Highway traffic situation awareness analysis method based on cloud computing technology

Abstract

The application relates to the technical field of expressway traffic state analysis, and discloses an expressway traffic situation perception analysis method based on cloud computing technology. The expressway traffic situation perception analysis method based on cloud computing technology scores the congestion of the expressway by carrying out multi-data fusion on the actual weather, the number of vehicles, the average speed of the vehicles and the congestion period of the expressway section, generates a no-passing signal or a limited-passing signal according to the congestion score, and sends the generated no-passing signal or limited-passing signal to the traffic management department. The traffic management department can formulate corresponding countermeasures for the congested road section in time according to the received signal, controls the entrances of the expressway sections in the corresponding area, alleviates the number of vehicles on the congested road section, avoids the entry of vehicles into the congested area when the vehicles cannot normally travel, increases the congestion of the area, and helps the traffic management department to dredge the congested road section, thereby reducing the difficulty of traffic dredging of the traffic management department.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of expressway traffic state analysis, in particular to an expressway traffic situation awareness analysis method based on cloud computing technology. BACKGROUND

[0002] Expressway traffic situation awareness relies on a variety of sensors and technologies, including but not limited to coil vehicle detectors, video cameras, radar devices, etc. These technologies can capture traffic flow data in real time, such as vehicle speed, traffic volume, vehicle position, etc., so as to achieve comprehensive perception of traffic situation, in order to realize real-time monitoring of the road surface state of the expressway.

[0003] However, at present, video monitoring is mainly relied on to judge the road condition of the expressway, and it is impossible to fuse multiple data to comprehensively analyze the road condition of the expressway. When in bad weather, it is impossible to timely estimate whether the expressway surface is abnormal, so that the decision of the traffic management department is delayed, more vehicles cannot timely avoid the congested road section, more vehicles are blocked on the expressway, and the length of the congested road section is increased, which brings difficulty to traffic relief. SUMMARY

[0004] (I) Technical problems solved

[0005] In view of the shortcomings of the prior art, the present application provides an expressway traffic situation awareness analysis method based on cloud computing technology, which has the advantages that the traffic management department can formulate corresponding countermeasures for the congested road section according to the received signals, control the entrance of the corresponding area of the expressway section, relieve the number of vehicles in the congested road section, avoid that vehicles still enter when congestion occurs and normal driving is impossible, increase the area of congestion, and help the traffic management department to dredge the congested road section, reduce the difficulty of traffic relief of the traffic management department, etc., and solve the above problems.

[0006] (II) Technical solutions

[0007] In order to achieve the above-mentioned purpose, the present application provides the following technical solutions: an expressway traffic situation awareness analysis method based on cloud computing technology, comprising the following steps:

[0008] S1, dividing the expressway surface into a plurality of areas, and forming continuous unique digital numbers for these areas one by one;

[0009] S2, obtaining road surface data of each area, the road surface data including vehicle quantity data and vehicle speed data in the area, obtaining the number of vehicles entering the area, and obtaining the number of vehicles staying in the service area in each area;

[0010] S3. Calculate the growth rate of the number of vehicles in each area and the average speed of vehicles in each area, and determine whether the corresponding area is congested based on the growth rate and average speed of vehicles.

[0011] S4. When the corresponding area is determined to be a congested road section, obtain the weather data and time period data for that area, and calculate the congestion score by combining the weather data, the number of vehicles entering the corresponding area, the number of vehicles staying in the corresponding service area, and the time period data.

[0012] S5. After calculating the congestion score, compare the congestion score with the congestion score threshold. If the congestion score exceeds the congestion score threshold, it means that the highway in this area is severely congested and the highway entrance needs to be closed to prevent vehicles from entering in order to alleviate the congestion. If the congestion score is below the congestion score threshold, it means that the highway in this area is congested but passable, and the number of vehicles entering the highway entrance needs to be controlled to alleviate the congestion.

[0013] Preferably, the number of vehicles in the area is represented as follows: The vehicle speed data is represented as Furthermore, the total number of vehicle speed data obtained is the same as the number of vehicles obtained, and the number of vehicles entering the entrance is represented as follows: The number of vehicles staying in the service area is represented as follows: .

[0014] Preferably, the expression for calculating the growth rate of vehicle numbers in each region is as follows:

[0015]

[0016] In the formula, , as well as Subscript This represents a unique numerical identifier corresponding to each region. This indicates the growth rate of the number of vehicles in the region. This indicates the vehicle quantity data for the area obtained this time, compared to the vehicle quantity data for the area obtained previously. This represents the difference between the current vehicle count data and the previous vehicle count data for the same area. Dividing this difference by the previous vehicle count data yields the vehicle growth rate for that area. A negative value indicates a decreasing trend in the number of vehicles in the area. When the value is positive, it indicates that the number of vehicles in the area is on the rise. When the value is 0, it means that the number of vehicles in the area has not changed.

[0017] Preferably, the vehicle average speed calculation expression in each area is:

[0018]

[0019] In the formula, the subscript represents the unique digital number corresponding to each area, and the superscript represents the vehicle speed data corresponding to each vehicle in the area, and the denominator represents the number of vehicles in the area . The value of .

[0020] Preferably, the method for determining whether the corresponding area is congested is:

[0021] A1, compare the calculated vehicle quantity growth rate of each area with the preset vehicle quantity growth rate threshold, and when the vehicle quantity growth rate of a certain area or the vehicle quantity growth rates of multiple areas exceeds the preset vehicle quantity growth rate threshold, generate congestion signal one;

[0022] A2, compare the calculated vehicle average speed in each area with the limited minimum driving speed of the expressway, and when the vehicle average speed of a certain area or the vehicle average speeds of multiple areas is lower than the limited minimum driving speed of the expressway, generate congestion signal two;

[0023] A3, when congestion signal one and congestion signal two are generated at the same time, it is determined that the corresponding area is in a congested state.

[0024] Preferably, the weather data is sunny, overcast, or rainy, snowy, and foggy, wherein sunny is represented by , overcast is represented by , and rainy, snowy, and foggy is represented by , the corresponding area entrance vehicle entry quantity data is represented by , the subscript is the digital number of the corresponding area, the corresponding area service area vehicle quantity data is represented by , the subscript represents the digital number of the corresponding area, and the time period data includes morning, noon, or night, wherein morning is represented by , noon is represented by , and night is represented by .

[0025] Preferably, when the weather data is sunny, the weather data is represented by , , and the value is 0.2, when the weather data is overcast, the weather data is represented by , 0.2, when the weather data is rain, snow or fog, the weather data is represented as , 0.8.

[0026] Preferably, when the time period data is morning, the time period data is represented as , 0.1, when the time period data is noon, the time period data is represented as , 0.3, when the time period data is night, the time data is represented as , 0.8.

[0027] Preferably, the congestion score calculation expression is:

[0028]

[0029] In the formula, represents sunny day or cloudy day or rain, snow or fog day , represents that the time period data is morning or noon or night , , , and are weight one, weight two, weight three and weight four respectively, and + + + =1, , , and are constant terms, represents the congestion score.

[0030] Preferably, when the congestion score exceeds the congestion score threshold, a highway entrance prohibition signal is generated;

[0031] When the congestion score is lower than the congestion score threshold, a highway entrance restriction signal is generated.

[0032] Compared with the prior art, the present application provides a highway traffic situation awareness analysis method based on cloud computing technology, which has the following beneficial effects:

[0033] The present application scores the congestion of the expressway by fusing the actual weather, the number of vehicles, the average speed of vehicles, and the time period when congestion occurs of the expressway section, generates a no-passing signal or a limited-passing signal according to the congestion score, and sends the generated no-passing signal or limited-passing signal to the traffic control department, so that the traffic control department can formulate corresponding countermeasures for the congested section in time according to the received signal, controls the entrance of the expressway section of the corresponding area, relieves the number of vehicles of the congested section, avoids vehicles entering when congestion occurs and cannot normally drive, increases the area of congestion, and at the same time, helps the traffic control department to dredge the congested section, and reduces the difficulty of the traffic control department to dredge traffic. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The present application is a method step schematic diagram. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0036] The actual weather, the number of vehicles, the average speed of vehicles, and the time period when congestion occurs of the expressway section are fused, the congestion of the expressway is scored, and corresponding measures are taken according to the score to help the traffic control department to better adjust the traffic flow of the expressway, and avoid relying on the traditional video monitoring method to monitor the state of the expressway, and therefore a highway traffic situation awareness analysis method based on cloud computing technology is proposed, please refer to Figure 1 , which comprises the following steps:

[0037] S1, divide the expressway surface into a plurality of areas, and form continuous unique digital numbers for these areas one by one, the digital numbers can be divided according to the name of the expressway section, the kilometer number of the section division, the direction of travel and other data, so that the divided areas all have displacement digital numbers, which can be used to quickly determine the congested section and select the adjacent section to the congested section to intervene the traffic of the expressway.

[0038] S2, obtain the road surface data of each area, the road surface data includes the number of vehicles in the area and the vehicle speed data, obtains the number of vehicles entering each area, and obtains the number of vehicles staying in the service area in each area. The number of vehicles in the area is represented as , and the vehicle speed data is represented as Furthermore, the total number of vehicle speed data obtained is the same as the number of vehicles obtained. The number of vehicles entering the entrance is represented as follows: The number of vehicles parked in the service area is represented as By acquiring data on the number of vehicles within a region, vehicle entry data at each region's entrance, vehicle speed, and vehicles staying in service areas within each region, the potential load status of highways within the region can be grasped in a timely manner. This data can then be used for analysis to determine whether highway congestion is occurring.

[0039] S3. Calculate the vehicle growth rate and average vehicle speed in each area, and determine whether the corresponding area is congested based on the vehicle growth rate and average vehicle speed. Wherein:

[0040] The formulas for calculating the growth rate of vehicle numbers in each region are as follows:

[0041]

[0042] In the formula, , as well as Subscript This represents a unique numerical identifier corresponding to each region. This indicates the growth rate of the number of vehicles in the region. This indicates the vehicle quantity data for the area obtained this time, compared to the vehicle quantity data for the area obtained previously. This represents the difference between the current vehicle count data and the previous vehicle count data for the same area. Dividing this difference by the previous vehicle count data yields the vehicle growth rate for that area. When the value is negative, it indicates that the number of vehicles in the area is decreasing. When the value is positive, it indicates that the number of vehicles in the area is on the rise. A value of 0 indicates that the number of vehicles in the area has not changed. For example, if the area is A, the growth rate of the number of vehicles in the area is: .

[0043] The formula for calculating the average vehicle speed in each region is as follows:

[0044]

[0045] In the formula, subscript Indicates the unique numerical code corresponding to each region, superscript ~ This represents the vehicle speed data for each car within that area, with the denominator containing... Data representing the number of vehicles in the area The value is , and when the region is A, the vehicle constant speed result in the region is ;

[0046] By calculating the vehicle speed increase of each region, the increase of vehicle flow of each road section in the time interval can be objectively fed back, and by calculating the vehicle constant speed of each region, the speed of vehicle driving in each region can be objectively fed back. When the speed is faster, it indicates that the road surface of the region is more unblocked, and when the speed is slower, it indicates that the road surface of the region is more congested. Therefore, whether the road surface of the corresponding region is congested can be determined according to the combination of the two values, and the determination method is as follows:

[0047] 1. The calculated vehicle quantity speed increase of each region is compared with the preset vehicle quantity speed increase threshold value. When the vehicle quantity speed increase of a region or the vehicle quantity speed increase of multiple regions exceeds the preset vehicle quantity speed increase threshold value, congestion signal one is generated.

[0048] 2. The calculated vehicle constant speed of each region is compared with the limited minimum driving speed of the expressway. When the vehicle constant speed of a region or the vehicle constant speed of multiple regions is lower than the limited minimum driving speed of the expressway, congestion signal two is generated.

[0049] 3. When congestion signal one and congestion signal two are generated at the same time, it is determined that the corresponding region is in a congested state.

[0050] When only congestion signal one appears, it indicates that there are more vehicles entering the road section, but it does not affect the normal passing of vehicles according to the speed limit of the expressway. When only congestion signal two appears, it indicates that the number of vehicles in the region has not increased, but some vehicles are driving slowly due to their own reasons, resulting in a decrease in the vehicle constant speed of the region, but it does not affect the normal passing of vehicles in the region. When both signals appear at the same time, it indicates that the number of vehicles in the region has increased and affected the speed of vehicle passing in the region. This situation can be understood as a congested state in the region, which has affected the normal passing of vehicles in the region.

[0051] S4. When it is determined that the corresponding region is a congested road section, the weather data and time period data of the region are obtained, and the weather data, the number of vehicles entering the corresponding region, the number of vehicles staying in the service area of the corresponding region, and the time period data are used to calculate the congestion score. The weather data is sunny, overcast, or rainy, snowy, and foggy, wherein sunny indicates , overcast indicates , and rainy, snowy, and foggy indicate . The number of vehicles entering the corresponding region is represented as , and the subscript is the digital number of the corresponding region. The number of vehicles staying in the service area of the corresponding region is represented as , and the subscript denotes a digital number corresponding to a region, the time period data includes morning or noon or night, wherein the morning is denoted as , the noon is denoted as , and the night is denoted as , and the congestion score calculation expression is:

[0052]

[0053] In the formula, denotes sunny weather or cloudy weather or rainy, snowy, or foggy weather , denotes that the time period data is morning or noon or night , , , and are weight one, weight two, weight three, and weight four, respectively, and + + + = 1, , , and are constant terms, denotes the congestion score, and it should be noted that:

[0054] when the weather data is sunny, the weather data is denoted as , with a value of 0.2, when the weather data is cloudy, the weather data is denoted as , with a value of 0.2, when the weather data is rainy, snowy, or foggy, the weather data is denoted as , with a value of 0.8, when the time period data is morning, the time period data is denoted as , with a value of 0.1, when the time period data is noon, the time period data is denoted as , with a value of 0.3, when the time period data is night, the time data is denoted as , The value is 0.8, and different weather and time periods are valued, so as to express the influence of different weather and different time periods on congestion. Generally, rain, snow and fog have a greater influence on highway traffic, and when congestion occurs, the influence is greater. In the morning and at noon, the visibility is good, and the driver can observe the road conditions in front and respond in time. However, at noon, the driver is prone to drowsiness due to the influence of time. Therefore, the value at noon is greater than the value in the morning. At night, the driver's visibility is poor and he is prone to drowsiness. Therefore, when the highway is congested, the congestion degree at night is greater, which includes the driver falling asleep due to drowsiness and causing rear-end collision on the congested road section, and the driver falling asleep for a short time when queuing for a long time, thereby increasing the duration of congestion. The number of vehicles parked at the service area and the number of vehicles entering at the entrance are also combined to score the potential congestion of the road section, so as to analyze the potential state of the highway pavement congestion in the region.

[0055] S5, after calculating the congestion score, the congestion score is compared with the congestion score threshold. When the congestion score exceeds the congestion score threshold, it indicates that the highway pavement in the region is severely congested, and the highway entrance needs to be closed to prohibit vehicles from entering to alleviate the congestion. When the congestion score is lower than the congestion score threshold, it indicates that the highway pavement in the region is congested, but can be passed, and the number of vehicles entering at the highway entrance needs to be controlled to alleviate the congestion.

[0056] It should be noted that the above-mentioned obtained data are all sent to the cloud computing platform for summary and analysis, and the cloud computing platform is also interconnected with the monitoring department of the traffic management center. When the congestion score calculated by the cloud computing platform exceeds the congestion score threshold set by the traffic management department, a signal for prohibiting the highway entrance from passing is generated. When the congestion score is lower than the congestion score threshold, a signal for limiting the highway entrance from passing is generated, and the generated signal for prohibiting passing or limiting passing is sent to the traffic management department. The traffic management department can formulate corresponding countermeasures for the congested road section in time according to the received signal, and control the entrance of the corresponding region of the highway section to alleviate the number of vehicles on the congested road section, so as to avoid the increase of the congested area when the congestion cannot be normally driven. At the same time, it is helpful for the traffic management department to dredge the congested road section, and reduces the difficulty of dredging traffic for the traffic management department.

[0057] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A highway traffic situation awareness analysis method based on cloud computing technology, characterized in that: The method comprises the following steps: S1, dividing the highway pavement into several regions, and sequentially forming continuous unique digital numbers for the regions; S2, obtaining pavement data of each region, the pavement data comprising vehicle quantity data and vehicle speed data in the region, obtaining entry vehicle quantity data in each region, and obtaining vehicle quantity data of vehicles staying in service areas in each region; S3, calculating vehicle quantity growth rate of each region and average vehicle speed in each region, and judging whether the corresponding region is congested according to the vehicle growth rate and the average vehicle speed; S4, when it is judged that the corresponding region is a congested section, obtaining weather data and time period data of the region, and calculating a congestion score according to the weather data, entry vehicle quantity data of the corresponding region, vehicle quantity data of vehicles staying in service areas of the corresponding region, and time period data; The weather data is sunny day, cloudy day or rainy and snowy day, wherein the sunny day is represented as , the cloudy day is represented as , and the rainy and snowy day is represented as , the corresponding area entrance vehicle entering quantity data is represented as , the subscript is the digital number of the corresponding area, the corresponding area service area staying vehicle quantity data is represented as , the subscript is the digital number of the corresponding area, and the time period data includes morning, noon or night, wherein the morning is represented as , the noon is represented as , and the night is represented as . when the weather data is sunny, 0.2 when the weather data is cloudy, 0.2 when the weather data is rainy, snowy or foggy, 0.

8. when the time period data is morning, when the time period data is noon, when the time period data is night, when the time period data is night, The congestion score calculation expression is: In the formula, represents a sunny day or a cloudy day or a rainy, snowy or foggy day , represents a time period data as morning or noon or night , , , and are weight one, weight two, weight three and weight four respectively, and + + + = 1, , , and are constant terms, represents a congestion score; S5, after the congestion score is calculated, comparing the congestion score with a congestion score threshold value, when the congestion score exceeds the congestion score threshold value, it indicates that the highway pavement of the region is seriously congested, and the highway entry needs to be closed to prohibit vehicles from entering to relieve the congestion, when the congestion score is lower than the congestion score threshold value, it indicates that the highway pavement of the region is congested, but can be passed, and the number of vehicles entering the highway needs to be controlled to relieve the congestion. 2.The expressway traffic situation awareness analysis method based on cloud computing technology according to claim 1, characterized in that: The vehicle quantity data in the area is represented as The vehicle driving speed data is represented as The total number of the acquired vehicle driving speed data is the same as the acquired vehicle quantity data, the entry vehicle driving-in quantity data is represented as The vehicle quantity data staying in the service area is represented as . 3.The expressway traffic situation awareness analysis method based on cloud computing technology according to claim 2, characterized in that: The vehicle quantity growth rate calculation expression of each region is as follows: In the formula, , and , subscript represents the unique digital number corresponding to each region, represents the growth rate of the number of vehicles in the region, represents the last acquired regional vehicle quantity data of the current acquired regional vehicle quantity data, represents the difference between the current acquired regional vehicle quantity data and the last acquired regional vehicle quantity data, which is divided by the last acquired regional vehicle quantity data to obtain the growth rate of the number of vehicles in the region, when is negative, it indicates that the number of vehicles in the region is decreasing, when is positive, it indicates that the number of vehicles in the region is increasing, and when is 0, it indicates that the number of vehicles in the region has not changed. 4.The expressway traffic situation awareness analysis method based on cloud computing technology according to claim 3, characterized in that: The average vehicle speed calculation expression in each region is: In the formula, subscript represents the unique digital number corresponding to each region, superscript ~ represents the vehicle speed data corresponding to each vehicle in the region, and the denominator represents the number of vehicles in the region The value of is 5.The expressway traffic situation awareness analysis method based on cloud computing technology according to claim 4, characterized in that: The method for judging whether the corresponding region is congested is: A1, comparing the calculated vehicle quantity growth rate of each region with a preset vehicle quantity growth rate threshold value, when the vehicle quantity growth rate of a region or the vehicle quantity growth rates of multiple regions exceed the preset vehicle quantity growth rate threshold value, a congestion signal one is generated; A2, comparing the calculated average vehicle speed of each region with a limited minimum driving speed of the highway, when the average vehicle speed of a region or the average vehicle speeds of multiple regions are lower than the limited minimum driving speed of the highway, a congestion signal two is generated; A3, when the congestion signal one and the congestion signal two are generated at the same time, it is judged that the corresponding region is in a congested state. 6.The expressway traffic situation awareness analysis method based on cloud computing technology according to claim 5, characterized in that: When the congestion score exceeds the congestion score threshold value, a highway entry prohibition passing signal is generated; When the congestion score is lower than the congestion score threshold value, a highway entry restriction passing signal is generated.

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