A traffic light control method and system

Abstract

The application discloses a traffic light control method and system, the traffic light control method comprises the following steps: determining a control mode, a pedestrian flow factor and a vehicle flow factor; determining a state parameter according to the control mode; determining control data according to the state parameter, the pedestrian flow factor and the vehicle flow factor; and controlling the traffic light according to the control data. Compared with the prior art, the application has the following beneficial effects: by determining the pedestrian flow factor and the vehicle flow factor, the control data of the traffic light can be dynamically adjusted according to real-time traffic conditions, the waiting time of vehicles and pedestrians can be reduced, the operation efficiency of the overall traffic system can be improved, traffic flow can be more effectively managed, and congestion can be reduced; by combining the control mode and the state parameter, the accuracy of determining the control data can be improved, the control of the traffic light is more refined, and the needs of different traffic scenes can be met.

Description

Technical Field

[0001] This invention relates to the field of traffic light control technology, and in particular to a traffic light control method and system. Background Technology

[0002] Traditional traffic light control systems typically use fixed time allocation to control traffic signals. While this approach considers traffic flow in most situations during design, in actual operation, the dynamic changes in traffic flow caused by varying vehicle and pedestrian volumes during weekday rush hours, weekends, and public holidays, as well as unpredictable factors, make it difficult to improve traffic congestion simply by adjusting the traffic light duration. This can lead to a series of problems, such as "high vehicle volume on one side causing traffic congestion, while low vehicle volume on the other side results in only a few vehicles or no vehicles crossing during the green light period," "high pedestrian volume but short green light time at crosswalks," and "low pedestrian volume or even no pedestrians crossing crosswalks but long green light time," posing significant safety hazards to our traffic systems.

[0003] Therefore, existing technologies for controlling traffic lights have the problem of not being able to dynamically adjust the time allocation according to changes in traffic flow. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a traffic light control method and system that can solve the problem that the prior art cannot dynamically adjust the time allocation according to changes in traffic flow.

[0005] To solve the above problems, the present invention is implemented according to the following solution:

[0006] A traffic light control method is provided, including:

[0007] Determine the control mode, pedestrian flow factor, and vehicle flow factor;

[0008] Determine the state parameters based on the control mode;

[0009] The control data is determined based on the state parameters, the pedestrian flow factor, and the vehicle flow factor.

[0010] The traffic lights are controlled according to the control data.

[0011] Compared with existing technologies, the traffic light control method provided by this invention has the following advantages: By determining the pedestrian flow factor and the vehicle flow factor, the control data of the traffic lights can be dynamically adjusted according to the real-time traffic conditions, which can reduce the waiting time of vehicles and pedestrians, improve the overall operating efficiency of the traffic system, and thus manage traffic flow more effectively and reduce congestion; by combining control modes and state parameters, the accuracy of determining control data can be improved, making the control of traffic lights more refined, thereby adapting to the needs of different traffic scenarios.

[0012] Optionally, the control mode includes a first mode and a second mode, and determining the control mode includes:

[0013] Obtain the target state and determine whether the target state meets the first preset condition;

[0014] If the conditions are met, the first mode is used; otherwise, the second mode is used.

[0015] Optionally, determining the pedestrian flow factor includes:

[0016] Obtain the facial recognition results;

[0017] Infrared detection is performed on the facial recognition results to determine the body temperature result of the facial recognition results;

[0018] Based on the body temperature results, facial recognition results that do not meet the second preset condition will be filtered out;

[0019] Determine the number of face recognition results that meet the second preset condition;

[0020] When the number of facial recognition results exceeds a preset threshold, weight data is determined;

[0021] The pedestrian flow factor is determined based on the weight data and the preset weight data.

[0022] Optionally, determining the traffic flow factor includes:

[0023] Obtain the number of vehicles and roads;

[0024] The traffic flow factor is determined based on the number of vehicles and the number of roads.

[0025] Optionally, obtaining the number of vehicles and the number of roads includes:

[0026] The license plates are identified, and the number of identified license plates is determined as the number of vehicles.

[0027] Determine whether the road is divided into lanes. If the road is divided into lanes, determine the number of lanes as the number of roads. If the road is not divided into lanes, determine the road width and determine the number of roads based on the road width and a preset width.

[0028] Optionally, determining the state parameters according to the control mode includes:

[0029] When the control mode is the first mode, the state parameter exists;

[0030] Based on location information, obtain weather conditions through the weather system;

[0031] When the weather condition is at level one, the first preset parameter is determined as the state parameter;

[0032] When the weather condition is at level two, the second preset parameter is determined as the state parameter;

[0033] When the weather condition is level three, the third preset parameter is determined as the state parameter;

[0034] When the control mode is the second mode, the state parameter does not exist.

[0035] Optionally, determining the control data based on the state parameters, the pedestrian flow factor, and the vehicle flow factor includes:

[0036] Determine the minimum green light duration for pedestrians, the minimum green light duration for lanes, and the total number of seconds for traffic to pass through a round of intersections;

[0037] Target data is determined based on the state parameters, the pedestrian flow factor, the vehicle flow factor, the minimum green light duration for pedestrians, and the minimum green light duration for lanes.

[0038] The control data is determined based on the target data and the total number of seconds of passage.

[0039] Optionally, the target data includes the target minimum green light duration for pedestrians, the target minimum green light duration for lanes, the target pedestrian flow factor, and the target vehicle flow factor. Determining the target data based on the state parameters, the pedestrian flow factor, the vehicle flow factor, the minimum green light duration for pedestrians, and the minimum green light duration for lanes includes:

[0040] When the state parameter exists:

[0041] The target minimum green light duration for the pedestrian crossing is determined based on the state parameters and the minimum green light duration for the pedestrian crossing.

[0042] The minimum green light duration for the target lane is determined based on the state parameters, the minimum green light duration for the pedestrian crossing, and the minimum green light duration for the lane.

[0043] The target pedestrian flow factor is determined based on the state parameters and the pedestrian flow factor.

[0044] The target traffic flow factor is determined based on the state parameters and the traffic flow factor.

[0045] When the state parameter does not exist:

[0046] The minimum number of seconds for the pedestrian green light is determined as the target minimum number of seconds for the pedestrian green light.

[0047] The minimum green light duration for the specified lane is determined as the minimum green light duration for the target lane;

[0048] The pedestrian flow factor is determined as the target pedestrian flow factor;

[0049] The traffic flow factor is determined as the target traffic flow factor.

[0050] Optionally, the control data includes pedestrian control data and lane control data, and determining the control data based on the target data and the total number of seconds of passage includes:

[0051] The pedestrian flow factor, the vehicle flow factor, and the total number of seconds of passage are calculated to determine the green light duration for the pedestrian crossing;

[0052] When the green light duration for the pedestrian crossing is less than the minimum green light duration for the target pedestrian crossing, the minimum green light duration for the target pedestrian crossing is determined as the pedestrian crossing control data.

[0053] When the green light duration for the pedestrian crossing is greater than or equal to the minimum green light duration for the target pedestrian crossing, the green light duration for the pedestrian crossing is determined as the pedestrian crossing control data.

[0054] The target pedestrian flow factor, the vehicle flow factor, and the total number of seconds of passage are calculated to determine the green light seconds for the lane;

[0055] When the green light duration of the lane is less than the minimum green light duration of the target lane, the minimum green light duration of the target lane is determined as the lane control data;

[0056] When the green light duration of the lane is greater than or equal to the minimum green light duration of the target lane, the green light duration of the lane is determined as the lane control data.

[0057] A traffic light control system is also provided, applied to the aforementioned traffic light control method, comprising:

[0058] Algorithm module, used for:

[0059] Determine the control mode, pedestrian flow factor, and vehicle flow factor;

[0060] Determine the state parameters based on the control mode;

[0061] The control data is determined based on the state parameters, the pedestrian flow factor, and the vehicle flow factor.

[0062] The control module controls the traffic lights based on the control data. Attached Figure Description

[0063] Figure 1 This is a flowchart of the traffic light control method of the present invention. Detailed Implementation

[0064] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0065] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0066] See Figure 1 As shown, the present invention provides a traffic light control method, comprising:

[0067] S1: Determine the control mode, pedestrian flow factor, and vehicle flow factor, including:

[0068] The control modes include a first mode and a second mode. The control mode is determined as follows:

[0069] The target state is obtained, and it is determined whether the target state meets the first preset condition. If it meets the condition, the first mode is adopted; if it does not meet the condition, the second mode is adopted. The target state is whether the weather status acquisition module is enabled. The first preset condition is that the weather status acquisition module is enabled. The weather status acquisition module is used to obtain the weather status in real time from the weather system based on the location information.

[0070] Determine the pedestrian flow factor:

[0071] First, the facial recognition results are obtained. When a face is recognized, infrared detection is performed on the facial recognition results to determine the body temperature of the face. Based on the body temperature, facial recognition results that do not meet the second preset condition are filtered out. The second preset condition is that the body temperature is between 34 and 44 degrees Celsius. Facial recognition results that meet the second preset condition are identified as real people. By detecting the body temperature corresponding to the facial recognition results through infrared sensing, the accuracy of facial recognition can be improved and the misidentification of non-real faces, such as faces on billboards, can be prevented.

[0072] The number of facial recognition results that meet the second preset condition is determined, that is, the number of facial recognition results that are identified as real people is counted to determine the actual flow of people; when the number of facial recognition results is greater than a preset threshold, the weight data is determined; here the preset threshold is 50, when the actual flow of people exceeds 50 people, the weight data is obtained through weight sensing, and the weight data is the total weight at the time of measurement.

[0073] Finally, based on the weight data and the preset weight data, the flow factor was determined; the preset weight data was 64.42152364087126 kg, which is the average human weight, and its calculation principle is as follows:

[0074] According to the "Report on Nutrition and Chronic Diseases of Chinese Residents (2020)," the average weight of adult men in China is approximately 69.6 kg, and the average weight of adult women is approximately 59 kg. Meanwhile, based on the national population data at the end of 2022, the male population was 722.06 million and the female population was 689.69 million. Using the total population data and the corresponding average weights for men and women, we can calculate: (69.6 * 722.06 + 59 * 689.69) ÷ (722.06 + 689.69) = 64.42152364087126, which is the average weight of 64.42152364087126 kg.

[0075] The formula for calculating the pedestrian flow factor is as follows:

[0076]

[0077] in, Human traffic factor For weight data, The calculation result of the traffic flow factor based on the preset weight data is rounded to the nearest integer.

[0078] Determine traffic flow factors:

[0079] First, license plates are recognized, and the number of recognized license plates is determined as the number of vehicles. Next, it is determined whether the road is divided into lanes. If the road is divided into lanes, the number of lanes is determined as the number of roads. If the road is not clearly divided into lanes, the road width is determined, and the number of roads is determined based on the road width and a preset width. Finally, the traffic flow factor is determined based on the number of vehicles and the number of roads, and the calculation formula is as follows:

[0080]

[0081] in, Traffic flow factor For the number of vehicles, The number of roads is used, and the result of the traffic flow factor calculation is rounded to the nearest integer.

[0082] S2: Determine the state parameters based on the control mode, including:

[0083] When the control mode is the first mode, the weather status acquisition module is turned on, meaning that the weather status acquisition module can obtain the weather status from the weather system based on the location information, and the status parameter exists at this time; when the control mode is the second mode, the weather status acquisition module is not turned on, meaning that the weather status acquisition module cannot obtain the weather status from the weather system in real time based on the location information, and the status parameter does not exist at this time because the weather status cannot be obtained.

[0084] The weather conditions are divided into three levels. The first level includes sunny, cloudy, partly cloudy, and light to moderate rain. The second level includes heavy rain, torrential rain, and snow. The third level includes thunderstorms, sandstorms, and hail.

[0085] When the weather condition is Level 1, meaning the weather is good and will not affect pedestrians and vehicles, the first preset parameter is set to 0. When the weather condition is Level 2, meaning the weather is moderate and will have some impact on pedestrians and vehicles, the second preset parameter is set to 0.1. When the weather condition is Level 3, meaning the weather is poor and will have a significant impact on pedestrians and vehicles, the third preset parameter is set to 0.2. In other words, the larger the value of the state parameter, the worse the weather condition.

[0086] S3: Determine control data based on state parameters, pedestrian flow factor, and vehicle flow factor, including:

[0087] When state parameters exist, the original minimum green light duration for pedestrians and lanes needs to be dynamically adjusted based on these parameters to obtain dynamically adjusted target data. Then, control data is determined based on the target data and the total number of seconds of passage. The target data includes the target minimum green light duration for pedestrians, the target minimum green light duration for lanes, the target pedestrian flow factor, and the target vehicle flow factor. The calculation formula for the target data is as follows:

[0088] Minimum green light duration for the target pedestrian crossing = Minimum green light duration for the original pedestrian crossing * (1 + status parameter);

[0089] Minimum green light duration for the target lane = Minimum green light duration for the original lane - (Minimum green light duration for the original pedestrian crossing * Weather parameters);

[0090] Target pedestrian flow factor = pedestrian flow factor * (1 + state parameter);

[0091] Target traffic flow factor = traffic flow factor * (1 + state parameter).

[0092] When the state parameter does not exist, there is no need to dynamically adjust the original minimum green light duration for pedestrians and the original minimum green light duration for lanes. Instead, the original minimum green light duration for pedestrians is determined as the target minimum green light duration for pedestrians, the minimum green light duration for lanes is determined as the target minimum green light duration for lanes, the pedestrian flow factor is determined as the target pedestrian flow factor, and the vehicle flow factor is determined as the target vehicle flow factor.

[0093] Control data includes pedestrian control data and lane control data; after determining the target data, the control data is determined based on the target data and the total number of seconds of passage. The specific implementation logic is as follows:

[0094] The calculation of pedestrian green light seconds is as follows: If the calculated pedestrian green light seconds are less than the target minimum pedestrian green light seconds, it indicates that the calculated pedestrian green light seconds do not meet the minimum requirement, meaning that the pedestrian green light seconds are insufficient to allow pedestrians enough time to cross the road. Therefore, the target minimum pedestrian green light seconds are determined as the pedestrian control data. If the calculated pedestrian green light seconds are greater than or equal to the target minimum pedestrian green light seconds, it indicates that the calculated pedestrian green light seconds meet the minimum requirement, meaning that the pedestrian green light seconds are sufficient to allow pedestrians enough time to cross the road. Therefore, the pedestrian green light seconds are determined as the pedestrian control data. The formula for calculating the pedestrian green light seconds is: Pedestrian green light seconds = Total crossing seconds * Pedestrian flow factor ÷ (Pedestrian flow factor + Vehicle flow factor).

[0095] The calculation of lane green light seconds is performed. If the calculated lane green light seconds are less than the target lane minimum green light seconds, it indicates that the calculated lane green light seconds do not meet the minimum requirement, meaning the green light seconds are insufficient to allow vehicles enough time to cross the road. Therefore, the target lane minimum green light seconds are determined as the lane control data. If the calculated lane green light seconds are greater than or equal to the target lane minimum green light seconds, it indicates that the calculated lane green light seconds meet the minimum requirement, meaning the green light seconds are sufficient to allow vehicles enough time to cross the road. Therefore, the lane green light seconds are determined as the lane control data. The formula for calculating lane green light seconds is: Lane green light seconds = Total travel time * Traffic flow factor ÷ (Pedestrian flow factor + Traffic flow factor).

[0096] S4: Control the traffic lights based on the control data.

[0097] The traffic light control method provided by this invention can dynamically adjust the control data of traffic lights according to real-time traffic conditions by determining pedestrian flow factors and vehicle flow factors. This can reduce the waiting time of vehicles and pedestrians, improve the overall operating efficiency of the traffic system, and thus more effectively manage traffic flow and reduce congestion. By combining control modes and state parameters, the accuracy of determining control data can be improved, making the control of traffic lights more refined and adapting to the needs of different traffic scenarios.

[0098] The present invention also provides a traffic light control system, comprising:

[0099] Algorithm module, used for:

[0100] Determine the control mode, pedestrian flow factor, and vehicle flow factor;

[0101] Determine the state parameters based on the control mode;

[0102] The control data is determined based on the state parameters, the pedestrian flow factor, and the vehicle flow factor.

[0103] The control module controls the traffic lights based on the control data.

[0104] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A traffic light control method characterized by, The application relates to a traffic light control method and device. The method comprises the following steps: determining a control mode, a people flow factor and a vehicle flow factor; determining a state parameter according to the control mode; determining control data according to the state parameter, the people flow factor and the vehicle flow factor; controlling the traffic light according to the control data; the control mode comprises a first mode and a second mode, and the control mode is determined by the following steps: acquiring a target state, and judging whether the target state meets a first preset condition, wherein the target state is whether to start a weather state acquisition module; if the first preset condition is met, the first mode is adopted, and if the first preset condition is not met, the second mode is adopted; the people flow factor is determined by the following steps: acquiring a face recognition result; performing infrared detection on the face recognition result to determine a body temperature result of the face recognition result; screening out the face recognition result that does not meet a second preset condition according to the body temperature result; determining the number of face recognition results that meet the second preset condition; when the number of face recognition results is greater than a preset threshold, determining weight data; 2. The traffic light control method of claim 1, wherein, determining the people flow factor according to the weight data and preset weight data. the vehicle flow factor is determined by the following steps: acquiring the number of vehicles and the number of road strips; 3. The traffic light control method of claim 2, wherein, determining the vehicle flow factor according to the number of vehicles and the number of road strips. the number of vehicles and the number of road strips are acquired by the following steps: recognizing license plates, and determining the number of recognized license plates as the number of vehicles; 4. The traffic light control method of claim 1, wherein, determining whether a road is divided into lanes, when the road is divided into lanes, determining the number of road lane divisions as the number of road strips, and when the road is not divided into lanes, determining the width of the road, and determining the number of road strips according to the width of the road and a preset width. the state parameter is determined according to the control mode by the following steps: when the control mode is the first mode, the state parameter exists; acquiring a weather state through a weather system according to positioning information; when the weather state is a first level, a first preset parameter is determined as the state parameter; when the weather state is a second level, a second preset parameter is determined as the state parameter; when the weather state is a third level, a third preset parameter is determined as the state parameter; 5. The traffic light control method of claim 4, wherein, when the control mode is the second mode, the state parameter does not exist. the control data is determined according to the state parameter, the people flow factor and the vehicle flow factor by the following steps: determining a minimum number of sidewalk green lights, a minimum number of lane green lights and a total number of passages; determining target data according to the state parameter, the people flow factor, the vehicle flow factor, the minimum number of sidewalk green lights and the minimum number of lane green lights; 6. The traffic light control method of claim 5, wherein, determining the control data according to the target data and the total number of passages. the target data comprises a target minimum number of sidewalk green lights, a target minimum number of lane green lights, a target people flow factor and a target vehicle flow factor, and the target data is determined according to the state parameter, the people flow factor, the vehicle flow factor, the minimum number of sidewalk green lights and the minimum number of lane green lights by the following steps: when the state parameter exists: determining the target pedestrian green light minimum seconds according to the state parameter and the pedestrian green light minimum seconds; determining the target lane green light minimum seconds according to the state parameter, the pedestrian green light minimum seconds and the lane green light minimum seconds; determining the target pedestrian flow factor according to the state parameter and the pedestrian flow factor; determining the target vehicle flow factor according to the state parameter and the vehicle flow factor; when the state parameter does not exist: determining the pedestrian green light minimum seconds as the target pedestrian green light minimum seconds; determining the lane green light minimum seconds as the target lane green light minimum seconds; determining the pedestrian flow factor as the target pedestrian flow factor; determining the vehicle flow factor as the target vehicle flow factor.

7. The traffic light control method of claim 6, wherein, The control data includes pedestrian control data and lane control data, and the control data is determined according to the target data and the total passing seconds, including: calculating the target pedestrian flow factor, the vehicle flow factor and the total passing seconds to determine the pedestrian green light seconds; when the pedestrian green light seconds is less than the target pedestrian green light minimum seconds, determining the target pedestrian green light minimum seconds as the pedestrian control data; when the pedestrian green light seconds is greater than or equal to the target pedestrian green light minimum seconds, determining the pedestrian green light seconds as the pedestrian control data; calculating the target pedestrian flow factor, the vehicle flow factor and the total passing seconds to determine the lane green light seconds; when the lane green light seconds is less than the target lane green light minimum seconds, determining the target lane green light minimum seconds as the lane control data; when the lane green light seconds is greater than or equal to the target lane green light minimum seconds, determining the lane green light seconds as the lane control data.

8. A traffic light control system applied to the traffic light control method of any one of claims 1-7, characterized in that, including: algorithm module, used for: determining control mode, pedestrian flow factor, vehicle flow factor; determining state parameter according to the control mode; determining control data according to the state parameter, the pedestrian flow factor and the vehicle flow factor; controlling module, used for controlling the traffic light according to the control data.

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