CN112863205B - Signal lamp timing method, device, equipment and storage medium based on big data - Google Patents
Signal lamp timing method, device, equipment and storage medium based on big data Download PDFInfo
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Abstract
The invention discloses a big data-based signal lamp timing method, a big data-based signal lamp timing device, a big data-based signal lamp timing equipment and a big data-based signal lamp timing storage medium, wherein the method comprises the following steps: acquiring running vehicle information acquired by a road sensor arranged at an intersection; carrying out big data analysis according to the running vehicle information to obtain vehicle flow information; obtaining the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase according to the vehicle flow information; according to the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase, time prediction is carried out through a preset signal lamp time model, and signal lamp control time is obtained; and time distribution is carried out on the signal lamp according to the signal lamp control time, so that the signal lamp control time is automatically calculated according to the current traffic flow information, the control of the signal lamp is timely realized according to the signal lamp control time, manual control is avoided, and the intelligence of traffic signal lamp control is greatly improved.
Description
Technical Field
The invention relates to the technical field of traffic control, in particular to a signal lamp timing method, a signal lamp timing device, signal lamp timing equipment and a storage medium based on big data.
Background
In the existing traffic signal control, signal lamp control is generally carried out according to fixed time, but the overload of vehicles on a one-way road caused by congested road sections or special reasons cannot be solved only by the traditional fixed signal lamp control.
Therefore, for the road sections which are easy to have traffic jam, the traffic police often adopts a manual mode to control, namely the traffic police can judge the current road more intuitively to control the signal lamp, but the attendance frequency of the traffic police is greatly improved in an artificial mode.
Disclosure of Invention
The invention mainly aims to provide a signal lamp timing method, a signal lamp timing device, signal lamp timing equipment and a signal lamp timing storage medium based on big data, and aims to solve the technical problem that laser thickness measurement cannot be performed on irregular objects in the prior art.
In order to achieve the above object, the present invention provides a big data based signal lamp timing method, which comprises the following steps:
acquiring running vehicle information acquired by a road sensor arranged at an intersection;
carrying out big data analysis according to the running vehicle information to obtain vehicle flow information;
Obtaining the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase according to the vehicle flow information;
carrying out time prediction through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase, so as to obtain signal lamp control time;
and carrying out time distribution on the signal lamp according to the signal lamp control time.
Optionally, the time prediction is performed through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase, and before obtaining the signal lamp control time, the method further includes:
acquiring the period duration, the period loss time, the traffic flow phase information and the sampled traffic flow information of a signal lamp;
obtaining the effective period duration of the signal lamp according to the period duration and the period loss time of the signal lamp;
obtaining a flow ratio parameter corresponding to the phase according to the traffic flow phase information and the sampled traffic flow information;
and establishing the preset signal lamp time model according to the effective period duration and the flow ratio parameter corresponding to the phase.
Optionally, before time allocation is performed on the signal lamp according to the signal lamp control time, the method further includes:
Obtaining vehicle flow change information in a preset time period according to the vehicle flow information;
acquiring current time information and current area information;
obtaining time adjustment quantity according to the vehicle flow change information, the current time information and the current region information;
adjusting the signal lamp control time according to the time adjustment quantity;
time distribution is carried out on the signal lamps according to the signal lamp control time, and the time distribution method comprises the following steps:
and distributing the time of the signal lamp according to the adjusted signal lamp control time.
Optionally, before time allocation is performed on the signal lamp according to the signal lamp control time, the method further includes:
judging whether the signal lamp control time exceeds a preset time threshold value or not;
when the signal lamp control time exceeds a preset time threshold value, obtaining target signal lamp control time through a preset time calculation strategy;
the time distribution of the signal lamps according to the signal lamp control time comprises the following steps:
and time distribution is carried out on the signal lamp according to the target signal lamp control time.
Optionally, the obtaining of the target signal lamp control time through the preset time calculation strategy includes:
acquiring a signal phase number, phase signal loss time and periodic red light time;
Obtaining the periodic loss time according to the signal phase number, the phase signal loss time and the periodic red light time;
obtaining a traffic flow ratio of a critical lane according to the single-phase traffic flow and a road preset saturation amount;
obtaining an intersection traffic flow ratio according to the critical lane traffic flow ratio;
obtaining cycle duration according to the ratio of the cycle loss time to the intersection traffic flow;
and obtaining the control time of the target signal lamp according to the period duration, the period loss time and the traffic flow ratio of the critical lane.
Optionally, before acquiring the signal phase number, the method further includes:
acquiring intersection road information acquired by a road sensor;
synchronous data screening is carried out on the intersection road information to obtain road information in a synchronous time period;
extracting the characteristics of the road information to obtain road characteristic information;
performing attribute identification on the road characteristic information to obtain road attribute information;
performing data fusion according to the road attribute information to obtain target road information;
and determining the signal phase number according to the target road information.
Optionally, the time prediction is performed through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase, and before the signal lamp control time is obtained, the method further includes:
Obtaining the vehicle congestion condition according to the vehicle flow information;
inquiring a vehicle congestion level list according to the vehicle congestion condition to obtain a vehicle congestion level;
and when the vehicle congestion level reaches a preset congestion level, performing time prediction through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase to obtain signal lamp control time.
In addition, in order to achieve the above object, the present invention further provides a signal lamp timing device based on big data, where the signal lamp timing device based on big data includes:
the acquisition module is used for acquiring the information of running vehicles acquired by a road sensor arranged at an intersection;
the analysis module is used for carrying out big data analysis according to the running vehicle information to obtain vehicle flow information;
the acquisition module is further used for acquiring the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase according to the vehicle flow information;
the prediction module is used for predicting time through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase to obtain signal lamp control time;
And the distribution module is used for distributing time to the signal lamps according to the signal lamp control time.
Furthermore, to achieve the above object, the present invention also proposes an apparatus comprising: the system comprises a memory, a processor and a big data-based signal timing program stored on the memory and capable of running on the processor, wherein the big data-based signal timing program is configured to realize the steps of the big data-based signal timing method.
In addition, to achieve the above object, the present invention further provides a storage medium, on which a big data based beacon timing program is stored, and the big data based beacon timing program, when executed by a processor, implements the steps of the big data based beacon timing method as described above.
The signal lamp timing method based on big data provided by the invention acquires the information of running vehicles collected by a road sensor arranged at an intersection; carrying out big data analysis according to the running vehicle information to obtain vehicle flow information; obtaining the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase according to the vehicle flow information; according to the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase, time prediction is carried out through a preset signal lamp time model, and signal lamp control time is obtained; and time distribution is carried out on the signal lamp according to the signal lamp control time, so that the signal lamp control time is automatically calculated according to the current traffic flow information, the control of the signal lamp is timely realized according to the signal lamp control time, manual control is avoided, and the intelligence of traffic signal lamp control is greatly improved.
Drawings
Fig. 1 is a schematic structural diagram of a big-data-based signal lamp timing device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flowchart of a first embodiment of a big-data-based signal lamp timing method according to the present invention;
FIG. 3 is a schematic diagram of a system framework of an intelligent road signal timing system according to an embodiment of a big-data-based signal lamp timing method of the present invention;
FIG. 4 is a schematic diagram of a traffic information collection device according to an embodiment of a big data-based signal lamp timing method of the present invention;
FIG. 5 is a schematic flowchart of a second embodiment of a big-data-based signal lamp timing method according to the present invention;
fig. 6 is a schematic functional block diagram of a signal lamp timing device based on big data according to a first embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, e.g. a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may comprise a Display screen (Display), an input unit such as keys, and the optional user interface 1003 may also comprise a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the large data based signal timing apparatus configuration shown in fig. 1 does not constitute a limitation of the large data based signal timing apparatus and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a big data-based signal timing program.
In the big data-based signal lamp timing device shown in fig. 1, the network interface 1004 is mainly used for connecting a server and communicating data with the server; the user interface 1003 is mainly used for connecting a user terminal and performing data communication with the terminal; the big-data-based signal lamp timing device calls the big-data-based signal lamp timing program stored in the memory 1005 through the processor 1001, and executes the big-data-based signal lamp timing method provided by the embodiment of the invention.
Based on the hardware structure, the embodiment of the signal lamp timing method based on the big data is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of a big data-based signal lamp timing method according to the present invention.
In a first embodiment, the big-data-based signal lamp timing method includes the following steps:
in step S10, traveling vehicle information collected by a road sensor installed at the intersection is acquired.
It should be noted that, the execution main body of the embodiment may be a signal lamp timing device based on big data, the signal lamp timing device based on big data is provided with a signal lamp timing program based on big data, and may also be other devices that can achieve the same or similar functions.
The traffic signal control device is internally provided with a multi-core multi-CPU structure and can bear various value-added computing capabilities. The mode of combining network and computing is adopted, a high-performance network switch is built in, the integration of the traffic field network is realized, various network interfaces such as Ethernet and the like are included, rich network services based on a TCP/IP protocol are supported, and various applications such as traffic control, traffic perception, traffic video application, traffic cloud data processing and the like are highly integrated.
The control device is connected with the traffic signal machine and the traffic management data center, and can realize the sharing of area control and detection data under the condition of no center. And local distribution and storage of the traffic perception data are realized. The system framework of the intelligent timing system for road signals shown in fig. 3 comprises a front-end device and a traffic signal machine, wherein, the front-end device can comprise a traffic signal control device, the front-end device comprises a power supply, a data storage unit, a data processing control unit, a coder-decoder unit and a data transceiver unit, wherein, the traffic collection unit sends the collected data to the front-end device, the front-end device sends the data to the traffic signal machine through 232 or 485 serial ports, and other connection modes are also available, the embodiment is not limited to this, the traffic signal machine comprises a control signal output unit, a data processing unit, a data storage unit, a codec unit and a data transceiver unit, the data transceiver unit can send the data to the data center, and the control signal output unit outputs control information to the traffic signal lamp so as to control the traffic signal lamp.
As shown in fig. 4, various traffic information collecting devices are disposed at the intersections, where the traffic information collecting devices include video collecting devices, geomagnetic sensors, coil sensors, microwave sensors, or the like, and may further include other data collecting devices, which are not limited in this embodiment, and the traffic information collecting devices are used as automobile flow collectors to obtain flow data of each intersection of the road, and send the flow data of each intersection to the data center.
And step S20, performing big data analysis according to the running vehicle information to obtain vehicle flow information.
In this embodiment, a signal management and control device based on traffic road traffic data is established, that is, traffic data at each intersection of a road is synchronously transmitted to a central data platform and the management and control device through a network (private network, wireless and the like) by a traffic information acquisition device (a video, geomagnetic, coil, microwave and other automobile traffic acquisition devices), and the management and control device mainly realizes statistics, analysis, calculation and real-time signal output control on real-time traffic data and historical data, that is, a diversified, real-time and efficient signal control comprehensive scheme is provided for a road network according to traffic flow changes at different directions of the intersection and by combining with time-interval and regional traffic demand changes.
It can be understood that, the signal management and control device can analyze according to the vehicle information when acquiring the vehicle information collected by the flow collection device to obtain the vehicle flow information, and can also analyze big data according to the historical vehicle information by collecting the historical vehicle information, so that the data collected by the current sensor and the historical vehicle data are combined and analyzed to obtain the vehicle flow information, and the accuracy of vehicle flow information identification is realized.
And step S30, obtaining the signal lamp effective period duration and the flow ratio parameter corresponding to the phase according to the vehicle flow information.
The vehicle flow information may also be obtained by performing learning analysis on historical big data to obtain historical vehicle flow information, and the vehicle flow information is obtained according to the historical vehicle flow information and the current vehicle flow information acquired by the current sensor.
And step S40, performing time prediction through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase, and obtaining signal lamp control time.
In this embodiment, the preset signal lamp time model can be obtained through convolutional neural network training, and can also be obtained through other training modes, this embodiment does not limit this, in this embodiment, to obtain preset signal lamp time model through convolutional neural network training as an example and explain, include the corresponding relation of current vehicle flow information and signal lamp control time through preset signal lamp time model, regard current vehicle flow information as the input, output signal lamp control time, thereby avoid through manual operation, automatically obtain signal lamp control time through vehicle flow information, improve the intellectuality of vehicle road signal lamp control.
In an embodiment, before the step S40, the method further includes:
obtaining the vehicle congestion condition according to the vehicle flow information; inquiring a vehicle congestion level list according to the vehicle congestion condition to obtain a vehicle congestion level; when the vehicle congestion level reaches a preset congestion level, step S40 is executed.
In this embodiment, the vehicle congestion level list may be a list that records correspondence between traffic flow information and a vehicle congestion level, and the corresponding vehicle congestion level may be obtained by querying the vehicle congestion level list through the traffic flow information, for example, the traffic flow information is a, and since the vehicle congestion level list corresponds to a correspondence between a traffic flow range and a corresponding congestion level, the traffic flow range is determined as B according to the traffic flow information, and the congestion level corresponding to B is queried as a second level through the vehicle congestion level list, so as to obtain the congestion level of the vehicle.
It should be noted that the vehicle congestion level may be divided into three levels, a first level, a second level, and a third level, and may also be divided into more levels, which is not limited in this embodiment, the three levels are taken as an example, the congestion level is preset as the second level, and may also be other levels.
And step S50, distributing the time of the signal lamp according to the signal lamp control time.
In this embodiment, according to signal lamp control time carries out time distribution to the signal lamp, include, acquire the original control time of green light signal lamp, according to signal lamp control time adjusts the original control time of green light signal lamp, still can also be for acquireing the original control time of red light signal lamp, according to signal lamp control time adjusts the original control time of red light signal lamp, calculates each road crossing traffic flow quantity in city through the traffic big data, to the highway section that takes place to block up, through intelligent control system, the not equidirectional signal lamp current time of automatically regulated road reduces the one-way road surface traffic overload that causes because of the special reason, because the phenomenon of the vehicle condition aggravation that blocks up that the restriction of signal lamp standard time leads to. According to the actual overload condition, the traffic time of each lane is intelligently controlled, the signal change is not executed according to the curing time any more, and the attendance frequency of the traffic police is reduced.
According to the scheme, the running vehicle information acquired by the road sensor arranged at the intersection is acquired; carrying out big data analysis according to the running vehicle information to obtain vehicle flow information; obtaining the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase according to the vehicle flow information; carrying out time prediction through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase, so as to obtain signal lamp control time; the signal lamp is subjected to time distribution according to the signal lamp control time, so that the signal lamp control time is automatically calculated according to the current traffic flow information, the control over the signal lamp is timely realized according to the signal lamp control time, manual control is avoided, and the intelligence of traffic signal lamp control is greatly improved.
In an embodiment, as shown in fig. 5, a second embodiment of the signal lamp timing method based on big data according to the present invention is proposed based on the first embodiment, and before the step S40, the method further includes:
step S401, signal lamp period duration, period lost time, traffic flow phase information and sampled traffic flow information are obtained.
It should be noted that the traffic flow phase information is the vehicle driving direction information of the current road, for example, the current road defines the first phase representation, the red light is turned on the road in the north-south direction, the vehicle is prohibited from driving, the green light is turned on the road in the east-west direction, the vehicle can pass, the second phase is just opposite, the red light is turned on the road in the east-west direction, the vehicle is prohibited from driving, the green light is turned on the road in the north-south direction, the vehicle can pass, and the three-phase and the four-phase may be set.
In the present embodiment, the signal lamp cycle duration is represented by T, and the cycle loss time is represented by TlThe presentation, the traffic flow phase information is represented by n, and the sampled traffic flow information is represented by Z.
And S402, obtaining the effective period duration of the signal lamp according to the period duration and the period lost time of the signal lamp.
In the present embodiment, the effective period duration is TiThe effective period duration is obtained by subtracting the period loss time from the signal lamp period duration, namely T-T l=Ti。
And S403, obtaining a flow ratio parameter corresponding to the phase according to the traffic flow phase information and the sampled traffic flow information.
In this embodiment, the phase-corresponding flow rate ratio parameter is represented by α, wherein,
(ii) a Wherein Z isiTraffic flow information representing the current phase.
And S404, establishing the preset signal lamp time model according to the effective period duration and the flow ratio parameter corresponding to the phase.
In specific implementation, the preset signal lamp time model, namely T, is established according to the effective period duration and the flow ratio parameter corresponding to the phasej=αTiWherein, TjAnd controlling the time of the signal lamp, so that the determination of the control time of the signal lamp is realized according to a preset signal lamp time model.
In an embodiment, before the step S40, the method further includes:
obtaining vehicle flow change information in a preset time period according to the vehicle flow information; acquiring current time information and current region information; obtaining time adjustment quantity according to the vehicle flow change information, the current time information and the current region information; and adjusting the signal lamp control time according to the time adjustment amount.
In the embodiment, according to the traffic flow change of different directions at the intersection, the control time of the signal lamp is adjusted by combining the traffic demand change of a time interval type and a regional type, so that the accuracy of the signal lamp control is improved.
In an embodiment, before the step S40, the method further includes:
judging whether the signal lamp control time exceeds a preset time threshold value or not; and when the signal lamp control time exceeds a preset time threshold value, obtaining the target signal lamp control time through a preset time calculation strategy.
It should be noted that, since it is necessary to ensure that the control time of the signal lamp is within the normal time range when the control time of the signal lamp exceeds the normal time range, the control time of the target signal lamp needs to be obtained again according to the preset time calculation strategy, so that the accuracy of the control time of the target signal lamp is improved.
In this embodiment, the preset time threshold may be 10s, and may also be other time parameter information, which is not limited in this embodiment, when the signal lamp control time exceeds 10s, and when the signal lamp control time exceeds the preset time threshold, the target signal lamp control time is obtained through a preset time calculation strategy, where the preset time calculation strategy may be to obtain the target signal lamp control time according to the cycle duration, the cycle loss time, and the critical lane traffic flow ratio, and may also be obtained through other calculation methods, which is not limited in this embodiment.
In an embodiment, the obtaining of the target signal lamp control time through the preset time calculation strategy includes:
acquiring a signal phase number, phase signal loss time and periodic red light time; obtaining the periodic loss time according to the signal phase number, the phase signal loss time and the periodic red light time; obtaining a critical lane traffic flow ratio according to the single-phase traffic flow and a road preset saturation amount; obtaining an intersection traffic flow ratio according to the critical lane traffic flow ratio; obtaining cycle duration according to the ratio of the cycle loss time to the intersection traffic flow; and obtaining the control time of the target signal lamp according to the period duration, the period loss time and the traffic flow ratio of the critical lane.
In this embodiment, the number of signal phases is represented as n, the phase signal loss time is represented as l, and the periodic red light time is represented as TrUsing T in terms of cycle loss timel=nl+Tr。
The single-phase traffic flow is represented by v, the preset road saturation is represented by s, and the critical lane traffic flow ratio y = v/s and the intersection traffic flow ratio are obtained
Duration of a cycle
；
Target signal lamp control time:
in an embodiment, before acquiring the signal phase number, the method further includes:
Acquiring intersection road information acquired by a road sensor; synchronous data screening is carried out on the intersection road information to obtain road information in a synchronous time period; extracting the characteristics of the road information to obtain road characteristic information; performing attribute identification on the road characteristic information to obtain road attribute information; performing data fusion according to the road attribute information to obtain target road information; and determining the signal phase number according to the target road information.
In the embodiment, the target signal lamp control time is obtained by combining the single-phase traffic flow, the preset road saturation amount and the critical lane traffic flow ratio of the intersection, so that more accurate signal lamp control time is obtained according to the actual situation, and the accuracy of signal lamp control is improved.
The invention further provides a signal lamp timing device based on the big data.
Referring to fig. 6, fig. 6 is a functional module schematic diagram of a first embodiment of a big data-based signal lamp timing device according to the present invention.
In a first embodiment of the big data based signal lamp timing device of the present invention, the big data based signal lamp timing device includes:
the acquisition module 10 is used for acquiring the information of the running vehicles acquired by the road sensors arranged at the intersection.
The traffic signal control device is internally provided with a multi-core multi-CPU structure and can bear various value-added computing capabilities. The mode of combining network and computing is adopted, a high-performance network switch is built in, the integration of the traffic field network is realized, various network interfaces such as Ethernet and the like are included, rich network services based on a TCP/IP protocol are supported, and various applications such as traffic control, traffic perception, traffic video application, traffic cloud data processing and the like are highly integrated.
The control device is connected with the traffic signal machine and the traffic management data center, and can realize the sharing of area control and detection data under the condition of no center. And local distribution and storage of the traffic perception data are realized. The system framework of the intelligent timing system for road signals shown in fig. 3 comprises a front-end device and a traffic signal machine, wherein, the front-end device can comprise a traffic signal control device, the front-end device comprises a power supply, a data storage unit, a data processing control unit, a coder-decoder unit and a data transceiver unit, wherein, the traffic collection unit sends the collected data to the front-end device, the front-end device sends the data to the traffic signal machine through 232 or 485 serial ports, and other connection modes are also available, the embodiment is not limited to this, the traffic signal machine comprises a control signal output unit, a data processing unit, a data storage unit, a codec unit and a data transceiver unit, the data transceiver unit can send the data to the data center, and the control signal output unit outputs control information to the traffic signal lamp so as to control the traffic signal lamp.
As shown in fig. 4, various traffic information collecting devices are disposed at the intersection, where the traffic information collecting devices include a video collecting device, a geomagnetic sensor, a coil sensor, or a microwave sensor, and may further include other data collecting devices, which are not limited in this embodiment, and the traffic information collecting devices are used as car flow collectors, so as to obtain flow data at each intersection of the road.
And the analysis module 20 is configured to perform big data analysis according to the running vehicle information to obtain vehicle flow information.
In this embodiment, a signal management and control device based on traffic road traffic data is established, that is, traffic data at each intersection of a road is synchronously transmitted to a central data platform and the management and control device through a network (private network, wireless and the like) by a traffic information acquisition device (a video, geomagnetic, coil, microwave and other automobile traffic acquisition devices), and the management and control device mainly realizes statistics, analysis, calculation and real-time signal output control on real-time traffic data and historical data, that is, a diversified, real-time and efficient signal control comprehensive scheme is provided for a road network according to traffic flow changes at different directions of the intersection and by combining with time-interval and regional traffic demand changes.
It can be understood that, the signal management and control device can analyze according to the vehicle information when acquiring the vehicle information collected by the flow collection device to obtain the vehicle flow information, and can also analyze big data according to the historical vehicle information by collecting the historical vehicle information, so that the data collected by the current sensor and the historical vehicle data are combined and analyzed to obtain the vehicle flow information, and the accuracy of vehicle flow information identification is realized.
The obtaining module 10 is further configured to obtain a signal lamp effective period duration and a flow ratio parameter corresponding to the phase according to the vehicle flow information.
The vehicle flow information may also be obtained by performing learning analysis on historical big data to obtain historical vehicle flow information, and the vehicle flow information is obtained according to the historical vehicle flow information and the current vehicle flow information acquired by the current sensor.
And the prediction module 30 is configured to perform time prediction through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase, so as to obtain signal lamp control time.
In this embodiment, the preset signal lamp time model can be obtained through convolutional neural network training, and can also be obtained through other training modes, this embodiment does not limit this, in this embodiment, to obtain preset signal lamp time model through convolutional neural network training as an example and explain, include the corresponding relation of current vehicle flow information and signal lamp control time through preset signal lamp time model, regard current vehicle flow information as the input, output signal lamp control time, thereby avoid through manual operation, automatically obtain signal lamp control time through vehicle flow information, improve the intellectuality of vehicle road signal lamp control.
In an embodiment, the big data-based signal lamp timing device further includes a query module;
the query module is used for obtaining the vehicle congestion condition according to the vehicle flow information; and inquiring a vehicle congestion level list according to the vehicle congestion condition to obtain the vehicle congestion level.
In this embodiment, the vehicle congestion level list may be a list that records correspondence between traffic flow information and a vehicle congestion level, and the corresponding vehicle congestion level may be obtained by querying the vehicle congestion level list through the traffic flow information, where for example, the vehicle traffic flow information is a, and since the vehicle congestion level list corresponds to a correspondence between a traffic flow range and a corresponding congestion level, the vehicle traffic flow range is determined as B according to the traffic flow information, and the congestion level corresponding to B is queried as a second level through the vehicle congestion level list, so as to obtain a congestion level of the vehicle.
It should be noted that the congestion level of the vehicle may be divided into three levels, a first level, a second level, and a third level, and may also be divided into more levels, which is not limited in this embodiment, the three levels are taken as an example, and the congestion level is preset as the second level, and may also be other levels.
And the distribution module 40 is used for distributing time to the signal lamps according to the signal lamp control time.
In the embodiment, the number of automobile flows at each road intersection of a city is calculated through traffic big data, and for road sections with congestion, the traffic light passing time of different directions of the road is automatically adjusted through an intelligent control system, so that the phenomena that the traffic flow on the one-way road is overloaded due to special reasons and the vehicle congestion condition is aggravated due to the standard time limitation of the traffic light are reduced. According to the actual overload condition, the traffic time of each lane is intelligently controlled, the signal change is not executed according to the curing time any more, and the attendance frequency of the traffic police is reduced.
According to the scheme, the embodiment acquires the running vehicle information acquired by the road sensor arranged at the intersection; carrying out big data analysis according to the running vehicle information to obtain vehicle flow information; obtaining the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase according to the vehicle flow information; according to the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase, time prediction is carried out through a preset signal lamp time model, and signal lamp control time is obtained; and time distribution is carried out on the signal lamp according to the signal lamp control time, so that the signal lamp control time is automatically calculated according to the current traffic flow information, the control of the signal lamp is timely realized according to the signal lamp control time, manual control is avoided, and the intelligence of traffic signal lamp control is greatly improved.
In an embodiment, the big data-based signal lamp timing device further comprises an establishing module, and the establishing module is further configured to obtain signal lamp period duration, period loss time, traffic flow phase information and sampled traffic flow information; obtaining the effective period duration of the signal lamp according to the period duration and the period loss time of the signal lamp; obtaining a flow ratio parameter corresponding to the phase according to the traffic flow phase information and the sampled traffic flow information; and establishing the preset signal lamp time model according to the effective period duration and the flow ratio parameter corresponding to the phase.
In an embodiment, the distribution module 40 is further configured to obtain vehicle flow change information within a preset time period according to the vehicle flow information; acquiring current time information and current region information; obtaining time adjustment quantity according to the vehicle flow change information, the current time information and the current region information; and adjusting the signal lamp control time according to the time adjustment amount.
In an embodiment, the allocating module 40 is further configured to determine whether the signal lamp control time exceeds a preset time threshold; when the signal lamp control time exceeds a preset time threshold value, obtaining target signal lamp control time through a preset time calculation strategy
In an embodiment, the distribution module 40 is further configured to obtain a signal phase number, a phase signal loss time, and a periodic red light time; obtaining the periodic loss time according to the signal phase number, the phase signal loss time and the periodic red light time; obtaining a traffic flow ratio of a critical lane according to the single-phase traffic flow and a road preset saturation amount; obtaining an intersection traffic flow ratio according to the critical lane traffic flow ratio; obtaining cycle duration according to the ratio of the cycle loss time to the intersection traffic flow; and obtaining the control time of the target signal lamp according to the period duration, the period loss time and the traffic flow ratio of the critical lane.
In an embodiment, the distribution module 40 is further configured to obtain intersection road information acquired by a road sensor; synchronous data screening is carried out on the intersection road information to obtain road information in a synchronous time period; extracting the characteristics of the road information to obtain road characteristic information; performing attribute identification on the road characteristic information to obtain road attribute information; performing data fusion according to the road attribute information to obtain target road information; and determining the signal phase number according to the target road information.
In addition, in order to achieve the above object, the present invention further provides a big data-based signal lamp timing device, where the big data-based signal lamp timing device includes: the system comprises a memory, a processor and a big-data-based signal timing program which is stored on the memory and can run on the processor, wherein the big-data-based signal timing program is configured to realize the steps of the big-data-based signal timing method.
Furthermore, an embodiment of the present invention further provides a storage medium, where a big data-based signal timing program is stored, and the area identification program, when executed by a processor, implements the steps of the big data-based signal timing method as described above.
Since the storage medium adopts all technical solutions of all the above embodiments, at least all the beneficial effects brought by the technical solutions of the above embodiments are achieved, and details are not repeated herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or system in which the element is included.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be substantially or partially embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling an intelligent terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A big data-based signal lamp timing method is characterized by comprising the following steps:
acquiring running vehicle information acquired by a road sensor arranged at an intersection;
carrying out big data analysis according to the running vehicle information to obtain vehicle flow information;
obtaining the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase according to the vehicle flow information;
according to the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase, time prediction is carried out through a preset signal lamp time model, and signal lamp control time is obtained;
distributing the time of the signal lamp according to the control time of the signal lamp;
the time distribution of the signal lamps according to the signal lamp control time comprises the following steps:
time distribution is carried out on signal lamps according to target signal lamp control time, wherein the target signal lamp control time is obtained by obtaining a signal phase number, phase signal loss time and periodic red lamp time; obtaining the periodic loss time according to the signal phase number, the phase signal loss time and the periodic red light time; obtaining a critical lane traffic flow ratio according to the single-phase traffic flow and a road preset saturation amount; obtaining an intersection traffic flow ratio according to the critical lane traffic flow ratio; obtaining cycle duration according to the ratio of the cycle loss time to the intersection traffic flow; obtaining the time length of the period, the period loss time and the traffic flow ratio of the critical lane;
The method comprises the following specific steps: the phase number of the signal is represented as n, the loss time of the phase signal is represented as l, and the red light time of the period is represented as TrAccording to the period loss time by Tl＝nl+Tr；
The single-phase traffic flow is represented by v, the preset road saturation is represented by s, and the traffic flow ratio of the critical lane y is obtained as v/s, and the traffic flow ratio of the intersection is obtained
Duration of cycle
Target signal lamp control time
2. The signal lamp timing method based on big data as claimed in claim 1, wherein the time prediction is performed by a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase, before the signal lamp control time is obtained, further comprising:
acquiring the period duration, the period loss time, the traffic flow phase information and the sampled traffic flow information of a signal lamp;
obtaining the effective period duration of the signal lamp according to the period duration and the period loss time of the signal lamp;
obtaining a flow ratio parameter corresponding to the phase according to the traffic flow phase information and the sampled traffic flow information;
and establishing the preset signal lamp time model according to the effective period duration and the flow ratio parameter corresponding to the phase.
3. The big-data-based signal timing method as claimed in claim 1, wherein before the time allocation of the signal lights according to the signal control time, further comprising:
Obtaining vehicle flow change information in a preset time period according to the vehicle flow information;
acquiring current time information and current area information;
obtaining time adjustment quantity according to the vehicle flow change information, the current time information and the current region information;
adjusting the signal lamp control time according to the time adjustment quantity;
time distribution is carried out on the signal lamps according to the signal lamp control time, and the time distribution method comprises the following steps:
and distributing the time of the signal lamp according to the adjusted signal lamp control time.
4. The big-data-based signal timing method as claimed in claim 1, wherein before the time allocation of the signal lights according to the signal control time, further comprising:
judging whether the signal lamp control time exceeds a preset time threshold value or not;
and when the signal lamp control time exceeds a preset time threshold value, obtaining the target signal lamp control time through a preset time calculation strategy.
5. The big-data-based signal lamp timing method according to claim 1, wherein before the obtaining the number of signal phases, the method further comprises:
acquiring intersection road information acquired by a road sensor;
Synchronous data screening is carried out on the intersection road information to obtain road information in a synchronous time period;
extracting the characteristics of the road information to obtain road characteristic information;
performing attribute identification on the road characteristic information to obtain road attribute information;
performing data fusion according to the road attribute information to obtain target road information;
and determining the signal phase number according to the target road information.
6. The signal lamp timing method based on the big data as claimed in any one of claims 1 to 5, wherein the time prediction is performed by a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase, before the signal lamp control time is obtained, further comprising:
obtaining the vehicle congestion condition according to the vehicle flow information;
inquiring a vehicle congestion level list according to the vehicle congestion condition to obtain a vehicle congestion level;
and when the vehicle congestion level reaches a preset congestion level, performing time prediction through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase to obtain signal lamp control time.
7. The signal lamp timing device based on the big data is characterized by comprising the following components:
the acquisition module is used for acquiring running vehicle information acquired by a road sensor arranged at an intersection;
the analysis module is used for carrying out big data analysis according to the running vehicle information to obtain vehicle flow information;
the acquisition module is further used for acquiring the effective period duration of the signal lamp and the flow ratio parameter corresponding to the phase according to the vehicle flow information;
the prediction module is used for predicting time through a preset signal lamp time model according to the signal lamp effective period duration and the flow ratio parameter corresponding to the phase to obtain signal lamp control time;
the distribution module is used for distributing the time of the signal lamp according to the control time of the signal lamp;
the distribution module is also used for distributing time of the signal lamp according to the control time of a target signal lamp, wherein the control time of the target signal lamp is the time of obtaining the signal phase number, the phase signal loss time and the periodic red light time; obtaining the periodic loss time according to the signal phase number, the phase signal loss time and the periodic red light time; obtaining a critical lane traffic flow ratio according to the single-phase traffic flow and a road preset saturation amount; obtaining an intersection traffic flow ratio according to the critical lane traffic flow ratio; obtaining cycle duration according to the ratio of the cycle loss time to the intersection traffic flow; obtaining the time length of the period, the period loss time and the traffic flow ratio of the critical lane;
The method comprises the following specific steps: the phase number of the signal is represented as n, the loss time of the phase signal is represented as l, and the red light time of the period is represented as TrAccording to the period loss time by Tl＝nl+Tr；
The single-phase traffic flow is represented by v, the preset road saturation amount is represented by s, and the traffic flow ratio of the critical lane is obtainedV/s, intersection traffic flow ratio
Duration of cycle
Target signal lamp control time
8. A big data based signal lamp timing device, characterized in that, the big data based signal lamp timing device includes: a memory, a processor, and a big-data based semaphore timing program stored on the memory and executable on the processor, the big-data based semaphore timing program configured to implement the steps of the big-data based semaphore timing method according to any of claims 1-6.
9. A storage medium, characterized in that the storage medium has stored thereon a big-data based signal timing program, which when executed by a processor implements the steps of the big-data based signal timing method according to any one of claims 1 to 6.
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Denomination of invention: Method, device, equipment, and storage medium for signal light timing based on big data Granted publication date: 20220628 Pledgee: Wuhan Caidian Branch of Hubei Bank Co.,Ltd. Pledgor: WUHAN ZONGHENG SMART CITY CO.,LTD. Registration number: Y2024980021347 |