CN117058877B - Urban traffic intelligent monitoring method and system - Google Patents
Urban traffic intelligent monitoring method and system Download PDFInfo
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- CN117058877B CN117058877B CN202311100933.3A CN202311100933A CN117058877B CN 117058877 B CN117058877 B CN 117058877B CN 202311100933 A CN202311100933 A CN 202311100933A CN 117058877 B CN117058877 B CN 117058877B
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- 238000012545 processing Methods 0.000 claims description 26
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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Abstract
The application relates to an intelligent monitoring method and system for urban traffic, and relates to the technical field of traffic monitoring, wherein the method comprises the steps of obtaining vehicle path information, vehicle speed information and signal lamp period of each road; calculating according to the vehicle path information, the vehicle speed information and the signal lamp period to determine the road where the vehicle of the vehicle is located at each time point; counting according to the determined roads on which the single vehicle is located at a single time point to determine a predicted number of vehicles; judging whether the predicted quantity of the vehicles is larger than the preset possible congestion quantity or not; if the predicted number of the vehicles is not greater than the possible congestion number, no action is performed; if the predicted number of the vehicles is greater than the possible congestion number, defining the road where the vehicles are located as the possible congestion road, defining the corresponding time point as the congestion time point, and outputting the road condition according to the congestion time point and the possible congestion road. The application has the effect of improving the overall monitoring effect on the road.
Description
Technical Field
The application relates to the field of traffic monitoring technology, in particular to an intelligent monitoring method and system for urban traffic.
Background
With the development of the age, vehicles are rapidly increased, so that greater pressure is given to urban traffic, and therefore, the movement condition of each vehicle in the urban traffic needs to be monitored so as to determine the road traffic condition, and traffic management personnel can intervene in time to process when the road congestion condition occurs.
In the related art, the current judgment on the traffic jam condition is basically based on two data of the vehicle queuing length and the vehicle moving speed, when the vehicle queuing length and the vehicle moving speed are low, the traffic jam condition of the road can be considered, and at the moment, traffic managers can intervene in processing.
In view of the above related art, the inventor considers that the traffic manager intervenes to process only when the road is congested, and at this time, the congested road easily causes congestion on several associated roads, so that the traffic manager needs more time and workload to dredge traffic, and the overall monitoring effect on the road is poor, and there is still room for improvement.
Disclosure of Invention
In order to improve the overall monitoring effect on roads, the application provides an intelligent monitoring method and system for urban traffic.
In a first aspect, the present application provides an intelligent urban traffic monitoring method, which adopts the following technical scheme:
an intelligent monitoring method for urban traffic, comprising the following steps:
acquiring vehicle path information, vehicle speed information and signal lamp period of each road;
calculating according to the vehicle path information, the vehicle speed information and the signal lamp period to determine the road where the vehicle of the vehicle is located at each time point;
counting according to the determined roads on which the single vehicle is located at a single time point to determine a predicted number of vehicles;
judging whether the predicted quantity of the vehicles is larger than the preset possible congestion quantity or not;
if the predicted number of the vehicles is not greater than the possible congestion number, no action is performed;
if the predicted number of the vehicles is greater than the possible congestion number, defining the road where the vehicles are located as the possible congestion road, defining the corresponding time point as the congestion time point, and outputting the road condition according to the congestion time point and the possible congestion road.
By adopting the technical scheme, in the urban road traffic monitoring process, the moving condition of the vehicle on each road is analyzed to determine the road to which the vehicle can move at the corresponding time point, so that whether the congestion condition can occur or not is determined by analyzing the theoretical traffic flow, the congestion condition can be pre-judged in advance, the predictability of the congestion condition determination is improved, and the overall monitoring effect on the road is improved.
Optionally, the step of acquiring the vehicle path information includes:
acquiring a navigation use state of a vehicle and a current road;
judging whether the navigation using state is consistent with a preset operation state;
if the navigation using state is consistent with the operation state, acquiring vehicle path information;
if the navigation using state is inconsistent with the operation state, acquiring vehicle license plate information, establishing a fixed interval with the width of a preset fixed duration on a preset time axis, and enabling the rear end point of the fixed interval to coincide with the current time point;
determining a next moving road of the vehicle on the current road according to the license plate information of the vehicle in the fixed interval;
counting according to each next moving road to determine the next moving number, determining the next moving number with the largest value according to a preset ordering rule, and defining the next moving road corresponding to the next moving number as a possible moving road;
repeatedly determining a next moving road according to the possible moving roads until the next moving road does not exist;
and connecting lines according to the current road and all possible moving roads to determine vehicle path information.
By adopting the technical scheme, in the process of determining the vehicle path information of the vehicle, the vehicle path information is determined according to the historical movement condition aiming at the vehicle without starting navigation, so that the road on which the vehicle is positioned is convenient to determine subsequently.
Optionally, when there are at least two next movement numbers with the largest values, the defining step of the possible movement path further includes:
defining a possible moving road having at least two next moving numbers with the largest numerical value as a bifurcation analyzing road, and defining a corresponding road which is the possible moving road as a candidate road;
defining a point in time when a road on which a vehicle is located coincides with a bifurcation analyzing road as a bifurcation-to-point;
calculating according to the current time point and the bifurcation to the site to determine theoretical demand duration;
determining the required waiting time of a signal green light corresponding to a road to be selected according to the signal lamp period, the current time point and the theoretical required time in the bifurcation analysis road;
and determining the demand waiting time with the minimum value according to the ordering rule, and defining the candidate road corresponding to the demand waiting time as a possible moving road.
By adopting the technical scheme, when at least two roads which can move are present, the roads are analyzed according to the green light condition.
Optionally, the method further includes a step of determining vehicle speed information, the step including:
defining the predicted quantity of the vehicles corresponding to the condition that the vehicles are on the road where the vehicles are located as the quantity of the vehicle pressure;
Determining a theoretical quantity range according to the quantity of the vehicle pressure and a preset adjustment quantity;
determining the actual number of the vehicles on each road according to the license plate information of the vehicles in the fixed section;
judging whether the actual number of the vehicles is in the theoretical number range or not;
if the actual number of the vehicles is not in the theoretical number range, defining a road corresponding to the actual number of the vehicles as an invalid road;
if the actual number of the vehicles is in the theoretical number range, defining a road corresponding to the actual number of the vehicles as an effective road;
and acquiring historical driving speeds in the effective road according to the vehicle license plate information, and calculating according to all the historical driving speeds to determine vehicle speed information.
By adopting the technical scheme, more accurate vehicle speed information is determined according to the traffic flow condition on the road.
Optionally, the step of calculating to determine the vehicle speed information based on all the historical driving speeds includes:
calculating a difference value according to the actual number of the vehicles and the pressure number of the vehicles to determine the difference value number of the vehicles;
determining a speed correction coefficient corresponding to the number of the vehicle difference values according to a preset coefficient matching relation;
Calculating according to the speed correction coefficient and the corresponding historical running speed to update the historical running speed;
and carrying out average value calculation according to all updated historical driving speeds to determine vehicle speed information.
By adopting the technical scheme, the historical driving speed can be corrected according to the difference value condition of the vehicle flow, so that more accurate vehicle speed information can be determined conveniently.
Optionally, after the predicted number of vehicles is determined, the intelligent monitoring method for urban traffic further comprises:
defining the empty vehicles in the fixed interval according to the vehicle license plate information to be the first vehicles, and defining the roads where the vehicles with the determined predicted quantity of the vehicles are located to be target roads;
determining a first driving path according to the first vehicle in the fixed interval;
determining a shortest travel path according to each road of the first travel path and the target road, and defining the shortest travel path determined by the current road and the target road as the target path;
judging whether the path length of the target path is smaller than the path length of each shortest running path;
if the path length of the target path is not smaller than the path length of each shortest driving path, defining the first vehicle as an invalid vehicle;
If the path length of the target path is smaller than the path length of each shortest driving path, defining the first vehicle as an effective vehicle;
determining an effective vehicle which can move onto a target road at a corresponding time point according to a preset fixed speed as a theoretical possible vehicle in the effective vehicles;
counting all theoretically possible vehicles to determine a theoretical additional quantity, and calculating according to the theoretical additional quantity and a preset theoretical duty ratio to determine a correction quantity;
and carrying out summation calculation according to the correction quantity and the vehicle prediction quantity so as to update the vehicle prediction quantity.
By adopting the technical scheme, the route analysis can be carried out on the vehicles which are not recorded in the case, so that the traffic flow of each road can be accurately determined.
Optionally, the method further comprises a theoretical duty ratio determining step, which comprises the following steps:
determining a first theoretical value corresponding to the predicted quantity of the vehicle according to a preset first value matching relation;
determining the number of historical vehicles of the target road at the corresponding time point in the fixed interval;
determining a second theoretical value corresponding to the number of the historical vehicles according to a preset second value matching relation;
And carrying out average value calculation according to the first theoretical value and the second theoretical value to determine the theoretical duty ratio.
By adopting the technical scheme, the more accurate theoretical duty ratio can be determined according to the traffic flow condition.
In a second aspect, the present application provides an intelligent monitoring system for urban traffic, which adopts the following technical scheme:
an intelligent monitoring system for urban traffic, comprising:
the acquisition module is used for acquiring vehicle path information, vehicle speed information and signal lamp period of each road;
the processing module is connected with the acquisition module and the judging module and is used for storing and processing information;
the judging module is connected with the acquisition module and the processing module and is used for judging information;
the processing module calculates according to the vehicle path information, the vehicle speed information and the signal lamp period to determine the road where the vehicle of the vehicle is located at each time point;
the processing module counts according to the determined roads on which the single vehicle is located at a single time point to determine the predicted quantity of the vehicles;
the judging module judges whether the predicted quantity of the vehicles is larger than the preset possible congestion quantity or not;
if the judging module judges that the predicted quantity of the vehicles is not greater than the possible congestion quantity, no action is generated;
If the judging module judges that the predicted quantity of the vehicles is larger than the possible congestion quantity, the processing module defines the road where the vehicles are located as the possible congestion road, defines the corresponding time point as the congestion time point, and outputs the road condition according to the congestion time point and the possible congestion road.
By adopting the technical scheme, in the urban road traffic monitoring process, the processing module analyzes the moving condition of the vehicle on each road to determine the road to which the vehicle can move at the corresponding time point, so that the judging module determines whether the congestion condition can occur or not by analyzing the theoretical vehicle flow, the processing module pre-judges the congestion condition in advance, the predictability of the congestion condition determination is improved, and the overall monitoring effect of the road is improved.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method has the advantages that the movement condition of the road vehicles can be analyzed in the urban traffic monitoring process so as to determine the theoretical traffic flow of each road at each time point, so that the congestion condition can be determined in advance, and the overall monitoring effect of the roads is improved;
2. the more accurate vehicle path information can be determined by combining the vehicle navigation condition and the vehicle history movement condition, so that the subsequent analysis and determination of each road condition are facilitated;
3. The accurate vehicle speed information can be determined according to the road traffic flow conditions, so that the analysis and the determination of each road condition can be conveniently carried out later.
Drawings
Fig. 1 is a flow chart of an intelligent monitoring method for urban traffic.
Fig. 2 is a flowchart of a vehicle path determination method.
Fig. 3 is a flow chart of a possible mobile road definition method.
Fig. 4 is a flowchart of a vehicle speed determination method.
FIG. 5 is a flow chart of a vehicle speed calculation accurate method.
Fig. 6 is a flowchart of a vehicle predicted quantity updating method.
Fig. 7 is a flowchart of a theoretical duty cycle determination method.
Fig. 8 is a block flow diagram of an intelligent monitoring method for urban traffic.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to fig. 1 to 8 and the embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The embodiment of the application discloses an urban traffic intelligent monitoring method, in the process of urban traffic monitoring, the movement condition of each vehicle on each road is obtained, so that the road where the vehicle is likely to be located at each subsequent time point is analyzed, the traffic flow condition of each road at each subsequent time point is determined, the condition of possible congestion can be prejudged in advance, and the overall monitoring effect of the road is improved.
Referring to fig. 1, the method flow of the intelligent monitoring method for urban traffic comprises the following steps:
step S100: vehicle path information, vehicle speed information and signal lamp period of each road are obtained.
The path corresponding to the vehicle path information is the path that the vehicle needs to travel on the road at present, and is described below a specific acquisition method, and details are not repeated here; the speed value corresponding to the vehicle speed information is an average speed value of the vehicle moving under each road, the signal lamp period is the duration of the traffic light of the intersection corresponding to each road, which is displayed in each direction, wherein the road is defined by a worker and is in a region where congestion caused by more vehicles is likely to occur.
Step S101: and calculating according to the vehicle path information, the vehicle speed information and the signal lamp period to determine the road where the vehicle of the vehicle is located at each time point.
The road where the vehicle is located is the road where the determined vehicle is located at the corresponding time point, the movable distance of the vehicle at each time point can be known according to the vehicle path information and the vehicle speed information, the time point is corrected according to the signal lamp period to exclude the waiting time of the red lamp, and the road where the vehicle is located is determined according to the determined distance.
Step S102: the number of predicted vehicles is determined by counting at a single point in time based on the determined roads along which the single vehicle is located.
The predicted number of vehicles is a number value of roads on which the vehicle is determined to be located among roads corresponding to a single point in time, that is, the number of vehicles that the road would theoretically have at the corresponding point in time.
Step S103: and judging whether the predicted number of the vehicles is larger than a preset possible congestion number.
The possible congestion amount is the minimum predicted amount of vehicles when the congestion situation is considered to be possible due to more vehicles and set by the staff, and the purpose of the judgment is to know whether the congestion situation is possible on the road.
Step S1031: if the predicted number of vehicles is not greater than the possible congestion number, no action is taken.
When the predicted number of vehicles is not greater than the possible congestion number, the road is basically free from congestion at the corresponding time point, and no additional operation is needed.
Step S1032: if the predicted number of the vehicles is greater than the possible congestion number, defining the road where the vehicles are located as the possible congestion road, defining the corresponding time point as the congestion time point, and outputting the road condition according to the congestion time point and the possible congestion road.
When the predicted number of vehicles is greater than the possible congestion number, the possible congestion condition of the road at the corresponding time point is indicated, and the possible congestion road and the congestion time point are defined at the moment, so that the road condition can be displayed on the management interface of the traffic manager, and the traffic manager can intervene in advance to process the road condition, so that the subsequent road congestion condition is reduced.
Referring to fig. 2, the vehicle path information acquisition step includes:
step S200: and acquiring the navigation using state of the vehicle and the current road.
The navigation use state is the use condition of the vehicle on the road for map navigation, the state comprises an unworked state and an operating state, wherein the navigation use condition can be bound with navigation use equipment through the vehicle, and one-vehicle navigation is realized; the road on which the determined vehicle is currently located is the road on which the determined vehicle is currently located.
Step S201: and judging whether the navigation using state is consistent with a preset operation state.
The operation state is a state set by a worker when the user uses navigation during driving, and the purpose of judgment is to know whether the current path of the vehicle which needs to move can be determined by directly navigating.
Step S2011: and if the navigation using state is consistent with the operation state, acquiring the vehicle path information.
When the navigation use state is consistent with the operation state, the user driving the vehicle at present is stated to use navigation, and the vehicle path information is directly acquired according to the content in the navigation.
Step S2012: if the navigation using state is inconsistent with the operation state, acquiring vehicle license plate information, establishing a fixed interval with the width of a preset fixed duration on a preset time axis, and enabling the rear end point of the fixed interval to coincide with the current time point.
When the navigation use state is inconsistent with the operation state, the vehicle path information cannot be acquired through navigation, and further analysis is needed at the moment; the license plate corresponding to the license plate information of the vehicle is the license plate of the vehicle to be detected, the license plate can be acquired on an image shot by a camera of an intersection when the vehicle enters the road, a time axis is a coordinate axis formed by combining all time points, the fixed duration is the duration which is set by a worker and can analyze the historical driving condition of the vehicle, for example, one year, and the driving data of the vehicle in the fixed duration can be acquired by establishing a fixed interval so as to facilitate the subsequent analysis.
Step S202: and determining the next moving road of the vehicle on the current road according to the license plate information of the vehicle in the fixed interval.
The next moving road is the road to which the vehicle of the license plate corresponding to the license plate information of the vehicle in the fixed interval moves after moving to the road where the vehicle is currently located.
Step S203: and counting according to each next moving road to determine the next moving number, determining the next moving number with the largest value according to a preset ordering rule, and defining the next moving road corresponding to the next moving number as a possible moving road.
The number value corresponding to the next moving number is the number value of times that the user moves to the corresponding next moving road after moving to the road where the user is currently located, and the number of times that the same next moving road appears in the fixed interval can be determined; the sorting rule is a method which is set by a worker and can sort the numerical values, such as an bubbling method, and the next moving quantity with the largest numerical value can be determined through the sorting rule, namely, the next moving road corresponding to the next moving quantity is the road which is most likely to move by the user, and at the moment, the possible moving roads are defined to realize the distinction of different next moving roads, so that the subsequent determination and analysis of the vehicle path information are facilitated.
Step S204: and repeatedly determining the next moving road according to the possible moving roads until the next moving road does not exist.
The possible moving road is used to simulate the current road to re-determine the next moving road of the possible moving road, so that the moving condition of the vehicle can be analyzed.
Step S205: and connecting lines according to the current road and all possible moving roads to determine vehicle path information.
Starting from the current road, all possible moving roads are traversed to form a path along which the vehicle moves, i.e., vehicle path information.
Referring to fig. 3, when there are at least two next movement numbers with the largest values, the defining step of the possible movement path further includes:
step S300: the possible moving road where there are at least two next moving numbers with the largest value is defined as a bifurcation analysis road, and the road corresponding to the possible moving road is defined as a candidate road.
When at least two next movement numbers with the largest numerical value exist, the fact that the possible movement roads cannot be determined only through the ordering rule is indicated, and further analysis is needed; the bifurcation analyzing road is defined to distinguish the possible moving road which needs to be analyzed currently, and the candidate road is defined to distinguish the possible moving road which can be used as the bifurcation analyzing road, so that the follow-up analysis is convenient.
Step S301: the point in time when the road on which the vehicle is located coincides with the bifurcation analyzing road is defined as bifurcation-to-point.
The bifurcation to site is defined to distinguish the point in time when the vehicle moves to the bifurcation analysis path, facilitating subsequent analysis.
Step S302: and calculating according to the current time point and the bifurcation to the site to determine the theoretical demand duration.
The theoretical required time is the time required when the vehicle on the current road moves along the path corresponding to the vehicle path information to move to the bifurcation analysis road.
Step S303: and determining the required waiting time of the signal green light corresponding to the road to be selected according to the signal lamp period, the current time point and the theoretical required time in the bifurcation analysis road.
The signal green light corresponding to the road to be selected is a signal green light when a vehicle can move from the bifurcation analysis road to the road to be selected, for example, the road to be selected is a left front road of the bifurcation analysis road, then the signal green light is a left turn green light of the bifurcation analysis road, the waiting time is the waiting time needed when a signal lamp corresponding to the road to be selected is displayed as a green light after the vehicle moves to a bifurcation analysis road from a bifurcation site, the signal lamp condition when the vehicle moves to the bifurcation analysis road can be determined through the signal lamp period, the current time point and the theoretical required time, and the waiting time when each signal green light needs to be turned on can be determined by utilizing the signal lamp period and the signal lamp condition.
Step S304: and determining the demand waiting time with the minimum value according to the ordering rule, and defining the candidate road corresponding to the demand waiting time as a possible moving road.
And determining the minimum required waiting time length through the ordering rule so as to determine the roads which can be used by the user to run on the same line more quickly, thereby defining the road to be selected as a possible moving road, and ensuring that the determined possible moving road is more accurate.
Referring to fig. 4, the method further includes a step of determining vehicle speed information, the step including:
step S400: the predicted number of vehicles corresponding to the vehicle on the road where the vehicle is located is defined as the number of vehicle pressures.
The amount of vehicle pressure is defined to determine the traffic flow conditions of the road the vehicle is required to move, facilitating subsequent analysis.
Step S401: and determining a theoretical quantity range according to the quantity of the vehicle pressure and the preset adjustment quantity.
The theoretical quantity range is a vehicle flow range when the speed of the vehicle moving in the range of the vehicle flow set by the staff differs little from the speed of the vehicle moving in the quantity of the vehicle pressure, the adjustment quantity is a fixed value quantity set by the staff, the lower limit value of the theoretical quantity range is determined by subtracting the adjustment quantity from the quantity of the vehicle pressure, and the upper limit value of the theoretical quantity range is determined by adding the adjustment quantity to the quantity of the vehicle pressure.
Step S402: and determining the actual number of the vehicles on each road according to the license plate information of the vehicles in the fixed section.
The actual number of vehicles is the number of vehicles owned by the road when the vehicles run on the road in the fixed section, and can be obtained through statistics through the condition that the vehicles pass through the road junction.
Step S403: and judging whether the actual number of the vehicles is in the theoretical number range.
The purpose of the judgment is to know whether the current running situation of the vehicle is the same as the current running situation.
Step S4031: if the actual number of the vehicles is not in the theoretical number range, defining the road corresponding to the actual number of the vehicles as an invalid road.
When the actual number of the vehicles is not in the theoretical number range, the condition that the vehicles run on the road is indicated to have low similarity with the current condition, and an invalid road is defined at the moment to realize the distinction of different roads, so that the follow-up analysis is convenient.
Step S4032: if the actual number of vehicles is within the theoretical number range, the road corresponding to the actual number of vehicles is defined as an effective road.
When the actual number of the vehicles is in the theoretical number range, the situation that the vehicles run on the road is higher in similarity with the current situation, the current movement situation can be analyzed by utilizing the previous movement situation on the road, and an effective road is defined at the moment to realize the distinction of different roads, so that the follow-up analysis is convenient.
Step S404: and acquiring historical driving speeds in the effective road according to the vehicle license plate information, and calculating according to all the historical driving speeds to determine vehicle speed information.
The historical driving speed is an average speed value of the vehicle driving on the effective road, and the vehicle speed information of the current vehicle on the road can be determined by using all the historical driving speeds, and specific determining steps are as follows.
Referring to fig. 5, the step of calculating from all the historic travel speeds to determine vehicle speed information includes:
step S500: and carrying out difference calculation according to the actual number of the vehicles and the pressure number of the vehicles to determine the difference number of the vehicles.
The number of vehicle difference values is a number difference between the currently determined theoretical number of vehicles and the actual number of vehicles in the effective road determined in the history, and the number of vehicle pressure is subtracted from the actual number of vehicles.
Step S501: and determining a speed correction coefficient corresponding to the number of the vehicle difference values according to a preset coefficient matching relation.
The different vehicle difference numbers indicate that the number of vehicles on the road is inconsistent, when the number of the vehicle difference numbers is large, the speed of the vehicle determined under the historical condition is lower than that of the vehicle determined under the historical condition when the number of the vehicle difference numbers is large compared with that of the vehicle at present, the speed correction coefficient at the moment is a parameter for correcting the historical speed, so that the corrected speed can approach to the moving speed of the vehicle under the current number of the vehicle, and the coefficient matching relationship between the two is determined in advance by staff and is not repeated.
Step S502: and calculating according to the speed correction coefficient and the corresponding historical running speed to update the historical running speed.
The historical travel speed is multiplied by the speed correction coefficient to update the historical travel speed, so that the determined historical travel speed can be more effectively used for determining current vehicle speed information.
Step S503: and carrying out average value calculation according to all updated historical driving speeds to determine vehicle speed information.
And calculating the average value by using the updated historical driving speed to determine the vehicle speed information, so that the determined vehicle speed information is accurate, and the subsequent analysis of the vehicle movement condition is facilitated.
Referring to fig. 6, after the predicted number of vehicles is determined, the intelligent monitoring method for urban traffic further includes:
step S600: and defining the empty vehicles with road conditions determined according to the vehicle license plate information in the fixed interval as first vehicles, and defining the roads where the vehicles with the determined predicted quantity of the vehicles are located as target roads.
Determining the condition of the road and being empty means that the condition that the vehicle of the license plate corresponding to the license plate information of the vehicle runs on the current road cannot be obtained in the fixed interval, namely, the vehicle runs on the current road for the first time and does not have corresponding navigation information, and the vehicle is defined as the first-time vehicle for identification at the moment so as to facilitate subsequent analysis; the target road is the road for determining the predicted quantity of the vehicles, and the road where different vehicles are located is distinguished through the definition of the target road, so that the subsequent analysis is convenient.
Step S601: and determining a first driving path according to the first vehicle in the fixed interval.
The first driving path is a path formed by connecting roads which are taken by the first vehicle in the current driving process.
Step S602: and determining the shortest travel path according to each road of the first travel path and the target road, and defining the shortest travel path determined by the current road and the target road as the target path.
The shortest travel path is the path with the shortest overall path which can be moved when a certain node road on the first travel path moves to the target road, and the target path is defined to distinguish the shortest travel path required by the vehicle to move from the current road to the target road, so that the follow-up analysis is convenient.
Step S603: and judging whether the path length of the target path is smaller than the path length of each shortest running path.
The purpose of the determination is to determine whether the vehicle is likely to move to the target road by knowing whether the vehicle is approaching the target road.
Step S6031: if the path lengths of the target paths are not all smaller than the path lengths of the shortest travel paths, the first vehicle is defined as an invalid vehicle.
When the path length of the target path is not smaller than the path length of each shortest driving path, the vehicle is basically not moved to the target road, and the first-time vehicle is determined to be an invalid vehicle of the target road for identification, so that the different first-time vehicles of the target road are distinguished, and the subsequent analysis is facilitated.
Step S6032: if the path length of the target path is smaller than the path length of each shortest travel path, the first vehicle is defined as an effective vehicle.
When the path length of the target path is smaller than that of each shortest driving path, the vehicle is possibly moved to the target road, and the first-time vehicle is determined to be an effective vehicle of the target road for identification, so that the different first-time vehicles of the target road are distinguished, and subsequent analysis is facilitated.
Step S604: and determining the effective vehicle which can move onto the target road at the corresponding time point according to the preset fixed speed as a theoretical possible vehicle in the effective vehicles.
The fixed speed is a fixed speed of the vehicle set by the staff during normal running, and it is theoretically possible that the vehicle is an effective vehicle capable of moving to the target road at a corresponding point in time.
Step S605: the vehicles are counted according to all theoretical possibilities to determine a theoretical additional quantity, and the correction quantity is calculated according to the theoretical additional quantity and a preset theoretical duty cycle.
The theoretical additional quantity is the total quantity value of first vehicles which can theoretically move to the target road, the theoretical duty ratio is the vehicle duty ratio of the theoretical additional quantity of vehicles which can move to the target road under the general condition, the duty ratio can be a fixed value set by a worker, and can also be determined according to the condition of the vehicles, and the determination method of the application is described below; the corrected number is the total number of first vehicles that would normally move to the target road, and is determined by multiplying the theoretical additional number by the theoretical duty cycle.
Step S606: and carrying out summation calculation according to the correction quantity and the vehicle prediction quantity so as to update the vehicle prediction quantity.
And adding the correction quantity to the vehicle prediction quantity to update the vehicle prediction quantity so as to ensure that the subsequent determination of the road congestion condition is more accurate.
Referring to fig. 7, the method further includes a theoretical duty ratio determining step including:
step S700: and determining a first theoretical value corresponding to the predicted quantity of the vehicle according to a preset first value matching relation.
The first theoretical value is the ratio of the theoretical additional number of vehicles to the target road in the actual process, different predicted numbers of vehicles indicate that the current lane is different in popularity, and when the predicted number of vehicles is larger, the higher the driving importance of the road is, the more vehicles move to the road at the moment, namely the larger the first theoretical value is, and the first value matching relation between the two is recorded and stored in advance for staff.
Step S701: and determining the historical vehicle quantity of the target road at the corresponding time point in the fixed interval.
The historical vehicle number is an average value of the vehicle numbers of the target roads determined in the fixed interval and existing at corresponding time points, and the average value can be obtained by calculating the average value of the vehicle numbers at a plurality of same time points, for example, the currently determined target road is A, the corresponding time point is seven-point at night, the vehicle number of the road A at seven-point at night is determined in the fixed interval, and then the average value is calculated.
Step S702: and determining a second theoretical value corresponding to the number of the historical vehicles according to a preset second value matching relation.
The second theoretical value is the ratio of the theoretical additional number of vehicles to the target road in the actual process, different historical vehicle numbers can reflect the importance degree of the road, the relationship between the historical vehicle numbers is the same as that between the first theoretical value and the predicted number of the vehicles, different second theoretical values are corresponding to the different historical vehicle numbers, and the second numerical matching relationship between the historical vehicle numbers and the predicted number of the vehicles is also determined in advance by staff and recorded and stored.
Step S703: and carrying out average value calculation according to the first theoretical value and the second theoretical value to determine the theoretical duty ratio.
And carrying out mean value calculation by using the first theoretical value and the second theoretical value to determine the theoretical duty ratio, so that the theoretical duty ratio which is more in line with the current road condition can be obtained, and the subsequent congestion condition determination is more accurate.
Referring to fig. 8, based on the same inventive concept, an embodiment of the present invention provides an intelligent monitoring method for urban traffic, including:
the acquisition module is used for acquiring vehicle path information, vehicle speed information and signal lamp period of each road;
the processing module is connected with the acquisition module and the judging module and is used for storing and processing information;
the judging module is connected with the acquisition module and the processing module and is used for judging information;
the processing module calculates according to the vehicle path information, the vehicle speed information and the signal lamp period to determine the road where the vehicle of the vehicle is located at each time point;
the processing module counts according to the determined roads on which the single vehicle is located at a single time point to determine the predicted quantity of the vehicles;
the judging module judges whether the predicted quantity of the vehicles is larger than the preset possible congestion quantity or not;
If the judging module judges that the predicted quantity of the vehicles is not greater than the possible congestion quantity, no action is generated;
if the judging module judges that the predicted quantity of the vehicles is larger than the possible congestion quantity, the processing module defines the road where the vehicles are located as the possible congestion road, defines the corresponding time point as the congestion time point, and outputs the road condition according to the congestion time point and the possible congestion road;
the vehicle path information determining module is used for determining vehicle path information of vehicles on a road;
the possible moving road definition module is used for defining the possible moving roads for the condition that at least two next moving roads exist;
the vehicle speed information determining module is used for determining more accurate vehicle speed information according to the condition of the road traffic flow;
the vehicle speed information calculation module corrects the historical speed according to the road traffic flow condition so as to realize accurate calculation of the vehicle speed information;
the vehicle prediction quantity updating module is used for analyzing the unregistered vehicles to update the vehicle prediction quantity so that the determined vehicle prediction quantity is closer to the actual situation;
and the theoretical duty ratio determining module is used for determining a more accurate theoretical duty ratio.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
Claims (7)
1. An intelligent monitoring method for urban traffic is characterized by comprising the following steps:
acquiring vehicle path information, vehicle speed information and signal lamp period of each road;
calculating according to the vehicle path information, the vehicle speed information and the signal lamp period to determine the road where the vehicle of the vehicle is located at each time point;
counting according to the determined road where the single vehicle is located at a single time point to determine the predicted number of vehicles, wherein the predicted number of vehicles is the number of vehicles which the road theoretically would have at the corresponding time point;
judging whether the predicted quantity of the vehicles is larger than the preset possible congestion quantity or not;
If the predicted number of the vehicles is not greater than the possible congestion number, no action is performed;
if the predicted number of the vehicles is greater than the possible congestion number, defining the road where the vehicles are located as the possible congestion road, defining the corresponding time point as the congestion time point, and outputting the road condition according to the congestion time point and the possible congestion road;
the vehicle path information acquisition step includes:
acquiring a navigation use state of a vehicle and a current road;
judging whether the navigation use state is consistent with a preset operation state, wherein the operation state is a state set by a worker when a user drives the vehicle and uses navigation;
if the navigation using state is consistent with the operation state, acquiring vehicle path information;
if the navigation using state is inconsistent with the operation state, acquiring vehicle license plate information, establishing a fixed interval with the width of a preset fixed duration on a preset time axis, and enabling the rear end point of the fixed interval to coincide with the current time point, wherein the fixed duration is a duration which is set by a worker and can be used for analyzing the historical driving condition of the vehicle;
determining a next moving road of the vehicle on the current road according to the vehicle license plate information in the fixed interval, wherein the next moving road is a road to which the vehicle of the license plate corresponding to the vehicle license plate information in the fixed interval moves next after moving to the current road;
Counting according to each next moving road to determine the next moving number, determining the next moving number with the largest value according to a preset sorting rule, and defining the next moving road corresponding to the next moving number as a possible moving road, wherein the number value corresponding to the next moving number is the number value of times that a user moves to the corresponding next moving road after moving to the current road;
repeatedly determining a next moving road according to the possible moving roads until the next moving road does not exist;
and connecting lines according to the current road and all possible moving roads to determine vehicle path information.
2. The intelligent urban traffic monitoring method according to claim 1, wherein the step of defining the possible moving road when there are at least two next moving numbers having the largest values further comprises:
defining a possible moving road having at least two next moving numbers with the largest numerical value as a bifurcation analyzing road, and defining a corresponding road which is the possible moving road as a candidate road;
defining a point in time when a road on which a vehicle is located coincides with a bifurcation analyzing road as a bifurcation-to-point;
calculating according to the current time point and the bifurcation to the site to determine a theoretical demand time length, wherein the theoretical demand time length is the time length required when a vehicle on the current road moves along a path corresponding to the vehicle path information to move to the bifurcation analysis road;
Determining the required waiting time of a signal green light corresponding to a road to be selected according to the signal lamp period, the current time point and the theoretical required time in the bifurcation analysis road;
and determining the demand waiting time with the minimum value according to the ordering rule, and defining the candidate road corresponding to the demand waiting time as a possible moving road.
3. The intelligent urban traffic monitoring method according to claim 1, further comprising a step of determining vehicle speed information, comprising:
defining the predicted quantity of the vehicles corresponding to the condition that the vehicles are on the road where the vehicles are located as the quantity of the vehicle pressure;
determining a theoretical quantity range according to the vehicle pressure quantity and a preset adjustment quantity, wherein the theoretical quantity range is a vehicle flow range when the moving speed of the vehicle in the range of the vehicle flow set by a worker is not greatly different from the moving speed in the vehicle pressure quantity, the adjustment quantity is a fixed value quantity set by the worker, the lower limit value of the theoretical quantity range is determined by subtracting the adjustment quantity from the vehicle pressure quantity, and the upper limit value of the theoretical quantity range is determined by adding the adjustment quantity to the vehicle pressure quantity;
determining the actual number of the vehicles on each road according to the license plate information of the vehicles in the fixed section;
Judging whether the actual number of the vehicles is in the theoretical number range or not;
if the actual number of the vehicles is not in the theoretical number range, defining a road corresponding to the actual number of the vehicles as an invalid road;
if the actual number of the vehicles is in the theoretical number range, defining a road corresponding to the actual number of the vehicles as an effective road;
and acquiring historical driving speeds in the effective road according to the vehicle license plate information, and calculating according to all the historical driving speeds to determine vehicle speed information.
4. The intelligent urban traffic monitoring method according to claim 3, wherein the step of calculating to determine the vehicle speed information based on all the historic traveling speeds comprises:
calculating a difference value according to the actual number of the vehicles and the pressure number of the vehicles to determine the difference value number of the vehicles;
determining a speed correction coefficient corresponding to the number of the vehicle difference values according to a preset coefficient matching relation;
calculating according to the speed correction coefficient and the corresponding historical running speed to update the historical running speed;
and carrying out average value calculation according to all updated historical driving speeds to determine vehicle speed information.
5. The intelligent urban traffic monitoring method according to claim 1, wherein after the predicted number of vehicles is determined, the intelligent urban traffic monitoring method further comprises:
Defining a vehicle which determines the road condition according to the vehicle license plate information and is empty in the fixed interval as a first vehicle, and defining a road where the vehicles with the determined predicted quantity of the vehicles are located as a target road, wherein the vehicle which determines the road condition and is empty means that the vehicle which cannot acquire the license plate corresponding to the vehicle license plate information in the fixed interval runs on the current road;
determining a first driving path according to the first vehicle in the fixed interval;
determining a shortest travel path according to each road of the first travel path and the target road, and defining the shortest travel path determined by the current road and the target road as the target path;
judging whether the path length of the target path is smaller than the path length of each shortest running path;
if the path length of the target path is not smaller than the path length of each shortest driving path, defining the first vehicle as an invalid vehicle;
if the path length of the target path is smaller than the path length of each shortest driving path, defining the first vehicle as an effective vehicle;
determining an effective vehicle capable of moving onto a target road at a corresponding time point as a theoretically possible vehicle according to a preset fixed speed in the effective vehicle;
Counting all theoretically possible vehicles to determine a theoretical additional quantity, and calculating according to the theoretical additional quantity and a preset theoretical duty ratio to determine a correction quantity;
and carrying out summation calculation according to the correction quantity and the vehicle prediction quantity so as to update the vehicle prediction quantity.
6. The intelligent urban traffic monitoring method according to claim 5, further comprising a theoretical duty cycle determining step comprising:
determining a first theoretical value corresponding to the predicted quantity of the vehicle according to a preset first value matching relation;
determining the number of historical vehicles of the target road at the corresponding time point in the fixed interval;
determining a second theoretical value corresponding to the number of the historical vehicles according to a preset second value matching relation;
and carrying out average value calculation according to the first theoretical value and the second theoretical value to determine the theoretical duty ratio.
7. An intelligent monitoring system for urban traffic, comprising:
the acquisition module is used for acquiring vehicle path information, vehicle speed information and signal lamp period of each road;
the processing module is connected with the acquisition module and the judging module and is used for storing and processing information;
The judging module is connected with the acquisition module and the processing module and is used for judging information;
the processing module calculates according to the vehicle path information, the vehicle speed information and the signal lamp period to determine the road where the vehicle of the vehicle is located at each time point;
the processing module counts according to the determined roads on which the single vehicle is located at a single time point to determine the predicted quantity of the vehicles;
the judging module judges whether the predicted quantity of the vehicles is larger than the preset possible congestion quantity or not;
if the judging module judges that the predicted quantity of the vehicles is not greater than the possible congestion quantity, no action is generated;
if the judging module judges that the predicted quantity of the vehicles is larger than the possible congestion quantity, the processing module defines the road where the vehicles are located as the possible congestion road, defines the corresponding time point as the congestion time point, and outputs the road condition according to the congestion time point and the possible congestion road;
the vehicle path information acquisition step includes:
the method comprises the steps that an acquisition module acquires a navigation use state of a vehicle and a road where the vehicle is currently located;
the judging module judges whether the navigation using state is consistent with a preset operation state;
if the judging module judges that the navigation using state is consistent with the operation state, the acquiring module acquires the vehicle path information;
If the judging module judges that the navigation using state is inconsistent with the operation state, the acquiring module acquires vehicle license plate information, and enables the processing module to establish a fixed interval with the width of a preset fixed duration on a preset time axis, and enables the rear end point of the fixed interval to coincide with the current time point;
the processing module determines a next moving road of the vehicle on the current road according to the license plate information of the vehicle in the fixed interval;
the processing module counts according to each next moving road to determine the next moving quantity, determines the next moving quantity with the largest value according to a preset ordering rule, and defines the next moving road corresponding to the next moving quantity as a possible moving road;
the processing module repeatedly determines a next moving road according to the possible moving roads until the next moving road does not exist;
the processing module is used for connecting lines according to the current road and all possible moving roads to determine the vehicle path information.
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Denomination of invention: A method and system for intelligent monitoring of urban transportation Granted publication date: 20240227 Pledgee: Qilu Bank Co.,Ltd. Jinan Chemical Fiber Factory Road Branch Pledgor: Jiaze Ruian Group Co.,Ltd. Registration number: Y2024980016491 |
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