Traffic dispersion control method based on digital block

Abstract

The invention relates to the technical field of digital traffic, and discloses a traffic dispersion control method based on a digital block, and the method comprises the steps: obtaining target road data and target dynamic data of a target block, judging the traffic condition of the target block, carrying out the traffic evaluation of the target block, carrying out the traffic early warning of an abnormal collection point of the target block, and carrying out the early warning of the abnormal collection point. According to the traffic dispersion control method based on the digital block, whether the road of the block has traffic congestion is judged, whether vehicle driving and pedestrian flow change are normal is judged, whether a behavior possibly causing traffic congestion exists in the block is estimated, and the traffic dispersion control method based on the digital block is used for performing traffic dispersion and arranging and updating a traffic feedback data set. The traffic jam condition in the block is reduced, traffic dispersion is carried out timely and quickly when the traffic jam occurs, traffic accidents are reduced, and the safety of passing vehicles and personnel is guaranteed.

Description

Technical Field

[0001] The invention relates to the field of digital traffic technology, in particular to a traffic diversion control method based on digital blocks. Background Art

[0002] The traffic diversion and control method based on digital blocks combines modern digital technology and intelligent transportation concepts, and aims to optimize traffic management and control through real-time data collection and analysis. It can not only improve traffic efficiency and reduce congestion, but also provide effective support for the sustainable development of the city. With the acceleration of urbanization, traffic demand continues to increase, and problems such as traffic congestion, increased accidents and environmental pollution are becoming increasingly prominent. Traditional traffic management methods can no longer meet the needs of modern cities and need to be reformed with the help of digital means. Through urban traffic management, digital technology, Internet of Things (IoT), big data analysis and intelligent transportation systems, by integrating advanced data acquisition and analysis technologies, combined with intelligent control mechanisms and public participation, it will help improve the efficiency and safety of urban traffic. Through continuous optimization and adjustment, the flexibility and adaptability of urban traffic management will be improved, and ultimately a more intelligent transportation system will be realized; The existing traffic diversion and control methods based on digital blocks cannot determine whether there is traffic congestion on the roads in the block, cannot determine whether the changes in vehicle driving and pedestrian flow are normal, and cannot predict whether there are behaviors in the block that may cause traffic congestion, making the block prone to traffic congestion and unable to conduct traffic diversion in a timely and rapid manner, which is prone to traffic accidents and affects the safety of passing vehicles and personnel. Its practicality has certain limitations. Summary of the invention

[0003] The present invention provides a traffic diversion control method based on digital blocks, which is used to promote the solution of the problems mentioned in the background technology.

[0004] The present invention provides the following technical solution: a traffic diversion control method based on a digital block, comprising:

[0005] Acquire an image of a target block and define it as a target image;

[0006] Acquire road data in the target image and define it as target road data;

[0007] Acquire dynamic data in the target image and define it as target dynamic data;

[0008] Determine the traffic conditions of the target block based on the target road data and target dynamic data;

[0009] If the target block has smooth traffic, then conduct a traffic assessment on the target block;

[0010] If the traffic in the target block is orderly, then the target block can continue to be monitored;

[0011] If the traffic in the target block is disordered, the data collection point where the traffic in the target collection point is disordered is extracted and determined as an abnormal collection point;

[0012] Provide traffic warnings for abnormal collection points in target blocks;

[0013] If the target block is congested, obtain a traffic feedback dataset;

[0014] According to the traffic feedback data set and the traffic conditions of the target block, the traffic in the target block is diverted until the traffic in the target block is smooth;

[0015] Obtain traffic congestion data for the target block and organize and update the traffic feedback dataset.

[0016] As an optional solution of the traffic diversion control method based on digital blocks described in the present invention, the traffic conditions of the target blocks are judged according to the target road data and the target dynamic data, specifically:

[0017] Acquiring target road data, wherein the target road data includes unit road data and special road data;

[0018] Obtain all unit road data within the target block to form a unit road set;

[0019] Identify each element in the unit road set as a target unit element in turn;

[0020] Get the current traffic flow of the target unit element;

[0021] Get the unit of the target unit element to determine the traffic flow;

[0022] If the current traffic volume ≥ the unit determined traffic volume × 80%, the traffic flow of the unit road is determined to be normal;

[0023] If the current traffic volume is less than the unit determined traffic volume × 80%, the unit road traffic volume is determined to be abnormal;

[0024] If all elements in the unit road set determine that the traffic flow of the unit road is normal, then the unit road data is determined to be normal;

[0025] If there are elements in the unit road set that determine that the traffic flow on the unit road is abnormal, then the unit road data is determined to be abnormal;

[0026] Obtain all special road data within the target block to form a special road set;

[0027] Identify each element in the special road set as a target special element in turn;

[0028] Obtain the analysis of vehicle flow and pedestrian flow of target special elements;

[0029] Obtain the special judgment vehicle flow and special judgment human flow of the target special element;

[0030] If the analyzed vehicle flow ≥ the special determined vehicle flow × 80% and the analyzed pedestrian flow ≥ the special determined pedestrian flow × 80%, the traffic flow on the special road is judged to be normal;

[0031] If the analyzed vehicle flow is less than the special determined vehicle flow × 80% or the analyzed pedestrian flow is less than the special determined pedestrian flow × 80%, the traffic flow on the special road is determined to be abnormal;

[0032] If all elements in the unit road set determine that the flow rate of the special road is normal, then the special road data is determined to be normal;

[0033] If there are elements in the unit road set that determine that the flow of the special road is abnormal, then the special road data is determined to be abnormal;

[0034] If the unit road data is judged to be normal and the special road data is judged to be normal, the traffic in the target block is judged to be smooth;

[0035] If the unit road data is determined to be abnormal or the special road data is determined to be abnormal, the target block is determined to be traffic congested.

[0036] As an optional solution of the traffic diversion control method based on digital blocks described in the present invention, the traffic evaluation of the target block is specifically as follows:

[0037] Set up several data collection points in the target neighborhood;

[0038] Define each data collection point as a target collection point;

[0039] Conduct vehicle analysis on target collection points;

[0040] If all vehicles at the target collection point determine that the target vehicle is traveling at a normal speed and that the target vehicle is traveling normally, then the vehicle at the target collection point is determined to be traveling normally;

[0041] If there is a vehicle at the target collection point and the target vehicle is judged to be traveling at an abnormal speed or is judged to be traveling abnormally, then the vehicle at the target collection point is traveling abnormally;

[0042] Conduct crowd flow analysis at target collection points;

[0043] If the vehicles at the target collection point are running normally and the flow of people at the target collection point is normal, then the traffic at the target collection point is judged to be orderly;

[0044] If the vehicle driving at the target collection point is abnormal or the flow of people at the target collection point is abnormal, the traffic at the target collection point is judged to be disorderly;

[0045] If all data collection points determine that the traffic at the target collection point is orderly, then the traffic in the target block is determined to be orderly;

[0046] If there is a data collection point that determines that the traffic at the target collection point is disorderly, then the traffic in the target block is determined to be disorderly.

[0047] As an optional solution of the traffic diversion control method based on digital blocks described in the present invention, vehicle analysis is performed on the target collection point, including vehicle trajectory analysis, specifically:

[0048] Obtain all vehicles collected at the target collection point to form a vehicle set;

[0049] Identify each element in the vehicle set as a target vehicle in turn;

[0050] Obtain the road section to which the target collection point belongs, and define it as the target road section;

[0051] Obtain the driving trajectory of the target vehicle from entering the target road section to the target collection point, and define it as the target trajectory;

[0052] Set a number of data points on the target trajectory as trajectory points;

[0053] Get all trajectory points to form a trajectory point set;

[0054] Get the coordinates of each element in the trajectory point set and set them as trajectory coordinates;

[0055] Each trajectory coordinate is matched one by one with each element in the trajectory point set to form a trajectory set;

[0056] Starting from the first element in the trajectory set, two adjacent elements in the trajectory set are extracted in sequence and set as the first element and the second element respectively;

[0057] Extract the trajectory coordinates of the first element and the second element, and define them as the first trajectory coordinates and the second trajectory coordinates, respectively and ;

[0058] Calculate the trajectory offset between the first element and the second element : ;

[0059] Get the lane width of the target road section, denoted as W;

[0060] Obtain the driving speed of the target vehicle on the target road section, denoted as S;

[0061] Calculate the speed factor k: ;

[0062] Among them, b and c are adjustable constants set according to actual traffic conditions, vehicle behavior and experience;

[0063] Calculate the target vehicle's offset threshold : ;

[0064] like , it is determined that the target vehicle is traveling normally on this trajectory;

[0065] like , then the target vehicle is determined to have changed lanes, and the number of lane changes is recorded and updated;

[0066] Set the judgment time;

[0067] The number of lane changes of the target vehicle within the determination time is obtained, which is defined as the determination number;

[0068] Set the number of comparisons;

[0069] If the number of determinations of the target vehicle is less than or equal to the number of comparisons, it is determined that the lane change of the target vehicle meets the first condition;

[0070] If the number of determinations of the target vehicle is greater than the number of comparisons, it is determined that the lane change of the target vehicle does not meet the first condition;

[0071] Get the speed of the target vehicle before changing lanes, recorded as ;

[0072] Set the ratio threshold α;

[0073] Calculate speed change threshold : ;

[0074] Get the speed of the target vehicle after changing lanes, recorded as ;

[0075] like , it is determined that the target vehicle lane change meets the second condition;

[0076] like , it is determined that the target vehicle lane change does not meet the second condition;

[0077] If it is determined that the target vehicle's lane change meets the first condition and it is determined that the target vehicle's lane change meets the second condition, then it is determined that the target vehicle is traveling normally on the trajectory;

[0078] If it is determined that the target vehicle lane change does not meet the first condition or it is determined that the target vehicle lane change does not meet the second condition, then it is determined that the target vehicle is driving abnormally on the trajectory;

[0079] If all elements in the trajectory set determine that the target vehicle is driving normally on the trajectory, then the target vehicle is determined to be driving normally;

[0080] If there is an element in the trajectory set that determines that the target vehicle is driving abnormally on the trajectory, then it is determined that the target vehicle is driving abnormally.

[0081] As an optional solution of the traffic diversion control method based on digital blocks described in the present invention, vehicle analysis is performed on the target collection point, including vehicle speed analysis, specifically:

[0082] Get the target vehicle;

[0083] Acquire all images of the target vehicle collected by the target collection point to form an image set;

[0084] Arbitrarily extract two adjacent images from the image set and define them as the first image and the second image respectively;

[0085] Obtaining a vehicle center point of the target vehicle in the first image as a first center point;

[0086] Obtaining a vehicle center point of the target vehicle in the second image as a second center point;

[0087] Get the coordinates of the first center point and the second center point respectively, set them as the first coordinate and the second coordinate, respectively recorded as and ;

[0088] Get the actual width of the target vehicle, recorded as ;

[0089] Get the pixel width of the target vehicle in the image, recorded as ;

[0090] Get the video frame rate, recorded as fps;

[0091] Calculate the speed v of the target vehicle: ;

[0092] Set the speed threshold range;

[0093] If the speed v> the maximum value of the speed threshold range or the speed v< the minimum value of the speed threshold range, it is determined that the target vehicle is traveling at an abnormal speed;

[0094] If the speed v ≤ the maximum value of the speed threshold range and the speed v ≥ the minimum value of the speed threshold range, it is determined that the target vehicle's driving speed is normal.

[0095] As an optional solution of the traffic diversion control method based on digital blocks described in the present invention, the crowd flow analysis is performed on the target collection point, specifically:

[0096] Get the current time;

[0097] Get the sub-period set;

[0098] Get the cycle number corresponding to the current time and set it as the current cycle number;

[0099] Get all the times corresponding to the current period number in the sub-period set and set them as historical time;

[0100] Get all historical time to form a historical time set;

[0101] Obtain the crowd density of the target collection point at each historical time in the historical time set, and define it as the historical crowd density;

[0102] Get the number of elements in the historical time set and define it as the number of historical collections;

[0103] Calculate the historical average population density, historical average population density = sum of all historical population densities ÷ historical collection quantity;

[0104] Obtain the crowd density of the target collection point at the current time, which is defined as the current crowd density;

[0105] If the current population density is ≥ the historical average population density × (1-15%) and the current population density is ≤ the historical average population density × (1+15%), then the flow of people at the target collection point is determined to be normal;

[0106] If the current population density is less than the historical average population density × (1-15%) or the current population density is greater than the historical average population density × (1+15%), the population flow at the target collection point is judged to be abnormal.

[0107] As an optional solution of the traffic diversion control method based on digital blocks described in the present invention, the sub-period set is obtained, specifically:

[0108] Get the current time;

[0109] Set the maximum collection time;

[0110] Set the unit duration;

[0111] Calculate the collection start time, collection start time = current time - (collection duration + unit duration);

[0112] Calculate the collection end time, collection end time = current time - unit duration;

[0113] The collection start time is taken as the starting time, and the collection end time is taken as the ending time to form a collection cycle;

[0114] Set the minimum collection time;

[0115] Taking the collection start time as the starting time, each interval is a small collection time, forming a small cycle collection point within the collection cycle;

[0116] Get the collection start time and all small periodic collection points to form a periodic point set;

[0117] Sequentially extract two adjacent elements in the periodic point set to form a sub-collection period;

[0118] All sub-collection periods within a collection period are integrated to form a sub-period set.

[0119] As an optional solution of the traffic diversion control method based on digital blocks of the present invention, traffic diversion for the target block is specifically performed as follows:

[0120] Acquire road data for determining abnormal unit road data or abnormal special road data, and define it as an abnormal road;

[0121] Obtain the starting point of congestion in abnormal roads;

[0122] Get traffic feedback dataset;

[0123] If the traffic feedback data set has the congestion starting point, then the traffic diversion operation corresponding to the congestion starting point in the traffic feedback data set is obtained and defined as the historical operation;

[0124] Traffic diversion can be carried out on abnormal roads based on historical operations;

[0125] If the traffic feedback data set does not contain the congestion starting point, then obtain the target road data corresponding to the congestion starting point;

[0126] If the congestion starting point is a special road, adjust the device data of the congestion starting point;

[0127] If the starting point of the congestion is a unit road, the passenger flow at the starting point of the congestion is obtained and determined as the congested passenger flow;

[0128] If there is crowd congestion, adjust the flow of people;

[0129] If there is no traffic congestion, adjust the vehicle movement.

[0130] As an optional solution of the traffic diversion control method based on digital blocks described in the present invention, the traffic congestion data of the target block is obtained, and the traffic feedback data set is sorted and updated, specifically:

[0131] Obtain all abnormal roads and congestion starting points;

[0132] Obtain target road data corresponding to each abnormal road and its congestion starting point, and define them as congested road data;

[0133] Obtain the congested passenger flow corresponding to each abnormal road and its congestion starting point, and define it as abnormal congested passenger flow;

[0134] Obtain the traffic diversion operation corresponding to each abnormal road and its congestion starting point, and define it as a congestion operation;

[0135] Obtain the time when the target block is judged to be congested, and set it as the congestion start time;

[0136] The time when traffic in the target block is smooth for the first time after the congestion start time is obtained and is defined as the congestion end time;

[0137] Integrate all abnormal roads and congestion starting points, congested road data, abnormal congested passenger flow, congestion operations, congestion start time and congestion end time to form a single-day congestion data set;

[0138] Upload the single-day congestion dataset to the traffic feedback dataset;

[0139] Update traffic feedback dataset.

[0140] The present invention has the following beneficial effects:

[0141] 1. The traffic diversion control method based on digital blocks determines whether there is traffic congestion on the roads in the blocks by obtaining road data, vehicle driving data and pedestrian flow data in the blocks. When the vehicle flow and pedestrian flow on the road exceed the normal threshold range, it indicates that there may be traffic congestion. At this time, the corresponding traffic diversion is carried out quickly according to the starting position of the traffic congestion to reduce the occurrence of traffic accidents and ensure the safety of passing vehicles and personnel.

[0142] 2. The traffic diversion control method based on digital blocks obtains the vehicle traffic conditions in places where traffic congestion is prone to occur, and determines whether the speed of vehicles in the road section is within the normal threshold range. If it exceeds the normal threshold range, it means that the vehicle speed is too fast or too slow, which is likely to cause traffic congestion. Timely warnings are needed to reduce traffic congestion in the blocks. When traffic congestion occurs, timely and rapid traffic diversion is carried out to reduce the occurrence of traffic accidents and ensure the safety of passing vehicles and personnel.

[0143] 3. The traffic diversion control method based on digital blocks obtains the vehicle traffic conditions in places prone to traffic congestion to determine whether there are abnormal lane changes in the road section. If there are multiple lane changes in a short period of time or the speed is reduced immediately after changing lanes, it means that the vehicle has abnormal lane changes, which is likely to cause traffic congestion. Timely warnings are needed to reduce traffic congestion in the blocks. When traffic congestion occurs, timely and rapid traffic diversion is carried out to reduce the occurrence of traffic accidents and ensure the safety of passing vehicles and personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0144] Figure 1 The figure is a flow chart of the traffic diversion control method based on digital blocks of the present invention. DETAILED DESCRIPTION

[0145] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0146] Embodiment 1, a traffic diversion control method based on digital blocks, see Figure 1 ,include:

[0147] Acquire an image of a target block and define it as a target image;

[0148] Obtaining road data in the target image as target road data, wherein the road data is the distribution of roads in the target block, including the distribution of intersections, motor vehicle lanes, non-motor vehicle lanes, and other roads;

[0149] Acquire dynamic data in the target image and define it as target dynamic data, wherein the dynamic data is the flow of people and the driving conditions of vehicles in the target block;

[0150] Determine the traffic conditions of the target block based on the target road data and target dynamic data;

[0151] If the target block has smooth traffic, then conduct a traffic assessment on the target block;

[0152] If the traffic in the target block is orderly, then the target block can continue to be monitored;

[0153] If the traffic in the target block is disordered, the data collection point where the traffic in the target collection point is disordered is extracted and determined as an abnormal collection point;

[0154] Issue traffic warnings for abnormal collection points in target blocks, that is, focus on monitoring vehicles in target blocks and promptly control vehicles that may affect traffic to leave as soon as possible to avoid traffic congestion;

[0155] If the target block is congested, a traffic feedback data set is obtained, wherein the traffic feedback data set is a data set formed by integrating the causes of each historical traffic congestion in the target block, the duration of traffic diversion, and the traffic diversion method;

[0156] According to the traffic feedback data set and the traffic conditions of the target block, the traffic in the target block is diverted until the traffic in the target block is smooth;

[0157] Obtain traffic congestion data for the target block and organize and update the traffic feedback dataset.

[0158] Among them, traffic diversion is carried out in the target blocks, specifically:

[0159] Acquire road data for determining abnormal unit road data or abnormal special road data, and define it as an abnormal road;

[0160] Obtaining a congestion starting point in the abnormal road, wherein the congestion starting point is a location point where vehicles cannot travel on the traffic congested road section;

[0161] Get traffic feedback dataset;

[0162] If the traffic feedback data set has the congestion starting point, then the traffic diversion operation corresponding to the congestion starting point in the traffic feedback data set is obtained and defined as the historical operation;

[0163] Traffic diversion can be carried out on abnormal roads based on historical operations;

[0164] If the traffic feedback data set does not contain the congestion starting point, then obtain the target road data corresponding to the congestion starting point;

[0165] If the starting point of the congestion is a special road, the equipment data of the starting point of the congestion is adjusted, and the equipment data is a traffic light, that is, the congestion may be caused by unreasonable allocation of traffic light time. At this time, the time of the traffic light needs to be adjusted in time to allow vehicles to pass as soon as possible;

[0166] If the starting point of the congestion is a unit road, the passenger flow at the starting point of the congestion is obtained and determined as the congested passenger flow;

[0167] If there is a crowd congestion, adjust the pedestrian flow position. That is, the congestion may be caused by a large number of people crossing the road. In this case, the traffic police need to guide the pedestrians to cross the road through a location with fewer vehicles so that vehicles can pass as quickly as possible, or guide the pedestrians and vehicles to alternately pass through the congested section.

[0168] If there is no congestion, the vehicle movement will be adjusted. That is, the congestion may be caused by a car accident or a sudden lane change. At this time, the monitoring equipment will be combined with the traffic lights at the intersection at the end of the road section to make adjustments, and guide the vehicles to return to an orderly situation as soon as possible, and monitor to prevent vehicles from causing traffic congestion again due to sudden lane changes.

[0169] This embodiment also provides for obtaining traffic congestion data of a target block and collating and updating a traffic feedback data set, specifically:

[0170] Obtain all abnormal roads and congestion starting points;

[0171] Obtain target road data corresponding to each abnormal road and its congestion starting point, and define them as congested road data;

[0172] Obtain the congested passenger flow corresponding to each abnormal road and its congestion starting point, and define it as abnormal congested passenger flow;

[0173] Obtain the traffic diversion operation corresponding to each abnormal road and its congestion starting point, and define it as a congestion operation;

[0174] Obtain the time when the target block is judged to be congested, and set it as the congestion start time;

[0175] The time when traffic in the target block is smooth for the first time after the congestion start time is obtained and is defined as the congestion end time;

[0176] Integrate all abnormal roads and congestion starting points, congested road data, abnormal congested passenger flow, congestion operations, congestion start time and congestion end time to form a single-day congestion data set;

[0177] Upload the single-day congestion dataset to the traffic feedback dataset;

[0178] Update traffic feedback dataset.

[0179] Through the above method, it is possible to determine whether there is traffic congestion on the roads in the block, whether the changes in vehicle driving and pedestrian flow are normal, and whether there are behaviors in the block that may cause traffic congestion. Traffic congestion in the block can be reduced, and traffic can be diverted in a timely and rapid manner when traffic congestion occurs to reduce the occurrence of traffic accidents and ensure the safety of passing vehicles and personnel.

[0180] Embodiment 2: This embodiment is an improvement made on the basis of embodiment 1. The traffic diversion control method based on digital blocks determines the traffic conditions of the target blocks according to the target road data and the target dynamic data. Specifically:

[0181] Acquire target road data, wherein the target road data includes unit road data and special road data, wherein the unit road data is the distribution of straight sections of each motor vehicle lane, and the special road data is the distribution of sections where there are interfaces between motor vehicle lanes and non-motor vehicle lanes, such as crossroads, T-junctions, etc.;

[0182] Obtain all unit road data within the target block to form a unit road set;

[0183] Identify each element in the unit road set as a target unit element in turn;

[0184] Obtaining the current traffic flow of the target unit element, where the current traffic flow is the number of vehicles passing through the straight section of the motorway per minute;

[0185] Obtaining a unit determined vehicle flow rate of the target unit element, wherein the unit determined vehicle flow rate is the number of vehicles passing through the straight section of the motor vehicle lane per minute under normal circumstances;

[0186] If the current traffic volume ≥ the unit determined traffic volume × 80%, the traffic flow of the unit road is determined to be normal;

[0187] If the current traffic volume is less than the unit determined traffic volume × 80%, the unit road traffic volume is determined to be abnormal;

[0188] If all elements in the unit road set determine that the traffic flow of the unit road is normal, then the unit road data is determined to be normal;

[0189] If there are elements in the unit road set that determine that the traffic flow on the unit road is abnormal, then the unit road data is determined to be abnormal;

[0190] Obtain all special road data within the target block to form a special road set;

[0191] Identify each element in the special road set as a target special element in turn;

[0192] Obtain the analyzed vehicle flow and analyzed pedestrian flow of the target special element, wherein the analyzed vehicle flow is the number of vehicles passing through the intersection per minute, and the analyzed pedestrian flow is the number of people passing through the intersection per minute;

[0193] Obtain the special determination vehicle flow and special determination human flow of the target special element, wherein the special determination vehicle flow is the number of vehicles passing through the intersection per minute under normal circumstances, and the special determination human flow is the number of people passing through the intersection per minute under normal circumstances;

[0194] If the analyzed vehicle flow ≥ the special determined vehicle flow × 80% and the analyzed pedestrian flow ≥ the special determined pedestrian flow × 80%, the traffic flow on the special road is judged to be normal;

[0195] If the analyzed vehicle flow is less than the special determined vehicle flow × 80% or the analyzed pedestrian flow is less than the special determined pedestrian flow × 80%, the traffic flow on the special road is determined to be abnormal;

[0196] If all elements in the unit road set determine that the flow rate of the special road is normal, then the special road data is determined to be normal;

[0197] If there are elements in the unit road set that determine that the flow of the special road is abnormal, then the special road data is determined to be abnormal;

[0198] If the unit road data is judged to be normal and the special road data is judged to be normal, the traffic in the target block is judged to be smooth;

[0199] If the unit road data is determined to be abnormal or the special road data is determined to be abnormal, the target block is determined to be traffic congested.

[0200] Embodiment 3: This embodiment is an improvement made on the basis of embodiment 2. In this embodiment, vehicle analysis is performed on the target collection point, including vehicle trajectory analysis, specifically:

[0201] Obtain all vehicles collected at the target collection point to form a vehicle set;

[0202] Identify each element in the vehicle set as a target vehicle in turn;

[0203] Obtain the road section to which the target collection point belongs, and define it as the target road section;

[0204] Obtain the driving trajectory of the target vehicle from entering the target road section to the target collection point, and define it as the target trajectory;

[0205] Set a number of data points on the target trajectory as trajectory points;

[0206] Get all trajectory points to form a trajectory point set;

[0207] Get the coordinates of each element in the trajectory point set and set them as trajectory coordinates;

[0208] Each trajectory coordinate is matched one by one with each element in the trajectory point set to form a trajectory set;

[0209] Starting from the first element in the trajectory set, two adjacent elements in the trajectory set are extracted in sequence and set as the first element and the second element respectively;

[0210] Extract the trajectory coordinates of the first element and the second element, and define them as the first trajectory coordinates and the second trajectory coordinates, respectively and ;

[0211] Calculate the trajectory offset between the first element and the second element : ;

[0212] Get the lane width of the target road section, denoted as W;

[0213] Obtain the driving speed of the target vehicle on the target road section, denoted as S;

[0214] Calculate the speed factor k: ;

[0215] Among them, b and c are adjustable constants set according to actual traffic conditions, vehicle behavior and experience;

[0216] Calculate the target vehicle's offset threshold : ;

[0217] like , it is determined that the target vehicle is traveling normally on this trajectory;

[0218] like , then the target vehicle is determined to have changed lanes, and the number of lane changes is recorded and updated;

[0219] Set the judgment time, which is 5 minutes;

[0220] The number of lane changes of the target vehicle within the determination time is obtained, which is defined as the determination number;

[0221] Set the comparison times, which is 2;

[0222] If the number of determinations of the target vehicle is less than or equal to the number of comparisons, it is determined that the lane change of the target vehicle meets the first condition;

[0223] If the number of determinations of the target vehicle is greater than the number of comparisons, it is determined that the lane change of the target vehicle does not meet the first condition;

[0224] Get the speed of the target vehicle before changing lanes, recorded as ;

[0225] Setting a proportional threshold α, wherein the proportional threshold α is a speed reduction proportional threshold, such as 0.1 or 0.2;

[0226] Calculate speed change threshold : ;

[0227] Get the speed of the target vehicle after changing lanes, recorded as ;

[0228] like , it is determined that the target vehicle lane change meets the second condition;

[0229] like , it is determined that the target vehicle lane change does not meet the second condition;

[0230] If it is determined that the target vehicle's lane change meets the first condition and it is determined that the target vehicle's lane change meets the second condition, then it is determined that the target vehicle is traveling normally on the trajectory;

[0231] If it is determined that the target vehicle lane change does not meet the first condition or it is determined that the target vehicle lane change does not meet the second condition, then it is determined that the target vehicle is driving abnormally on the trajectory;

[0232] If all elements in the trajectory set determine that the target vehicle is driving normally on the trajectory, then the target vehicle is determined to be driving normally;

[0233] If there is an element in the trajectory set that determines that the target vehicle is driving abnormally on the trajectory, then it is determined that the target vehicle is driving abnormally.

[0234] This embodiment also provides for performing vehicle analysis on the target collection point, including vehicle speed analysis, specifically:

[0235] Get the target vehicle;

[0236] Acquire all images of the target vehicle collected by the target collection point to form an image set;

[0237] Arbitrarily extract two adjacent images from the image set and define them as the first image and the second image respectively;

[0238] Obtaining a vehicle center point of the target vehicle in the first image as a first center point;

[0239] Obtaining a vehicle center point of the target vehicle in the second image as a second center point;

[0240] Get the coordinates of the first center point and the second center point respectively, set them as the first coordinate and the second coordinate, respectively recorded as and ;

[0241] Get the actual width of the target vehicle, recorded as , the actual width is in meters;

[0242] Get the pixel width of the target vehicle in the image, recorded as , the unit of pixel width is pixel;

[0243] Get the video frame rate, recorded as fps, the unit of video frame rate is frame / second;

[0244] Calculate the speed v of the target vehicle, the unit of speed v is kilometers per hour: ;

[0245] Setting a speed threshold range, wherein the speed threshold is a speed range within which the road section can be driven;

[0246] If the speed v> the maximum value of the speed threshold range or the speed v< the minimum value of the speed threshold range, it is determined that the target vehicle is traveling at an abnormal speed;

[0247] If the speed v ≤ the maximum value of the speed threshold range and the speed v ≥ the minimum value of the speed threshold range, it is determined that the target vehicle's driving speed is normal.

[0248] This embodiment also provides for performing crowd flow analysis on the target collection point, specifically:

[0249] Get the current time;

[0250] Get the sub-period set;

[0251] Get the cycle number corresponding to the current time and set it as the current cycle number. The cycle number is the day of the week corresponding to the date. For example, if January 1 is Wednesday, the cycle number of January 1 is 3.

[0252] Get all the times corresponding to the current period number in the sub-period set and set them as historical time;

[0253] Get all historical time to form a historical time set;

[0254] Obtain the crowd density of the target collection point at each historical time in the historical time set, and define it as the historical crowd density;

[0255] Get the number of elements in the historical time set and define it as the number of historical collections;

[0256] Calculate the historical average population density, historical average population density = sum of all historical population densities ÷ historical collection quantity;

[0257] Obtain the crowd density of the target collection point at the current time, which is defined as the current crowd density;

[0258] If the current human density is ≥ the historical average human density × (1-15%) and the current human density is ≤ the historical average human density × (1+15%), the human flow at the target collection point is judged to be normal, that is, the historical human flow data used for reference is relatively accurate and consistent with the current situation. At the same time, the human flow is also within the normal range, which means that the human flow data at the collection point is normal;

[0259] If the current population density is less than the historical average population density × (1-15%) or the current population density is greater than the historical average population density × (1+15%), the target collection point is judged to have abnormal population flow, that is, the historical population flow data used for reference is not accurate enough and does not match the current situation, but the population flow is also within the normal range. Therefore, it means that the population flow data at the collection point is abnormal and needs to be monitored closely to avoid sudden congestion.

[0260] Among them, the sub-period set is obtained as follows:

[0261] Get the current time;

[0262] Set the maximum collection period, which is 30 days;

[0263] Set the unit duration, which is 1 day;

[0264] Calculate the collection start time, collection start time = current time - (collection duration + unit duration);

[0265] Calculate the collection end time, collection end time = current time - unit duration;

[0266] The collection start time is taken as the starting time, and the collection end time is taken as the ending time to form a collection cycle;

[0267] Set a short collection period, which is 7 days;

[0268] Taking the collection start time as the starting time, each interval is a small collection time, forming a small cycle collection point within the collection cycle;

[0269] Get the collection start time and all small periodic collection points to form a periodic point set;

[0270] Sequentially extract two adjacent elements in the periodic point set to form a sub-collection period;

[0271] All sub-collection periods within a collection period are integrated to form a sub-period set, that is, all dates within a collection period are integrated according to the period number to form a set.

[0272] In this embodiment, it is determined whether there is traffic congestion on the roads in the block, whether the changes in vehicle driving and pedestrian flow are normal, and whether there are behaviors in the block that may cause traffic congestion, so as to reduce traffic congestion in the block, and to promptly and quickly divert traffic when traffic congestion occurs, so as to reduce the occurrence of traffic accidents and ensure the safety of passing vehicles and personnel.

Claims

1. A traffic diversion control method based on digital blocks, characterized by: include: Acquire an image of a target block and define it as a target image; Acquire road data in the target image and define it as target road data; Acquire dynamic data in the target image and define it as target dynamic data; Determine the traffic conditions of the target block based on the target road data and target dynamic data; If the target block has smooth traffic, then conduct a traffic assessment on the target block; If the traffic in the target block is orderly, then the target block can continue to be monitored; If the traffic in the target block is disordered, the data collection point where the traffic in the target collection point is disordered is extracted and determined as an abnormal collection point; Provide traffic warnings for abnormal collection points in target blocks; If the target block is congested, obtain a traffic feedback dataset; According to the traffic feedback data set and the traffic conditions of the target block, the traffic in the target block is diverted until the traffic in the target block is smooth; Obtain traffic congestion data for the target block and organize and update the traffic feedback dataset.

2. The traffic diversion control method based on digital blocks according to claim 1 is characterized in that: According to the target road data and target dynamic data, the traffic conditions of the target block are judged as follows: Acquiring target road data, wherein the target road data includes unit road data and special road data; Obtain all unit road data within the target block to form a unit road set; Identify each element in the unit road set as a target unit element in turn; Get the current traffic flow of the target unit element; Get the unit of the target unit element to determine the traffic flow; If the current traffic volume ≥ the unit determined traffic volume × 80%, the traffic flow of the unit road is determined to be normal; If the current traffic volume is less than the unit determined traffic volume × 80%, the unit road traffic volume is determined to be abnormal; If all elements in the unit road set determine that the traffic flow of the unit road is normal, then the unit road data is determined to be normal; If there are elements in the unit road set that determine that the traffic flow on the unit road is abnormal, then the unit road data is determined to be abnormal; Obtain all special road data within the target block to form a special road set; Identify each element in the special road set as a target special element in turn; Obtain the analysis of vehicle flow and pedestrian flow of target special elements; Obtain the special judgment vehicle flow and special judgment human flow of the target special element; If the analyzed vehicle flow ≥ the special determined vehicle flow × 80% and the analyzed pedestrian flow ≥ the special determined pedestrian flow × 80%, the traffic flow on the special road is judged to be normal; If the analyzed vehicle flow is less than the special determined vehicle flow × 80% or the analyzed pedestrian flow is less than the special determined pedestrian flow × 80%, the traffic flow on the special road is determined to be abnormal; If all elements in the unit road set determine that the flow rate of the special road is normal, then the special road data is determined to be normal; If there are elements in the unit road set that determine that the flow of the special road is abnormal, then the special road data is determined to be abnormal; If the unit road data is judged to be normal and the special road data is judged to be normal, the traffic in the target block is judged to be smooth; If the unit road data is determined to be abnormal or the special road data is determined to be abnormal, the target block is determined to be traffic congested.

3. The traffic diversion control method based on digital blocks according to claim 1 is characterized in that: Conduct a traffic assessment of the target neighborhoods, specifically: Set up several data collection points in the target neighborhood; Define each data collection point as a target collection point; Conduct vehicle analysis on target collection points; If all vehicles at the target collection point determine that the target vehicle is traveling at a normal speed and that the target vehicle is traveling normally, then the vehicle at the target collection point is determined to be traveling normally; If there is a vehicle at the target collection point and the target vehicle is judged to be traveling at an abnormal speed or is judged to be traveling abnormally, then the vehicle at the target collection point is traveling abnormally; Conduct crowd flow analysis at target collection points; If the vehicles at the target collection point are running normally and the flow of people at the target collection point is normal, then the traffic at the target collection point is judged to be orderly; If the vehicle driving at the target collection point is abnormal or the flow of people at the target collection point is abnormal, the traffic at the target collection point is judged to be disorderly; If all data collection points determine that the traffic at the target collection point is orderly, then the traffic in the target block is determined to be orderly; If there is a data collection point that determines that the traffic at the target collection point is disorderly, then the traffic in the target block is determined to be disorderly.

4. The traffic diversion control method based on digital blocks according to claim 3 is characterized in that: Carry out vehicle analysis on the target collection point, including vehicle trajectory analysis, specifically: Obtain all vehicles collected at the target collection point to form a vehicle set; Identify each element in the vehicle set as a target vehicle in turn; Obtain the road section to which the target collection point belongs, and define it as the target road section; Obtain the driving trajectory of the target vehicle from entering the target road section to the target collection point, and define it as the target trajectory; Set a number of data points on the target trajectory as trajectory points; Get all trajectory points to form a trajectory point set; Get the coordinates of each element in the trajectory point set and set them as trajectory coordinates; Each trajectory coordinate is matched one by one with each element in the trajectory point set to form a trajectory set; Starting from the first element in the trajectory set, two adjacent elements in the trajectory set are extracted in sequence and set as the first element and the second element respectively; Extract the trajectory coordinates of the first element and the second element, and define them as the first trajectory coordinates and the second trajectory coordinates, respectively recorded as (x t ,y t ) and (x t+1 ,y t+1 ); Calculate the trajectory offset Δd between the first and second elements: ； Get the lane width of the target road section, denoted as W; Obtain the driving speed of the target vehicle on the target road section, denoted as S; Calculate the speed factor k: ； Among them, b and c are adjustable constants set according to actual traffic conditions, vehicle behavior and experience; Calculate the target vehicle's offset threshold D threshold : ； If Δd≤D threshold , it is determined that the target vehicle is traveling normally on this trajectory; If Δd>D threshold , then the target vehicle is determined to have changed lanes, and the number of lane changes is recorded and updated; Set the judgment time; The number of lane changes of the target vehicle within the determination time is obtained, which is defined as the determination number; Set the number of comparisons; If the number of determinations of the target vehicle is less than or equal to the number of comparisons, it is determined that the lane change of the target vehicle meets the first condition; If the number of determinations of the target vehicle is greater than the number of comparisons, it is determined that the lane change of the target vehicle does not meet the first condition; Get the speed of the target vehicle before changing lanes, denoted as S 变 ; Set the ratio threshold α; Calculate the speed change threshold Δv threshold : ； Get the speed of the target vehicle after changing lanes, denoted as S 判 ; If S 判 ≥Δv threshold , it is determined that the target vehicle lane change meets the second condition; If S 判 <Δv threshold , it is determined that the target vehicle lane change does not meet the second condition; If it is determined that the target vehicle's lane change meets the first condition and it is determined that the target vehicle's lane change meets the second condition, then it is determined that the target vehicle is traveling normally on the trajectory; If it is determined that the target vehicle lane change does not meet the first condition or it is determined that the target vehicle lane change does not meet the second condition, then it is determined that the target vehicle is driving abnormally on the trajectory; If all elements in the trajectory set determine that the target vehicle is driving normally on the trajectory, then the target vehicle is determined to be driving normally; If there is an element in the trajectory set that determines that the target vehicle is driving abnormally on the trajectory, then it is determined that the target vehicle is driving abnormally.

5. The traffic diversion control method based on digital blocks according to claim 4 is characterized in that: Carry out vehicle analysis on the target collection point, including vehicle speed analysis, specifically: Get the target vehicle; Acquire all images of the target vehicle collected by the target collection point to form an image set; Arbitrarily extract two adjacent images from the image set and define them as the first image and the second image respectively; Obtaining a vehicle center point of the target vehicle in the first image as a first center point; Obtaining a vehicle center point of the target vehicle in the second image as a second center point; Obtain the coordinates of the first center point and the second center point respectively, set them as the first coordinate and the second coordinate, and record them as (x1, y1) and (x2, y2) respectively; Get the actual width of the target vehicle, denoted as W real ; Get the pixel width of the target vehicle in the image, denoted as W pixel ; Get the video frame rate, recorded as fps; Calculate the speed v of the target vehicle: ； Set the speed threshold range; If the speed v> the maximum value of the speed threshold range or the speed v< the minimum value of the speed threshold range, it is determined that the target vehicle is traveling at an abnormal speed; If the speed v ≤ the maximum value of the speed threshold range and the speed v ≥ the minimum value of the speed threshold range, it is determined that the target vehicle's driving speed is normal.

6. The traffic diversion control method based on digital blocks according to claim 5 is characterized in that: Conduct crowd flow analysis at the target collection point, specifically: Get the current time; Get the sub-period set; Get the cycle number corresponding to the current time and set it as the current cycle number; Get all the times corresponding to the current period number in the sub-period set and set them as historical time; Get all historical time to form a historical time set; Obtain the crowd density of the target collection point at each historical time in the historical time set, and define it as the historical crowd density; Get the number of elements in the historical time set and define it as the number of historical collections; Calculate the historical average population density, historical average population density = sum of all historical population densities ÷ historical collection quantity; Obtain the crowd density of the target collection point at the current time, which is defined as the current crowd density; If the current population density is ≥ the historical average population density × (1-15%) and the current population density is ≤ the historical average population density × (1+15%), then the flow of people at the target collection point is determined to be normal; If the current population density is less than the historical average population density × (1-15%) or the current population density is greater than the historical average population density × (1+15%), the population flow at the target collection point is judged to be abnormal.

7. The traffic diversion control method based on digital blocks according to claim 6 is characterized in that: Get the sub-period set, specifically: Get the current time; Set the maximum collection time; Set the unit duration; Calculate the collection start time, collection start time = current time - (collection duration + unit duration); Calculate the collection end time, collection end time = current time - unit duration; The collection start time is taken as the starting time, and the collection end time is taken as the ending time to form a collection cycle; Set the minimum collection time; Taking the collection start time as the starting time, each interval is a small collection time, forming a small cycle collection point within the collection cycle; Get the collection start time and all small periodic collection points to form a periodic point set; Sequentially extract two adjacent elements in the periodic point set to form a sub-collection period; All sub-collection periods within a collection period are integrated to form a sub-period set.

8. The traffic diversion control method based on digital blocks according to claim 1 is characterized in that: Traffic diversion in target blocks, specifically: Acquire road data for determining abnormal unit road data or abnormal special road data, and define it as an abnormal road; Obtain the starting point of congestion in abnormal roads; Get traffic feedback dataset; If the traffic feedback data set has the congestion starting point, then the traffic diversion operation corresponding to the congestion starting point in the traffic feedback data set is obtained and defined as the historical operation; Traffic diversion can be carried out on abnormal roads based on historical operations; If the traffic feedback data set does not contain the congestion starting point, then obtain the target road data corresponding to the congestion starting point; If the congestion starting point is a special road, adjust the device data of the congestion starting point; If the starting point of the congestion is a unit road, the passenger flow at the starting point of the congestion is obtained and determined as the congested passenger flow; If there is crowd congestion, adjust the flow of people; If there is no traffic congestion, adjust the vehicle movement.

9. The traffic diversion control method based on digital blocks according to claim 1 is characterized in that: Obtain traffic congestion data for the target block and organize and update the traffic feedback dataset, specifically: Obtain all abnormal roads and congestion starting points; Obtain target road data corresponding to each abnormal road and its congestion starting point, and define them as congested road data; Obtain the congested passenger flow corresponding to each abnormal road and its congestion starting point, and define it as abnormal congested passenger flow; Obtain the traffic diversion operation corresponding to each abnormal road and its congestion starting point, and define it as a congestion operation; Obtain the time when the target block is judged to be congested, and set it as the congestion start time; The time when traffic in the target block is smooth for the first time after the congestion start time is obtained and is defined as the congestion end time; Integrate all abnormal roads and congestion starting points, congested road data, abnormal congested passenger flow, congestion operations, congestion start time and congestion end time to form a single-day congestion data set; Upload the single-day congestion dataset to the traffic feedback dataset; Update traffic feedback dataset.

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