School import and export pedestrian traffic management system and method based on internet of things

Abstract

The application relates to the technical field of traffic management, in particular to a school import and export pedestrian traffic management system and method based on the Internet of Things. The steps of the method comprise the following: obtaining road traffic monitoring data of school import and export during school dismissal time; evaluating road congestion degree by extracting vehicle distribution data; evaluating traffic interference degree by identifying vehicle driving direction and vehicle and pedestrian mixed driving behavior; evaluating student dismissal speed according to the road congestion degree and the traffic interference degree, and adjusting the school dismissal strategy according to the student dismissal speed. The application quantifies the road congestion degree and the traffic interference degree of the school import and export, and then evaluates the student dismissal speed, thereby improving the student dismissal efficiency and reducing the road traffic safety risk of the school import and export.

Description

Technical Field

[0001] This application relates to the field of traffic management technology, and in particular to a pedestrian traffic management system and method for school entrances and exits based on the Internet of Things. Background Technology

[0002] With the increasing demand for motorized travel in cities, the traffic environment at school entrances and exits is deteriorating. Traffic congestion and disorder near schools during morning and evening rush hours have gradually become the norm in cities. This not only poses safety hazards to students going to and from school, but also seriously affects the normal passage of surrounding residents, increasing the management pressure and difficulty for traffic management departments.

[0003] The main reason for traffic problems at school entrances and exits is that vehicles picking up and dropping off students and vehicles going to and from get off work gather at school entrances and exits and surrounding roads in a short period of time, far exceeding the road's carrying capacity. At the same time, due to limited road space and high pedestrian density, the phenomenon of pedestrians and vehicles sharing the road is common. In addition, some vehicles park and pass in an irregular manner, which further exacerbates the road congestion.

[0004] Existing technologies often overlook the impact of mixed traffic of vehicles and pedestrians on road conditions when assessing traffic conditions at school entrances and exits, resulting in low accuracy in road congestion assessment. At the same time, traditional dismissal strategies lack dynamic response to real-time traffic conditions and cannot adjust dismissal strategies in a timely manner according to the actual congestion level of roads at school entrances and exits. Furthermore, the concentrated departure of students from school further increases road safety hazards and congestion at school entrances and exits. Summary of the Invention

[0005] To overcome the shortcomings and deficiencies of existing technologies, this application provides a pedestrian traffic management system and method for school entrances and exits based on the Internet of Things. By quantifying the degree of road congestion and traffic interference at school entrances and exits, the system assesses the speed at which students leave school, thereby improving the efficiency of students leaving school and reducing road traffic safety risks at school entrances and exits.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, this application provides an Internet of Things-based method for pedestrian traffic management at school entrances and exits, comprising the following steps:

[0008] Obtain road traffic monitoring data at school entrances and exits during dismissal hours;

[0009] By extracting vehicle distribution data, the degree of road congestion can be assessed;

[0010] The degree of traffic disruption is assessed by identifying the direction of vehicle travel and the mixing of vehicles and pedestrians.

[0011] Assess student departure speed based on road congestion and traffic disruption levels, and adjust dismissal strategies accordingly.

[0012] Optionally, the assessment of road congestion includes:

[0013] Acquire road traffic monitoring data and extract vehicle distribution data from the road traffic monitoring data;

[0014] The road congestion index is calculated based on vehicle distribution data. The road congestion index is used to quantitatively assess the degree of road congestion. The formula for calculating the road congestion index is:

[0015] ;

[0016] In the formula Indicates the first The road area occupied by each vehicle Indicates the effective area of ​​the road. Indicates the number of vehicles. Indicates the first The distance between each vehicle and the center point of the school's entrance and exit. Indicates the width of the road. Indicates the first regulating factor. This indicates the road congestion index.

[0017] Optionally, the assessment of the degree of traffic disruption includes:

[0018] Acquire road traffic monitoring data and identify vehicle driving directions and mixed traffic behavior between vehicles and pedestrians in the road traffic monitoring data;

[0019] The consistency coefficient of vehicle driving direction and the road mixing coefficient are calculated based on the vehicle driving direction and the mixed traffic behavior of vehicles and pedestrians, respectively. The formula for calculating the consistency coefficient of vehicle driving direction is:

[0020] ;

[0021] In the formula Indicates the first The angle between the vehicle's direction of travel and the prescribed direction of travel on the road. This represents the average angle between the vehicle's direction of travel and the prescribed direction of travel on the road. Indicates the number of vehicles. Indicates the consistency coefficient of vehicle travel direction;

[0022] The traffic interference index is calculated based on the vehicle direction consistency coefficient and the road congestion coefficient. The traffic interference index is used to quantitatively assess the degree of traffic interference. The formula for calculating the traffic interference index is:

[0023] ;

[0024] In the formula This represents the consistency coefficient of vehicle travel direction. Indicates the road mixed traffic coefficient. Indicates the third regulatory factor. This indicates the traffic disruption index.

[0025] Optionally, calculating the road mixing coefficient includes:

[0026] The monitored road area in the road traffic monitoring data is divided into One monitoring grid;

[0027] Extract the first The vehicle and pedestrian data in each monitoring grid are collected, and the cross-traffic coefficient of the monitoring grid is calculated. The formula for calculating the cross-traffic coefficient of the monitoring grid is as follows:

[0028] ;

[0029] In the formula Indicates the first The number of vehicles in each monitoring grid Indicates the first The number of pedestrians in each monitoring grid Indicates the first The road occupancy area of ​​vehicles in each monitoring grid Indicates the first The area of ​​road occupied by pedestrians in each monitoring grid. Indicates the first The effective area of ​​roads within each monitoring grid Indicates the second regulating factor. Indicates the first The mixing coefficient of each monitoring grid;

[0030] The road mixing coefficient is calculated based on the mixing coefficient of the monitoring grid. The road mixing coefficient is used to quantitatively assess the degree of road mixing. The formula for calculating the road mixing coefficient is as follows:

[0031] ;

[0032] In the formula Indicates the first The mixing coefficient of each monitoring grid Indicates the number of monitoring grids. This indicates the road mixed traffic coefficient.

[0033] Optionally, the assessment of student departure speed includes:

[0034] Obtain the road congestion index and traffic disruption index;

[0035] The student departure speed is calculated based on the road congestion index and traffic disruption index. The formula for calculating the student departure speed is as follows:

[0036] ;

[0037] In the formula The road congestion index indicates... Indicates the traffic disruption index. Indicates road congestion weight, Indicates the traffic interference weight. This represents the historical average rate of students leaving school. This indicates the speed at which students leave school.

[0038] Optionally, adjusting the dismissal strategy based on students' departure speed includes:

[0039] Get the student departure speed and the number of students in each grade;

[0040] The required school departure time for a given grade is obtained by calculating the ratio of the number of students in that grade to the rate at which students leave school, and this school departure time is used as the school dismissal interval.

[0041] It should be noted that the values ​​of the first adjustment factor, second adjustment factor, third adjustment factor, road congestion weight, and traffic interference weight are determined as follows: 5000 sets of road traffic monitoring data are collected, and the students' departure speed is differentiated based on whether it meets safety requirements. The road traffic monitoring data is then substituted into the student departure speed calculation formula for calculation. The calculated student departure speed and the differentiation results are simultaneously imported into the fitting software, and the optimal first adjustment factor, second adjustment factor, third adjustment factor, road congestion weight, and traffic interference weight that match the differentiation accuracy are output.

[0042] Secondly, this application provides an Internet of Things-based pedestrian traffic management system for school entrances and exits, including:

[0043] The data acquisition module is used to acquire road traffic monitoring data at school entrances and exits during dismissal time.

[0044] The first assessment module is used to assess road congestion by extracting vehicle distribution data;

[0045] The second assessment module is used to assess the degree of traffic interference by identifying the direction of vehicle travel and the mixed traffic behavior of vehicles and pedestrians.

[0046] The strategy adjustment module is used to assess students' departure speed based on road congestion and traffic disruption, and adjust the dismissal strategy accordingly.

[0047] The control module is used to control the operation of the data acquisition module, the first evaluation module, the second evaluation module, and the strategy adjustment module.

[0048] Thirdly, this application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an Internet of Things-based pedestrian traffic management method for school entrances and exits by calling the computer program stored in the memory.

[0049] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an Internet of Things-based pedestrian traffic management method for school entrances and exits.

[0050] Compared with the prior art, this application has the following advantages and beneficial effects:

[0051] This application assesses road congestion by extracting vehicle distribution data from school entrances and exits, evaluates traffic interference by identifying vehicle travel directions and the mixed traffic behavior of vehicles and pedestrians, and then assesses student departure speed based on road congestion and traffic interference. Based on student departure speed, the application adjusts the dismissal strategy to improve student departure efficiency while reducing road traffic safety risks at school entrances and exits. Attached Figure Description

[0052] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0053] Figure 1 This is a schematic diagram of the overall process of the Internet of Things-based pedestrian traffic management method for school entrances and exits provided in the embodiments of this application;

[0054] Figure 2 This is a schematic diagram of the vehicle parking space distribution characteristics provided in the embodiments of this application;

[0055] Figure 3 This is a schematic diagram of the structure of the Internet of Things-based pedestrian traffic management system for school entrances and exits provided in the embodiments of this application;

[0056] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0057] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0058] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall process of the Internet of Things-based pedestrian traffic management method for school entrances and exits provided in this application embodiment, which specifically includes the following steps:

[0059] S110: Obtain road traffic monitoring data at school entrances and exits during dismissal time.

[0060] S120: Assess road congestion by extracting vehicle distribution data;

[0061] The disorderly parking of vehicles at school entrances and exits is a major factor affecting traffic order and road congestion, especially during peak school hours. Some vehicles park haphazardly or occupy road space for extended periods, further reducing the passage space for pedestrians and other vehicles. The spatial distribution of parked vehicles also significantly impacts road congestion. For example, some parents park motor vehicles or non-motor vehicles near school entrances and exits, and this uneven parking often concentrates in the vicinity, further increasing traffic pressure in that area. This not only affects the normal passage of vehicles and pedestrians but may also lead to traffic jams and safety accidents. Therefore, when assessing road congestion, the spatial distribution of parked vehicles must be fully considered, including:

[0062] Acquire road traffic monitoring data and extract vehicle distribution data from the road traffic monitoring data. Use target detection algorithms (such as Faster R-CNN, SSD and YOLO) to extract vehicle distribution data from the road traffic monitoring data. The vehicle distribution data includes the number of vehicles, the road area occupied by vehicles, and the distance between vehicles and the center point of the school entrance and exit.

[0063] Please see Figure 2 , Figure 2 This is a schematic diagram of the distribution characteristics of vehicle parking space provided in the embodiments of this application. By analyzing the distance distribution between vehicles and the center point of school entrances and exits in motor vehicle lanes and non-motor vehicle lanes, as well as the degree of occupation of road space resources, the degree of road congestion can be assessed.

[0064] The road congestion index is calculated based on vehicle distribution data. The road congestion index is used to quantitatively assess the degree of road congestion. The formula for calculating the road congestion index is: ;

[0065] In the formula Indicates the first The road area occupied by each vehicle Indicates the effective area of ​​the road. Indicates the number of vehicles. Used to describe the degree of occupancy of road space resources, that is, the ratio of the total road area occupied by vehicles to the effective road area. Indicates the first The distance between each vehicle and the center point of the school's entrance and exit. This indicates that vehicles that are closer together contribute more to road congestion. Indicates the width of the road. Indicates the first regulating factor. This indicates the road congestion index.

[0066] S130: Assess the degree of traffic disruption by identifying vehicle travel direction and the mixing of vehicles and pedestrians;

[0067] Mixed traffic behavior at school entrances and exits refers to the situation during peak school hours where motor vehicles, non-motor vehicles, and a large number of pedestrians simultaneously appear in the same traffic space without clear diversion measures or traffic rules, resulting in various traffic flows intertwining. This mixed traffic behavior disrupts normal traffic flow, increases road traffic uncertainty, and further exacerbates traffic congestion, especially when vehicles do not travel in the prescribed direction or frequently weave in and out of traffic. This mixed traffic behavior not only affects the consistency of traffic flow but also increases the dynamic complexity of road congestion. Assessing the degree of traffic interference includes:

[0068] Acquire road traffic monitoring data and identify the vehicle driving direction and mixed traffic behavior of vehicles and pedestrians in the road traffic monitoring data. The steps for identifying the vehicle driving direction in the road traffic monitoring data include: (1) vehicle detection, which is performed by processing the road traffic monitoring data with computer vision technology; (2) trajectory tracking, which is performed by matching the detection box algorithm and combining Kalman filtering and Hungarian algorithm to track the vehicle position; (3) driving direction identification, which is performed by identifying the vehicle driving direction based on the position change of the vehicle in consecutive frames, for example, by calculating the vehicle driving direction by the position coordinate difference of the vehicle center point in adjacent frames.

[0069] The consistency coefficient of vehicle driving direction and the road mixing coefficient are calculated based on the vehicle driving direction and the mixed traffic behavior of vehicles and pedestrians, respectively. The formula for calculating the consistency coefficient of vehicle driving direction is: ;

[0070] In the formula Indicates the first The angle between the vehicle's direction of travel and the prescribed direction of travel on the road. , Used to assess the degree of deviation of a vehicle's driving direction from the prescribed driving direction on the road, wherein, This indicates that the vehicle's direction of travel is completely consistent with the road's prescribed direction of travel. This indicates that the vehicle is traveling in the opposite direction to the prescribed road direction, i.e., the vehicle is traveling in the wrong direction. This represents the average angle between the vehicle's direction of travel and the prescribed direction of travel on the road. , Indicates the number of vehicles. Indicates the consistency coefficient of vehicle travel direction;

[0071] The traffic interference index is calculated based on the vehicle direction consistency coefficient and the road congestion coefficient. The traffic interference index is used to quantitatively assess the degree of traffic interference. The formula for calculating the traffic interference index is: ;

[0072] In the formula This represents the consistency coefficient of vehicle travel direction. Indicates the road mixed traffic coefficient. Indicates the third regulatory factor. Indicates the traffic disruption index;

[0073] The mixed traffic behavior of vehicles and pedestrians typically manifests as pedestrians and vehicles sharing the road, vehicles randomly changing lanes or stopping, pedestrians crossing between vehicles, and even situations where the direction of pedestrian flow conflicts with the direction of oncoming vehicles. The road mixed traffic coefficient is calculated as follows:

[0074] The monitored road area in the road traffic monitoring data is divided into One monitoring grid;

[0075] Extracting the first target using object detection algorithms The vehicle and pedestrian data in each monitoring grid are collected, and the cross-traffic coefficient of the monitoring grid is calculated. The formula for calculating the cross-traffic coefficient of the monitoring grid is as follows: ;

[0076] In the formula Indicates the first The number of vehicles in each monitoring grid Indicates the first The number of pedestrians in each monitoring grid Used to measure the quantity relationship between vehicles and pedestrians. Indicates the first The road occupancy area of ​​vehicles in each monitoring grid Indicates the first The area of ​​road occupied by pedestrians in each monitoring grid. Indicates the first The effective area of ​​roads within each monitoring grid Indicates the second regulating factor. Indicates the first The mixing coefficient of each monitoring grid;

[0077] The road mixing coefficient is calculated based on the mixing coefficient of the monitoring grid. The road mixing coefficient is used to quantitatively assess the degree of road mixing. The formula for calculating the road mixing coefficient is as follows:

[0078] ;

[0079] In the formula Indicates the first The mixing coefficient of each monitoring grid Indicates the number of monitoring grids. This indicates the road mixed traffic coefficient.

[0080] S140: Assess student departure speed based on road congestion and traffic disruption, and adjust dismissal strategies accordingly.

[0081] When the road congestion index is high, road traffic flow is poor, and students spend more time at school entrances and exits, leading to a slower overall departure speed. The traffic disruption index reflects the impact of mixed vehicle and pedestrian traffic on traffic flow; chaotic traffic at school entrances and exits further reduces student departure speed. Assessing student departure speed includes:

[0082] Obtain the road congestion index and traffic disruption index;

[0083] The student departure speed is calculated based on the road congestion index and traffic disruption index. The formula for calculating the student departure speed is as follows:

[0084] ;

[0085] In the formula The road congestion index indicates... Indicates the traffic disruption index. Indicates road congestion weight, Indicates the traffic interference weight. This represents the historical average rate of students leaving school. Indicates the speed at which students leave school;

[0086] Dynamically adjusting dismissal strategies based on actual traffic conditions at school entrances and exits can effectively avoid traffic congestion and road safety hazards caused by students from all grades leaving school simultaneously, optimize traffic flow in the school gate area, and improve overall dismissal efficiency. Adjusting dismissal strategies according to students' departure speed includes:

[0087] Get the student departure speed and the number of students in each grade;

[0088] The required school departure time for a given grade is obtained by calculating the ratio of the number of students in that grade to the rate at which students leave school, and this school departure time is used as the school dismissal interval.

[0089] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a school entrance / exit pedestrian traffic management system based on the Internet of Things (IoT) provided in this embodiment. This embodiment provides a school entrance / exit pedestrian traffic management system based on the Internet of Things (IoT), including:

[0090] Data acquisition module 210 is used to acquire road traffic monitoring data at school entrances and exits during school dismissal time;

[0091] The first assessment module 220 is used to assess the degree of road congestion by extracting vehicle distribution data;

[0092] The second assessment module 230 is used to assess the degree of traffic interference by identifying the direction of vehicle travel and the mixed traffic behavior of vehicles and pedestrians.

[0093] The strategy adjustment module 240 is used to assess the speed at which students leave school based on the degree of road congestion and traffic interference, and to adjust the dismissal strategy accordingly.

[0094] The control module 250 is used to control the operation of the data acquisition module, the first evaluation module, the second evaluation module, and the strategy adjustment module.

[0095] In this embodiment of the application, the first evaluation module 220 is used to evaluate the degree of road congestion by extracting vehicle distribution data. The evaluation of the degree of road congestion includes:

[0096] Acquire road traffic monitoring data and extract vehicle distribution data from the road traffic monitoring data;

[0097] The road congestion index is calculated based on vehicle distribution data. The road congestion index is used to quantitatively assess the degree of road congestion. The formula for calculating the road congestion index is:

[0098] ;

[0099] In the formula Indicates the first The road area occupied by each vehicle Indicates the effective area of ​​the road. Indicates the number of vehicles. Indicates the first The distance between each vehicle and the center point of the school's entrance and exit. Indicates the width of the road. Indicates the first regulating factor. This indicates the road congestion index.

[0100] The second assessment module 230 is used to assess the degree of traffic interference by identifying the vehicle's direction of travel and the mixed traffic behavior of vehicles and pedestrians. The assessment of the degree of traffic interference includes:

[0101] Acquire road traffic monitoring data and identify vehicle driving directions and mixed traffic behavior between vehicles and pedestrians in the road traffic monitoring data;

[0102] The consistency coefficient of vehicle driving direction and the road mixing coefficient are calculated based on the vehicle driving direction and the mixed traffic behavior of vehicles and pedestrians, respectively. The formula for calculating the consistency coefficient of vehicle driving direction is:

[0103] ;

[0104] In the formula Indicates the first The angle between the vehicle's direction of travel and the prescribed direction of travel on the road. This represents the average angle between the vehicle's direction of travel and the prescribed direction of travel on the road. Indicates the number of vehicles. Indicates the consistency coefficient of vehicle travel direction;

[0105] The traffic interference index is calculated based on the vehicle direction consistency coefficient and the road congestion coefficient. The traffic interference index is used to quantitatively assess the degree of traffic interference. The formula for calculating the traffic interference index is:

[0106] ;

[0107] In the formula This represents the consistency coefficient of vehicle travel direction. Indicates the road mixed traffic coefficient. Indicates the third regulatory factor. Indicates the traffic disruption index;

[0108] Calculating the road mixing factor includes:

[0109] The monitored road area in the road traffic monitoring data is divided into One monitoring grid;

[0110] Extract the first The vehicle and pedestrian data in each monitoring grid are collected, and the cross-traffic coefficient of the monitoring grid is calculated. The formula for calculating the cross-traffic coefficient of the monitoring grid is as follows:

[0111] ;

[0112] In the formula Indicates the first The number of vehicles in each monitoring grid Indicates the first The number of pedestrians in each monitoring grid Indicates the first The road occupancy area of ​​vehicles in each monitoring grid Indicates the first The area of ​​road occupied by pedestrians in each monitoring grid. Indicates the first The effective area of ​​roads within each monitoring grid Indicates the second regulating factor. Indicates the first The mixing coefficient of each monitoring grid;

[0113] The road mixing coefficient is calculated based on the mixing coefficient of the monitoring grid. The road mixing coefficient is used to quantitatively assess the degree of road mixing. The formula for calculating the road mixing coefficient is as follows:

[0114] ;

[0115] In the formula Indicates the first The mixing coefficient of each monitoring grid Indicates the number of monitoring grids. This indicates the road mixed traffic coefficient.

[0116] The strategy adjustment module 240 is used to assess student departure speed based on road congestion and traffic disruption levels, and adjust the dismissal strategy accordingly. The assessment of student departure speed includes:

[0117] Obtain the road congestion index and traffic disruption index;

[0118] The student departure speed is calculated based on the road congestion index and traffic disruption index. The formula for calculating the student departure speed is as follows:

[0119] ;

[0120] In the formula The road congestion index indicates... Indicates the traffic disruption index. Indicates road congestion weight, Indicates the traffic interference weight. This represents the historical average rate of students leaving school. Indicates the speed at which students leave school;

[0121] Adjusting dismissal strategies based on students' departure speed includes:

[0122] Get the student departure speed and the number of students in each grade;

[0123] The required school departure time for a given grade is obtained by calculating the ratio of the number of students in that grade to the rate at which students leave school, and this school departure time is used as the school dismissal interval.

[0124] The parameters and steps for implementing the corresponding functions of each unit module in the IoT-based school entrance and exit pedestrian traffic management system of this application can be referred to the parameters and steps in the embodiments of the IoT-based school entrance and exit pedestrian traffic management method above, and will not be repeated here.

[0125] like Figure 4 As shown, embodiments of the present invention also provide an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores a method for pedestrian traffic management at school entrances and exits based on the Internet of Things, which can be loaded by the processor 320 and executed as provided in the above embodiments.

[0126] The memory 310 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the IoT-based pedestrian traffic management method for school entrances and exits provided in the above embodiments. The data storage area may store data involved in the IoT-based pedestrian traffic management method for school entrances and exits provided in the above embodiments.

[0127] Processor 320 may include one or more processing cores. Processor 320 executes instructions, programs, code sets, or instruction sets stored in memory 310, and calls data stored in memory 310 to perform various functions and process data as described in this application. Processor 320 may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of processor 320 may also be other types, and this application embodiment does not specifically limit the specific devices used.

[0128] The communication bus 330 may include a path for transmitting information between the aforementioned components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0129] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments: a school entrance and exit pedestrian traffic management method based on the Internet of Things.

[0130] In this embodiment, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), staging random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0131] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0132] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for pedestrian traffic management at school entrances and exits based on the Internet of Things, characterized in that: Includes the following steps: Obtain road traffic monitoring data at school entrances and exits during dismissal hours; By extracting vehicle distribution data, the degree of road congestion can be assessed; including: Acquire road traffic monitoring data and extract vehicle distribution data from the road traffic monitoring data; The road congestion index is calculated based on vehicle distribution data. The road congestion index is used to quantitatively assess the degree of road congestion. The formula for calculating the road congestion index is: ； In the formula Indicates the first The road area occupied by each vehicle Indicates the effective area of ​​the road. Indicates the number of vehicles. Indicates the first The distance between each vehicle and the center point of the school's entrance and exit. Indicates the width of the road. Indicates the first regulating factor. Indicates the road congestion index; Assess the degree of traffic disruption by identifying vehicle travel directions and the mixing of vehicles and pedestrians; including: Acquire road traffic monitoring data and identify vehicle driving directions and mixed traffic behavior between vehicles and pedestrians in the road traffic monitoring data; The consistency coefficient of vehicle driving direction and the road mixing coefficient are calculated based on the vehicle driving direction and the mixed traffic behavior of vehicles and pedestrians, respectively. The formula for calculating the consistency coefficient of vehicle driving direction is: ； In the formula Indicates the first The angle between the vehicle's direction of travel and the prescribed direction of travel on the road. This represents the average angle between the vehicle's direction of travel and the prescribed direction of travel on the road. Indicates the number of vehicles. Indicates the consistency coefficient of vehicle travel direction; The traffic interference index is calculated based on the vehicle direction consistency coefficient and the road congestion coefficient. The traffic interference index is used to quantitatively assess the degree of traffic interference. The formula for calculating the traffic interference index is: ； In the formula This represents the consistency coefficient of vehicle travel direction. Indicates the road mixed traffic coefficient. Indicates the third regulatory factor. The traffic interference index is represented by the road mixing coefficient, which includes: The monitored road area in the road traffic monitoring data is divided into One monitoring grid; Extract the first The vehicle and pedestrian data in each monitoring grid are collected, and the cross-traffic coefficient of the monitoring grid is calculated. The formula for calculating the cross-traffic coefficient of the monitoring grid is as follows: ； In the formula Indicates the first The number of vehicles in each monitoring grid Indicates the first The number of pedestrians in each monitoring grid Indicates the first The road occupancy area of ​​vehicles in each monitoring grid Indicates the first The area of ​​road occupied by pedestrians in each monitoring grid. Indicates the first The effective area of ​​roads within each monitoring grid Indicates the second regulating factor. Indicates the first The mixing coefficient of each monitoring grid; The road mixing coefficient is calculated based on the mixing coefficient of the monitoring grid. The road mixing coefficient is used to quantitatively assess the degree of road mixing. The formula for calculating the road mixing coefficient is as follows: ； In the formula Indicates the first The mixing coefficient of each monitoring grid Indicates the number of monitoring grids. Indicates the road mixed traffic coefficient; Assess student departure speed based on road congestion and traffic disruption levels, and adjust dismissal strategies accordingly.

2. The method for pedestrian traffic management at school entrances and exits based on the Internet of Things as described in claim 1, characterized in that, The assessment of student departure speed includes: Obtain the road congestion index and traffic disruption index; The student departure speed is calculated based on the road congestion index and traffic disruption index. The formula for calculating the student departure speed is as follows: ； In the formula Indicates the road congestion index. Indicates the traffic disruption index. Indicates road congestion weight, Indicates the traffic interference weight. This represents the historical average rate of students leaving school. This indicates the speed at which students leave school.

3. The method for pedestrian traffic management at school entrances and exits based on the Internet of Things as described in claim 1, characterized in that, The adjustment of the dismissal strategy based on the students' departure speed includes: Get the student departure speed and the number of students in each grade; The required school departure time for a given grade is obtained by calculating the ratio of the number of students in that grade to the rate at which students leave school, and this school departure time is used as the school dismissal interval.

4. A pedestrian traffic management system for school entrances and exits based on the Internet of Things (IoT), applied to any one of the IoT-based pedestrian traffic management methods for school entrances and exits as described in claims 1-3, characterized in that, The system includes: The data acquisition module is used to acquire road traffic monitoring data at school entrances and exits during dismissal time. The first assessment module is used to assess road congestion by extracting vehicle distribution data; The second assessment module is used to assess the degree of traffic interference by identifying the direction of vehicle travel and the mixed traffic behavior of vehicles and pedestrians. The strategy adjustment module is used to assess students' departure speed based on road congestion and traffic disruption, and adjust the dismissal strategy accordingly. The control module is used to control the operation of the data acquisition module, the first evaluation module, the second evaluation module, and the strategy adjustment module.

5. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the Internet of Things-based pedestrian traffic management method for school entrances and exits as described in any one of claims 1-3 by calling the computer program stored in the memory.

6. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the Internet of Things-based pedestrian traffic management method for school entrances and exits as described in any one of claims 1-3.

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