A traffic flow simulation method and system based on cellular automaton

Abstract

The application relates to a vehicle flow simulation method and system based on a cellular automaton, and relates to the field of expressway management.The method comprises the following steps: acquiring lane and vehicle information of a first road section, and mapping the first road section into a two-dimensional cellular space; according to a minimum unit of lane and vehicle movement, the cellular space is divided into cells; the vehicles are randomly distributed in the cells, the state of the cells comprises a first state and a second state, and the types of the vehicles comprise first vehicles and second vehicles; the time slice is iterated, the new positions of the vehicles are calculated, and the state of the cells is updated according to the new positions; a model is established based on a cellular automaton, vehicle density ratio parameters are inputted for iterative simulation, and average vehicle flow is outputted; the vehicle density ratio parameters comprise the ratio of the first vehicle density and the second vehicle density. The application determines the optimal vehicle flow density ratio based on simulation results, provides decision support for expressway vehicle management, and guarantees the safety of the expressway.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of highway management, and in particular to a traffic flow simulation method and system based on cellular automata. BACKGROUND

[0002] At present, large trucks account for a large proportion in China's highways, and the traffic flow of large trucks has a great impact on the overall road conditions of highways. The driving speed, acceleration performance and braking performance of large trucks are significantly different from those of small cars, and are more likely to cause mobile bottlenecks and deteriorate traffic conditions. The management of large trucks on highways has always been the focus and difficulty of highway vehicle management. Highways often have lane restrictions, speed restrictions and passage time restrictions for large trucks, which means that the traffic characteristics of large trucks are very different from those of general social vehicles. It is necessary to manage large and medium-sized trucks differently when managing highways. Current vehicle simulation systems for highways do not distinguish between trucks and cannot accurately predict the impact of truck traffic on highway conditions.

[0003] Therefore, it is desirable to provide a traffic flow simulation method and system based on cellular automata, which simulates the traffic flow of highways based on the characteristics of heavy trucks, solves the problem of predicting truck traffic on highways, accurately simulates and predicts heavy truck traffic on highways, provides data reference for highway truck regulation, helps to improve vehicle mobile bottleneck effect, provides decision support for highway vehicle management, and ensures highway safety. SUMMARY

[0004] According to some embodiments of the present application, a traffic flow simulation method based on cellular automata is provided, which is applied to a terminal (such as a management system, etc.). The method can include obtaining lane and vehicle information of a first road section, mapping the first road section to a two-dimensional cellular space; dividing the cellular space into cells according to the minimum unit of lane and vehicle movement; randomly distributing the vehicles in the cells, the state of the cells including a first state and a second state, the first state being no vehicle, and the second state being a vehicle, the type of the vehicle including a first vehicle and a second vehicle; iterating time slices, calculating new positions of the vehicles, and updating the state of the cells according to the new positions; establishing a model based on cellular automata, inputting vehicle density ratio parameters for iterative simulation, and outputting average traffic flow; the vehicle density ratio parameters include the ratio of the first vehicle density and the second vehicle density.

[0005] In some embodiments, the output average traffic flow specifically includes determining the maximum average traffic flow based on different vehicle density ratio parameters; determining the corresponding optimal vehicle density ratio based on the maximum average traffic flow; based on the optimal vehicle density ratio, if the first vehicle density is known, determining the second vehicle density; if the second vehicle density is known, determining the first vehicle density.

[0006] In some embodiments, the calculating of the new position of the vehicle specifically includes obtaining the position X of the vehicle at time t. n (t), n is the number of vehicles; determine the speed v of the vehicle at time t n (t); Iterate one time slice to time t+1 and calculate the new position X of the vehicle n (t+1), X n (t+1)=X n (t)+v n (t)×1.

[0007] In some embodiments, the determination of the vehicle's speed v at time t n (t), specifically including obtaining the speed limit information of the first road section, the speed limit information including a first speed limit and a second speed limit, corresponding to the first vehicle and the second vehicle respectively; determining the vehicle type and vehicle operation, the vehicle operation including a first operation and a second operation; when the first operation is performed, determining that the minimum value of the calculated value and the speed limit information is the speed v corresponding to the vehicle at time t n (t); When the second operation is performed, the maximum value of the calculated value and the zero value is determined to be the speed v at time t corresponding to the vehicle n (t).

[0008] In some embodiments, determining the vehicle operation specifically includes obtaining the safe distance d of the vehicle safe Determine the real-time speed of the vehicle and its distance d from the vehicle in front of it in the same lane n , the real-time speed complies with the speed limit information; determine the vehicle distance d n Is it less than the safe distance d? safe If it is less than the safe distance, d n <d safe , determine whether there is a vehicle within a first distance of the adjacent lane, the first distance being less than the front sight distance; if there is a vehicle within the first distance, determine that the vehicle performs the second operation; if there is no vehicle within the first distance, determine whether the speed of the vehicle within the front sight distance L is not less than the real-time speed of the vehicle; if it is less than the real-time speed of the vehicle, determine that the vehicle performs the second operation; if it is not less than the safe distance, that is, d n ≥d safe , determining that the vehicle performs a first operation.

[0009] In some embodiments, the determination of whether the speed of the vehicle within the forward sight distance L is not less than the real-time speed of the vehicle specifically includes: if it is not less than the real-time speed of the vehicle, according to the lane change probability P Change If the vehicle changes lanes, it changes lanes to the adjacent lane at the real-time speed; if the vehicle does not change lanes, it is determined that the vehicle performs the second operation; if the vehicle is of the first type, the first type of vehicle is restricted from changing lanes to the leftmost lane, that is, P Change =0.

[0010] In some embodiments, when there is no obstruction within the forward sight distance L, the method specifically includes causing the vehicle to perform a second operation according to a probability P in the next time slice.

[0011] In some embodiments, the model is established based on cellular automata and adopts periodic boundary conditions.

[0012] In some embodiments, the periodic boundary condition specifically includes the right boundary of the cellular space serving as a virtual neighbor to the left of the left boundary; and the left boundary of the cellular space serving as a virtual neighbor to the right of the right boundary.

[0013] According to a second aspect of some embodiments of the present application, a system is provided, comprising: a memory configured to store data and instructions; a processor communicating with the memory, wherein when executing instructions in the memory, the processor is configured to: obtain lane and vehicle information of a first road section, and map the first road section into a two-dimensional cellular space; divide the cellular space into cells according to the minimum units of lane and vehicle movement; randomly distribute the vehicles in the cells, the states of the cells comprising a first state and a second state, the first state having no vehicle and the second state having a vehicle, and the types of the vehicles comprising a first vehicle and a second vehicle; iterate time slices, calculate a new position of the vehicle, and update the state of the cell according to the new position; establish a model based on a cellular automaton, input a vehicle density ratio parameter for iterative simulation, and output an average traffic flow; the vehicle density ratio parameter comprises a ratio of a first vehicle density to a second vehicle density.

[0014] Therefore, according to the cellular automaton-based traffic flow simulation method and system in some embodiments of the present application, highway traffic flow simulation is performed based on the characteristics of heavy-loaded trucks, the problem of highway truck flow prediction is solved, and accurate simulation and prediction of heavy-loaded truck flow on highways are achieved, providing data reference for highway truck regulation, helping to improve the bottleneck effect of vehicle movement, providing decision support for highway vehicle management, and ensuring highway safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] For better understanding and illustrating some embodiments of the present application, the following will refer to the description of embodiments in conjunction with the drawings, in which the same reference numerals indicate corresponding parts in the drawings.

[0016] Figure 1 is an exemplary schematic diagram of a cellular automaton-based traffic simulation system according to some embodiments of the present application.

[0017] Figure 2 is an exemplary flowchart of a cellular automaton-based traffic simulation method according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present application as defined by the claims and their equivalents. These embodiments include various specific details to assist in that understanding but these are to be regarded as merely illustrative. Accordingly, those of ordinary skill in the art will recognize that various changes, modifications, and substitutions can be made to the various embodiments described and that such changes, modifications, and substitutions can be made without departing from the scope and spirit of the present application. In addition, for brevity and clarity, descriptions of well-known functions and constructions are omitted so as to not unnecessarily obscure the disclosure before the present application.

[0019] The terms and phrases used in the following description and claims are not limited to the literal meanings of the terms and phrases but are only for enabling a clear and consistent understanding of the present application. Accordingly, it is understood that the description of the various embodiments of the present application is merely provided for illustrative purposes and is not intended to limit the scope of the appended claims and their equivalents.

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in some embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of the present application.

[0021] It should be noted that the terms used in the embodiments of the present application are merely for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a", "an", and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application means and includes any or all possible combinations of one or more associated listed items. The expressions "first", "second", "the first", and "the second" are used to modify the corresponding elements without considering the order or importance, and are merely used to distinguish one element from another element, without limiting the corresponding elements.

[0022] The terminal according to some embodiments of the present application can be a smart terminal, a platform, equipment and / or electronic device, etc.; the smart terminal can include a management and control system, etc. The platform can include a cloud control platform, etc., and the platform can include a system platform composed of one or more electronic devices; the equipment can include an intelligent connected vehicle (ICV); the electronic device can include one or a combination of several of a personal computer (PC, such as a tablet computer, a desktop computer, a notebook computer, a netbook, a palm computer PDA), a client device, a virtual reality device (VR), an augmented reality device (AR), a mixed reality device (MR), an XR device, a rendering machine, a smart phone, a mobile phone, an electronic book reader, a portable multimedia player (PMP), an audio / video player (MP3 / MP4), a camera, and a wearable device, etc. According to some embodiments of the present application, the wearable device can include an accessory type (such as a watch, a ring, a bracelet, glasses, or a head-mounted device (HMD)), an integrated type (such as electronic clothing), a decoration type (such as a skin pad, a tattoo, or an internal electronic device), etc., or a combination of several thereof. In some embodiments of the present application, the electronic device can be flexible, not limited to the above-mentioned devices, or can be a combination of one or several of the above-mentioned various devices. In the present application, the term "user" can indicate a person using an electronic device or a device (such as an artificial intelligence electronic device) using an electronic device.

[0023] The embodiments of the present application provide a car flow simulation method and system based on a cellular automaton. In order to facilitate understanding of the embodiments of the present application, the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 is an exemplary schematic diagram of a car flow simulation system based on a cellular automaton according to some embodiments of the present application. As shown in Figure 1The cellular automation-based traffic flow simulation system 100 may include a network 110, a control terminal 120, a user terminal 130, and a server 140. Specifically, the control terminal 120 and the user terminal 130 establish communication via the network. For example, the control terminal 120 and the user terminal 130 may communicate in the same local area network (e.g., a network environment with the same router). Furthermore, the control terminal 120 may be connected to the network 110 via a wired (e.g., network cable) or wireless (e.g., cloud server), and the user terminal 130 may establish a communication connection with the network 110 via a wired or wireless (e.g., WIFI). In some embodiments, the user terminal 130 may send vehicle location information, real-time speed, and other information to the control terminal 120 and the server 140. Furthermore, the control terminal 120 and the server 140 may provide feedback to the user terminal 130 on vehicle motion predictions and traffic flow status. Based on the feedback, the user terminal 130 may make adjustments based on the vehicle motion predictions and traffic flow status. As an example, the server 140 and / or the control terminal 120 may simulate the evolution state of traffic flow based on pre-set evolution rules.

[0025] According to some embodiments of the present application, the control terminal 120 and the user terminal 130 may be the same or different terminal devices. The terminal devices may include, but are not limited to, a control system, a cloud control platform, a smart terminal, a mobile terminal, a computer, etc. In an intelligent transportation scenario, the control terminal 120 may include a control system, etc., and the user terminal 130 may include a display device, etc. In some embodiments, the control terminal 120 and the user terminal 130 may be integrated into a single device, such as a control system. In some embodiments, the server 140 is a type of computer that has advantages over ordinary computers, such as faster operation and higher load capacity, but is correspondingly more expensive. In a network environment, the server can provide computing or application services to other clients (e.g., PCs, smartphones, terminals such as ATMs, and large-scale equipment such as transportation systems). The server has high-speed CPU computing power, long-term reliable operation, strong I / O external data throughput, and improved scalability. The services that the server can provide include, but are not limited to, the ability to respond to service requests, provide services, and ensure services. As an electronic device, the server has an extremely complex internal structure, including an internal structure similar to that of an ordinary computer. As an example, the internal structure of the server may include a central processing unit (CPU), a hard disk, a memory, a system, a system bus, etc.

[0026] In some embodiments of the present application, the cellular automaton-based traffic simulation system 100 can omit one or more elements, or can further include one or more other elements. As an example, the cellular automaton-based traffic simulation system 100 can include a plurality of user terminals 130, such as a plurality of smart devices, etc. As another example, the cellular automaton-based traffic simulation system 100 can include one or more control terminals 120. As a further example, the cellular automaton-based traffic simulation system 100 can include a plurality of servers 140, etc. In some embodiments, the cellular automaton-based traffic simulation system 100 can include, but is not limited to, a system for issuing a control policy based on a smart traffic scenario in a city, etc. The network 110 can be any type of communication network, which can include a computer network (e.g., a local area network (LAN) or a wide area network (WAN)), the Internet, and / or a telephone network, etc., or a combination of several. In some embodiments, the network 110 can be another type of wireless communication network. The wireless communication can include microwave communication and / or satellite communication, etc. The wireless communication can include cellular communication, such as global system for mobile communications (GSM), code division multiple access (CDMA), the 3rd generation telecommunication (3G), the 4th generation mobile communication (4G), the 5th generation mobile communication (5G), the 6th generation mobile communication (6G), long term evolution (LTE), LTE-advanced (LTE-A), wideband code division multiple access (WCDMA), universal mobile telecommunications system (UMTS), wireless broadband (WiBro), etc., or a combination of several.In some embodiments, the user terminal 130 can be other equipment and / or electronic devices with equivalent functions, which can include one or a combination of an intelligent connected vehicle (ICV), a virtual reality device (VR), a rendering machine, a personal computer (PC, such as a tablet, a desktop, a notebook, a netbook, a palm computer PDA), a smart phone, a mobile phone, an electronic book reader, a portable multimedia player (PMP), an audio / video player (MP3 / MP4), a camera, and a wearable device, etc.

[0027] In some embodiments, the WIFI can be other types of wireless communication technologies. According to some embodiments of the present application, the wireless communication can include wireless fidelity (WiFi), Bluetooth, Bluetooth low energy (BLE), ZigBee, near field communication (NFC), magnetic secure transmission, radio frequency, body area network (BAN), etc., or a combination of several. According to some embodiments of the present application, the wired communication can include global navigation satellite system (Glonass / GNSS), global positioning system (GPS), Beidou navigation satellite system, or Galileo (European global satellite navigation system), etc. The wired communication can include universal serial bus (USB), high-definition multimedia interface (HDMI), recommend standard 232 (RS-232), and / or plain old telephone service (POTS), etc., or a combination of several.

[0028] It should be noted that the above description of the traffic flow simulation system 100 based on cellular automata is for convenience of description only and does not limit the present application to the scope of the embodiments cited. It is understandable that those skilled in the art, based on the principles of the present system, may arbitrarily combine the various components, or form a subsystem connected with other components without departing from the principles, and make various modifications and changes in form and details to the application fields of the above methods and systems. For example, the control terminal 120 / user terminal 130 can be integrated into the management and control system, etc. Such variations are all within the scope of protection of the present application.

[0029] Figure 2 FIG. 1 is an exemplary flow chart of a traffic flow simulation method based on cellular automata according to some embodiments of the present application. Figure 2 The process 200 can be implemented by the cellular automaton-based traffic flow simulation system 100. In some embodiments, the cellular automaton-based traffic flow simulation method 200 can be automatically initiated or initiated by an instruction. The instruction can include a system instruction, a device instruction, a user instruction, an action instruction, or a combination thereof.

[0030] At 201, lane and vehicle information for a first road section is obtained, and the first road section is mapped into a two-dimensional cellular space. Operation 201 can be implemented by the control terminal 120 and the server 140 of the cellular automaton-based traffic flow simulation system 100. In some embodiments, the control terminal 120 and / or the server 140 can obtain lane and vehicle information for the first road section and map the first road section into a two-dimensional cellular space. In some embodiments, the first road section may include a monitored section of a highway, and the monitored section may include a vehicle movement bottleneck section, an accident-prone section, a section with high traffic volume, etc.

[0031] In some embodiments, the lane and vehicle information includes static parameters, dynamic parameters, and set parameters. The static parameters may include road parameters, vehicle physical parameters, etc. The road parameters may include road length, number of lanes, and speed limits for each lane. The vehicle physical parameters may include vehicle type, vehicle acceleration, braking deceleration, and maximum speed limit. The dynamic parameters may include vehicle flow rate, etc. As an example, the set parameters may include vehicle lane change probability, random deceleration probability, etc.

[0032] At 202, the cellular space is divided into cells based on the lanes and the minimum unit of vehicle movement. Operation 202 can be implemented by the control terminal 120 and the server 140 of the cellular automaton-based traffic flow simulation system 100. In some embodiments, the control terminal 120 and / or the server 140 can divide the cellular space into cells based on the lanes and the minimum unit of vehicle movement.

[0033] At 203, the vehicles are randomly distributed in the cells, where the states of the cells include a first state and a second state, where there are no vehicles in the first state and there are vehicles in the second state, and the types of the vehicles include a first vehicle and a second vehicle. Operation 203 can be implemented by the control terminal 120 and the server 140 of the traffic flow simulation system 100 based on cellular automata. In some embodiments, the control terminal 120 and / or the server 140 can randomly distribute the vehicles in the cells, where the states of the cells include a first state and a second state. For example, there are no vehicles in the first state and there are vehicles in the second state, and the types of the vehicles include a first vehicle and a second vehicle. The first vehicles can include large vehicles or heavy vehicles, such as trucks. The second vehicles can include small vehicles, etc.

[0034] At 204, the time slices are iterated to calculate the new position of the vehicle, and the state of the cell is updated based on the new position. Operation 204 can be implemented by the server 140 and / or the control terminal 120 of the cellular automaton-based traffic flow simulation system 100. In some embodiments, the server 140 and / or the control terminal 120 can iterate the time slices to calculate the new position of the vehicle, and update the state of the cell based on the new position.

[0035] In some embodiments, calculating the new position of the vehicle may specifically include obtaining the position X of the vehicle at time t n (t), n is the number of vehicles; determine the speed v of the vehicle at time t n (t); Iterate one time slice to time t+1 and calculate the new position X of the vehicle n (t+1), X n (t+1)=X n (t)+v n (t) × 1. The time slice is a unit of processing time for a simulation, for example, a time slice of one step may be one second (1s).

[0036] In some embodiments, the determination of the vehicle's speed v at time t n (t) may specifically include obtaining speed limit information of the first road section, the speed limit information including a first speed limit and a second speed limit, corresponding to the first vehicle and the second vehicle respectively; determining the vehicle type and vehicle operation, the vehicle operation including a first operation and a second operation; when the first operation is performed, determining that the minimum value between the calculated value and the speed limit information is the speed v corresponding to the vehicle at time t n (t); When the second operation is performed, the maximum value of the calculated value and the zero value is determined to be the speed v at time t corresponding to the vehicle n (t).

[0037] As an example, the first operation may include an acceleration operation, and the second operation may include a deceleration operation, a braking operation, etc. For example, the maximum speed limit v of the second vehicle (such as a small vehicle) is set. limit =33.3m / s, the maximum speed limit of the first vehicle (such as a truck) v limit ′ =27.8m / s; the first vehicle and the second vehicle have different acceleration and deceleration characteristics. The speed of the vehicle changes in a time slice of one step. The time slice is set to 1 second. When the first operation (such as acceleration) is performed, the speed of the truck is the minimum value v of the calculated value and the maximum speed limit. t+1 ′=min{v t +1.4, 27.8}, the speed of small vehicles is the minimum of the calculated value and the maximum speed limit v t+1 =min{v t +3.3, 33.3}; When the second operation (such as deceleration) is performed, the speed of the truck takes the maximum value v of the calculated value and zero value t+1 ′=max{v t -2.3, 0}, the speed of the small vehicle takes the maximum value of the calculated value and zero value v t+1 =

[0038] max{v t -11.1, 0}; With the iteration of the time slice, the location of the vehicle will change, and the state of the cell corresponding to the new location is updated to the second state (vehicle), and the state of the cell corresponding to the old location is updated to the first state (no vehicle).

[0039] In some embodiments, determining the vehicle operation may specifically include obtaining the safe distance d of the vehicle safe Determine the real-time speed of the vehicle and its distance d from the vehicle in front of it in the same lane n , the real-time speed complies with the speed limit information; determine the vehicle distance d n Is it less than the safe distance d? safe If it is less than the safe distance, d n <d safe , determine whether there is a vehicle within a first distance of the adjacent lane, the first distance being less than the front sight distance; if there is a vehicle within the first distance, determine that the vehicle performs the second operation; if there is no vehicle within the first distance, determine whether the speed of the vehicle within the front sight distance L is not less than the real-time speed of the vehicle; if it is less than the real-time speed of the vehicle, determine that the vehicle performs the second operation; if it is not less than the safe distance, that is, d n ≥d safe , determining that the vehicle performs a first operation.

[0040] In some embodiments, the determination of whether the speed of the vehicle within the forward sight distance L is not less than the real-time speed of the vehicle may specifically include: if it is not less than the real-time speed of the vehicle, according to the lane change probability P Change If the vehicle changes lanes, it changes lanes to the adjacent lane at the real-time speed; if the vehicle does not change lanes, it is determined that the vehicle performs the second operation; if the vehicle is of the first type, the first type of vehicle is restricted from changing lanes to the leftmost lane, that is, P Change = 0. As an example, the first vehicle (truck) is not allowed to change lanes to the leftmost lane.

[0041] As an example, when the distance d to the vehicle ahead in the same lane is n <d safe When , it can be further judged that there is no vehicle within the distance d in the adjacent lane, and the speed of the vehicle ahead within the front sight distance L is v m Not less than the vehicle's real-time speed v n , the vehicle changes lanes with probability P Change Change lanes to the adjacent lane, and maintain the real-time speed v n If no lane change occurs, the second operation (such as deceleration) is performed. For another example, when there is a vehicle within a distance d ahead in the adjacent lane, or the speed of the vehicle ahead within a sight distance L in the adjacent lane is v m Less than the vehicle's real-time speed v n , the vehicle performs a second operation (such as a deceleration operation). The front distance d is less than the front sight distance L. The vehicle real-time speed v n Less than the current vehicle type speed limit v limit or v limit ′ For example, when the distance d from the vehicle in front of the same lane is n ≥d safe , the vehicle may perform a first operation (such as an acceleration operation).

[0042] In some embodiments, when there is no obstruction within the forward sight distance L, the vehicle may be caused to perform a second operation (e.g., a deceleration operation) in the next time slice according to probability P. For example, the deceleration operation performed according to probability P can simulate a sudden failure or a driver's random deceleration in reality, thereby improving the accuracy of the model simulation.

[0043] At 205 , a model is established based on cellular automata, a vehicle density ratio parameter is input, an iterative simulation is performed, and an average vehicle flow rate is output; the vehicle density ratio parameter includes the ratio of the first vehicle density to the second vehicle density. Operation 205 can be implemented by the server 140 and / or the control terminal 120 of the cellular automata-based traffic flow simulation system 100 . In some embodiments, the server 140 and / or the control terminal 120 can establish a model based on cellular automata, input the vehicle density ratio parameter, an iterative simulation is performed, and the average vehicle flow rate is output. The vehicle density ratio parameter may include the ratio of the first vehicle density to the second vehicle density.

[0044] In some embodiments, the output average traffic flow may specifically include determining the maximum average traffic flow based on different vehicle density ratio parameters; determining the corresponding optimal vehicle density ratio based on the maximum average traffic flow; based on the optimal vehicle density ratio, if the first vehicle density is known, determining the second vehicle density; if the second vehicle density is known, determining the first vehicle density.

[0045] As an example, after the model is built, iterative simulations are performed using different vehicle density parameters for cars and trucks. The average traffic flow corresponding to each vehicle density is recorded. Based on the different vehicle densities, the relationship between vehicle density and average traffic flow is calculated to determine the optimal vehicle density ratio that maximizes the average traffic flow. For example, based on the optimal vehicle density ratio, if the large vehicle density is determined, the optimal small vehicle density is calculated; conversely, if the small vehicle density is determined, the optimal large vehicle density is calculated.

[0046] According to some embodiments of the present application, process 200 may further include establishing a model based on a cellular automation, employing periodic boundary conditions. The periodic boundary conditions may specifically include the right boundary of the cellular space serving as a virtual neighbor to the left of the left boundary, and the left boundary of the cellular space serving as a virtual neighbor to the right of the right boundary. The periodic boundary forms a ring on the simulated map.

[0047] As an example, the leftmost and rightmost sides of the first road segment are adjacent in the model logic. For example, a vehicle moving from left to right can enter the left side after leaving the right boundary. This prevents the randomness of new individuals from interfering with the simulation results. Furthermore, the model data is calculated after 2000 time slice iterations to eliminate the impact of the initial random state on the simulation results.

[0048] According to some embodiments of the present application, the vehicle behavior prediction and traffic flow status feedback information obtained based on the simulation results can be displayed in the user interface (UI) of the user terminal 130. The display scenarios of the vehicle behavior prediction and traffic flow status feedback information can include but are not limited to scene display through any form or combination of VR, AR, MR, and XR.

[0049] It should be noted that the above description of process 200 is for ease of description only and does not limit the present application to the scope of the illustrated embodiments. It is understood that those skilled in the art, based on the principles of this system, may arbitrarily combine the various operations, or construct sub-processes and combinations with other operations, without departing from such principles, and make various modifications and changes in the form and details of the functions implementing the above-mentioned processes and operations. For example, process 200 may further include operations such as modeling based on cellular automata and employing periodic boundary conditions. Such variations are within the scope of protection of this application.

[0050] In summary, according to the cellular automaton-based traffic flow simulation method and system of the embodiment of the present application, highway traffic flow simulation is performed based on the characteristics of heavy-loaded trucks, the problem of highway truck flow prediction is solved, and accurate simulation and prediction of heavy-loaded truck flow on highways are achieved, providing data reference for highway truck regulation, helping to improve the bottleneck effect of vehicle movement, providing decision support for highway vehicle management, and ensuring highway safety.

[0051] It should be noted that the above embodiments are merely examples, and the present application is not limited to such examples, but may be modified in various ways.

[0052] It should be noted that, in this specification, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0053] Finally, it should be noted that the above series of processes include not only processes executed in time series in the order described herein, but also processes executed in parallel or separately rather than in time series.

[0054] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented through hardware associated with computer program instructions. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0055] The above disclosures are only some preferred embodiments of the present application and are not intended to limit the scope of the present application. A person skilled in the art will understand that all or part of the processes of the above embodiments and equivalent changes made in accordance with the claims of the present application are still within the scope of the invention.

Claims

1. A traffic flow simulation method based on cellular automata, characterized in that: include: Acquire lane and vehicle information of a first road section, and map the first road section into a two-dimensional cellular space; Dividing the cellular space into cells according to the lane and the minimum unit of vehicle movement; Randomly distributing the vehicles in the cells, the states of the cells include a first state and a second state, the first state has no vehicles, the second state has vehicles, and the types of the vehicles include a first vehicle and a second vehicle; Iterate the time slice, calculate the new position of the vehicle, and update the state of the cell according to the new position; A model is established based on cellular automation, a vehicle density ratio parameter is input for iterative simulation, and an average vehicle flow rate is output; the vehicle density ratio parameter includes a ratio of a first vehicle density to a second vehicle density; The determination of the vehicle's speed v at time t n (t), including: Obtaining speed limit information for the first road section, the speed limit information including a first speed limit and a second speed limit, corresponding to the first vehicle and the second vehicle, respectively; determining the vehicle type and the vehicle operation, wherein the vehicle operation includes a first operation and a second operation; When the first operation is performed, the minimum value between the calculated value and the speed limit information is determined to be the speed v corresponding to the time t of the vehicle. n (t); When the second operation is performed, the maximum value between the calculated value and the zero value is determined to be the speed v at time t corresponding to the vehicle. n (t); The determining of the vehicle operation specifically includes: Get the safe distance d of the vehicle safe and forward sight distance L; Determine the real-time speed of the vehicle and the distance d between the vehicle and the vehicle ahead in the same lane n , the real-time speed complies with the speed limit information; Determine the vehicle distance d n Is it less than the safe distance d? safe ; If it is less than the safe distance, that is, d n <d safe , determining whether there is a vehicle within a first distance of an adjacent lane, the first distance being less than the forward sight distance; If there is a vehicle within the first distance, determining that the vehicle performs a second operation; If there is no vehicle within the first distance, determining whether the speed of the vehicle within the forward sight distance L is not less than the real-time speed of the vehicle; If the speed is less than the real-time speed of the vehicle, determining that the vehicle performs a second operation; If it is not less than the safe distance, that is, d n ≥d safe , determining that the vehicle performs a first operation.

2. The method according to claim 1, characterized in that The output average traffic flow specifically includes: Determine the maximum average traffic flow according to different vehicle density ratio parameters; Based on the maximum average traffic flow, determine the corresponding optimal vehicle density ratio; Based on the optimal vehicle density ratio, if the first vehicle density is known, the second vehicle density is determined; if the second vehicle density is known, the first vehicle density is determined.

3. The method according to claim 2, characterized in that The calculating of the new position of the vehicle specifically includes: Get the vehicle's position X at time t n (t), n is the number of vehicles; Determine the vehicle's speed v at time t n (t); Iterate one time slice to time t+1 and calculate the new position X of the vehicle n (t+1), X n (t+1)=X n (t)+v n (t)×1.

4. The method according to claim 3, characterized in that The determining whether the speed of the vehicle within the forward sight distance L is not less than the real-time speed of the vehicle specifically includes: If it is not less than the real-time speed of the vehicle, according to the lane change probability P Change If the vehicle changes lanes, it changes lanes to an adjacent lane at the real-time speed; If the vehicle does not change lanes, determining that the vehicle performs a second operation; If the vehicle is of the first type, the first type of vehicle is restricted from changing lanes to the leftmost lane, i.e., P Change =0.

5. The method according to claim 4, characterized in that When there is no obstruction within the forward sight distance L, the following steps are specifically performed: In the next time slice, according to probability P, the vehicle is made to perform a second operation.

6. The method according to claim 1, characterized in that The model is established based on cellular automata and adopts periodic boundary conditions.

7. The method according to claim 6, characterized in that The periodic boundary conditions specifically include: The right boundary of the cell space serves as a virtual neighbor to the left of the left boundary; The left boundary of the cell space serves as a virtual neighbor to the right of the right boundary.

8. A system characterized in that include: a memory configured to store data and instructions; A processor in communication with a memory, wherein when executing instructions in the memory, the processor is configured to: Acquire lane and vehicle information of a first road section, and map the first road section into a two-dimensional cellular space; Dividing the cellular space into cells according to the lane and the minimum unit of vehicle movement; Randomly distributing the vehicles in the cells, the states of the cells include a first state and a second state, the first state has no vehicles, the second state has vehicles, and the types of the vehicles include a first vehicle and a second vehicle; Iterate the time slice, calculate the new position of the vehicle, and update the state of the cell according to the new position; A model is established based on cellular automation, a vehicle density ratio parameter is input for iterative simulation, and an average vehicle flow rate is output; the vehicle density ratio parameter includes a ratio of a first vehicle density to a second vehicle density; The determination of the vehicle's speed v at time t n (t), including: Obtaining speed limit information for the first road section, the speed limit information including a first speed limit and a second speed limit, corresponding to the first vehicle and the second vehicle, respectively; determining the vehicle type and the vehicle operation, wherein the vehicle operation includes a first operation and a second operation; When the first operation is performed, the minimum value between the calculated value and the speed limit information is determined to be the speed v corresponding to the time t of the vehicle. n (t); When the second operation is performed, the maximum value between the calculated value and the zero value is determined to be the speed v at time t corresponding to the vehicle. n (t); The determining of the vehicle operation specifically includes: Get the safe distance d of the vehicle safe and forward sight distance L; Determine the real-time speed of the vehicle and the distance d between the vehicle and the vehicle ahead in the same lane n , the real-time speed complies with the speed limit information; Determine the vehicle distance d n Is it less than the safe distance d? safe ; If it is less than the safe distance, that is, d n <d safe , determining whether there is a vehicle within a first distance of an adjacent lane, the first distance being less than the forward sight distance; If there is a vehicle within the first distance, determining that the vehicle performs a second operation; If there is no vehicle within the first distance, determining whether the speed of the vehicle within the forward sight distance L is not less than the real-time speed of the vehicle; If the speed is less than the real-time speed of the vehicle, determining that the vehicle performs a second operation; If it is not less than the safe distance, that is, d n ≥d safe , determining that the vehicle performs a first operation.

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