CN115547055B - Traffic signal lamp coordination control method and device, storage medium and equipment - Google Patents
Traffic signal lamp coordination control method and device, storage medium and equipment Download PDFInfo
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Abstract
The invention discloses a traffic signal lamp coordination control method, a device, a storage medium and equipment, wherein a traffic data set is obtained by carrying out time-interval statistics on a road section to be predicted in advance according to a decision tree algorithm, and a coordination data set consisting of optimal signal lamp setting data of each time interval is calculated in advance according to the traffic data set for machine learning, so that three structural models with different data types in the traffic data set as attributes are generated; the traffic data set comprises a vehicle data set, a weather data set and a road segment data set; acquiring real-time data of a time period to be predicted, and performing decision analysis by adopting a structural model with more or less attributes according to data corresponding to the attributes of the structural model in the real-time data; and regulating and controlling the signal lamp by taking the analysis result as the signal lamp setting data of the time period to be predicted. The real-time prediction of the traffic signal lamp management scheme can be realized, and the traffic efficiency is improved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a traffic signal lamp coordination control method, a traffic signal lamp coordination control device, a storage medium and traffic signal lamp coordination control equipment.
Background
With the increasing number of automobiles, the conflict between intersections is increasingly excited, in order to safely and effectively manage intersections, the traffic light management method is one of the most effective methods, and the conventional traffic light control method is mainly divided into two types, wherein one type is a scheme for manually setting different time intervals according to experience and then automatically switching the time intervals, and the other type is that intersection monitoring is carried out by depending on hardware and then real-time edge calculation is carried out; then informing the intersection signal lamp switching scheme; the former depends on personal experience and is not real-time, the latter can automatically coordinate, but the adjustable mode is also preset by hands, and the communication between intersections needs to depend on the judgment of the calculation result of the upper level, so that the automatic adaptation can not be really realized, and only the post reaction can be realized; the control of the traffic signal lamp in the existing traffic signal control method cannot realize real-time prediction, and the traffic efficiency of the scheme is low.
Disclosure of Invention
In order to solve the technical problems, the invention provides a traffic signal lamp coordination control method, a traffic signal lamp coordination control device, a storage medium and traffic signal lamp coordination control equipment, which can realize real-time prediction of a traffic signal lamp management scheme and improve traffic efficiency.
The embodiment of the invention provides a traffic signal lamp coordination control method, which comprises the following steps:
carrying out time-interval statistics on a road section to be predicted in advance according to a decision tree algorithm to obtain a traffic data set, and calculating a coordination data set consisting of optimal signal lamp setting data of each time interval according to the traffic data set in advance to carry out machine learning so as to generate three structural models with different data types in the traffic data set as attributes; the traffic data set comprises a vehicle data set, a weather data set and a road segment data set;
acquiring real-time data of a time period to be predicted, and performing decision analysis by adopting a first structural model with the largest number of attributes according to data corresponding to the attributes of the first structural model in the real-time data;
when the first structural model cannot output an analysis result, performing decision analysis by adopting a second structural model with a second number of attributes according to data corresponding to the attributes of the second structural model in the real-time data;
when the second structural model cannot output an analysis result, performing decision analysis by adopting a third structural model with the minimum number of attributes according to data corresponding to the attributes of the third structural model in the real-time data;
and when the first structural model, the second structural model or the third structural model outputs an analysis result, the analysis result is used as signal lamp setting data of the time period to be predicted to regulate and control the signal lamp.
Preferably, the calculation method of the coordination data set includes:
traversing and calculating the set time of all signal lamps in a preset signal lamp time length setting interval according to the passing data set of each time interval, and determining the optimal signal lamp set data with the shortest vehicle passing time in the time interval; and taking the optimal signal lamp setting data with the shortest vehicle passing time in all periods as the coordination data set.
As an improvement of the above, the method further comprises:
obtaining verification data regulated and controlled by a signal lamp, wherein the verification data comprises vehicle passing time of the time period to be predicted;
calculating the error between the vehicle passing time and the shortest passing time corresponding to the signal lamp setting data in the analysis result;
and when the error is not less than a preset error threshold value, judging that the prediction is abnormal, and performing decision analysis again on the real-time data acquired in the previous period of the period to be predicted according to the third structural model so as to revise the signal lamp setting data of the period to be predicted on the same day according to the reanalysis result.
Preferably, the method further comprises:
recalculating the corresponding coordination data set of each time period from the traffic data sets counted in all time periods in the day; and inputting the traffic data set and the corresponding coordination data set in each time period into a decision tree algorithm for machine learning, and optimizing the three structural models.
Preferably, the attributes of the first structural model include date, time period, temperature, wind direction, wind speed, and rainfall;
the attributes of the second structure model comprise time period, temperature, wind direction, wind speed and rainfall;
the attributes of the third structural model include temperature, wind direction, wind speed, and rainfall.
Preferably, the vehicle data set comprises a vehicle unique code, a transit time period, a transit time and a road segment unique code;
the weather data set includes: the data is unique code, date, time period, weather condition, temperature, rainfall, wind direction and wind speed;
the road segment data set includes: a road segment unique code, a road segment name, and a road segment length.
Preferably, the setting time of all signal lamps existing in the preset signal lamp time length setting interval is subjected to traversal calculation according to the passing data set of each time interval, and the optimal signal lamp setting data with the shortest vehicle passing time in the time interval is determined; taking the optimal signal lamp setting data with the shortest vehicle passing time in all periods as the coordination data set, specifically comprising:
for each passing time period, acquiring the road section length, the passing time period and the passing time corresponding to the unique code of each vehicle in the passing data set and the calculated average speed to obtain a passing record set of all vehicles;
randomly setting signal lamp setting data which comprises all traffic signal lamps in the road section to be predicted and accords with a red lamp setting interval, a green lamp setting interval and a yellow lamp setting interval in the signal lamp time length setting interval;
taking a preset time node as the time of all vehicles entering the road section to be predicted in the traffic data set, calculating the traffic time consumed when all vehicles completely pass through the road section to be predicted under the current signal lamp setting data, and taking the longest traffic time consumed by all vehicles as the total time consumed corresponding to the current signal lamp setting data;
traversing all signal lamp setting data which accord with the signal lamp time length setting interval, and keeping the total time consumption with the shortest time length in the total time consumption obtained by calculating all the signal lamp setting data as the optimal signal lamp setting data of the current time period;
and traversing and calculating the optimal signal setting data of each period to obtain the coordination data set.
The embodiment of the invention provides a traffic signal lamp coordination control device, which comprises:
the model training module is used for carrying out time-interval statistics on a road section to be predicted in advance according to a decision tree algorithm to obtain a traffic data set, calculating a coordination data set consisting of optimal signal lamp setting data of each time interval according to the traffic data set in advance, carrying out machine learning, and generating three structural models taking different data types in the traffic data set as attributes; the traffic data set comprises a vehicle data set, a weather data set and a road segment data set;
the first analysis module is used for acquiring real-time data of a time period to be predicted and carrying out decision analysis by adopting a first structural model with the largest number of attributes according to data corresponding to the attributes of the first structural model in the real-time data;
the second analysis module is used for performing decision analysis by adopting a second structure model with the second number of attributes according to data corresponding to the attributes of the second structure model in the real-time data when the first structure model cannot output an analysis result;
the third analysis module is used for performing decision analysis by adopting a third structural model with the minimum number of attributes according to data corresponding to the attributes of the third structural model in the real-time data when the second structural model cannot output an analysis result;
and the output module is used for regulating and controlling the signal lamp by taking the analysis result as the signal lamp setting data of the time period to be predicted when the first structural model, the second structural model or the third structural model outputs the analysis result.
Further, the process of the model training module calculating the coordination data set comprises:
traversing and calculating the set time of all signal lamps in a preset signal lamp time length setting interval according to the passing data set of each time interval, and determining the optimal signal lamp set data with the shortest vehicle passing time in the time interval; and taking the optimal signal lamp setting data with the shortest vehicle passing time in all periods as the coordination data set.
Further, the apparatus further comprises:
an anomaly analysis module to:
obtaining verification data regulated and controlled by a signal lamp, wherein the verification data comprises the vehicle passing time of the time period to be predicted;
calculating the error between the vehicle passing time and the shortest passing time corresponding to the signal lamp setting data in the analysis result;
and when the error is not less than a preset error threshold value, judging that the prediction is abnormal, and performing decision analysis again on the real-time data acquired in the previous period of the period to be predicted according to the third structural model so as to revise the signal lamp setting data of the period to be predicted on the same day according to the reanalysis result.
Preferably, the apparatus further comprises:
the optimization module is used for recalculating the coordination data set corresponding to each time period from the traffic data sets counted in all time periods on the day; and inputting the passing data set and the corresponding coordination data set of each time period into a decision tree algorithm for machine learning, and optimizing the three structural models.
Preferably, the attributes of the first structural model include date, time period, temperature, wind direction, wind speed, and rainfall;
attributes of the second structural model include time period, temperature, wind direction, wind speed and rainfall;
the attributes of the third structural model include temperature, wind direction, wind speed, and rainfall.
Preferably, the vehicle data set comprises a vehicle unique code, a transit time period, a transit time and a road segment unique code;
the weather data set includes: the data is unique code, date, time period, weather condition, temperature, rainfall, wind direction and wind speed;
the road segment data set includes: a road segment unique code, a road segment name, and a road segment length.
Preferably, the model training module is further specifically configured to:
for each passing time period, acquiring the road section length, the passing time period and the passing time corresponding to the unique code of each vehicle in the passing data set and the calculated average speed to obtain a passing record set of all vehicles;
randomly setting signal lamp setting data which comprise all traffic signal lamps in the road section to be predicted and accord with a red lamp setting section, a green lamp setting section and a yellow lamp setting section in the signal lamp time length setting section;
taking a preset time node as the time of all vehicles entering the road section to be predicted in the traffic data set, calculating the traffic time consumed when all vehicles completely pass through the road section to be predicted under the current signal lamp setting data, and taking the longest traffic time consumed by all vehicles as the total time consumed corresponding to the current signal lamp setting data;
traversing all signal lamp setting data which accord with the signal lamp time length setting interval, and keeping the total time consumption with the shortest time length in the total time consumption obtained by calculating all the signal lamp setting data as the optimal signal lamp setting data of the current time period;
and traversing and calculating the optimal signal setting data of each period to obtain the coordination data set.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the traffic signal lamp coordination control method in any one of the above embodiments.
The embodiment of the present invention further provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the traffic signal light coordination control method according to any one of the above embodiments is implemented.
According to the traffic signal lamp coordination control method, the traffic signal lamp coordination control device, the storage medium and the equipment, a traffic data set is obtained by carrying out time-interval statistics on a road section to be predicted in advance according to a decision tree algorithm, and a coordination data set consisting of optimal signal lamp setting data of each time interval is calculated in advance according to the traffic data set to carry out machine learning, so that three structural models with different data types in the traffic data set as attributes are generated; the traffic data set comprises a vehicle data set, a weather data set and a road segment data set; acquiring real-time data of a time period to be predicted, and performing decision analysis by adopting a first structural model with the largest number of attributes according to data corresponding to the attributes of the first structural model in the real-time data; when the first structural model cannot output an analysis result, performing decision analysis by adopting a second structural model with a second number of attributes according to data corresponding to the attributes of the second structural model in the real-time data; when the second structural model cannot output an analysis result, performing decision analysis by adopting a third structural model with the least number of attributes according to data corresponding to the attributes of the third structural model in the real-time data; and when the first structural model, the second structural model or the third structural model outputs an analysis result, the analysis result is used as signal lamp setting data of the time period to be predicted to regulate and control the signal lamp. The real-time prediction of the traffic signal lamp management scheme can be realized, and the traffic efficiency is improved.
Drawings
Fig. 1 is a schematic flow chart of a traffic signal light coordination control method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a traffic light coordination control method according to another embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a first structural model provided by an embodiment of the invention;
FIG. 4 is a schematic structural diagram of a second structural model provided in an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a third structural model provided in an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a traffic signal light coordination control device according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a traffic signal lamp coordination control method, and relates to fig. 1, which is a flow schematic diagram of the traffic signal lamp coordination control method provided by the embodiment of the invention, and the method comprises the following steps of S1-S5:
s1, performing machine learning on a traffic data set obtained by performing time-interval statistics on a road section to be predicted in advance according to a decision tree algorithm and a coordination data set composed of optimal signal lamp setting data calculated in each time interval in advance according to the traffic data set to generate three structural models taking different data types in the traffic data set as attributes; the traffic data set comprises a vehicle data set, a weather data set and a road segment data set;
s2, acquiring real-time data of the current time period, and performing decision analysis by adopting a first structural model with the maximum number of attributes according to data corresponding to the attributes of the first structural model in the real-time data;
s3, when the first structural model cannot output an analysis result, performing decision analysis by adopting a second structural model with a second attribute number according to data corresponding to the attribute of the second structural model in the real-time data;
s4, when the second structural model cannot output an analysis result, performing decision analysis by adopting a third structural model with the minimum number of attributes according to data corresponding to the attributes of the third structural model in the real-time data;
and S5, when the first structural model, the second structural model or the third structural model outputs an analysis result, the analysis result is used as signal lamp setting data of a corresponding time period to regulate and control the signal lamp.
In the specific implementation of this embodiment, it is necessary to acquire data of a road segment to be predicted in advance, and perform specific division according to different time periods according to the acquired data, and set the time period, taking half an hour as a time slice, for example, dividing the time in one day into 0:00-0: 30. 0:30-1: 00. 1:00-1:30, waiting for 48 half-hour time periods, performing prediction by taking half an hour as a unit during each prediction, and determining the traffic data sets of the road section to be predicted in different time periods; the traffic data set comprises information of vehicles passing in the time period, weather information and road section condition information in the time period; vehicle information, weather information and road section condition information in different time periods respectively form a vehicle data set, a weather data set and a road section data set; the traffic data set can record the relation between various factors of the road section and the passing vehicles along with time and weather; and the vehicle data set of the passing vehicles can calculate the vehicle passing condition of the road section to be predicted and describe the traffic condition of the road section to be predicted.
The optimal signal lamp setting data under the condition of weather data corresponding to each time period can be calculated through the acquired traffic data set, and a coordination data set is formed through the calculated optimal signal lamp setting; the signal lamp setting data includes the setting time of the red lamp, the yellow lamp and the green lamp.
Performing learning according to data of each time period in the traffic data set and optimal signal lamp setting data calculated in the corresponding time period by a decision tree algorithm to generate three structural models with different data types in the traffic data set as attributes; the prediction accuracy, the response rate and the calculation speed of the structure models which are used as attributes of different data types are different; the fewer the attributes, the higher the response rate and the calculation speed, but the lower the prediction accuracy; the more the attributes are, the lower the response rate and the calculation speed are, and the higher the prediction precision is;
therefore, in the application, the real-time data of the time period to be predicted is obtained, and the first structural model with the largest number of attributes is adopted to perform decision analysis according to the data corresponding to the attributes of the first structural model in the real-time data;
when the first structural model outputs an analysis result, the analysis result is used as signal lamp setting data of a time period to be predicted to regulate and control a signal lamp;
when the first structural model cannot output an analysis result, performing decision analysis by adopting a second structural model with a second number of attributes according to data corresponding to the attributes of the second structural model in the real-time data;
when the second structure model outputs an analysis result, the analysis result is used as signal lamp setting data of a time period to be predicted to regulate and control the signal lamp;
when the second structural model cannot output an analysis result, performing decision analysis by adopting a third structural model with the least number of attributes according to data corresponding to the attributes of the third structural model in the real-time data;
when the third structural model outputs an analysis result, the analysis result is used as signal lamp setting data of a time period to be predicted to regulate and control the signal lamp;
preferentially adopting the structural model with a large number of attributes to perform decision analysis, improving the accuracy of an output result, and ensuring the output of a prediction result by taking the third structural model with the least attributes as a bottom-preserving measure;
the method is used in an actual application scene, the investment of manpower and hardware can be effectively reduced, the process is automatic, and along with the increase of a traffic data set, the accuracy of a scheme for setting the traffic signal lamp can be continuously improved.
In another embodiment provided by the present invention, the method for calculating the coordination data set includes:
traversing and calculating the set time of all signal lamps in a preset signal lamp time length setting interval according to the passing data set of each time interval, and determining the optimal signal lamp set data with the shortest vehicle passing time in the time interval; and taking the optimal signal lamp setting data with the shortest vehicle passing time in all periods as the coordination data set.
In this embodiment, the coordination data set specifically includes:
traversing and calculating the set time of all signal lamps in a preset signal lamp time length setting interval according to the passing data set of each time interval, and determining the optimal signal lamp set data with the shortest vehicle passing time in the time interval; traversing all possible setting time in a preset signal lamp time length setting interval according to the speed information of the passing vehicles in the passing data set, calculating the optimal signal lamp setting data with the shortest passing time, and taking the optimal signal lamp setting data with the shortest passing time of the vehicles in all time intervals as the coordination data set. In the traversing process, the minimum increasing unit of the time length of the red light, the yellow light and the green light is second, and a rule is set, so that the red light and the yellow light stop the movement of the vehicle.
The vehicle passing time in each time period is also related to the corresponding weather data, so that the optimal signal lamp setting data in each time period are calculated, the corresponding weather data are also considered, and the corresponding weather data can be synthesized when decision analysis is subsequently carried out according to the optimal signal lamp setting data, so that the signal lamp prediction precision is improved.
In another embodiment provided by the present invention, the method further comprises:
obtaining verification data regulated and controlled by a signal lamp, wherein the verification data comprises the vehicle passing time of the time period to be predicted;
calculating the error between the vehicle passing time and the shortest passing time corresponding to the signal lamp setting data in the analysis result;
and when the error is not less than a preset error threshold value, judging that the prediction is abnormal, and carrying out decision analysis again on the real-time data acquired in the previous period of the period to be predicted according to the third structural model so as to revise the signal lamp setting data of the period to be predicted in the current day according to the reanalysis result.
In the specific implementation of the present embodiment, refer to fig. 2, which is a schematic flow chart of a traffic signal light coordination control method according to another embodiment of the present invention;
when the signal lamp coordination control is carried out, the following steps are specifically executed:
acquiring information, namely acquiring real-time data of a time period to be predicted, wherein the real-time data comprises corresponding weather information and corresponding time period information;
performing smod1 analysis, namely performing decision analysis by adopting a first structural model smod1 with the largest number of attributes according to data corresponding to the attributes of the first structural model smod1 in the real-time data;
when the first structure model smod1 has an analysis result, returning the result, and regulating and controlling the signal lamp by taking the analysis result as signal lamp setting data of a time period to be predicted;
when the first structural model smod1 has no analysis result, the second structural model smod2 with the second number of attributes is adopted to perform decision analysis according to the data corresponding to the attributes of the second structural model smod2 in the real-time data;
when the second structure model smod2 has an analysis result, returning the result, and regulating and controlling the signal lamp by taking the analysis result as signal lamp setting data of a time period to be predicted;
when the second structure model smod2 has no analysis result, the third structure model smod3 with the least number of attributes is adopted to perform decision analysis according to the data corresponding to the attributes of the third structure model smod3 in the real-time data;
when the third structure model smod3 outputs an analysis result, the analysis result is used as signal lamp setting data of a time period to be predicted to regulate and control the signal lamp;
obtaining verification data regulated and controlled by a signal lamp, wherein the verification data comprises the vehicle passing time of the time period to be predicted;
calculating the error between the vehicle passing time and the shortest passing time corresponding to the signal lamp setting data in the analysis result;
and when the error is not less than 2% of the preset error, judging that the prediction is abnormal, and acquiring data of the previous period, namely acquiring real-time data acquired in the previous period of the period to be predicted, and performing decision analysis by adopting a third structural model again to revise the signal lamp setting data of the period to be predicted on the same day according to the reanalysis result.
It should be noted that, in this embodiment, the error threshold is set to be 2%, and in other embodiments, the error threshold may be set to be other values.
The signal lamp is regulated and controlled according to the prediction result, and the result obtained through decision analysis of the structure model is subjected to anomaly analysis, so that the accuracy of the signal lamp setting time prediction time is improved.
In another embodiment provided by the present invention, the method further comprises:
recalculating the corresponding coordination data set of each time period from the traffic data sets counted in all time periods in the day; and inputting the passing data set and the corresponding coordination data set of each time period into a decision tree algorithm for machine learning, and optimizing the three structural models.
In this embodiment, referring to fig. 2, the method further includes:
collecting data, and performing model optimization, namely counting the traffic data sets of all time periods in the day, recalculating the traffic data sets counted in all time periods in the day, and recalculating the coordination data set corresponding to each time period;
and inputting the passing data set and the corresponding coordination data set of each time period into a decision tree algorithm for machine learning, optimizing the three structural models, and revising the optimal signal lamp setting time scheme today.
The data volume of machine learning is continuously improved through the data acquired in real time, and the accuracy of the scheme set for the traffic signal lamp is improved.
In yet another embodiment provided by the present invention, the attributes of the first structural model include date, time period, temperature, wind direction, wind speed, and rainfall;
the attributes of the second structure model comprise time period, temperature, wind direction, wind speed and rainfall;
the attributes of the third structural model include temperature, wind direction, wind speed, and rainfall.
In the specific implementation of the present embodiment, refer to fig. 3, which is a schematic structural diagram of a first structural model provided in the embodiment of the present invention; the first structural model is trained by using data of date (without consideration of year), time period, temperature, wind direction, wind speed and rainfall as attributes, and signal lamp setting data is used as an analysis result; different dates were obtained at 1 month, 1 day, 2 months, 1 day, etc., 1:00-1:30 and 2:00-2:30, the structural model for gradual analysis and decision making in different time periods and different weather data comprises the weather conditions such as sunny weather or rain weather, different temperature states such as 30 degrees or 20 degrees, different wind directions such as south wind or north wind, different wind power sizes such as three-level or four-level, and analysis results of optimal signal lamp setting data in different rainfall such as 0.5mm or 2 mm; along with the richness of the data volume of weather in the training process, the output response rate of the analysis result of the model is continuously improved, and more complex real-time data can be adapted;
it should be noted that, in this embodiment, an embodiment of attributes of the first structural model and a structural schematic diagram of the corresponding first model are provided, in other embodiments, the attributes of the first structural model may adopt different setting methods, and the corresponding structure is changed due to different attributes, and does not affect the specific implementation of the scheme.
Fig. 4 is a schematic structural diagram of a second structural model provided in the embodiment of the present invention; the second structure model is trained by using attributes such as time interval, temperature, wind direction, wind speed, rainfall and the like, and signal lamp setting data is used as an analysis result; obtained in the following ratio of 1:00-1:30 and 2:00-2:30, the structural model for gradual analysis and decision making in different time periods and different weather data comprises the weather conditions such as sunny weather or rain weather, different temperature states such as 30 degrees or 20 degrees, different wind directions such as south wind or north wind, different wind power sizes such as three-level or four-level, and analysis results of optimal signal lamp setting data in different rainfall such as 0.5mm or 2 mm; along with the richness of the data volume of weather in the training process, the output response rate of the analysis result of the model is continuously improved, and more complex real-time data can be adapted;
it should be noted that, in this embodiment, an embodiment of attributes of the second structure model and a structural schematic diagram of the corresponding second structure model are provided, in other embodiments, the attributes of the second structure model may adopt different setting methods, and the corresponding structure may also change due to different attributes, and does not affect the specific implementation of the solution.
Fig. 5 is a schematic structural diagram of a third structural model provided in the embodiment of the present invention; the third structural model is trained by using attributes such as temperature, wind direction, wind speed, rainfall and the like, and signal lamp setting data is used as an analysis result; obtaining a structural model for gradual analysis and decision making under different weather data, wherein the weather data in the structural model comprises analysis results of optimal signal lamp setting data under different rainfall, such as 0.5mm or 2mm, and different wind directions, such as south wind or north wind, different wind magnitudes, such as three levels or four levels, and different temperature states, such as 30 ℃ or 20 ℃ and the like; along with the richness of the data volume of weather in the training process, the output response rate of the analysis result of the model is continuously improved, and more complex real-time data can be adapted;
it should be noted that, in this embodiment, an embodiment of attributes of the third structural model and a structural schematic diagram of the corresponding third structural model are provided, in other embodiments, the attributes of the third structural model may adopt different setting methods, and the corresponding structure is also changed due to different attributes, and does not affect the specific implementation of the scheme.
Decision analysis is carried out through the structural models with different attribute numbers, and the requirements of different precision, calculation speed and response rate can be met.
In yet another embodiment provided by the present invention, the vehicle data set includes a vehicle unique code, a transit time period, a transit time, and a road segment unique code;
the weather data set includes: the data is unique code, date, time period, weather condition, temperature, rainfall, wind direction and wind speed;
the road segment data set includes: a road segment unique code, a road segment name, and a road segment length.
In this embodiment, the process of acquiring the traffic data set specifically includes:
each road segment itself has a corresponding road segment data set pre-stored therein, and table 1 gives an example of the road segment data set, where the road segment data set includes attributes such as a unique code of the road segment, a name of the road segment, and a length of the road segment, and gives a corresponding attribute identifier and a corresponding description:
table 1 road section data set example table
Collecting data with a time interval as a unit through sensing equipment such as an intersection sensor and camera equipment, collecting total time of a vehicle with a license plate as a main vehicle passing through a road section in the time interval as a vehicle data set, wherein an example of the vehicle data set is given in table 2, and the vehicle data set comprises a unique vehicle code, a passing time period, passing time and a unique road section code;
TABLE 2 vehicle data set example Table
Collecting the current weather condition in a time period, wherein an example of a weather data set is given in table 3, the weather data set comprises a data unique code, a date, a time period, a weather condition, a temperature, rainfall, a wind direction and a wind speed, and the weather condition is recorded only once in each time period;
table 3 example table of weather data set
A weather data set tdt, a vehicle data set ctd and a road section data set rdt are formed after arrangement, the data are stored in files according to the names of the files named by dates, one file is generated every day, for example, 20220102.Tdt is generated, and the data are stored, so that subsequent model training is facilitated.
In another embodiment provided by the present invention, the traversing calculation is performed on the setting time of all signal lamps existing in the preset signal lamp time length setting interval according to the passing data set of each time interval, and the optimal signal lamp setting data with the shortest vehicle passing time in the time interval is determined; taking the optimal signal lamp setting data with the shortest vehicle passing time in all periods as the coordination data set, specifically comprising:
for each passing time period, acquiring the road section length, the passing time period and the passing time corresponding to the unique code of each vehicle in the passing data set and the calculated average speed to obtain a passing record set of all vehicles;
randomly setting signal lamp setting data which comprise all traffic signal lamps in the road section to be predicted and accord with a red lamp setting section, a green lamp setting section and a yellow lamp setting section in the signal lamp time length setting section;
taking a preset time node as the time of all vehicles entering the road section to be predicted in the traffic data set, calculating the traffic time consumed when all vehicles completely pass through the road section to be predicted under the current signal lamp setting data, and taking the longest traffic time consumed by all vehicles as the total time consumed corresponding to the current signal lamp setting data;
traversing all signal lamp setting data which accord with the signal lamp time length setting interval, and keeping the total consumed time with the shortest time length in the total consumed time obtained by calculating all signal lamp setting data as the optimal signal lamp setting data in the current time period;
and traversing and calculating the optimal signal setting data of each period to obtain the coordination data set.
In the specific implementation of this embodiment, the formula S = V × T is applied;
the distance S is the total length of the road section in the road section data set, the distance T is the passing time recorded in the vehicle data set, and the calculation speed V = S/T; for example, a 4 km road section is finished in 40 seconds, and the average speed is 4000/40=100m/s;
calculating each vehicle in the vehicle data set in a certain period, writing the calculation result into the current record for storage, and obtaining a traffic record set of all vehicles, wherein table 4 is an example of a traffic data set;
table 4 traffic data set example table
Calculating optimal signal setting data in the coordination data set according to the determined passage record set;
the traffic record set comprises 5 vehicles in a time period 1. The path length is assumed to be 4000m.
Randomly setting signal lamp setting data which comprises all traffic signal lamps in the road section to be predicted and accords with a red lamp setting interval, a green lamp setting interval and a yellow lamp setting interval in the signal lamp time length setting interval, wherein the green lamp setting interval in the signal lamp time length setting interval is [5,60], the red lamp setting interval is [5,45], the yellow lamp is set to be 3, the unit is s, and the minimum growth unit is second; setting rules, and stopping the vehicle from moving by the red light and the yellow light; setting a scheme, namely setting a green light for 10s, a red light for 10s and a yellow light for 3s;
the intersection of the single-intersection road section is arranged at the tail end of the road section, and a preset time node is used as the time of all vehicles entering the road section to be predicted in the traffic data set;
in the embodiment, the starting time of the green light is taken as the time for entering the road section to be predicted, the road section is 4000 meters, the average speed of 40 meters/second theoretically finishes walking, if the red light and the green light are both set for 10 seconds, when the vehicle starts to finish walking the whole distance, the green-yellow-red light is subjected to 100/23 cycles, which is equal to 4.35 cycles; performing modulo operation on the set time for starting from the green light to enter and just returning to all the lights to obtain a modulo value of 0.35, and performing cycle time calculation on the modulo value, namely 23 × 0.35=8.05, wherein the time after the cycle calculation is less than the first green light time by 10s, so that the vehicle can directly pass through the road section to be predicted without stopping, and the time consumed by the vehicle to completely pass through the road section to be predicted can be directly calculated to be 100s;
the specific formula is calculated as: the calculation cycle period x = S/(t 1+ t2+ t 3); calculating a remainder y = x- [ x ]; calculating an interval value z = (t 1+ t2+ t 3) × y, wherein the green light time t1, the red light time t2 and the yellow light time t3;
judging whether the intersection is passed or not according to the interval value, judging the state of a signal lamp, judging whether the signal lamp needs to be waited or not, and further calculating the final passing time;
calculating the passing time of all vehicles passing the road section to be predicted completely under the current signal lamp setting data, and taking the longest passing time of all vehicles as the total time consumption corresponding to the current signal lamp setting data;
modifying a traffic light setting scheme, such as adding 1 second to a green light (a green light: 31s red light: 20s yellow light: 3 s) or subtracting 1 second (a green light: 29s red light: 20s yellow light: 3 s), and then recalculating the passage time until the conditions in all schemes in the signal light time setting interval are completely traversed;
obtaining the passing time consumption under all signal lamp setting schemes after the execution is finished, performing an ascending sorting algorithm, and keeping the total time consumption with the shortest duration in the total time consumption obtained by calculating all signal lamp setting data as the optimal signal lamp setting data in the current time period; and traversing and calculating the optimal signal setting data of each period to obtain the coordination data set.
The calculation of the multi-intersection condition is similar to that of a single intersection, and the calculation process and the process of generating the final data are also similar, but the following two differences exist in the calculation data:
(1) Multiple signal lamps may appear on a multi-intersection road, for example: a first signal lamp random arrangement scheme (a green lamp: 30s red lamp: 20s yellow lamp: 3 s) and a second signal lamp random arrangement scheme (a green lamp: 35s red lamp: 23s yellow lamp: 3 s);
when the exhaustive traversal step is carried out, the scheme combinations in all signal lamp duration setting intervals of the two signal lamps must be exhausted at the same time, the optimal time is obtained finally, the same is carried out on the plurality of signal lamps, and the more the intersections are, the larger the calculated amount is;
(2) The total length of the total route for calculating the multi-intersection road section must be added up by all intersections; for example, road section one 4000m, road section one 30m, road section two 3000m, road section two 30m, and final total length is 4000+30+3000+30=7060m.
And determining the optimal signal lamp setting data and determining a coordination data set by performing traversal calculation on the setting time of all signal lamps in the preset signal lamp time length setting interval.
In yet another embodiment provided by the present invention,
referring to fig. 6, it is a schematic structural diagram of a traffic signal light coordination control device provided in an embodiment of the present invention, where the device includes:
the model training module is used for carrying out machine learning on a traffic data set obtained by carrying out time-interval statistics on a road section to be predicted in advance according to a decision tree algorithm and a coordination data set formed by calculating optimal signal lamp setting data of each time interval in advance according to the traffic data set to generate three structural models taking different data types in the traffic data set as attributes; the traffic data set comprises a vehicle data set, a weather data set and a road segment data set;
the first analysis module is used for acquiring real-time data of a time period to be predicted and carrying out decision analysis by adopting a first structural model with the largest number of attributes according to data corresponding to the attributes of the first structural model in the real-time data;
the second analysis module is used for performing decision analysis by adopting a second structure model with the second number of attributes according to data corresponding to the attributes of the second structure model in the real-time data when the first structure model cannot output an analysis result;
the third analysis module is used for performing decision analysis by adopting a third structural model with the minimum number of attributes according to data corresponding to the attributes of the third structural model in the real-time data when the second structural model cannot output an analysis result;
and the output module is used for regulating and controlling the signal lamp by taking the analysis result as the signal lamp setting data of the time period to be predicted when the first structural model, the second structural model or the third structural model outputs the analysis result.
It should be noted that the traffic signal lamp coordination control device provided in the embodiment of the present invention can execute the traffic signal lamp coordination control method described in any embodiment of the foregoing embodiments, and specific functions of the traffic signal lamp coordination control device are not described herein again.
Fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program, such as a traffic light coordination control program, stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the embodiments of the traffic signal light coordination control method, such as steps S1 to S5 shown in fig. 1, are implemented. Alternatively, the processor implements the functions of the modules in the above device embodiments when executing the computer program.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device. For example, the computer program may be divided into modules, and the specific functions of the modules are not described again.
The terminal device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of a terminal device, and may include more or less components than those shown, or combine certain components, or different components, for example, the terminal device may also include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device and connects the various parts of the whole terminal device using various interfaces and lines.
The memory may be used for storing the computer programs and/or modules, and the processor may implement various functions of the terminal device by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the terminal device integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in code form, in object code form, in an executable file or in some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection therebetween, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A traffic signal lamp coordination control method is characterized by comprising the following steps:
carrying out time-interval statistics on a road section to be predicted in advance according to a decision tree algorithm to obtain a traffic data set, and calculating a coordination data set consisting of optimal signal lamp setting data of each time interval according to the traffic data set in advance to carry out machine learning so as to generate three structural models with different data types in the traffic data set as attributes; the traffic data set comprises a vehicle data set, a weather data set and a road segment data set;
acquiring real-time data of a time period to be predicted, and performing decision analysis by adopting a first structural model with the largest number of attributes according to data corresponding to the attributes of the first structural model in the real-time data;
when the first structural model cannot output an analysis result, performing decision analysis by adopting a second structural model with a second number of attributes according to data corresponding to the attributes of the second structural model in the real-time data;
when the second structural model cannot output an analysis result, performing decision analysis by adopting a third structural model with the least number of attributes according to data corresponding to the attributes of the third structural model in the real-time data;
and when the first structural model, the second structural model or the third structural model outputs an analysis result, the analysis result is used as signal lamp setting data of the time period to be predicted to regulate and control the signal lamp.
2. The traffic signal light coordination control method according to claim 1, characterized in that said coordination data set calculation method comprises:
traversing and calculating the setting time of all signal lamps in a preset signal lamp time length setting interval according to the traffic data set of each time period, and determining the optimal signal lamp setting data with the shortest vehicle traffic time in the time period; and taking the optimal signal lamp setting data with the shortest vehicle passing time in all periods as the coordination data set.
3. The traffic signal light coordination control method according to claim 2, characterized in that said method further comprises:
obtaining verification data regulated and controlled by a signal lamp, wherein the verification data comprises the vehicle passing time of the time period to be predicted;
calculating the error between the vehicle passing time and the shortest passing time corresponding to the signal lamp setting data in the analysis result;
and when the error is not less than a preset error threshold value, judging that the prediction is abnormal, and performing decision analysis again on the real-time data acquired in the previous period of the period to be predicted according to the third structural model so as to revise the signal lamp setting data of the period to be predicted on the same day according to the reanalysis result.
4. The coordinated control method of traffic signal lamps according to claim 2, characterized in that said method further comprises:
recalculating the corresponding coordination data set of each time period from the traffic data sets counted in all time periods in the day; and inputting the traffic data set and the corresponding coordination data set in each time period into a decision tree algorithm for machine learning, and optimizing the three structural models.
5. The traffic signal light coordination control method according to claim 1, characterized in that the attributes of said first structure model include date, time period, temperature, wind direction, wind speed and rainfall;
attributes of the second structural model include time period, temperature, wind direction, wind speed and rainfall;
the attributes of the third structural model include temperature, wind direction, wind speed, and rainfall.
6. The traffic signal light coordination control method according to claim 1, characterized in that said vehicle data set includes a vehicle unique code, a passage time period, a passage time, and a section unique code;
the weather data set includes: the data is unique code, date, time period, weather condition, temperature, rainfall, wind direction and wind speed;
the road segment data set includes: a road segment unique code, a road segment name, and a road segment length.
7. The traffic signal lamp coordination control method according to claim 2, characterized in that, according to the traffic data set of each time interval, the setting times of all signal lamps existing in the preset signal lamp time length setting interval are subjected to traversal calculation, and the optimal signal lamp setting data with the shortest vehicle passing time in the time interval is determined; taking the optimal signal lamp setting data with the shortest vehicle passing time in all periods as the coordination data set, specifically comprising:
for each passing time period, acquiring the road section length, the passing time period and the passing time corresponding to the unique code of each vehicle in the passing data set and the calculated average speed to obtain a passing record set of all vehicles;
randomly setting signal lamp setting data which comprises all traffic signal lamps in the road section to be predicted and accords with a red lamp setting interval, a green lamp setting interval and a yellow lamp setting interval in the signal lamp time length setting interval;
taking a preset time node as the time of all vehicles entering the road section to be predicted in the traffic data set, calculating the traffic time consumed when all vehicles completely pass through the road section to be predicted under the current signal lamp setting data, and taking the longest traffic time consumed by all vehicles as the total time consumed corresponding to the current signal lamp setting data;
traversing all signal lamp setting data which accord with the signal lamp time length setting interval, and keeping the total time consumption with the shortest time length in the total time consumption obtained by calculating all the signal lamp setting data as the optimal signal lamp setting data of the current time period;
and traversing and calculating the optimal signal setting data of each period to obtain the coordination data set.
8. A traffic signal light coordination control device, characterized in that the device comprises:
the model training module is used for carrying out time-interval statistics on a road section to be predicted in advance according to a decision tree algorithm to obtain a traffic data set, calculating a coordination data set consisting of optimal signal lamp setting data of each time interval according to the traffic data set in advance, carrying out machine learning, and generating three structural models taking different data types in the traffic data set as attributes; the traffic data set comprises a vehicle data set, a weather data set and a road segment data set;
the first analysis module is used for acquiring real-time data of a time period to be predicted and carrying out decision analysis by adopting a first structural model with the largest number of attributes according to data corresponding to the attributes of the first structural model in the real-time data;
the second analysis module is used for performing decision analysis by adopting a second structure model with the second number of attributes according to data corresponding to the attributes of the second structure model in the real-time data when the first structure model cannot output an analysis result;
the third analysis module is used for performing decision analysis by adopting a third structural model with the minimum number of attributes according to data corresponding to the attributes of the third structural model in the real-time data when the second structural model cannot output an analysis result;
and the output module is used for regulating and controlling the signal lamp by taking the analysis result as the signal lamp setting data of the time period to be predicted when the first structural model, the second structural model or the third structural model outputs the analysis result.
9. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls an apparatus to execute the traffic signal coordination control method according to any one of claims 1 to 7.
10. A terminal device, characterized by comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the traffic signal light coordination control method according to any one of claims 1 to 7 when executing the computer program.
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