time on the target road, and the target road corresponds to the target simulation road; and the generation module is used for performing simulation deduction by utilizing the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulated vehicles which run on the target simulated road in the continuous process of the simulation deduction, and the historical driving data represents vehicle driving data collected on the target road in the target time period of a historical period.
Optionally, the apparatus is further configured to: presetting a target event needing simulation deduction in the vehicle simulation system, wherein the target event indicates that a simulation event influencing the running speed of the target road occurs on the target simulation road in the target time period, and the simulation event is used for updating vehicle running data of the target simulation road in the target time period; and performing simulation deduction by using the initial simulation data, the target event and the historical driving data to generate target index data.
Optionally, the apparatus is further configured to perform at least one of the following methods: presetting a virtual traffic event in the vehicle simulation system, wherein the virtual traffic event represents that a traffic event which influences the running speed of the target road occurs on the target simulation road within the target time period; performing simulation deduction by using the initial simulation data, the virtual traffic event and the historical driving data to generate target index data; presetting a virtual control event in the vehicle simulation system, wherein the virtual control event indicates that a control event which influences the running speed of the target road occurs to the target simulation road within the target time period; performing simulation deduction by using the initial simulation data, the virtual management and control event and the historical driving data to generate target index data; presetting a virtual weather event in the vehicle simulation system, wherein the virtual weather event represents that a weather event which influences the running speed of the target road occurs on the target simulation road within the target time period; performing simulation deduction by using the initial simulation data, the virtual weather event and the historical driving data to generate target index data; presetting a virtual signal lamp event in the vehicle simulation system, wherein the virtual signal lamp event represents that a signal lamp event which influences the running speed of the target road occurs on the target simulation road within the target time period; and performing simulation deduction by using the initial simulation data, the virtual signal lamp events and the historical driving data to generate target index data.
Optionally, the apparatus is configured to generate target index data by performing simulation deduction using the initial simulation data and the historical travel data as follows: determining a target vehicle passing through the target road in the target time period on a historical cycle according to the historical driving data; adding a target simulation vehicle to the target simulation road according to a preset time interval by using the initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle; and generating the target index data under the condition that the simulation deduction is carried out to the end time.
Optionally, the apparatus is configured to determine a target vehicle passing the target road in the target time period on a historical cycle from the historical travel data by: determining a plurality of target matrixes corresponding to the target time periods according to the historical driving data, wherein one target matrix is used for representing the sum of one type of vehicle which enters the target road in the target time period on a historical cycle; determining the target vehicle using the target matrix.
Optionally, the device is configured to add a target simulation vehicle to the target simulation road at preset time intervals by using the initial simulation data and the target vehicle, and perform simulation deduction: adding a first simulated vehicle to a first lane of the target simulated road according to the preset time interval under the condition that the target simulated lane comprises a plurality of lanes, wherein the first lane is a randomly selected lane in the plurality of lanes, and the target simulated vehicle comprises the first simulated vehicle; or in the case that the target simulated lane comprises a plurality of lanes and a first simulated vehicle has been added to a first lane, adding a second simulated vehicle to a second lane of the target simulated road at the preset time interval, wherein the first lane is a randomly selected lane of the plurality of lanes and the second lane is a randomly selected lane of the plurality of lanes except for the first lane.
Optionally, the apparatus is further configured to: under the condition that the simulation deduction is carried out to the end time, before the target index data is generated, determining an initial impedance set of the target simulation road according to the initial simulation data, wherein one initial impedance of the initial impedance set represents the driving time of one path in the target simulation road, and the target simulation road comprises a plurality of paths; dividing the target simulated vehicle into a first set of simulated vehicles using the initial set of impedances; setting a first set of simulated vehicles to travel according to a first path, setting a second set of simulated vehicles to travel according to a second path, wherein the travel time of the first path is shorter than the travel time of the second path, the number of simulated vehicles of the first set of simulated vehicles is greater than the number of simulated vehicles of the second set of simulated vehicles, and the first set of simulated vehicles comprises the first set of simulated vehicles and the second set of simulated vehicles.
Optionally, the apparatus is further configured to: setting a first simulation vehicle set to run according to a first path, setting a second simulation vehicle set to run according to a second path, then running along the first path according to the first simulation vehicle set, carrying out simulation deduction when the second simulation vehicle set runs along the second path, carrying out multi-round updating on the initial impedance set, and determining a target impedance set; dividing the target simulated vehicles into a second set of simulated vehicles by using the target impedance set, setting a third set of simulated vehicles to run according to the first path, and setting a fourth set of simulated vehicles to run according to the second path, wherein the second set of simulated vehicles comprises the third set of simulated vehicles and the fourth set of simulated vehicles; wherein updating the initial impedance set for each wheel comprises: according to the first simulation vehicle set, driving along the first path, and the second simulation vehicle set, driving along the second path, performing simulation deduction for one round, updating the impedance of the first path and the impedance of the second path, and determining the ratio of the impedance of the first path and the impedance of the second path; and under the condition that the change amplitude of the ratio in the updating processes of two adjacent rounds meets a preset condition, determining the impedance of the first path and the impedance of the second path determined in the last round as the target impedance set.
Optionally, the apparatus is configured to perform simulation deduction using the initial simulation data and the historical travel data to generate target index data by: determining a target simulation vehicle which needs to be added on the target simulation road in the continuous process of the simulation deduction according to the historical driving data, wherein the target simulation road comprises a plurality of lanes, and the target simulation vehicle allows lane change in the simulation deduction process; adjusting the driving behavior of the added target simulation vehicle by using a microscopic driving posture adjustment model, and performing simulation deduction to generate target index data, wherein the driving behavior comprises at least one of the following behaviors: lane changing, speed reducing, parking and course angle adjusting.
Optionally, the apparatus is configured to adjust the driving behavior of the added target simulated vehicle by using a microscopic driving posture adjustment model, and perform simulation deduction to generate target index data: when the simulation deduction reaches the end time, saving a microscopic driving track of the target simulation vehicle in the target time period; and generating congestion index data of the target road in the target time period according to the microscopic driving track, wherein the target index data comprises the congestion index data.
According to yet another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned road traffic simulation method when running.
According to yet another aspect of embodiments herein, there is provided a computer program product or computer program comprising computer instructions 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 performs the simulation method of road traffic as above.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory and a processor, where the memory stores therein a computer program, and the processor is configured to execute the above-mentioned road traffic simulation method through the computer program.
In the embodiment of the application, the target time period required to be subjected to simulation deduction is preset in the vehicle simulation system, wherein the target time period comprises the starting time of the simulation deduction and the ending time of the simulation deduction, the target time period corresponds to a target simulation road needing the simulation deduction, acquiring initial simulation data of the target simulation road at the starting time under the condition that the current time reaches the starting time, wherein the initial simulation data is determined by real-time driving data at the starting time, the real-time driving data represents vehicle driving data collected in real time on a target road, the target road corresponds to the target simulation road, simulation deduction is performed by using the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulated vehicles driving on the target simulated road in the continuous process of simulation deduction, the historical driving data represents the mode of vehicle driving data collected on the target road in the target time period of the historical period, the real-time driving data at the starting time is used as initial simulation data of the deduction simulation, then, a target time period required for simulation deduction is set, the whole simulation deduction flow is described, thus, the road traffic after the target time period realizes short-term prediction and can correspondingly output quantitative evaluation results, so that real-time twin upgrades from visualization to computable digital space, the decision is assisted through prediction, the virtual world and the physical world are connected, the purpose of finally realizing closed-loop control is achieved, thereby realizing the technical effects of improving the road traffic simulation efficiency and optimizing the utilization rate of real-time twin traffic simulation, and the technical problem of low utilization rate of real-time twin traffic simulation in road traffic simulation is solved.
In addition, various types of virtual events can be set, so that the application range of simulation deduction is further expanded, and the utilization rate of the real-time twin traffic simulation system is further improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram of an application environment of an alternative road traffic simulation method according to an embodiment of the application;
FIG. 2 is a schematic flow chart diagram illustrating an alternative road traffic simulation method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an alternative road traffic simulation method according to an embodiment of the present application;
FIG. 4 is a schematic diagram of yet another alternative method of simulating road traffic according to an embodiment of the present application;
FIG. 5 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application;
FIG. 6 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application;
FIG. 7 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application;
FIG. 8 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application;
FIG. 9 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application;
FIG. 10 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application;
FIG. 11 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application;
FIG. 12 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application;
FIG. 13 is a schematic structural diagram of an alternative road traffic simulation apparatus according to an embodiment of the present application;
FIG. 14 is a schematic diagram of an alternative road traffic simulation product according to an embodiment of the present application;
fig. 15 is a schematic structural diagram of an alternative electronic device according to an embodiment of the application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
First, partial nouns or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:
vehicle following algorithm: the longitudinal driving behavior of the vehicle in the microscopic simulation is determined by a vehicle following algorithm, and generally comprises a maximum driving speed and a minimum safe vehicle spacing, which respectively represent the maximum speed (such as road speed limit) which cannot be exceeded by the vehicle in the driving process and the minimum vehicle spacing which needs to be maintained all the time in the driving process. In each simulation step of a general following algorithm, the vehicle updates the acceleration/speed of the vehicle according to the position, speed and the like of the vehicle ahead, and after the speed is updated, the position of the vehicle is updated.
The present application is illustrated below with reference to examples:
according to an aspect of the embodiment of the present application, a method for simulating road traffic is provided, and optionally, in the embodiment, the method for simulating road traffic may be applied to a hardware environment formed by a server 101 and a terminal device 103 as shown in fig. 1. As shown in fig. 1, a server 101 is connected to a terminal 103 via a network, and may be used to provide services for the terminal or applications installed on the terminal, such as video applications, instant messaging applications, browser applications, educational applications, game applications, and the like. The database 105 may be provided on or separate from the server for providing data storage services for the server 101, such as a game data storage server, and the network may include, but is not limited to: a wired network, a wireless network, wherein the wired network comprises: a local area network, a metropolitan area network, and a wide area network, the wireless network comprising: bluetooth, WIFI, and other wireless communication enabled networks, terminal device 103 may be an application configured terminal, and may include, but is not limited to, at least one of: the Mobile phone (such as an Android Mobile phone, an iOS Mobile phone, etc.), a notebook computer, a tablet computer, a palm computer, an MID (Mobile Internet Devices), a PAD, a desktop computer, an intelligent television, an intelligent voice interaction device, an intelligent household appliance, a vehicle-mounted terminal, an aircraft, a Virtual Reality (VR) terminal, an Augmented Reality (AR) terminal, a Mixed Reality (MR) terminal, and other computer Devices, where the server may be a single server, a server cluster composed of multiple servers, or a cloud server.
As shown in fig. 1, the method for simulating road traffic may be implemented at the terminal device 103 by the following steps:
s1, presetting a target time period required to be subjected to simulation deduction in a vehicle simulation system on a terminal device 103, wherein the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, and the target time period corresponds to a target simulation road required to be subjected to the simulation deduction;
s2, under the condition that the current time reaches the starting time, acquiring initial simulation data of the target simulation road at the starting time on the terminal device 103, wherein the initial simulation data is determined by real-time running data of the starting time, the real-time running data represents vehicle running data acquired in real time on the target road, and the target road corresponds to the target simulation road;
and S3, performing simulation deduction on the terminal device 103 by using the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulation vehicles which run on the target simulation road in the continuous process of the simulation deduction, and the historical driving data represents vehicle driving data collected on the target road in the target time period of the historical period.
Optionally, in this embodiment, the simulation method of road traffic may also be implemented by a server, for example, implemented in the server 101 shown in fig. 1; or by both the terminal device and the server.
The above is merely an example, and the present embodiment is not particularly limited.
Optionally, as an optional implementation manner, as shown in fig. 2, the simulation method of road traffic includes:
s202, presetting a target time period needing simulation deduction in a vehicle simulation system, wherein the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, and the target time period corresponds to a target simulation road needing the simulation deduction;
optionally, in the present embodiment, the simulation method of road traffic may be applied to one or more types of simulation software, such as micro traffic simulation software, and the like, and the simulation software may include, but is not limited to, simulation software that needs networking or simulation software that does not need networking.
In an exemplary embodiment, the simulation method for road traffic may be based on an application of a simulation process to a simulation vehicle in a simulation road scene of an expressway, an urban road, an expressway, and the like, and may further include, but is not limited to, a simulation application of cargo transportation based on a specific type of transportation vehicle in a logistics transportation process, a simulation application of a daily operation process based on a network appointment vehicle, and the like.
Optionally, in this embodiment, the simulation method for vehicle driving may be applied to a simulation process of a traffic system such as an intelligent traffic system and an intelligent vehicle-road coordination system.
The Intelligent Transportation System (ITS) is also called an Intelligent Transportation System (Intelligent Transportation System), and is a comprehensive Transportation System which effectively and comprehensively applies advanced scientific technologies (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operational research, artificial intelligence and the like) to Transportation, service control and vehicle manufacturing, strengthens the relation among vehicles, roads and users, and accordingly ensures safety, improves efficiency, improves environment and saves energy. Or;
an Intelligent Vehicle Infrastructure Cooperative System (IVICS), referred to as a Vehicle Infrastructure Cooperative system for short, is a development direction of an Intelligent Transportation System (ITS). The vehicle-road cooperative system adopts the advanced wireless communication, new generation internet and other technologies, implements vehicle-vehicle and vehicle-road dynamic real-time information interaction in all directions, develops vehicle active safety control and road cooperative management on the basis of full-time dynamic traffic information acquisition and fusion, fully realizes effective cooperation of human and vehicle roads, ensures traffic safety, improves traffic efficiency, and thus forms a safe, efficient and environment-friendly road traffic system.
For example, fig. 3 is a schematic diagram of an optional simulation method of road traffic according to an embodiment of the present application, as shown in fig. 3, a set of roads that are allowed to be subjected to road traffic simulation may be viewed in a digital twin road simulation system, and all or a part of the roads are selected from the set of roads as the target simulated roads, for example, by performing a touch operation on the roads 1 and 2, so as to determine the roads 1 and 2 as the target simulated roads for performing road traffic simulation.
Optionally, in this embodiment, the target time period that needs to be subjected to simulation deduction may be flexibly set by a worker according to a business requirement or prior knowledge, where the starting time is a certain time after the current time, and the ending time is a certain time after the starting time, so as to predict a road traffic condition after a short time.
It should be noted that the number of the target simulation road may include, but is not limited to, one or more, and the target simulation road is a preset simulation road.
Exemplarily, fig. 4 is a schematic diagram of another alternative road traffic simulation method according to an embodiment of the present application, and as shown in fig. 4, the start time and the end time are set through an interactive interface of a simulation system, so that the simulation deduction process is executed after the system time reaches the start time.
S204, under the condition that the current time reaches the starting time, acquiring initial simulation data of the target simulation road at the starting time, wherein the initial simulation data is determined by real-time running data of the starting time, the real-time running data represents vehicle running data acquired in real time on the target road, and the target road corresponds to the target simulation road;
alternatively, in this embodiment, the current time may be understood as a system time of the vehicle simulation system, that is, when the system time of the vehicle simulation system reaches the starting time, the road simulation deduction process is considered to be started.
It should be noted that the initial simulation data may include, but is not limited to, real-time driving data collected at the start time for a target road, where the target road is a road corresponding to the target simulation road and allowing real-time driving data to be collected by using at least one collection device.
In an exemplary embodiment, the digital twin is a technical means for creating a virtual entity of a physical entity in a digital manner, and simulating, verifying, predicting and controlling the whole life cycle process of the physical entity by means of historical data, real-time data, algorithm models and the like.
The digital twin can carry out virtual parallel world establishment on the highway, carry out real-time and complete mapping on elements such as environment, vehicles, events and the like of a physical world of the highway, fully sense and dynamically monitor sensor data distributed in the highway, form accurate information expression and mapping of the virtual road on an entity road in information dimension, enable managers to master the global condition of the highway without being in the scene of the highway, and solve the problems of difficult detection of the whole road section, delayed event discovery, difficult event recollection and the like. It not only has simulation ability, but also has prediction and control ability.
In a road section area (corresponding to the target simulation road) which can be covered by the sensor, acquired information of multidimensional traffic facilities such as videos and radars is automatically carried and fused, and original incoherent target information acquired by various sensors is mutually verified and supplemented through a target fusion algorithm to form basically complete target attribute information, so that the accurate depiction of the vehicle running track on a high-speed trunk line is realized.
For example, the radar detected target and the video identified target are linked through the incidence relation of the map. Meanwhile, the real-time detection target is superposed on the high-precision map, the butt joint of the physical space and the virtual space is realized, and the holographic sensing of digital mapping is completed. And then the real-time reproduction simulation can be carried out in the simulation system, simulation deduction is carried out on the basis, core services such as traffic hidden danger, traffic events, traffic jam and the like are described, diagnosed, predicted and decided, real-time and efficient intelligent analysis and active control are achieved, and finally closed-loop control is achieved, so that the refinement, intellectualization, standardization and specialization of highway management are realized, and a solid foundation is laid for traffic management.
The sensing equipment deployed in the application can cover all or part of driving areas of a road network, can extract state information of structured roads and target objects, and achieves the functions of comprehensively sensing elements such as vehicles, roads and events, positioning target lanes at the centimeter level, integrating and searching targets and the like. By means of algorithms of target detection, visual pursuit, track splicing and the like in the twin technology, when the vehicle runs in a perception range, key information (such as vehicle type, speed, position, course angle and the like) of the vehicle can be resolved, and real-time determination of the vehicle running track is achieved.
By the aid of the full-coverage sensing device, lane-level track information of vehicles running in the sensing area can be collected, and each piece of vehicle track information data comprises the following fields:
id: a unique identification of the vehicle.
Vehicle type: the type of vehicle.
List of time stamps: the frequency at which the trace points are sensed/transmitted by the sensing device, e.g., once every 10 ms.
Location list: this list corresponds to a list of time stamps, i.e. each time stamp, will correspond to the vehicle's position at that moment, typically consisting of latitude and longitude, and elevation, where only latitude and longitude, i.e. x, y values, are considered, ignoring elevation z (or altitude).
Speed/heading angle list: and a list of timestamps, i.e., each timestamp, would correspond to the speed and heading angle of the vehicle at that time.
Because the time stamp interval is millisecond level, the actual running track of the vehicle can be sampled and collected at a high frequency, the position points (longitude and latitude) in the position lists are connected in a pairwise mode through straight line segments according to the time stamp sequence, a subsection broken line segment can be obtained, namely the straight line segments are arranged between the points corresponding to each adjacent time stamp, and the plurality of straight line segments form the track level track which is sensed and collected. Under the condition that the sampling points are dense enough (the frequency is high enough), the track of the vehicle with the segmented broken line segments reproduced by the track point list is close enough to the actual running track, and the position value of any time point (non-sampling moment) can be obtained between the adjacent data points of the two time points in a linear difference mode, namely the lane-level running track of the vehicle can be obtained, and the initial simulation data is generated according to the running track.
And S206, performing simulation deduction by using the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulation vehicles which run on the target simulation road in the continuous process of the simulation deduction, and the historical driving data represents vehicle driving data collected on the target road in the target time period of the historical period.
Optionally, in this embodiment, the historical driving data is vehicle driving data collected for the target road in the target time period in the historical period, for example, if the target time period is from 8 pm to 8 pm 10 minutes, vehicle driving data from 8 pm to 8 pm 10 minutes per day in the previous week or previous month may be queried, and then vehicles that need to be added to the target simulation road in the current 8 pm to 8 pm 10 minutes are estimated according to the vehicle driving data corresponding to the historical period.
It should be noted that, after the simulated vehicle to be added is determined, when the time of the simulation system reaches 8 pm today, simulation deduction for 10 minutes is started, and the simulated vehicle to be added is driven into the target simulated road according to a preset sequence within the 10 minutes, where the specific driving manner may include, but is not limited to, placing the simulated vehicles into the target simulated road one by one according to a preset time interval, and the like.
In an exemplary embodiment, for a target simulated road, a simulated vehicle to be added may be determined as a candidate vehicle set, and after the start time is reached, a vehicle is randomly selected from the candidate vehicle set, and is driven into the system from the start of the target simulated road, and is removed from the candidate vehicle set. The selection of the vehicle from the candidate vehicle set is not limited, and when there is no vehicle in the candidate vehicle set, it is proved that the departure task is completed, that is, all the simulated vehicles that need to be added are added.
By the embodiment, the target time period required to be subjected to simulation deduction is preset in the vehicle simulation system, wherein the target time period comprises the starting time of the simulation deduction and the ending time of the simulation deduction, the target time period corresponds to a target simulation road which needs to be subjected to the simulation deduction, acquiring initial simulation data of the target simulation road at the starting time under the condition that the current time reaches the starting time, wherein the initial simulation data is determined by real-time driving data at the starting time, the real-time driving data represents vehicle driving data collected in real time on a target road, the target road corresponds to the target simulation road, simulation deduction is performed by using the initial simulation data and historical driving data to generate target index data, wherein the historical travel data is used for adding simulated vehicles which travel on the target simulated road in the continuous process of simulation deduction, the historical travel data represents the mode of vehicle travel data collected on the target road in the target time period of the historical period, and by taking the real-time travel data at the starting time as the initial simulation data of the deduction simulation, then, a target time period required for simulation deduction is set, the whole simulation deduction flow is described, thus, the road traffic after the target time period realizes short-term prediction and can correspondingly output quantitative evaluation results, so that real-time twin upgrades from visualization to computable digital space, the decision is assisted through prediction, the virtual world and the physical world are connected, the purpose of finally realizing closed-loop control is achieved, thereby realizing the technical effects of improving the road traffic simulation efficiency and optimizing the utilization rate of real-time twin traffic simulation, and the technical problem of low utilization rate of real-time twin traffic simulation in road traffic simulation is solved.
As an optional solution, the method further includes: presetting a target event needing simulation deduction in a vehicle simulation system, wherein the target event represents that a simulation event influencing the running speed of a target road occurs on the target simulation road in a target time period, and the simulation event is used for updating vehicle running data of the target simulation road in the target time period; and performing simulation deduction by using the initial simulation data, the target event and the historical driving data to generate target index data.
Optionally, in this embodiment, the target events may be set by a worker according to a service requirement, and different target events may be combined to be derived as a branch simulation, or different target events are each independently derived as a branch simulation.
It should be noted that, when the target simulation road has a simulation event that affects the running speed of the target road within the target time period, it may be understood that the running speed of some or all of the simulation vehicles on the target simulation road may change after the target event is added, and the affecting may be beneficial or adverse.
Taking the beneficial effect as an example, a target event for removing the roadblock can be set, so that after the roadblock is removed in the target time period is predicted through simulation deduction, the traffic condition of the target simulation road can be simulated, and taking the adverse effect as an example, a target event corresponding to the traffic accident can be set, so that the traffic accident occurs in the target time period through simulation deduction, and the traffic condition of the target simulation road can be predicted.
Exemplarily, fig. 5 is a schematic diagram of a further alternative road traffic simulation method according to an embodiment of the present application, as shown in fig. 5, including the following steps:
s1, selecting a target event to be set in a simulation picture of a vehicle simulation system;
s2, selecting an event type expected to be set in a simulation picture of the vehicle simulation system;
s3, selecting a point (black point shown in fig. 5) and a time (10 shown in fig. 5, current system time is 9;
and S4, when the system time reaches 10.
The above is merely an example, and the present embodiment is not limited in any way.
As an optional solution, the method further includes at least one of: presetting a virtual traffic event in a vehicle simulation system, wherein the virtual traffic event represents that a traffic event which influences the running speed of a target road occurs on the target simulation road within a target time period; and performing simulation deduction by using the initial simulation data, the virtual traffic events and the historical driving data to generate target index data.
Optionally, in this embodiment, the virtual traffic event may include, but is not limited to, setting that a traffic accident occurs on the target simulated road, for example, the traffic accident may be set to different levels, including, but not limited to, scratch, rear-end collision, spontaneous combustion, and the like, where the traffic accidents at different levels have different degrees of influence on the traveling speed of the target road.
Presetting a virtual control event in a vehicle simulation system, wherein the virtual control event represents that a control event influencing the running speed of a target road occurs on the target simulation road within a target time period; and performing simulation deduction by using the initial simulation data, the virtual control event and the historical driving data to generate target index data.
Optionally, in this embodiment, the virtual control event may include, but is not limited to, setting that traffic control occurs on the target simulated road, for example, traffic control at different levels may be set, including, but not limited to, construction road block, welcome road block, holding event road block, and the like, where the traffic control at different levels has different degrees of influence on the traveling speed of the target road.
Presetting a virtual weather event in a vehicle simulation system, wherein the virtual weather event represents that a weather event influencing the running speed of a target road occurs on the target simulation road within a target time period; and performing simulation deduction by using the initial simulation data, the virtual weather event and the historical driving data to generate target index data.
Optionally, in this embodiment, the virtual weather event may include, but is not limited to, setting the target simulated road to have an extreme weather event, for example, the extreme weather may be set to different levels, including, but not limited to, fog, heavy rain, hail, etc., where the different levels of extreme weather affect the traveling speed of the target road to different degrees.
Presetting a virtual signal lamp event in a vehicle simulation system, wherein the virtual signal lamp event indicates that a signal lamp event which influences the running speed of a target road occurs on the target simulation road within a target time period; and performing simulation deduction by using the initial simulation data, the virtual signal lamp events and the historical driving data to generate target index data.
Optionally, in this embodiment, the virtual signal light event may include, but is not limited to, setting that a signal light event occurs on the target simulated road, for example, signal light events that may be set to different levels, including, but not limited to, signal light power failure, signal light error flashing, and the like, where the different levels of signal light events affect the running speed of the target road to different degrees.
As an alternative, performing simulation deduction by using the initial simulation data and the historical driving data to generate target index data, including: determining target vehicles passing through a target road in a target time period on a historical period according to historical driving data; adding target simulation vehicles to a target simulation road according to a preset time interval by using the initial simulation data and the target vehicles, and performing simulation deduction, wherein the target simulation vehicles are related to the target vehicles; when the simulation deduction is performed to the end time, target index data is generated.
Alternatively, in this embodiment, the target vehicle may be determined according to vehicle travel data that passes through the target road in the target time period in the historical cycle, for example, taking the target time period as 9.
Illustratively, taking the previous month as an example, the average of the number of vehicles that pass through the target road in the previous month from 9 to 00.
Alternatively, in this embodiment, the preset time interval may be preset by a worker, or may be dynamically determined according to a preset algorithm according to the number of vehicles already running on the target simulation lane, where the dynamic determination may be understood as detecting the number of vehicles already running on the target simulation lane every certain time, when the number of vehicles is large, the target simulation vehicles are added at longer time intervals, when the number of vehicles is small, the target simulation vehicles are added at shorter time intervals, or may be set to be in an opposite relationship, that is, when the number of vehicles is large, the target simulation vehicles are added at shorter time intervals, and when the number of vehicles is small, the target simulation vehicles are added at longer time intervals.
As an alternative, determining a target vehicle that passes through a target road in a target time period on a history cycle based on history travel data includes: determining a plurality of target matrixes corresponding to the target time period according to the historical driving data, wherein one target matrix is used for representing the sum of one type of vehicle which enters a target road in the target time period on the historical cycle; and determining the target vehicle by using the target matrix.
Alternatively, in this embodiment, the target matrix may be understood as a matrix formed by the number of vehicles that historically enter the target road in each cycle in the target time period, where the target road has a plurality of intersections, and then one element in the matrix represents the number of vehicles that enter the target road at one intersection.
Specifically, fig. 6 is a schematic diagram of yet another alternative road traffic simulation method according to the embodiment of the present application, as shown in fig. 6, when a unit time period is 1 minute, for example, the target simulation road has 7 entrances and exits (the sum of the number of entrances and exits is 7), so the matrix is 7 × 7, and the meaning of the elements in the matrix is that, in the target time period of 8.
If the sensed vehicle trajectory contains vehicle type information, the matrix can also be classified by vehicle type (e.g. car matrix of 8.
As an optional scheme, adding a target simulation vehicle to a target simulation road according to a preset time interval by using initial simulation data and the target vehicle, and performing simulation deduction, the method includes: under the condition that the target simulation lane comprises a plurality of lanes, adding a first simulation vehicle to a first lane of the target simulation road according to a preset time interval, wherein the first lane is a randomly selected lane in the plurality of lanes, and the target simulation vehicle comprises the first simulation vehicle; or in the case where the target simulated lane includes a plurality of lanes and the first simulated vehicle has been added to the first lane, adding a second simulated vehicle to a second lane of the target simulated road at preset time intervals, wherein the first lane is a randomly selected lane of the plurality of lanes and the second lane is a randomly selected lane of the plurality of lanes other than the first lane.
Optionally, in this embodiment, when the target simulation lane includes a plurality of lanes, the target simulation vehicles may be sequentially added according to a preset lane order, or the target simulation vehicles may be sequentially added according to a random lane order.
It should be noted that, the adding of the target simulation vehicle according to the random lane sequence may be configured such that, each time one lane is selected, the lane is deleted from the set of lanes to be selected, and after all lanes are selected, all lanes are restored to the set of lanes to be selected again, so as to make the probability that the target simulation vehicle enters each lane as close as possible.
In an exemplary embodiment, fig. 7 is a schematic diagram of a simulation method of alternative road traffic according to an embodiment of the present application, as shown in fig. 7, the first lane is a randomly selected lane 1, and the second lane is a randomly selected lane 2 in the remaining lanes, that is, after a first simulated vehicle is added to the lane 1, the randomly selected lane 2 is used as a lane to which a second simulated vehicle is added.
As an optional solution, before generating the target index data when the simulation deduction proceeds to the end time, the method further includes: determining an initial impedance set of the target simulation road according to the initial simulation data, wherein one initial impedance of the initial impedance set represents the driving time of one path in the target simulation road, and the target simulation road comprises a plurality of paths; dividing the target simulated vehicles into a first group of simulated vehicle sets by using the initial impedance sets; the method comprises the steps of setting a first set of simulated vehicles to travel according to a first path, setting a second set of simulated vehicles to travel according to a second path, wherein the travel time of the first path is shorter than the travel time of the second path, the number of simulated vehicles of the first set of simulated vehicles is larger than that of the second set of simulated vehicles, and the first set of simulated vehicles comprises the first set of simulated vehicles and the second set of simulated vehicles.
Alternatively, in this embodiment, the determining the initial impedance set of the target simulated road according to the initial simulation data may be understood as generating an initial impedance of each path according to the vehicle driving data of each simulated vehicle in the initial simulation data, and collectively constituting the initial impedance set.
It should be noted that, the dividing of the target simulated vehicles into the first set of simulated vehicles by using the initial impedance set may be understood as dividing a plurality of target simulated vehicles into a plurality of sets of simulated vehicles, where the first set of vehicles includes a plurality of sets of simulated vehicles, each set of simulated vehicles includes a number of simulated vehicles having a negative correlation with the initial impedance, and the higher the initial impedance is, the fewer simulated vehicles in the corresponding set of simulated vehicles are.
In an exemplary embodiment, the first path and the second path are different paths passing through the target simulated road, and when the initial impedances of the first path and the second path are different and the ratio is 1.
In another exemplary embodiment, fig. 8 is a schematic diagram of a simulation method of alternative road traffic according to an embodiment of the present application, and as shown in fig. 8, a target simulated road includes a plurality of simulated roads, i.e., a road 1, a road 2, a road 3, and a road 4, and then a first path is included from a starting point to an end point: the road 1 drives to the intersection 3 of the road and turns left to enter the road 3, then drives to the intersection 2 of the road and turns right to enter the road 2, finally drives to the intersection 4 of the road and enters the road 4 to reach the terminal point, the second route: the road 1 drives to the intersection of the road 4, turns left to enter the road 4, and reaches the terminal point through the road 4.
As an alternative, after the first set of simulated vehicles is set to travel according to the first path and the second set of simulated vehicles is set to travel according to the second path, the method further comprises: driving along a first path according to the first simulation vehicle set, performing simulation deduction when the second simulation vehicle set drives along a second path, performing multi-round updating on the initial impedance set, and determining a target impedance set; dividing the target simulated vehicles into a second simulated vehicle set by using the target impedance set, setting a third simulated vehicle set to run according to the first path, and setting a fourth simulated vehicle set to run according to the second path, wherein the second simulated vehicle set comprises the third simulated vehicle set and the fourth simulated vehicle set; wherein updating the initial impedance set for each pair comprises: performing simulation deduction for one round according to the fact that the first simulation vehicle set runs along the first path and the second simulation vehicle set runs along the second path, updating the impedance of the first path and the impedance of the second path in the round, and determining the ratio of the impedance of the first path and the impedance of the second path; and under the condition that the change amplitude of the ratio value in the two adjacent rounds of updating processes meets the preset condition, determining the impedance of the first path and the impedance of the second path determined in the last round as a target impedance set.
Alternatively, in this embodiment, the simulation deduction according to the first set of simulated vehicles traveling along the first path and the second set of simulated vehicles traveling along the second path is first-wheel simulation deduction based on initial impedance, after the simulation deduction is completed, the traveling trajectory of each simulated vehicle may be obtained again, the impedance of each first path and the impedance of each second path may be updated according to the new traveling trajectory, for example, the ratio of the initial impedance of the first path to the initial impedance of the second path is 1, for example, if the ratio of the impedances of the new round is 0.9, and the variation range of the ratio of the impedances of the two adjacent rounds is 0.09, and the preset condition is less than 3, the variation range satisfies the preset condition, and the 143 simulated vehicles are divided into the third set of simulated vehicles, and the 157 simulated vehicles are divided into the fourth set of simulated vehicles, so as to perform the subsequent simulation deduction.
As an alternative, performing simulation deduction by using the initial simulation data and the historical driving data to generate target index data, including:
determining a target simulation vehicle which needs to be added on a target simulation road in the continuous process of simulation deduction according to historical driving data, wherein the target simulation road comprises a plurality of lanes, and the target simulation vehicle allows lane change in the process of simulation deduction; and adjusting the driving behavior of the added target simulation vehicle by using the microscopic driving posture adjustment model, and performing simulation deduction to generate target index data, wherein the driving behavior comprises at least one of the following behaviors: lane changing, speed reducing, parking and course angle adjusting.
Alternatively, in the present embodiment, the added target simulation vehicle may be understood as a simulation vehicle that has traveled on the target simulation road, and due to too many simulation vehicles on the target simulation road or the occurrence of the target event, some simulation vehicles need to perform adjustment of the microscopic traveling posture, including but not limited to lane changing, deceleration, parking, heading angle adjustment, and the like.
In an exemplary embodiment, fig. 9 is a schematic diagram of still another alternative simulation method for road traffic according to an embodiment of the present application, as shown in fig. 9, when a virtual accident occurs at an accident position, at this time, an added simulated vehicle needs to change lanes, and the lane change process may include, but is not limited to, first determining whether a lane change condition from lane 1 to lane 2 is currently satisfied (whether front and rear vehicles are separated by a safe distance, whether the road section allows lane change, etc.), if the lane change condition is satisfied, changing the simulated vehicle from lane 1 to lane 2, and if the lane 2 is still an accident lane, determining again whether a lane change condition from lane 2 to lane 3 is currently satisfied (whether front and rear vehicles are separated by a safe distance, whether the road section allows lane change, etc.), and if the lane change condition is satisfied, changing the simulated vehicle from lane 2 to lane 3, so as to implement micro simulation deduction.
As an alternative, the step of adjusting the driving behavior of the added target simulation vehicle by using the microscopic driving posture adjustment model, and performing simulation deduction to generate target index data includes: when the simulation deduction reaches the end time, storing a microscopic running track of the target simulation vehicle running in the target time period; and generating congestion index data of the target road in the target time period according to the microscopic driving track, wherein the target index data comprises the congestion index data.
Optionally, in this embodiment, the microscopic simulation may record information such as a position and a speed of the simulated vehicle in each simulation step, which is equivalent to saving the microscopic trajectory of the simulated vehicle, and then may calculate traffic flow parameters of the selected section, or further calculate other evaluation indexes defined by the user in advance according to the trajectories, so as to obtain a quantitative evaluation result of various indexes in the virtual event, that is, if some event occurs, congestion conditions (speed, density, flow rate, etc.) of a road segment near the event may be calculated by the branch line simulation.
The present application is further illustrated by the following specific examples:
fig. 10 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application, and as shown in fig. 10, traffic data is a basic input of building a model and a simulation, and is divided into the following three categories:
dynamic data: the sensing equipment deployed in the application can be used for carrying out global coverage on all driving areas of a road network, extracting state information of a structured road and a target object, and realizing functions of comprehensively sensing elements such as vehicles, roads, events and the like, positioning target lanes at a centimeter level, integrating and searching targets and the like. By means of the following algorithm in the twin technology, when the vehicle runs in the sensing range, the key information (such as vehicle type, speed, position, course angle and the like) of the vehicle can be resolved, and the real-time tracking of the vehicle running track is realized, including but not limited to:
1. motor vehicle, non-motor vehicle, pedestrian target detection algorithm;
2. identifying an algorithm;
3. motor vehicles, non-motor vehicles, pedestrian vision pursuit algorithms;
4. constructing an algorithm for target observation;
5. a 3D target fusion detection algorithm;
6. a millimeter wave radar target detection and pursuit algorithm;
7. and (4) performing multipoint locus splicing and region deduplication algorithm.
Meanwhile, a radar of the roadside sensing equipment is accessed into the switch through a gigabit Ethernet interface and a camera through the front end of a PCIE interface, all essential data collected by the front end are communicated and gathered to the intelligent terminal with the integrated sensing edge at the end side through optical fibers by the access switch, data analysis, calculation and storage are carried out by the terminal, structural data and video unstructured data are pushed to the central side through a special traffic network to be integrated and mined, and data sharing is carried out with a service platform.
The perception data transmits the key information of the vehicle frame by frame at the frequency of 10 Hz, and the time delay from the appearance of the traffic target in the perception range to the display of the information of the vehicle in the simulation system is not more than 300ms.
Through the method, after the vehicle enters the sensing range, the vehicle can be reproduced in the simulation system in real time according to key information (vehicle type, position, speed, course angle and the like) of the vehicle, and as the vehicle continues to run in the sensing coverage range, sensing data is uploaded at low delay, so that all driving behaviors of the vehicle in the real world can be reproduced in the simulation system in real time, and the traffic state of the universe can be presented in real time.
Static data: the part comprises a high-precision map and other traffic elements required by establishing a basic road network, such as a timing scheme of a signal lamp, and the like, and a background traffic road network required by simulation can be established through the static data.
Historical data: a high-precision and low-delay real-time twin simulation system can be constructed through static data and dynamic data, OD (Origin Destination) production can be carried out through a historical perception track before the simulation system is formally operated, and the OD production can be used as a part of simulation system input in deduction.
In the present application, mainline simulation is a main process for performing simulation in a system according to sensing data acquired in real time. Firstly, modeling of a high-precision map and other elements (such as signal lamps) builds a simulated basic static element, and then reproducing the real sensed vehicle states (vehicle type, speed, position, course angle and the like) in a system one by one through a real-time sensing and fusion algorithm, so that the real-time twin simulation effect is realized. The main line simulation is used for reproducing the running state of the real vehicle in the system in a low-delay and high-accuracy mode.
The main line simulation takes dynamic and static data as input, and can reproduce the vehicle state in the real world in real time. Meanwhile, by utilizing historical OD data and combining traffic distribution, mesoscopic and microscopic traffic models and other traffic models, branch line simulation can be performed on the basis of main line simulation, and different functional applications such as event simulation, management and control simulation and the like are further realized.
The branch simulation mode adopts a real-time twin simulation mode based on perception full coverage to carry out simulation on traffic flow in the real world, and meanwhile, simulation branches can be cut out from a main line of real-time simulation at any time in the simulation according to different assumptions or real scenes to carry out deductive simulation on corresponding scenes, and short-time prediction is carried out on the time-space influence of the simulation branches, so that functional applications such as event simulation, control simulation and the like are realized on the basis of the simulation branches.
The main line simulation visualizes the real traffic flow in the form of microscopic simulation, but in order to make the simulation realize larger practical significance, the branch line deduction simulation is introduced here. The method is characterized in that a 'what if' prediction is made on the basis of reappearing a real traffic state, some virtual events (such as traffic accidents, construction road closing and the like) which can affect the traffic are added into a simulation, then the simulation is used for carrying out acceleration deduction in a system, the driving behavior of each vehicle is described in a microscopic simulation, so that the running of the vehicles can be simulated to simulate the reaction of the vehicles to different events (such as deceleration, lane changing and the like), the reaction of the vehicles is converged to form the influence on the whole traffic running state, quantitative evaluation indexes (such as average speed and flow of a road section or a section and the like) can be output by using a simulation result, and the time-space influence of the virtual events in a short time (such as the degree of the reduction of the average speed or the flow, the intersection to which the vehicle jam is queued and spread) is deduced according to the simulation result.
Meanwhile, the branch simulation does not need to receive real vehicle state data transmitted by the sensing device in real time, so that the technologies such as cloud data streaming calculation, distributed calculation, data synchronization and the like can be used for acceleration, and the effect of rapidly obtaining a simulation result in a short time is achieved, for example, a simulation result of 30 minutes (system time) in the future is obtained through simulation deduction in 1 minute (real time).
Fig. 11 is a schematic diagram of still another alternative road traffic simulation method according to an embodiment of the present application, in which a real-time twin simulation main line and an application branch line are shown in fig. 11, and the branch line simulation applications that can be implemented include, but are not limited to:
and (3) congestion prediction: and under the condition of not adding any virtual event, deducing the future 15-30 minutes from the current state, and predicting the influence on the space-time dimension such as the congestion position, the intensity and the like according to the simulation result.
Event simulation: adding virtual traffic accidents and other traffic events, deducing the future for 15-30 minutes from the current state, and predicting the influence on the space-time dimension such as congestion intensity caused by the events according to the simulation result.
Management and control simulation: virtual control measures (such as road closure and the like) are added, the future 15-30 minutes is deduced from the current state, and the influences on space-time dimensions such as congestion intensity and the like caused by the simulation results are predicted according to the simulation results.
Weather simulation: virtual weather (such as fog, heavy rain and the like) is added, the future 15-30 minutes is deduced from the current state, and the influence on the space-time dimension such as the congestion intensity caused by the simulation result is predicted according to the simulation result.
Other simulations: other events whose influence needs to be predicted can be added, and the influence of the events on the space-time dimension can be predicted by the method.
It should be noted that the traffic models required for the deduction from the main line simulation to the branch line simulation include, but are not limited to:
traffic distribution: the OD traffic data is distributed to the algorithm model on each road section, which is not described herein.
And (3) center view simulation: refers to a mesoscopic simulation model with relatively high simulation efficiency.
Microscopic simulation: refers to a microscopic simulation model with relatively high simulation precision.
Other models are as follows: the model other than the above model, which is required for the operation of the branch line simulation, is not limited herein, and may include, but is not limited to, an initial state model, a macro path setting model, a micro driving posture adjustment model, a road network parameter calibration model, and the like.
Wherein the initial state ("snapshot") model: when the main line simulation runs to a certain time T, the main line vehicle state (such as vehicle type, speed, acceleration, position, course angle and the like) at the time T is recorded as a state 'snapshot' in real time, and the state is used as the initial vehicle state of the branch line simulation.
Macro path setup model: for vehicles in the "snapshot", how to select the travel path at the next intersection. The probability of how the vehicle selects the downstream roads may be set to be related to the historical flow of the downstream roads at the intersection, which is not described herein.
And (3) microcosmic driving posture adjustment model: and (3) the planning method when switching from the original data to the traffic model control adjusts the vehicle driving behavior which is not positioned in the lane central line and the course angle is not 0 in the snapshot.
Road network parameter calibration model: the method and the process of the calibration are not limited, but the traffic parameters of the road network are reasonably calibrated before the branch line deduction simulation operation.
The flow from the main line to the branch line simulation is as follows:
by user setting the need to turn on the branchTime of simulation T 0 Duration T of the required operation of the leg simulation D (intra-simulation time, such as predicted future 15 minutes or 30 minutes) and events that require simulation testing in branch simulation (such as virtual traffic accidents or road closure events). The system injects the event to be tested into the system in a way that the event can be read in the simulation system and other simulation vehicles can react to the event, and the implementation mode in the system is not limited as long as the simulation vehicles can react to the event similar to that in reality.
Initial vehicle state: the real-time twin main line simulation runs continuously and runs to T in real time 0 At the moment, the traffic state 'snapshot' at the moment is taken. As the initial state of the leg simulation.
T D Type and number of vehicles entering the system over time: will be T when 0 To T 0 +T D The number of vehicles entering the road network is regarded as the same period (T) as the history 0 To T 0 +T D ) The same number of vehicles, and the history OD is used as input here.
T D The departure interval of the time interval entering system is as follows: the departure interval of each vehicle is set.
T D The time interval enters the exit lane of the system: and setting which lane center line of the road each vehicle should appear on.
T D Vehicle path of time slot entry system: since T is saved in the' snapshot 0 The position, speed, acceleration, etc. of all vehicles in the time system can be used to calculate the initial impedance (unit, time) of each link (link travel time = link length/link speed). The method for calculating the average speed from the "snapshot" is not limited herein, and may be based on T 0 The instantaneous speed of all vehicles on a certain road section at the moment is used for calculating the spatial average speed of the road section. At a known T 0 To T 0 +T D When the number of vehicles entering the system is within the range, calculating the path of the subsequent vehicles entering the system according to the following iterative steps:
s1, reading in time interval [ T 0 ，T 0 +T D ) History OD matrix of (1), assuming future T D The traffic demand of time is the same as the demand of history in the same time period;
s2, traffic distribution is carried out according to the travel time of the current road section, and the travel amount in the OD is distributed to each path and road section;
s3, performing mesoscopic traffic simulation according to the alternating current quantity distributed on the path and the road section, and outputting [ T ] 0 ，T 0 +T D ) The simulation result of (3), such as the flow rate and speed of each road section. Using the simulated travel time to the current time interval [ T 0 ，T 0 +T D ) Updating the travel time;
and S4, if the distribution result meets the convergence requirement (the change amplitude of two adjacent times is smaller than a certain value), taking the vehicle path at the moment as the driving path of the vehicle entering the system at the moment, and if the distribution result does not meet the convergence requirement, repeating the steps S2 and S3.
The maximum number of cycles can be set, the process is ended if the maximum cycle is reached and the requirement is not met, and the driving path of the vehicle is generated according to the last iteration result of the cycle.
The path result obtained here is the number of traffic flows passing through each possible path, and if there are 150 vehicles starting from the starting point O No. 1, 100 vehicles traveling to the ending point D No. 2, and there are two paths a and B, respectively, one possible result is q 12A =60，q 12B =40, i.e. the flow through the a path is 60 from 1 to 2 and the flow through the B path is 40. If there are 50 vehicles from the starting point of No. 1 to the ending point of No. 3 in addition to the ending point of No. 2, and there are two paths C and D between 1 and 3, q 13C =20，q 13C =30. When processing vehicles starting from the starting point No. 1, firstly marking the 150 vehicles according to different end points and paths, then placing the vehicles into a certain departure pool, randomly selecting one vehicle from the departure pool when the simulation runs to the moment that the departure is needed from the starting point No. 1, allowing the vehicle to be generated from the starting point No. 1, driving according to the corresponding end point and path, and so on until the departure pool is empty. In summary, the path results hereinAfter calculation, all vehicles entering the system from a starting point are marked according to the destination and the used path, and are placed into the departure pool corresponding to the starting point, and when the vehicles need to be generated, the vehicles are randomly taken from the pool until the departure pool is empty.
Branch line microscopic simulation: at the moment of determining T 0 Initial state of the moment, and at its subsequent T D After how the vehicle enters the system in a time period, the vehicle can be enabled to run in the whole road network along with the propulsion of the simulation step length in a microscopic simulation mode, virtual events needing to be tested are activated at the same time, and the vehicle manipulated by the microscopic model can make corresponding reactions to the virtual events, such as speed reduction or lane change and the like.
And (3) generating an evaluation index: the microscopic simulation can record the position, speed and other information of the vehicle in each simulation step length, equivalently, microscopic tracks of all simulated vehicles are stored, traffic flow parameters of a selected section (the overall process of each vehicle) are calculated, and other evaluation indexes defined by a user in advance can be further calculated according to the tracks, so that the quantitative evaluation result of each index under a virtual event is obtained, namely if some event occurs, the congestion condition (speed, density, flow and the like) of a road section near the event can be calculated through branch line simulation.
Fig. 12 is a schematic diagram of yet another alternative road traffic simulation method according to an embodiment of the present application, and a main line-to-branch line simulation flowchart is shown in fig. 12, which includes, but is not limited to, the following steps:
s1, setting a branch starting time T0, a duration Td and other configurations (such as virtual events and the like) by a user;
s2, acquiring a history OD matrix from T0 to TD;
s3, starting branch simulation at the T0 moment;
s4, taking the real-time simulation state at the current T0 moment as the initial state of a branch line, and realizing and activating virtual events and the like in the system;
s5, calculating initial road network impedance at the time of T0 according to the initial state;
s6, carrying out traffic distribution;
s7, performing mesoscopic traffic simulation;
s8, judging whether the distribution result is converged, if not, executing the step S9, and if yes, executing the step S10;
s9, recalculating the road network impedance according to the mesoscopic result;
s10, obtaining a vehicle path entering the system in the time period, and performing microscopic traffic simulation;
and S11, calculating indexes according to results after branch simulation is finished.
Through the embodiment, in real-time twin traffic simulation, the simulation system can reproduce the vehicle state of the real world in a high-precision and low-delay mode. On the basis, a traffic state 'snapshot' is defined as an initial state of the deduction simulation, then how a vehicle enters a system in a period needing to be predicted is set, and the whole branch deduction simulation process is described, so that short-term prediction is realized on traffic influence generated by a virtual event, a quantitative evaluation result can be correspondingly output, a real-time twin is upgraded from 'visualization' to a computable digital space, decision is assisted through prediction, a virtual world and a physical world are connected, and finally closed-loop control is realized.
It should be noted that, the above-mentioned spur simulation does not need to access real-time data, because the acceleration operation can be performed in various ways, and the way, method and required equipment for acceleration are not limited herein.
The convergence requirement of the traffic distribution result is not limited, and can be set as the value of the objective function after two adjacent iterations. The final objective here is to get the path of the vehicle to be driven in the road network balanced state.
When the branch simulation is performed on the virtual event, a branch simulation without the virtual event (the same as other inputs) can be performed at the same time, and then the branch simulation is compared with the result index with the virtual event to evaluate extra congestion and the like caused by the virtual event.
It can be assumed that T is present 0 To T 0 +T D Number of vehicles entering road network andhistory contemporaneous period (T) 0 To T 0 +T D ) The number of vehicles in the branch line simulation system is different, the current traffic state and the historical state can be compared, and the input can be adjusted correspondingly.
It is understood that in the specific implementation of the present application, related data such as user information, when the above embodiments of the present application are applied to specific products or technologies, user permission or consent needs to be obtained, and the collection, use and processing of related data need to comply with related laws and regulations and standards of related countries and regions.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
According to another aspect of the embodiment of the application, a road traffic simulation device for implementing the road traffic simulation method is also provided. As shown in fig. 13, the apparatus includes:
a setting module 1302, configured to preset a target time period that needs to be subjected to simulation deduction in a vehicle simulation system, where the target time period includes a start time of the simulation deduction and an end time of the simulation deduction, and the target time period corresponds to a target simulation road that needs to be subjected to simulation deduction;
an obtaining module 1304, configured to obtain initial simulation data of a target simulation road at the starting time when the current time reaches the starting time, where the initial simulation data is determined by real-time driving data of the starting time, the real-time driving data represents vehicle driving data acquired in real time on a target road, and the target road corresponds to the target simulation road;
a generating module 1306, configured to perform simulation deduction by using the initial simulation data and historical driving data, and generate target index data, where the historical driving data is used to add a simulation vehicle driving on the target simulation road during the duration of the simulation deduction, and the historical driving data represents vehicle driving data collected for the target road over the target time period of a historical period.
As an optional solution, the apparatus is further configured to: presetting a target event needing simulation deduction in the vehicle simulation system, wherein the target event indicates that a simulation event influencing the running speed of the target road occurs on the target simulation road in the target time period, and the simulation event is used for updating the vehicle running data of the target simulation road in the target time period; and performing simulation deduction by using the initial simulation data, the target event and the historical driving data to generate target index data.
As an optional solution, the apparatus is further configured to perform at least one of the following methods: presetting a virtual traffic event in the vehicle simulation system, wherein the virtual traffic event represents that the target simulation road has a traffic event which influences the running speed of the target road within the target time period; performing simulation deduction by using the initial simulation data, the virtual traffic event and the historical driving data to generate target index data; presetting a virtual control event in the vehicle simulation system, wherein the virtual control event represents that a control event which influences the running speed of the target road occurs on the target simulation road within the target time period; performing simulation deduction by using the initial simulation data, the virtual management and control event and the historical driving data to generate target index data; presetting a virtual weather event in the vehicle simulation system, wherein the virtual weather event represents that a weather event which influences the running speed of the target road occurs on the target simulation road within the target time period; performing simulation deduction by using the initial simulation data, the virtual weather event and the historical driving data to generate target index data; presetting a virtual signal lamp event in the vehicle simulation system, wherein the virtual signal lamp event represents that a signal lamp event which influences the running speed of the target road occurs on the target simulation road within the target time period; and performing simulation deduction by using the initial simulation data, the virtual signal lamp events and the historical driving data to generate target index data.
As an alternative, the apparatus is configured to perform simulation deduction using the initial simulation data and the historical travel data to generate target index data by: determining a target vehicle passing through the target road in the target time period on a historical period according to the historical driving data; adding a target simulation vehicle to the target simulation road according to a preset time interval by using the initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle; and generating the target index data when the simulation deduction is carried out to the end time.
As an alternative, the apparatus is configured to determine a target vehicle passing a target road in the target time period on a history cycle from the history travel data by: determining multiple target matrixes corresponding to the target time periods according to historical driving data, wherein one target matrix is used for representing the sum of one type of vehicles which enter a target road in the target time periods on the historical period; and determining the target vehicle by using the target matrix.
As an alternative, the apparatus is configured to add a target simulation vehicle to the target simulation road at preset time intervals by using the initial simulation data and the target vehicle, and perform simulation deduction: adding a first simulated vehicle to a first lane of the target simulated road according to the preset time interval under the condition that the target simulated lane comprises a plurality of lanes, wherein the first lane is a randomly selected lane in the plurality of lanes, and the target simulated vehicle comprises the first simulated vehicle; or in the case that the target simulated lane comprises a plurality of lanes and a first simulated vehicle has been added to a first lane, adding a second simulated vehicle to a second lane of the target simulated road at the preset time interval, wherein the first lane is a randomly selected lane of the plurality of lanes and the second lane is a randomly selected lane of the plurality of lanes except for the first lane.
As an optional solution, the apparatus is further configured to: under the condition that the simulation deduction is carried out to the end time, before the target index data are generated, determining an initial impedance set of the target simulation road according to the initial simulation data, wherein one initial impedance of the initial impedance set represents the driving duration of one path in the target simulation road, and the target simulation road comprises a plurality of paths; dividing the target simulated vehicle into a first set of simulated vehicles using the initial set of impedances; setting a first set of simulated vehicles to travel along a first path and a second set of simulated vehicles to travel along a second path, wherein a travel time period of the first path is shorter than a travel time period of the second path, a number of simulated vehicles of the first set of simulated vehicles is greater than a number of simulated vehicles of the second set of simulated vehicles, and the first set of simulated vehicles comprises the first set of simulated vehicles and the second set of simulated vehicles.
As an optional solution, the apparatus is further configured to: setting a first simulation vehicle set to run according to a first path, setting a second simulation vehicle set to run according to a second path, then running along the first path according to the first simulation vehicle set, carrying out simulation deduction when the second simulation vehicle set runs along the second path, carrying out multi-round updating on the initial impedance set, and determining a target impedance set; dividing the target simulated vehicles into a second set of simulated vehicles by using the target impedance set, setting a third set of simulated vehicles to run according to the first path, and setting a fourth set of simulated vehicles to run according to the second path, wherein the second set of simulated vehicles comprises the third set of simulated vehicles and the fourth set of simulated vehicles; wherein updating the initial impedance set for each wheel comprises: according to the first simulation vehicle set, driving along the first path, and the second simulation vehicle set, driving along the second path, performing simulation deduction for one round, updating the impedance of the first path and the impedance of the second path, and determining the ratio of the impedance of the first path and the impedance of the second path; and determining the impedance of the first path and the impedance of the second path determined in the last round as the target impedance set under the condition that the change amplitude of the ratio in the two adjacent rounds of updating processes meets a preset condition.
As an alternative, the apparatus is configured to perform simulation deduction using the initial simulation data and the historical travel data to generate target index data by: determining a target simulation vehicle which needs to be added on the target simulation road in the continuous process of the simulation deduction according to the historical driving data, wherein the target simulation road comprises a plurality of lanes, and the target simulation vehicle allows lane change in the simulation deduction process; adjusting the driving behaviors of the added target simulation vehicle by using a microscopic driving posture adjustment model, and performing simulation deduction to generate target index data, wherein the driving behaviors comprise at least one of the following: lane changing, speed reducing, parking and course angle adjusting.
As an alternative, the apparatus is configured to adjust the driving behavior of the added target simulation vehicle by using a microscopic driving posture adjustment model, and perform simulation deduction to generate target index data: when the simulation deduction reaches the end time, saving a microscopic driving track of the target simulation vehicle in the target time period; and generating congestion index data of the target road in the target time period according to the microscopic driving track, wherein the target index data comprises the congestion index data.
According to an aspect of the present application, there is provided a computer program product comprising a computer program/instructions containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 1409 and/or installed from the removable medium 1411. When executed by the central processing unit 1401, the computer program performs various functions provided by the embodiments of the present application.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Fig. 14 schematically shows a structural block diagram of a computer system of an electronic device for implementing the embodiment of the present application.
It should be noted that the computer system 1400 of the electronic device shown in fig. 14 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 14, the computer system 1400 includes a Central Processing Unit (CPU) 1401 which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 1402 or a program loaded from a storage portion 1408 into a Random Access Memory (RAM) 1403. In the random access memory 1403, various programs and data necessary for system operation are also stored. The central processor 1401, the read only memory 1402 and the random access memory 1403 are connected to each other via a bus 1404. An Input/Output interface 1405 (Input/Output interface, i.e., I/O interface) is also connected to the bus 1404.
The following components are connected to the input/output interface 1405: an input portion 1406 including a keyboard, a mouse, and the like; an output portion 1407 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage portion 1408 including a hard disk and the like; and a communication section 1409 including a network interface card such as a local area network card, a modem, or the like. The communication section 1409 performs communication processing via a network such as the internet. The driver 1410 is also connected to the input/output interface 1405 as necessary. A removable medium 1411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1410 as necessary, so that a computer program read out therefrom is installed into the storage section 1408 as necessary.
In particular, according to embodiments of the present application, the processes described in the various method flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion 1409 and/or installed from the removable medium 1411. When the computer program is executed by the central processing unit 1401, various functions defined in the system of the present application are executed.
According to still another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the simulation method of road traffic described above, where the electronic device may be a terminal device or a server shown in fig. 1. The present embodiment takes the electronic device as a terminal device as an example for explanation. As shown in fig. 15, the electronic device comprises a memory 1502, in which memory 1502 a computer program is stored, and a processor 1504 arranged to perform the steps of any of the above described method embodiments by means of the computer program.
Optionally, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of a computer network.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
the method comprises the following steps that S1, a target time period needing simulation deduction is preset in a vehicle simulation system, wherein the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, and the target time period corresponds to a target simulation road needing the simulation deduction;
s2, under the condition that the current time reaches the starting time, acquiring initial simulation data of the target simulation road at the starting time, wherein the initial simulation data is determined by real-time driving data of the starting time, the real-time driving data represents vehicle driving data acquired in real time on the target road, and the target road corresponds to the target simulation road;
and S3, performing simulation deduction by using the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulation vehicles driving on the target simulation road in the continuous process of the simulation deduction, and the historical driving data represents vehicle driving data collected for the target road in the target time period of the historical period.
Alternatively, it can be understood by those skilled in the art that the structure shown in fig. 15 is only an illustration, and the electronic device may be a smart phone (e.g., a mobile phone)
And terminal Devices such as tablet computers, palm computers, mobile Internet Devices (MID) and PADs. Fig. 15 is a diagram illustrating a structure of the electronic device. For example, the electronics may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 15, or have a different configuration than shown in FIG. 15.
The memory 1502 may be configured to store software programs and modules, such as program instructions/modules corresponding to the method and apparatus for simulating road traffic in the embodiment of the present application, and the processor 1504 executes various functional applications and data processing by running the software programs and modules stored in the memory 1502, so as to implement the above-mentioned method for simulating road traffic. The memory 1502 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1502 can further include memory located remotely from the processor 1504, which can be coupled to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The memory 1502 may be used for storing information such as real-time driving data, but is not limited thereto. As an example, as shown in fig. 15, the memory 1502 may include, but is not limited to, a setting module 1302, an obtaining module 1304, and a generating module 1306 in the road traffic simulation apparatus. In addition, the simulation apparatus may further include, but is not limited to, other module units in the simulation apparatus for road traffic, which is not described in detail in this example.
Optionally, the transmission device 1506 is used for receiving or transmitting data via a network. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 1506 includes a Network adapter (NIC) that can be connected to a router via a Network cable and other Network devices to communicate with the internet or a local area Network. In one example, the transmission device 1506 is a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In addition, the electronic device further includes: a display 1508 for displaying the simulation system; and a connection bus 1510 for connecting the respective module components in the above-described electronic apparatus.
In other embodiments, the terminal device or the server may be a node in a distributed system, where the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting a plurality of nodes through a network communication. The nodes may form a Peer-To-Peer (P2P) network, and any type of computing device, such as a server, a terminal, and other electronic devices, may become a node in the blockchain system by joining the Peer-To-Peer network.
According to one aspect of the present application, there is provided a computer readable storage medium from which computer instructions are read by a processor of a computer device, the processor executing the computer instructions to cause the computer device to perform a method of simulating road traffic as provided in the various alternative implementations of the simulation aspect of road traffic described above.
Alternatively, in the present embodiment, the above-mentioned computer-readable storage medium may be configured to store a computer program for executing the steps of:
the method comprises the following steps that S1, a target time period needing simulation deduction is preset in a vehicle simulation system, wherein the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, and the target time period corresponds to a target simulation road needing the simulation deduction;
s2, under the condition that the current time reaches the starting time, acquiring initial simulation data of the target simulation road at the starting time, wherein the initial simulation data is determined by real-time driving data of the starting time, the real-time driving data represents vehicle driving data acquired in real time on the target road, and the target road corresponds to the target simulation road;
and S3, performing simulation deduction by using the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulation vehicles which run on the target simulation road in the continuous process of the simulation deduction, and the historical driving data represents vehicle driving data collected on the target road in the target time period of the historical period.
Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the advantages and disadvantages of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (14)
1. A method of simulating road traffic, comprising:
presetting a target time period required to be subjected to simulation deduction in a vehicle simulation system, wherein the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, and the target time period corresponds to a target simulation road required to be subjected to the simulation deduction;
under the condition that the current time reaches the starting time, acquiring initial simulation data of a target simulation road at the starting time, wherein the initial simulation data is determined by real-time running data of the starting time, the real-time running data represents vehicle running data acquired in real time on the target road, and the target road corresponds to the target simulation road;
and performing simulation deduction by using the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulation vehicles which drive on the target simulation road in the continuous process of the simulation deduction, and the historical driving data represents vehicle driving data collected on the target road in the target time period of a historical period.
2. The method of claim 1, further comprising:
presetting a target event needing simulation deduction in the vehicle simulation system, wherein the target event indicates that a simulation event influencing the running speed of the target road occurs on the target simulation road in the target time period, and the simulation event is used for updating the vehicle running data of the target simulation road in the target time period;
and performing simulation deduction by using the initial simulation data, the target event and the historical driving data to generate target index data.
3. The method of claim 2, further comprising at least one of:
presetting a virtual traffic event in the vehicle simulation system, wherein the virtual traffic event represents that the target simulation road has a traffic event which influences the running speed of the target road within the target time period; performing simulation deduction by using the initial simulation data, the virtual traffic event and the historical driving data to generate target index data;
presetting a virtual control event in the vehicle simulation system, wherein the virtual control event indicates that a control event which influences the running speed of the target road occurs to the target simulation road within the target time period; performing simulation deduction by using the initial simulation data, the virtual control event and the historical driving data to generate target index data;
presetting a virtual weather event in the vehicle simulation system, wherein the virtual weather event represents that a weather event which influences the running speed of the target road occurs on the target simulation road within the target time period; performing simulation deduction by using the initial simulation data, the virtual weather event and the historical driving data to generate target index data;
presetting a virtual signal lamp event in the vehicle simulation system, wherein the virtual signal lamp event represents that a signal lamp event which influences the running speed of the target road occurs on the target simulation road within the target time period; and performing simulation deduction by using the initial simulation data, the virtual signal lamp events and the historical driving data to generate target index data.
4. The method of claim 1, wherein said performing a simulation deduction using said initial simulation data and historical driving data to generate target index data comprises:
determining a target vehicle passing through the target road in the target time period on a historical cycle according to the historical driving data;
adding a target simulation vehicle to the target simulation road according to a preset time interval by using the initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle;
and generating the target index data when the simulation deduction is carried out to the end time.
5. The method of claim 4, wherein said determining a target vehicle that has traveled the target road during the target time period over a historical period based on the historical travel data comprises:
determining a plurality of target matrixes corresponding to the target time periods according to the historical driving data, wherein one target matrix is used for representing the sum of one type of vehicle which enters the target road in the target time period on a historical cycle;
determining the target vehicle using the target matrix.
6. The method of claim 4, wherein the adding target simulation vehicles to the target simulation road at preset time intervals using the initial simulation data and the target vehicles and performing simulation deduction comprises:
adding a first simulated vehicle to a first lane of the target simulated road according to the preset time interval under the condition that the target simulated lane comprises a plurality of lanes, wherein the first lane is a randomly selected lane in the plurality of lanes, and the target simulated vehicle comprises the first simulated vehicle; or
And in the case that the target simulation lane comprises a plurality of lanes and a first simulation vehicle is already added to a first lane, adding a second simulation vehicle to a second lane of the target simulation road according to the preset time interval, wherein the first lane is a randomly selected lane in the plurality of lanes, and the second lane is a randomly selected lane except the first lane in the plurality of lanes.
7. The method of claim 4, wherein before generating the target metric data if the simulation deduction proceeded to the end time, the method further comprises:
determining an initial impedance set of the target simulation road according to the initial simulation data, wherein one initial impedance of the initial impedance set represents the travel time of one path in the target simulation road, and the target simulation road comprises a plurality of paths;
dividing the target simulated vehicle into a first set of simulated vehicles using the initial set of impedances;
setting a first set of simulated vehicles to travel along a first path and a second set of simulated vehicles to travel along a second path, wherein a travel time period of the first path is shorter than a travel time period of the second path, a number of simulated vehicles of the first set of simulated vehicles is greater than a number of simulated vehicles of the second set of simulated vehicles, and the first set of simulated vehicles comprises the first set of simulated vehicles and the second set of simulated vehicles.
8. The method of claim 7, wherein after the setting the first set of simulated vehicles to travel according to the first path and the second set of simulated vehicles to travel according to the second path, the method further comprises:
according to the fact that the first simulation vehicle set runs along the first path, the second simulation vehicle set runs along the second path to conduct simulation deduction, conducting multi-round updating on the initial impedance set, and determining a target impedance set;
dividing the target simulated vehicles into a second set of simulated vehicles by using the target impedance set, setting a third set of simulated vehicles to run according to the first path, and setting a fourth set of simulated vehicles to run according to the second path, wherein the second set of simulated vehicles comprises the third set of simulated vehicles and the fourth set of simulated vehicles;
wherein updating the initial impedance set for each pair comprises: performing simulation deduction for one round according to the fact that the first simulation vehicle set runs along the first path and the second simulation vehicle set runs along the second path, updating the impedance of the first path and the impedance of the second path for one round, and determining the ratio of the impedance of the first path to the impedance of the second path;
and determining the impedance of the first path and the impedance of the second path determined in the last round as the target impedance set under the condition that the change amplitude of the ratio in the two adjacent rounds of updating processes meets a preset condition.
9. The method of claim 1, wherein said performing a simulation deduction using said initial simulation data and historical driving data to generate target index data comprises:
determining a target simulation vehicle which needs to be added on the target simulation road in the continuous process of the simulation deduction according to the historical driving data, wherein the target simulation road comprises a plurality of lanes, and the target simulation vehicle allows lane change in the simulation deduction process;
adjusting the driving behavior of the added target simulation vehicle by using a microscopic driving posture adjustment model, and performing simulation deduction to generate target index data, wherein the driving behavior comprises at least one of the following behaviors: lane changing, deceleration, parking and course angle adjustment.
10. The method according to claim 9, wherein the adjusting the driving behavior of the added target simulation vehicle by using the microscopic driving posture adjustment model and performing simulation deduction to generate target index data comprises:
when the simulation deduction reaches the end time, saving a microscopic driving track of the target simulation vehicle in the target time period;
and generating congestion index data of the target road in the target time period according to the microscopic driving track, wherein the target index data comprises the congestion index data.
11. A road traffic simulation apparatus, comprising:
the simulation system comprises a setting module, a simulation module and a simulation module, wherein the setting module is used for presetting a target time period required to be subjected to simulation deduction in a vehicle simulation system, the target time period comprises a starting time of the simulation deduction and an ending time of the simulation deduction, and the target time period corresponds to a target simulation road required to be subjected to simulation deduction;
the acquisition module is used for acquiring initial simulation data of a target simulation road at the starting time under the condition that the current time reaches the starting time, wherein the initial simulation data is determined by real-time driving data of the starting time, the real-time driving data represents vehicle driving data acquired in real time on the target road, and the target road corresponds to the target simulation road;
and the generation module is used for performing simulation deduction by using the initial simulation data and historical driving data to generate target index data, wherein the historical driving data is used for adding simulated vehicles which run on the target simulated road in the continuous process of the simulation deduction, and the historical driving data represents vehicle driving data collected on the target road in the target time period of a historical period.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the program is executable by a terminal device or a computer to perform the method of any one of claims 1 to 10.
13. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method as claimed in any one of claims 1 to 10.
14. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 10 by means of the computer program.
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