Traffic monitoring system and method based on intelligent transportation Internet of Things

Abstract

The present application provides a traffic monitoring system and method based on the intelligent transportation Internet of Things. By deploying data collection devices, the traffic flow information of all traffic monitoring points at different sampling moments is collected. For each sampling moment, multiple traffic hubs and road network sub-areas of each traffic hub are extracted, and the traffic saturation of each road network sub-area is determined, thereby obtaining the traffic saturation of each road network sub-area at each sampling moment. The offset of the position information between different sampling moments is determined, and the comprehensive traffic index of the road network area at different sampling moments is determined by the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub point. The traffic situation of the road network area at different sampling moments is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things platform via a wireless network. By adopting the solution of the present application, the sub-area division can be adjusted in real time to improve the traffic response speed in the road network area.

Description

Technical Field

[0001] This application relates to the field of traffic monitoring technology, and more specifically, to a traffic monitoring system and method based on intelligent transportation Internet of Things. Background Art

[0002] Traffic monitoring involves a variety of technical means and equipment for real-time collection, analysis, and management of information such as traffic flow, vehicle behavior, and road conditions. Its main goals are to improve traffic efficiency, reduce congestion, enhance road safety, and support traffic management decisions. In terms of hardware, traffic monitoring typically includes video surveillance cameras, radar detectors, infrared sensors, ground sensing coils, and other equipment to collect traffic flow, vehicle speed, license plate information, and environmental parameters. In terms of software, technologies based on big data analysis, artificial intelligence, and the Internet of Things are widely used to process collected traffic data and generate results such as traffic situation assessment, anomaly detection, and optimization recommendations.

[0003] Traffic monitoring based on the Intelligent Transportation Internet of Things (ITI) is a crucial component of modern smart transportation systems. By deeply integrating IoT devices with traffic monitoring technologies, it enables real-time monitoring and management of traffic flow, vehicle dynamics, and road conditions. Using a variety of sensor devices (such as cameras, radar sensors, ground-sensing coils, and on-board equipment) as data collection terminals, it aggregates and analyzes the collected traffic data through an IoT platform. Existing IITI-based traffic monitoring typically collects traffic flow information from each sub-area to build a traffic database. This data is then mined for data that significantly deviates from expectations to monitor traffic anomalies within the road network. However, the Newman algorithm's relatively fixed sub-areas result in time lags in traffic conditions across different sub-areas (i.e., when traffic pressure in one sub-area suddenly increases, other sub-areas may not respond immediately). This results in a delayed response to traffic changes within the network, reducing the efficiency of anomaly detection. Therefore, how to adjust sub-area divisions in real time to improve traffic response speed within the network has become a challenge facing the industry. Summary of the Invention

[0004] The present application provides a traffic monitoring system and method based on the intelligent transportation Internet of Things, which can adjust the sub-area division in real time to improve the traffic response speed in the road network area.

[0005] In a first aspect, the present application provides a traffic monitoring method based on an intelligent transportation Internet of Things, wherein the intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform, and the data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network. The method comprises the following steps:

[0006] After deploying the data collection device at each traffic monitoring point in the road network area, the traffic flow information of all traffic monitoring points at different sampling times is collected;

[0007] At each sampling moment, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points using the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points;

[0008] Determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then obtaining the traffic saturation of each road network sub-area at each sampling moment;

[0009] Determining the offset of the location information of each traffic hub point between different sampling times, and determining the comprehensive traffic index of the road network area at different sampling times based on the traffic saturation of each road network sub-area and the offset of the location information of the corresponding traffic hub point;

[0010] The traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things platform via a wireless network.

[0011] In some embodiments, extracting multiple traffic hubs and road network sub-areas corresponding to each traffic hub from all traffic monitoring points based on the location information and traffic flow information of each traffic monitoring point specifically includes:

[0012] Based on the impact of different vehicle types on traffic conditions and the traffic flow information of each traffic monitoring point, information is integrated to obtain the traffic flow characteristic value of each traffic monitoring point;

[0013] Determine the degree of closeness of traffic conditions between every two traffic monitoring points through the location information of each traffic monitoring point and the traffic flow characteristic value of each traffic monitoring point;

[0014] Based on the closeness of the traffic situation between each two traffic monitoring points, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are divided among all traffic monitoring points.

[0015] In some embodiments, determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area specifically includes:

[0016] Select a road network sub-area as the selected road network sub-area;

[0017] Determine the traffic carrying capacity in the selected road network sub-area;

[0018] Determining the traffic saturation of the selected road network sub-area based on the traffic flow information of all traffic monitoring points in the selected road network sub-area and the traffic carrying capacity;

[0019] Continue to determine the traffic saturation of the remaining road network sub-areas.

[0020] In some embodiments, determining the offset of the location information of each transportation hub point between different sampling moments specifically includes:

[0021] Select a sampling moment as the selected sampling moment;

[0022] Get the adjacent sampling moments of the selected sampling moment;

[0023] Determine the offset of the position information of each transportation hub point between the selected sampling moment and the adjacent sampling moment by using the position information of each transportation hub point at the selected sampling moment and the position information of each transportation hub point at the adjacent sampling moment;

[0024] Continue to determine the offset of the location information of each transportation hub point between the remaining sampling moments and the corresponding adjacent sampling moments.

[0025] In some embodiments, determining the comprehensive traffic index of the road network area at different sampling times based on the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub location information specifically includes:

[0026] Setting a saturation factor and an offset factor according to the area type of the road network area;

[0027] Select a sampling moment as the selected sampling moment;

[0028] Select a road network area at the selected sampling time as the selected road network sub-area;

[0029] Get the traffic hub corresponding to the selected road network sub-area;

[0030] Determine a traffic index component of the selected road network sub-area at the selected sampling moment according to the traffic saturation of the selected road network sub-area, the offset of the traffic hub location information, the saturation factor and the offset factor;

[0031] Continuing to determine traffic index components of the remaining road network sub-areas at the selected sampling time, and then determining a comprehensive traffic index of the road network area at the selected sampling time;

[0032] Continue to determine the comprehensive traffic index of the road network area in the remaining sampling moments.

[0033] In some embodiments, performing abnormal monitoring of traffic situations in the road network area at different sampling times based on the comprehensive traffic index specifically includes:

[0034] Setting a traffic anomaly threshold value of the comprehensive traffic index in the road network area;

[0035] Perform abnormality judgment on the comprehensive traffic index corresponding to the road network area at different sampling times by using the traffic abnormality threshold;

[0036] Based on the judgment result, abnormal traffic conditions in the road network area at different sampling times are monitored.

[0037] In some embodiments, the data collection devices include radar sensors, global positioning system devices, and traffic cameras.

[0038] In a second aspect, the present application provides a traffic monitoring system based on an intelligent transportation Internet of Things, wherein the intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform, and the data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network. The system includes:

[0039] A collection module is used to collect traffic flow information of all traffic monitoring points at different sampling times after deploying the data collection device at each traffic monitoring point in the road network area;

[0040] a processing module configured to extract, at each sampling moment, a plurality of traffic hubs and a road network sub-area corresponding to each traffic hub from all traffic monitoring points using the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes a plurality of traffic monitoring points;

[0041] The processing module is further configured to determine the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, thereby obtaining the traffic saturation of each road network sub-area at each sampling moment;

[0042] The processing module is further configured to determine an offset of the location information of each traffic hub point between different sampling moments, and determine a comprehensive traffic index of the road network area at different sampling moments based on the traffic saturation of each road network sub-area and the offset of the location information of the corresponding traffic hub point;

[0043] The execution module is used to monitor the traffic situation of the road network area at different sampling times according to the comprehensive traffic index, and send the monitoring results to the Internet of Things platform via a wireless network.

[0044] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned traffic monitoring method based on the intelligent transportation Internet of Things when executing the computer program.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned traffic monitoring method based on the intelligent transportation Internet of Things.

[0046] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0047] In the traffic monitoring system and method based on the intelligent transportation Internet of Things provided in the present application, data collection devices are deployed at each traffic monitoring point in the road network area, thereby collecting traffic flow information of all traffic monitoring points at different sampling times; for each sampling time, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the position information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points; the traffic saturation of each road network sub-area is determined based on the traffic flow information of all traffic monitoring points in each road network sub-area, thereby obtaining the traffic saturation of each road network sub-area at each sampling time; the offset of the position information of each traffic hub point between different sampling times is determined, and the comprehensive traffic index of the road network area at different sampling times is determined through the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub point position information; the traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things platform via a wireless network.

[0048] It can be seen that in this application, first, by deploying data collection devices at various traffic monitoring points in the road network area, and then collecting traffic flow information of all traffic monitoring points at different sampling times, traffic hubs are dynamically extracted according to real-time sampling data, and road network sub-areas that match the current traffic situation are divided, so as to not rely on a fixed road network division method and be able to adapt to changes in traffic flow distribution at different times. Then, after determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, the traffic load situation of each road network sub-area is quickly quantified, especially for areas with sudden increase in traffic pressure, which can be perceived in time, thereby avoiding the occurrence of traffic saturation in each road network sub-area. The time lag phenomenon between different areas is determined. Subsequently, the offset of the position information of each traffic hub point between different sampling moments is determined, and the comprehensive traffic index of the road network area at different sampling moments is determined through the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub point position information. That is: the division of the road network sub-area is optimized in combination with the offset data to dynamically update the area boundary to avoid the problem of untimely response to traffic changes due to fixed division (time lag phenomenon). Finally, the traffic situation of the road network area at different sampling moments is monitored for abnormalities based on the comprehensive traffic index. In summary, this scheme can adjust the sub-area division in real time to improve the traffic response speed in the road network area. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a traffic monitoring method based on the intelligent transportation Internet of Things according to some embodiments of the present application;

[0050] Figure 2 This is a schematic diagram of the process of dividing transportation hubs according to some embodiments of the present application;

[0051] Figure 3 is a schematic diagram of a process for implementing abnormality monitoring according to some embodiments of the present application;

[0052] Figure 4 This is a flow chart of a traffic monitoring system based on the intelligent transportation Internet of Things according to some embodiments of the present application;

[0053] Figure 5 This is a diagram of the internal structure of a computer device for implementing a traffic monitoring method based on the intelligent transportation Internet of Things as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0054] In order to better understand the technical solution in this embodiment, the technical solution in this embodiment will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0055] refer to Figure 1 , which is a flow chart of a traffic monitoring method based on the intelligent transportation Internet of Things according to some embodiments of the present application. The traffic monitoring method 100 based on the intelligent transportation Internet of Things mainly includes the following steps:

[0056] In step 101, the data collection device is deployed at each traffic monitoring point in the road network area, and the traffic flow information of all traffic monitoring points at different sampling times is collected.

[0057] It should be noted that the traffic monitoring point mentioned in this application refers to the specific location of equipment or facilities established in the traffic network for collecting, monitoring and analyzing information such as traffic flow, vehicle speed, vehicle type, traffic conditions, etc. The data acquisition device includes radar sensors, global positioning system (GPS) equipment and traffic cameras. In addition, the traffic flow information includes the traffic flow and vehicle type corresponding to the traffic monitoring point within the preset sampling time. The preset sampling time is 10 minutes, and the vehicle types include small cars, buses and non-motor vehicles.

[0058] In step 102, for each sampling moment, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points.

[0059] In some embodiments, reference Figure 2 As shown in the figure, this figure is a schematic diagram of the process of dividing traffic hubs shown in some embodiments of the present application. The following steps can be used to extract multiple traffic hubs and road network sub-areas corresponding to each traffic hub from all traffic monitoring points based on the location information and traffic flow information of each traffic monitoring point:

[0060] Based on the impact of different vehicle types on traffic conditions and the traffic flow information of each traffic monitoring point, information is integrated to obtain the traffic flow characteristic value of each traffic monitoring point;

[0061] Determine the degree of closeness of traffic conditions between every two traffic monitoring points through the location information of each traffic monitoring point and the traffic flow characteristic value of each traffic monitoring point;

[0062] Based on the closeness of the traffic situation between every two traffic monitoring points, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are divided among all traffic monitoring points.

[0063] In some embodiments, based on the impact of different vehicle types on traffic conditions and the traffic flow information of each traffic monitoring point, information integration is performed to obtain the traffic flow characteristic value of each traffic monitoring point by using the following steps:

[0064] Selecting a traffic monitoring point as a selected traffic monitoring point;

[0065] Obtain traffic flow of different vehicle types from traffic flow information at selected traffic monitoring points;

[0066] Based on the impact of different vehicle types on the traffic situation, the traffic flow weight of each vehicle type on the traffic situation is set;

[0067] Determine a traffic flow characteristic value of a selected traffic monitoring point according to a traffic flow weight of each vehicle type to the traffic situation and a traffic flow of each vehicle type;

[0068] Continue to determine the traffic flow characteristic values ​​of the remaining traffic monitoring points.

[0069] In some preferred embodiments, the space occupied lengths of different vehicle types can be used as the impact of different vehicle types on the traffic situation. Therefore, the traffic flow weight of each vehicle type on the traffic situation is set based on the impact of different vehicle types on the traffic situation, that is, the traffic flow weight of each vehicle type on the traffic situation is set based on the space occupied lengths of different vehicle types. As a preferred embodiment, when the space occupied length of the vehicle type is large, for example, the space occupied length of a bus is 12 meters, the traffic flow weight of the bus on the traffic situation is set to 0.6; the space occupied length of a motorcycle is 2 meters, the traffic flow weight of the motorcycle on the traffic situation is set to 0.1, and the traffic flow weight is a constant with a value range of (0, 1).

[0070] In specific implementation, the traffic flow characteristic value of the selected traffic monitoring point is determined based on the traffic flow weight of each vehicle type to the traffic situation and the traffic flow of each vehicle type. This can be achieved in the following manner, namely: first, a vehicle type is selected as the selected vehicle type, and then the traffic flow weight of the selected vehicle type is obtained, the traffic flow weight is multiplied by the traffic flow of the selected vehicle type in the selected traffic monitoring point, and the results corresponding to the remaining vehicle types are continued to be determined. Finally, the value obtained by adding the results corresponding to all vehicle types is used as the traffic flow characteristic value of the fixed traffic monitoring point. In other embodiments, other methods can also be used for determination, which is not limited here.

[0071] It should be noted that the traffic flow characteristic value described in this application is a comprehensive quantitative indicator of the traffic flow at the traffic monitoring point, reflecting the complexity of the traffic situation at the corresponding traffic monitoring point. The larger the traffic flow characteristic value, the higher the complexity of the traffic situation at the corresponding traffic monitoring point. The smaller the traffic flow characteristic value, the lower the complexity of the traffic situation at the corresponding traffic monitoring point.

[0072] In specific implementation, the degree of closeness of the traffic situation between each two traffic monitoring points can be determined by the position information of each traffic monitoring point and the traffic flow characteristic value of each traffic monitoring point. This can be achieved in the following manner: first, after the position information and traffic flow characteristic value of each traffic monitoring point are formed into three-dimensional coordinates, the three-dimensional coordinates of all traffic monitoring points are used as input, and the Euclidean distance algorithm in the prior art is used to calculate the Euclidean distance between each two traffic monitoring points, and then the corresponding Euclidean distance is used as the degree of closeness of the traffic situation between each two traffic monitoring points. In other embodiments, other methods can also be used for determination, which is not limited here.

[0073] It should be noted that the degree of convergence of traffic situations described in this application is an indicator representing the degree of similarity of traffic situations between two traffic monitoring points. The greater the degree of convergence of traffic situations, the higher the degree of similarity of traffic situations between the two traffic monitoring points. The smaller the degree of convergence of traffic situations, the lower the degree of similarity of traffic situations between the two traffic monitoring points.

[0074] In specific implementation, based on the closeness of the traffic situation between every two traffic monitoring points, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are divided from all traffic monitoring points. This can be achieved in the following way: first, the closeness of the traffic situation between every two traffic monitoring points is used as the similarity index between every two traffic monitoring points in the K-means clustering algorithm, and then the existing K-means clustering algorithm is used to extract multiple center points and point clusters of each center point from all traffic monitoring points, and then each center point is used as a traffic hub point, and the area composed of the point clusters of each center point is used as the road network sub-area corresponding to each traffic hub point. In other embodiments, other methods can also be used for implementation, which are not limited here.

[0075] It should be noted that this application extracts multiple central points from all traffic monitoring points as the traffic hub points in this application, and thus the areas divided with each traffic hub point as the center in the road network area are used as corresponding road network sub-areas. This application reflects the real-time changes in traffic flow in the road network area through the changes in the positions of the traffic hub points at different sampling times.

[0076] In step 103, the traffic saturation of each road network sub-area is determined based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then the traffic saturation of each road network sub-area at each sampling moment is obtained.

[0077] In some embodiments, determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area can be achieved by using the following steps:

[0078] Select a road network sub-area as the selected road network sub-area;

[0079] Determine the traffic carrying capacity in the selected road network sub-area;

[0080] Determining the traffic saturation of the selected road network sub-area based on the traffic flow information of all traffic monitoring points in the selected road network sub-area and the traffic carrying capacity;

[0081] Continue to determine the traffic saturation of the remaining road network sub-areas.

[0082] In specific implementation, the traffic carrying capacity in the selected road network sub-area can be determined in the following manner: first, all roads in the selected road network sub-area are obtained, then the traffic flow threshold of each road is obtained, and finally, the traffic flow thresholds of all roads in the selected road network sub-area are added together as the traffic carrying capacity in the selected road network sub-area. In other embodiments, other methods can also be used for determination, which are not limited here.

[0083] It should be noted that the traffic flow threshold in this application refers to the maximum load threshold on the road. As a preferred embodiment, the traffic flow threshold can be obtained according to the type of road. For example, when the road is a main urban road, the traffic flow threshold of the road is set to 1500. When the road is a secondary urban road, the traffic flow threshold of the road is set to 1000. In other embodiments, other methods can also be used to obtain it, which is not limited here.

[0084] In specific implementation, the traffic saturation of the selected road network sub-area can be determined by the traffic flow information of all traffic monitoring points in the selected road network sub-area and the traffic carrying capacity. That is, first, the traffic flow characteristic value of each traffic monitoring point in the traffic flow information of all traffic monitoring points in the selected road network sub-area is obtained, and then, all the traffic flow characteristic values ​​are added up and compared with the traffic carrying capacity as the traffic saturation of the selected road network sub-area.

[0085] It should be noted that the traffic carrying capacity mentioned in this application refers to the maximum vehicle flow that can pass through a specific road network sub-area safely and efficiently within a unit time. In addition, the traffic saturation is used to characterize the relationship between traffic flow and road carrying capacity, which reflects the degree of traffic pressure in the road network sub-area. The greater the traffic saturation, the higher the degree of traffic pressure in the road network sub-area, and the smaller the traffic saturation, the lower the degree of traffic pressure in the road network sub-area.

[0086] In step 104, the offset of the location information of each traffic hub point between different sampling moments is determined, and the comprehensive traffic index of the road network area at different sampling moments is determined by the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub point location information.

[0087] In some embodiments, determining the offset of the location information of each transportation hub between different sampling moments may be achieved by using the following steps:

[0088] Select a sampling moment as the selected sampling moment;

[0089] Get the adjacent sampling moments of the selected sampling moment;

[0090] Determine the offset of the position information of each transportation hub point between the selected sampling moment and the adjacent sampling moment by using the position information of each transportation hub point at the selected sampling moment and the position information of each transportation hub point at the adjacent sampling moment;

[0091] Continue to determine the offset of the location information of each transportation hub point between the remaining sampling moments and the corresponding adjacent sampling moments.

[0092] In specific implementation, the offset of the position information of each transportation hub point between the selected sampling moment and the adjacent sampling moment is determined by the position information of each transportation hub point at the selected sampling moment and the position information of each transportation hub point at the adjacent sampling moment. This can be achieved in the following manner, namely: first, a transportation hub point is selected at the selected sampling moment, and then the position information of the transportation hub point at the adjacent sampling moment is obtained. Finally, after the position information of the transportation hub point and the position information at the adjacent sampling moment are used as input, the Euclidean distance algorithm in the prior art is used to calculate the Euclidean distance between the transportation hub point at the selected sampling moment and the adjacent sampling moment, and the Euclidean distance is used as the offset of the position information of the transportation hub point between the selected sampling moment and the adjacent sampling moment.

[0093] It should be noted that the offset of the location information described in this application refers to the spatial displacement of the geographical location of the traffic hub due to changes in traffic conditions between different sampling moments. It characterizes the degree of change in traffic conditions between different sampling moments. The larger the offset of the location information, the higher the degree of change in traffic conditions between different sampling moments. The smaller the offset of the location information, the lower the degree of change in traffic conditions between different sampling moments.

[0094] In some embodiments, determining the comprehensive traffic index of the road network area at different sampling times based on the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub location information can be achieved by the following steps:

[0095] Setting a saturation factor and an offset factor according to the area type of the road network area;

[0096] Select a sampling moment as the selected sampling moment;

[0097] Select a road network area at the selected sampling time as the selected road network sub-area;

[0098] Get the traffic hub corresponding to the selected road network sub-area;

[0099] Determine a traffic index component of the selected road network sub-area at the selected sampling moment according to the traffic saturation of the selected road network sub-area, the offset of the traffic hub location information, the saturation factor and the offset factor;

[0100] Continuing to determine traffic index components of the remaining road network sub-areas at the selected sampling time, and then determining a comprehensive traffic index of the road network area at the selected sampling time;

[0101] Continue to determine the comprehensive traffic index of the road network area in the remaining sampling moments.

[0102] Preferably, the present application sets the saturation factor and the offset factor by the area type of the road network area, that is: the values ​​of the saturation factor and the offset factor are set according to the different scenario ratios in the road network area. For example, in the highway scenario, the main focus is on the traffic load situation, and too high traffic saturation will lead to potential congestion and safety risks; in the urban main road scenario, due to the large traffic volume and moderate speed, congestion and traffic fluctuations are prone to occur, so it is necessary to pay attention to both the traffic load situation and the flow trend; in the urban secondary road or branch road scenario, due to the small traffic volume, but the vehicle dynamic changes are complex, and it is greatly affected by adjacent roads or events, so more attention is needed. Pay attention to dynamic changes of vehicles and emergencies. Therefore, preferably, when the proportion of highway scenes in the road network area is relatively large, the saturation factor is set to 0.8 and the offset factor is set to 0.2; when the proportion of urban main road scenes in the road network area is relatively large, the saturation factor is set to 0.6 and the offset factor is set to 0.4; when the proportion of urban secondary road or branch road scenes in the road network area is relatively large, the saturation factor is set to 0.4 and the offset factor is set to 0.6. In other embodiments, other methods can also be used for setting, which are not limited here.

[0103] It should be noted that the traffic index component described in the present application represents the degree of fluctuation of the traffic operation status of the road network sub-area at the corresponding sampling moment. The larger the traffic index component, the higher the degree of fluctuation of the traffic operation status of the road network sub-area at the corresponding sampling moment. The smaller the traffic index component, the lower the degree of fluctuation of the traffic operation status of the road network sub-area at the corresponding sampling moment. As a preferred embodiment, the traffic index component of the selected road network sub-area at the selected sampling moment is determined according to the traffic saturation of the selected road network sub-area, the offset of the traffic hub point location information, the saturation factor and the offset factor. The following method can be used, namely: first, the result of multiplying the traffic saturation of the selected road network sub-area by the saturation factor and the result of multiplying the offset of the traffic hub point location information corresponding to the selected road network sub-area by the offset factor are added, and the result obtained is used as the traffic index component of the selected road network sub-area at the selected sampling moment.

[0104] It should be noted that the comprehensive traffic index described in this application represents the degree of fluctuation of the traffic operation conditions in the road network area at the corresponding sampling moment. The larger the comprehensive traffic index is, the higher the degree of fluctuation of the traffic operation conditions in the road network area at the corresponding sampling moment. The smaller the comprehensive traffic index is, the lower the degree of fluctuation of the traffic operation conditions in the road network area at the corresponding sampling moment. As a preferred embodiment, determining the comprehensive traffic index of the road network area at the selected sampling moment can be achieved in the following manner, namely: first, obtaining the traffic index components of each road network sub-area at the selected sampling moment, and then taking the average value of all traffic index components as the comprehensive traffic index of the road network area at the selected sampling moment. In other embodiments, other methods can also be used for determination, which will not be repeated here.

[0105] In step 105, the traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things platform via a wireless network.

[0106] In some embodiments, reference Figure 3 As shown in FIG, this figure is a schematic diagram of a process for implementing abnormality monitoring shown in some embodiments of the present application. The abnormality monitoring of the traffic situation of the road network area at different sampling times according to the comprehensive traffic index can be implemented by the following steps:

[0107] First, in 1051, a traffic anomaly threshold of the comprehensive traffic index in the road network area is set;

[0108] Then, in 1052, the traffic anomaly threshold is used to determine the anomaly of the comprehensive traffic index of the road network area at different sampling times;

[0109] Finally, in 1053, based on the judgment result, abnormal monitoring of the traffic situation of the road network area at different sampling times is implemented.

[0110] It should be noted that, after obtaining the historical traffic flow information of all traffic monitoring points in the road network area, the present application respectively converts the historical traffic flow information into the traffic flow information in the present application, repeats the above process of determining the comprehensive traffic index from the traffic flow information, and obtains multiple historical comprehensive traffic indices of the road network area. Subsequently, the average value and standard deviation of each historical comprehensive traffic index are determined, and then the standard deviation is multiplied by the natural constant 2 and added to the average value as the traffic anomaly threshold. In other embodiments, other methods can also be used for setting, which are not limited here.

[0111] In specific implementation, the traffic anomaly threshold is used to perform abnormal judgment on the comprehensive traffic index corresponding to the road network area at different sampling moments. This can be achieved in the following manner, namely: first, the comprehensive traffic index of the road network area at a sampling moment is obtained; when the comprehensive traffic index is greater than or equal to the traffic anomaly threshold, the traffic situation of the road network area at the sampling moment is judged to be an abnormal mode; when the comprehensive traffic index is less than the traffic anomaly threshold, the traffic situation of the road network area at the sampling moment is judged to be a normal mode, and the above steps are repeated to perform abnormal judgment on the comprehensive traffic index of the road network area at the remaining sampling moments.

[0112] It should be noted that, based on the judgment result, the abnormal traffic situation of the road network area at different sampling moments is monitored, that is: when the traffic situation of the road network area is in normal mode, the processing is skipped and the judgment of the next sampling moment is waited for; when the traffic situation of the road network area is in abnormal mode, an alarm is sent to the traffic monitoring center of the road network area to prompt that traffic abnormalities may occur, and the comprehensive traffic index, sampling time and related information of the corresponding sampling moment are recorded into the traffic monitoring center, thereby realizing abnormal traffic situation monitoring of the road network area at different sampling moments. In addition, the present application utilizes wireless network technology (for example: 5G technology) to wirelessly transmit the collected data (comprehensive traffic index) to a cloud server (Internet of Things platform) for storage.

[0113] In addition, in another aspect of the present application, in some embodiments, the present application provides a traffic monitoring system based on an intelligent transportation Internet of Things, wherein the intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform, and the data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network, Figure 4 , which is a schematic diagram of the structure of a traffic monitoring system based on the intelligent transportation Internet of Things according to some embodiments of the present application. The traffic monitoring system 200 based on the intelligent transportation Internet of Things includes: a collection module 201, a processing module 202, and an execution module 203, which are described as follows:

[0114] The acquisition module 201 in this application is mainly used to deploy data acquisition devices at various traffic monitoring points in the road network area and collect traffic flow information at different sampling times at all traffic monitoring points;

[0115] Processing module 202, in this application, is mainly used to extract, at each sampling moment, multiple traffic hubs and road network sub-areas corresponding to each traffic hub from all traffic monitoring points based on the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points;

[0116] In addition, the processing module 202 in the present application is further configured to determine the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, thereby obtaining the traffic saturation of each road network sub-area at each sampling moment;

[0117] In addition, the processing module 202 in the present application is further configured to determine the offset of the location information of each traffic hub point between different sampling times, and determine the comprehensive traffic index of the road network area at different sampling times based on the traffic saturation of each road network sub-area and the offset of the location information of the corresponding traffic hub point;

[0118] Execution module 203. In this application, execution module 203 is mainly used to monitor the traffic situation of the road network area at different sampling times based on the comprehensive traffic index, and send the monitoring results to the Internet of Things platform via the wireless network.

[0119] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned traffic monitoring method based on the intelligent transportation Internet of Things.

[0120] In some embodiments, reference Figure 5 , which is an internal structure diagram of a computer device according to a traffic monitoring method based on the intelligent transportation Internet of Things shown in some embodiments of the present application. The traffic monitoring method based on the intelligent transportation Internet of Things in the above embodiment can be Figure 5 The computer device 300 shown in FIG. 1 is implemented as shown in FIG. 1 , and the computer device 300 includes at least one processor 301 , a communication bus 302 , a memory 303 , and at least one communication interface 304 .

[0121] The processor 301 may be a general-purpose central processing unit (CPU), or an application specific integrated circuit (ASIC) or one or more components for controlling the execution of the traffic monitoring method based on the intelligent transportation Internet of Things in this application.

[0122] The communication bus 302 is used to transmit information between the above components.

[0123] Memory 303 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD ROM) or other optical disk storage, an optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer. Memory 303 may exist independently and be connected to processor 301 via communication bus 302. Memory 303 may also be integrated with processor 301.

[0124] Memory 303 is used to store program code for executing the solution of the present application, and is controlled by processor 301 for execution. Processor 301 is used to execute the program code stored in memory 303. The program code may include one or more software modules. The traffic monitoring method based on the intelligent transportation Internet of Things in the above embodiment can be implemented by processor 301 and one or more software modules in the program code in memory 303.

[0125] The communication interface 304 uses any transceiver-like system for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0126] In a specific implementation, as an example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0127] The aforementioned computer device may be a general-purpose computer device or a dedicated computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of computer device.

[0128] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned traffic monitoring method based on the intelligent transportation Internet of Things.

[0129] In summary, in the traffic monitoring system and method based on the intelligent transportation Internet of Things disclosed in the embodiment of the present application, data collection devices are deployed at each traffic monitoring point in the road network area, thereby collecting traffic flow information of all traffic monitoring points at different sampling times; for each sampling time, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the position information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points; the traffic saturation of each road network sub-area is determined based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then the traffic saturation of each road network sub-area at each sampling time is obtained; the offset of the position information of each traffic hub point between different sampling times is determined, and the comprehensive traffic index of the road network area at different sampling times is determined through the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub point position information; the traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things via a wireless network; the sub-area division can be adjusted in real time to improve the traffic response speed in the road network area.

[0130] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0131] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if such changes and modifications fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such changes and modifications.

Claims

1. A traffic monitoring method based on intelligent transportation Internet of Things, wherein: The intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform. The data acquisition device transmits real-time data with the Internet of Things platform via a wireless network. The traffic monitoring method based on the intelligent transportation Internet of Things includes the following steps: After deploying the data collection device at each traffic monitoring point in the road network area, the traffic flow information of all traffic monitoring points at different sampling times is collected; At each sampling moment, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points using the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points; Determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then obtaining the traffic saturation of each road network sub-area at each sampling moment; Determining the offset of the location information of each traffic hub point between different sampling times, and determining the comprehensive traffic index of the road network area at different sampling times based on the traffic saturation of each road network sub-area and the offset of the location information of the corresponding traffic hub point; Perform abnormal monitoring of traffic conditions in the road network area at different sampling times based on the comprehensive traffic index, and send the monitoring results to the Internet of Things platform via a wireless network; Among them, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the location information and traffic flow information of each traffic monitoring point, including: Based on the impact of different vehicle types on traffic conditions and the traffic flow information of each traffic monitoring point, information is integrated to obtain the traffic flow characteristic value of each traffic monitoring point, wherein the traffic flow characteristic value is a comprehensive quantitative indicator of the traffic flow at the traffic monitoring point, reflecting the complexity of the traffic situation at the corresponding traffic monitoring point; Determine the degree of closeness of traffic conditions between every two traffic monitoring points through the location information of each traffic monitoring point and the traffic flow characteristic value of each traffic monitoring point; Based on the closeness of the traffic situation between every two traffic monitoring points, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are divided among all traffic monitoring points; The method of determining the comprehensive traffic index of the road network area at different sampling times by using the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub location information specifically includes: Setting a saturation factor and an offset factor according to the area type of the road network area; Select a sampling moment as the selected sampling moment; Select a road network area at the selected sampling time as the selected road network sub-area; Get the traffic hub corresponding to the selected road network sub-area; Determine a traffic index component of the selected road network sub-area at the selected sampling moment according to the traffic saturation of the selected road network sub-area, the offset of the traffic hub location information, the saturation factor and the offset factor; Continuing to determine traffic index components of the remaining road network sub-areas at the selected sampling time, and then determining a comprehensive traffic index of the road network area at the selected sampling time; Continue to determine the comprehensive traffic index of the road network area in the remaining sampling moments.

2. The method according to claim 1, wherein Determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area specifically includes: Select a road network sub-area as the selected road network sub-area; Determine the traffic carrying capacity in the selected road network sub-area; Determining the traffic saturation of the selected road network sub-area based on the traffic flow information of all traffic monitoring points in the selected road network sub-area and the traffic carrying capacity; Continue to determine the traffic saturation of the remaining road network sub-areas.

3. The method according to claim 1, wherein Determining the offset of the location information of each transportation hub between different sampling times specifically includes: Select a sampling moment as the selected sampling moment; Get the adjacent sampling moments of the selected sampling moment; Determine the offset of the position information of each transportation hub point between the selected sampling moment and the adjacent sampling moment by using the position information of each transportation hub point at the selected sampling moment and the position information of each transportation hub point at the adjacent sampling moment; Continue to determine the offset of the location information of each transportation hub point between the remaining sampling moments and the corresponding adjacent sampling moments.

4. The method according to claim 1, wherein The abnormal monitoring of the traffic situation of the road network area at different sampling times according to the comprehensive traffic index specifically includes: Setting a traffic anomaly threshold value of the comprehensive traffic index in the road network area; Perform abnormality judgment on the comprehensive traffic index corresponding to the road network area at different sampling times by using the traffic abnormality threshold; Based on the judgment result, abnormal traffic conditions in the road network area at different sampling times are monitored.

5. The method according to claim 1, wherein The data acquisition device includes a radar sensor, a global positioning system device and a traffic camera.

6. A traffic monitoring system based on an intelligent transportation internet of things, which uses the method according to any one of claims 1 to 5 to perform traffic monitoring, wherein: The intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform. The data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network. The system is characterized in that it includes: The acquisition module is used to deploy data acquisition devices at various traffic monitoring points in the road network area and collect traffic flow information at all traffic monitoring points at different sampling times; a processing module configured to extract, at each sampling moment, a plurality of traffic hubs and a road network sub-area corresponding to each traffic hub from all traffic monitoring points using the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes a plurality of traffic monitoring points; The processing module is further configured to determine the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, thereby obtaining the traffic saturation of each road network sub-area at each sampling moment; The processing module is further configured to determine an offset of the location information of each traffic hub point between different sampling moments, and determine a comprehensive traffic index of the road network area at different sampling moments based on the traffic saturation of each road network sub-area and the offset of the location information of the corresponding traffic hub point; The execution module is used to monitor the traffic situation of the road network area at different sampling times according to the comprehensive traffic index, and send the monitoring results to the Internet of Things platform via a wireless network.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the traffic monitoring method based on the intelligent transportation Internet of Things according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the traffic monitoring method based on the intelligent transportation Internet of Things as described in any one of claims 1 to 5 are implemented.

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