CN108922209B - Cloud intelligent traffic signal lamp system - Google Patents
Cloud intelligent traffic signal lamp system Download PDFInfo
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- CN108922209B CN108922209B CN201810805113.7A CN201810805113A CN108922209B CN 108922209 B CN108922209 B CN 108922209B CN 201810805113 A CN201810805113 A CN 201810805113A CN 108922209 B CN108922209 B CN 108922209B
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- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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Abstract
The invention provides a cloud intelligent traffic signal lamp system which comprises a congestion information acquisition module, a cloud server, a communication module and traffic signal lamps, wherein the congestion information acquisition module is used for determining the congestion condition of a real-time road and uploading the congestion condition of the real-time road to the cloud server, the cloud server sends the congestion condition of the real-time road to the traffic signal lamps through the communication module, the traffic signal lamps are arranged right above each road, and the red light time and the green light time of each road are determined according to the congestion condition of the real-time road. The invention has the beneficial effects that: the traffic signal lamp determines the time of the red light and the time of the green light by acquiring the real-time road congestion condition, and is beneficial to relieving traffic congestion.
Description
Technical Field
The invention relates to the technical field of signal lamps, in particular to a cloud intelligent traffic signal lamp system.
Background
The red light time and the green light time of the existing traffic signal lamp are preset, and cannot be adjusted in real time according to the road traffic condition.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a cloud intelligent traffic signal lamp system.
The purpose of the invention is realized by adopting the following technical scheme:
the cloud intelligent traffic signal lamp system comprises a congestion information acquisition module, a cloud server, a communication module and traffic signal lamps, wherein the congestion information acquisition module is used for determining the congestion condition of a real-time road and uploading the congestion condition of the real-time road to the cloud server, the cloud server sends the congestion condition of the real-time road to the traffic signal lamps through the communication module, the traffic signal lamps are installed right above each road, and the red light time and the green light time of each road are determined according to the congestion condition of the real-time road.
The invention has the beneficial effects that: the traffic signal lamp determines the time of the red light and the time of the green light by acquiring the real-time road congestion condition, and is beneficial to relieving traffic congestion.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a schematic structural view of the present invention;
reference numerals:
the system comprises a congestion information acquisition module 1, a cloud server 2, a communication module 3 and a traffic signal lamp 4.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the cloud intelligent traffic signal lamp system of the embodiment includes a congestion information acquisition module 1, a cloud server 2, a communication module 3 and a traffic signal lamp 4, where the congestion information acquisition module 1 is configured to determine a congestion condition of a real-time road and upload the congestion condition of the real-time road to the cloud server 2, the cloud server 2 sends the congestion condition of the real-time road to the traffic signal lamp 4 through the communication module 3, and the traffic signal lamp 4 is installed right above each road and determines red light time and green light time of each road according to the congestion condition of the real-time road.
The embodiment provides a cloud intelligent traffic signal lamp system, and the traffic signal lamp determines the time of a red light and the time of a green light by acquiring the real-time road congestion condition, so that the traffic congestion is relieved.
Preferably, the congestion information obtaining module 1 includes a traffic data collecting module, a congestion modeling module, a first congestion coefficient calculating module, a second congestion coefficient calculating module and a congestion factor determining module, the traffic data collecting module is configured to collect real-time data of road traffic, the congestion modeling module is configured to establish a road congestion model according to the real-time data of the road traffic, the first congestion coefficient calculating module is configured to determine a first congestion coefficient of the road according to the road congestion model, the second congestion coefficient calculating module is configured to determine a second congestion coefficient of the road according to the road congestion model, and the congestion factor determining module is configured to determine a congestion factor of the road according to the first congestion coefficient and the second congestion coefficient.
According to the preferred embodiment, the congestion model is established, and the first congestion coefficient and the second congestion coefficient are calculated, so that the congested road can be avoided, and the traffic congestion can be relieved.
Preferably, the congestion modeling module is configured to establish a road congestion model according to the road traffic real-time data, and specifically includes:
and sending the road traffic real-time data to a congestion modeling module, wherein if the distance between any two or more connected vehicles is less than S, and the S is between two meters and four meters, the connected vehicles form a congestion unit, and all congestion units on the road form a congestion model of the road.
According to the preferred embodiment, the congestion unit is defined according to the road traffic real-time data, the modeling of the road congestion model is realized, the foundation is laid for the calculation of the subsequent first congestion coefficient and the second congestion coefficient, and the model conforms to the congestion condition of the road, namely when the distance between the vehicles is not enough to drive another vehicle, the vehicles are in a congestion state.
Preferably, the first congestion coefficient calculation module is configured to determine a first congestion coefficient of a road according to a road congestion model, and specifically includes:
calculating a first congestion coefficient for the road using:
in the formula, E1A first congestion coefficient representing a road, p representing the number of congestion units on the road, niRepresenting vehicles in the ith congestion unit, L represents the length of a road, the position of the vehicles in the road is the distance from the vehicles to the entrance of the lane, and if the number of the vehicles in the ith congestion unit is odd, yiIndicating the position of the intermediate vehicle on the road, and if the number of vehicles in the ith congestion unit is even, yiIs shown as
The location of the vehicle on the road;
the congestion units with the same length have different functions at different positions of the road, and the first congestion coefficient is determined according to the positions of the congestion units, so that the accurate description of the road congestion condition is realized.
Preferably, the second congestion coefficient calculation module is configured to determine a second congestion coefficient of the road according to the road congestion model, and specifically includes:
a height G is set at the entrance of the road1A height of G2The detector calculates the first vehicle and the last vehicle and the height G of each congestion unit on the road1The included angle formed by the detector connecting lines, the first vehicle and the last vehicle of each congestion unit on the road and the height G2The included angle is formed by connecting lines of the detectors;
calculating a second congestion coefficient for the road using:
in the formula, E2A second congestion coefficient representing a road, p representing the number of congestion units on the road,
indicates the location of the lead vehicle of the ith congestion unit,
indicating the position of the last vehicle of the ith congestion unit;
according to the optimization implementation, the second congestion coefficient is described by using the angle of the congestion unit, and the second congestion coefficient is calculated by adopting the detectors with different heights, so that the road congestion condition can be more accurately expressed.
Preferably, the congestion factor determining module is configured to determine a congestion factor of the road according to the first congestion coefficient and the second congestion coefficient, and specifically includes:
calculating a congestion factor of the road according to the first congestion coefficient and the second congestion coefficient of the road:
E＝log[(E1+E2)2+1]+(E1+E2)2
in the formula, E represents a congestion factor of a road; the smaller the congestion factor of the road is, the less serious the traffic congestion condition of the road is.
According to the preferred embodiment, the congestion factor of the road is determined according to the first congestion coefficient and the second congestion coefficient of the road, the position of the congestion unit on the road and the angle formed by the congestion unit and the detector are comprehensively considered, the accuracy of congestion description is improved, and a foundation is laid for determining the red light time and the green light time of a subsequent traffic signal lamp and relieving traffic congestion.
From the above description of embodiments, it is clear for a person skilled in the art that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For a hardware implementation, a processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the procedures of an embodiment may be performed by a computer program instructing associated hardware. In practice, the program may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. Computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by the monitoring area of ordinary skill in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (1)
1. The cloud intelligent traffic signal lamp system is characterized by comprising a congestion information acquisition module, a cloud server, a communication module and traffic signal lamps, wherein the congestion information acquisition module is used for determining real-time road congestion conditions and uploading the real-time road congestion conditions to the cloud server, the cloud server sends the real-time road congestion conditions to the traffic signal lamps through the communication module, the traffic signal lamps are installed right above each road, and the red light time and the green light time of each road are determined according to the real-time road congestion conditions;
the congestion information acquisition module comprises a traffic data acquisition module, a congestion modeling module, a first congestion coefficient calculation module, a second congestion coefficient calculation module and a congestion factor determination module, wherein the traffic data acquisition module is used for acquiring real-time data of road traffic, the congestion modeling module is used for establishing a road congestion model according to the real-time data of the road traffic, the first congestion coefficient calculation module is used for determining a first congestion coefficient of a road according to the road congestion model, the second congestion coefficient calculation module is used for determining a second congestion coefficient of the road according to the road congestion model, and the congestion factor determination module is used for determining a congestion factor of the road according to the first congestion coefficient and the second congestion coefficient;
the congestion modeling module is used for establishing a road congestion model according to road traffic real-time data, and specifically comprises the following steps:
the road traffic real-time data are sent to a congestion modeling module, if the distance between any two or more than two connected vehicles is less than S, and the S is between two meters and four meters, the connected vehicles form a congestion unit, and all congestion units on the road form a congestion model of the road;
the first congestion coefficient calculation module is used for determining a first congestion coefficient of a road according to a road congestion model, and specifically comprises the following steps:
calculating a first congestion coefficient for the road using:
in the formula, E1A first congestion coefficient representing a road, p representing the number of congestion units on the road, niRepresenting vehicles in the ith congestion unit, L represents the length of a road, the position of the vehicles in the road is the distance from the vehicles to the entrance of the lane, and if the number of the vehicles in the ith congestion unit is odd, yiIndicating the position of the intermediate vehicle on the road, and if the number of vehicles in the ith congestion unit is even, yiIs shown as
The location of the vehicle on the road;
the second congestion coefficient calculation module is configured to determine a second congestion coefficient of the road according to the road congestion model, and specifically includes:
a height G is set at the entrance of the road1A height of G2The detector calculates the first vehicle and the last vehicle and the height G of each congestion unit on the road1The included angle formed by the detector connecting lines, the first vehicle and the last vehicle of each congestion unit on the road and the height G2The included angle is formed by connecting lines of the detectors;
calculating a second congestion coefficient for the road using:
in the formula, E2A second congestion coefficient representing a road, p representing the number of congestion units on the road,
indicates the location of the lead vehicle of the ith congestion unit,
indicating the position of the last vehicle of the ith congestion unit;
the congestion factor determination module is used for determining a congestion factor of a road according to the first congestion coefficient and the second congestion coefficient, and specifically comprises the following steps:
calculating a congestion factor of the road according to the first congestion coefficient and the second congestion coefficient of the road:
E＝log[(E1+E2)2+1]+(E1+E2)2
in the formula, E represents a congestion factor of a road; the smaller the congestion factor of the road is, the less serious the traffic congestion condition of the road is.
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