Traffic service management system based on big data

Abstract

The invention relates to a traffic service management system based on big data. The system comprises a data acquisition terminal, a server, a first user terminal and a second user terminal, the data acquisition terminal is used for acquiring accident data, vehicle data, weather data and 3D road data; the server is used for determining a corresponding matching strategy according to at least one of the vehicle data, the weather data and the 3D road data; the first user terminal is used for processing and combining the accident data, the vehicle data, the weather data and the 3D road data to obtain real-time traffic state information; the second user terminal is used for acquiring the accident data and generating a display interface according to the accident data, and the display interface is further used for receiving the command and dispatch information and sending the command and dispatch information to the server, so that the server adjusts the matching strategy according to the command and dispatch information. The traffic service management system based on the big data has the advantage that the traffic management efficiency can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a traffic service management system based on big data. Background Art

[0002] With the continued rapid advancement of urbanization and the increasing popularity of transportation, traffic problems have become increasingly prominent. Traffic congestion, weather interference, frequent traffic accidents, etc. have become key factors restricting urban development and affecting people's quality of life.

[0003] In order to effectively improve traffic conditions, there is an urgent need for a system that can accurately determine the corresponding management strategy based on multiple factors such as traffic accidents, vehicle congestion and weather in real time. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a traffic service management system based on big data, which has the advantage of being able to effectively improve traffic management efficiency.

[0005] In a first aspect, a traffic service management system based on big data is provided, the system comprising a data collection terminal, a server, a first user terminal and a second user terminal;

[0006] The data collection terminal is used to obtain accident data, vehicle data, weather data and 3D road data;

[0007] The server is used to determine a corresponding matching strategy according to at least one of the vehicle data, the weather data and the 3D road data, where the matching strategy is used to characterize and adjust the green light time in the road;

[0008] The first user terminal is used to process and combine the accident data, vehicle data, weather data and 3D road data to obtain real-time traffic status information, where the traffic status information includes the road congestion level and the user's estimated arrival time;

[0009] The second user terminal is used to obtain accident data and generate a display interface according to the accident data. The display interface is also used to receive command and dispatch information and send the command and dispatch information to the server so that the server adjusts the matching strategy according to the command and dispatch information.

[0010] Further, the data collection terminal includes a traffic data collection submodule, a weather data acquisition submodule and a road information collection submodule;

[0011] The traffic data collection submodule is used to collect current accident data and vehicle data, and transmit the accident data and vehicle data to the server terminal, wherein the accident data includes the accident handling time and the accident location;

[0012] The weather data acquisition submodule is used to obtain weather data;

[0013] The road information acquisition submodule is used to obtain the road information of the target area and perform 3D modeling based on the road information to obtain 3D road data. The road information includes road length and road speed limit.

[0014] Further, the server includes a data receiving submodule, a data verification and cleaning submodule, and a data processing submodule;

[0015] The data receiving submodule is used to receive vehicle data, weather data and 3D road data;

[0016] The data verification and cleaning submodule is used to verify and clean the acquired vehicle data, weather data and 3D road data to remove outliers and erroneous data;

[0017] The data processing submodule is used to determine a corresponding matching strategy according to at least one of the vehicle data, weather data and 3D road data.

[0018] Furthermore, according to the weather data, a corresponding matching strategy is determined, including:

[0019] Normalize the weather data to obtain the weather level corresponding to the weather data, and the weather levels include A, B and C;

[0020] Among them, the normalization formula is:

[0021]

[0022]

[0023] When the weather level is C, the matching strategy is determined to extend the green light time on the corresponding road section by the first time period;

[0024] When the weather level is A, the matching strategy is determined to shorten the green light time on the corresponding road section by a first time period.

[0025] Furthermore, a corresponding matching strategy is determined based on the vehicle data and the 3D road data, including:

[0026] The road length in the 3D road data is segmented to obtain the target road section;

[0027] The average speed of all vehicles that completely pass through the target road section within a time period is calculated based on vehicle data. The calculation formula used is:

[0028]

[0029] Wherein, V1 represents the average speed of vehicle 1 on the road section, V2 represents the average speed of vehicle 2 on the road section, V3 represents the average speed of vehicle 3 on the road section, and so on, VN represents the average speed of vehicle N on the road section;

[0030] Based on the average speed, the speed data is calculated, where the calculation formula used is:

[0031]

[0032] Among them, AV represents the average speed, and DS represents the speed limit of the road;

[0033] Based on the speed data, the congestion data is calculated, where the calculation formula used is:

[0034]

[0035] Congestion data = speed data * congestion index

[0036] Among them, VF represents the traffic volume, RC represents the road capacity;

[0037] According to the congestion data, the corresponding matching strategy is determined.

[0038] Furthermore, according to the congestion data, a corresponding matching strategy is determined, including:

[0039] The congestion data is processed to obtain the road congestion level, which includes smooth traffic, slow traffic, moderate congestion and severe congestion;

[0040] When the road congestion level is smooth, the matching strategy is determined to shorten the green light time on the corresponding road section by a first time period;

[0041] When the road congestion level is moderate congestion, the matching strategy is determined to extend the green light time on the corresponding road section by a first time period;

[0042] When the road congestion level is severe congestion, the matching strategy is determined to extend the green light time on the corresponding road section by a second time period;

[0043] The first time period is shorter than the second time period.

[0044] Furthermore, the accident data, vehicle data, weather data and 3D road data are processed and combined to obtain real-time traffic status information, including:

[0045] Based on the congestion data, road length and road speed limit, the user's estimated arrival time is calculated through a formula;

[0046] The formula is:

[0047] SAF = 1-exp(-k*congestion data)

[0048] AS=SL*SAF

[0049]

[0050] ATA=WT1+WT2+WT3+...+WTn

[0051] ETA=CT+ETT+ATA

[0052] Wherein, k is the adjustment factor coefficient, AS is the actual driving speed of the vehicle, ETT is the estimated driving time of the vehicle, ATA is the additional driving time of the vehicle, ETA is the estimated arrival time of the user, WT1 is the waiting time of the vehicle at traffic light 1, WT2 is the waiting time of the vehicle at traffic light 2, WT3 is the waiting time of the vehicle at traffic light 3, and so on, WTn is the waiting time of the vehicle at traffic light n; CT is the current time;

[0053] Get the road congestion level;

[0054] The real-time traffic status information including the user's estimated arrival time and the road congestion level is displayed on the first user terminal.

[0055] Furthermore, the matching strategy is adjusted according to the command and dispatch information, including:

[0056] When the command and dispatch information is in a processing state, the matching strategy is determined to extend the green light time on the corresponding road section by a third time period;

[0057] When the command and dispatch information is in a non-processing state, the matching strategy is determined to shorten the green light time on the corresponding road section by a fourth time period.

[0058] Furthermore, the first user terminal is further used for:

[0059] Obtain user terminal information;

[0060] The user terminal information is judged to obtain a first judgment result, where the first judgment result is used to indicate that the user of the first user terminal is a common user or a special user;

[0061] When the first judgment result indicates that the user of the first user terminal is a common user, the use authority of the corresponding user terminal is restricted.

[0062] In the second aspect, a method for traffic service management based on big data is provided, including:

[0063] Obtain accident data, vehicle data, weather data and 3D road data;

[0064] Determining a corresponding matching strategy according to at least one of the vehicle data, the weather data, and the 3D road data;

[0065] Process and combine accident data, vehicle data, weather data and 3D road data to obtain real-time traffic status information, including road congestion level and user's estimated arrival time;

[0066] A display interface is generated based on the accident data. The display interface is also used to receive command and dispatch information, and the command and dispatch information is used to adjust the matching strategy.

[0067] The technical solution provided by the present application includes at least the following technical effects: by providing a traffic service management system based on big data, the system includes a data acquisition terminal, a server, a first user terminal and a second user terminal; the data acquisition terminal is used to obtain accident data, vehicle data, weather data and 3D road data; the server is used to determine the corresponding matching strategy according to at least one of the vehicle data, weather data and 3D road data, and the matching strategy is used to characterize the adjustment of the green light time in the road; the first user terminal is used to process and combine the accident data, vehicle data, weather data and 3D road data to obtain real-time traffic status information, and the traffic status information includes the road congestion level and the user's estimated arrival time; the second user terminal is used to obtain accident data and generate a display interface according to the accident data, and the display interface is also used to receive command and dispatch information, and send the command and dispatch information to the server, so that the server adjusts the matching strategy according to the command and dispatch information, thereby achieving the technical effect of effectively improving the efficiency of traffic management.

[0068] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 This is a structural diagram of a traffic service management system based on big data in one embodiment of the present application;

[0071] Figure 2 This is a flow chart of a transportation service management method based on big data in one embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the application, so the present application is not limited by the specific embodiments disclosed below.

[0073] like Figure 1 As shown, in one embodiment of the present application, a traffic service management system based on big data is provided, the system includes a data collection terminal 101, a server 102, a first user terminal 103 and a second user terminal 104;

[0074] The data collection terminal 101 is used to obtain accident data, vehicle data, weather data and 3D road data;

[0075] The server 102 is used to determine a corresponding matching strategy according to at least one of the vehicle data, the weather data and the 3D road data, where the matching strategy is used to characterize and adjust the green light time in the road;

[0076] The first user terminal 103 is used to process and combine the accident data, vehicle data, weather data and 3D road data to obtain real-time traffic status information, where the traffic status information includes the road congestion level and the user's estimated arrival time;

[0077] The second user terminal 104 is used to obtain accident data and generate a display interface according to the accident data. The display interface is also used to receive command and dispatch information and send the command and dispatch information to the server so that the server adjusts the matching strategy according to the command and dispatch information.

[0078] Specifically, the first user terminal may be represented as a device used by a vehicle driver or a mobile device used by a traffic management personnel when going out; the second user terminal may be represented as a device used by a traffic management department in an office area.

[0079] A traffic service management system 100 based on big data is provided, which includes a data acquisition terminal 101, a server 102, a first user terminal 103 and a second user terminal 104; the data acquisition terminal 101 is used to obtain accident data, vehicle data, weather data and 3D road data; the server 102 is used to determine a corresponding matching strategy according to at least one of the vehicle data, weather data and 3D road data, and the matching strategy is used to characterize the adjustment of the green light time in the road; the first user terminal 103 is used to process and combine the accident data, vehicle data, weather data and 3D road data to obtain real-time traffic status information, and the traffic status information includes the road congestion level and the user's estimated arrival time; the second user terminal 104 is used to obtain accident data and generate a display interface according to the accident data, and the display interface is also used to receive command and dispatch information, and send the command and dispatch information to the server, so that the server adjusts the matching strategy according to the command and dispatch information, thereby achieving a technical effect that can effectively improve the efficiency of traffic management.

[0080] Further, the data collection terminal includes a traffic data collection submodule, a weather data acquisition submodule and a road information collection submodule;

[0081] (1) The traffic data collection submodule is used to collect current accident data and vehicle data, and transmit the accident data and vehicle data to the server terminal, wherein the accident data includes the accident handling time and the accident location;

[0082] (2) The weather data acquisition submodule is used to acquire weather data;

[0083] (3) The road information acquisition submodule is used to obtain the road information of the target area and perform 3D modeling based on the road information to obtain 3D road data. The road information includes road length and road speed limit.

[0084] Specifically, the traffic data collection submodule includes geomagnetic sensors, loop coil sensors, microwave radar sensors, video sensors and ultrasonic sensors; the weather data acquisition submodule can obtain weather data from the weather statistics device set up for each road information, or through the network interface opened by the meteorological department, where the weather data includes relevant data on factors such as rainfall value, fog value, snow value and geological disasters;

[0085] Among them, the road information collection submodule first collects data on the road, including field measurement of the road's topography and geometric parameters, and the use of drones, cameras and other equipment to collect images and video information of the road, and then organizes and classifies the obtained road data and establishes a data archive. Then, a terrain model is constructed based on the elevation data obtained from the field measurement, and the road geometry is constructed using 3D modeling software using the road geometry parameters. The extracted road photo texture information is mapped to the model, and then the model is optimized, and ancillary facilities such as road markings are added to set lighting and materials. The model is simplified to reduce complexity, and finally the model is verified. The accuracy and completeness are checked by comparing with field photos and measurement data, and the verified model is processed to obtain 3D road data.

[0086] Further, the server includes a data receiving submodule, a data verification and cleaning submodule, and a data processing submodule;

[0087] The data receiving submodule is used to receive vehicle data, weather data and 3D road data;

[0088] The data verification and cleaning submodule is used to verify and clean the acquired vehicle data, weather data and 3D road data to remove outliers and erroneous data;

[0089] The data processing submodule is used to determine a corresponding matching strategy according to at least one of the vehicle data, weather data and 3D road data.

[0090] Specifically, the data verification and cleaning submodule verifies and cleans vehicle data, weather data, and 3D road data. The methods for removing outliers and erroneous data include: on the one hand, programming tools and algorithms can be used for automated processing, such as using data filtering algorithms to set specific conditions to filter out data that does not meet the requirements, such as removing values ​​that exceed a reasonable range, and clustering algorithms can be used to group similar data points into one category, so that isolated outliers can be identified and processed. On the other hand, database management software can be used to find incomplete or erroneous data records through query statements and delete or correct them.

[0091] Furthermore, according to the weather data, a corresponding matching strategy is determined, including:

[0092] Normalize the weather data to obtain the weather level corresponding to the weather data, and the weather levels include A, B and C;

[0093] Among them, the normalization formula is:

[0094]

[0095]

[0096] When the weather level is C, the matching strategy is determined to extend the green light time on the corresponding road section by the first time period;

[0097] When the weather level is A, the matching strategy is determined to shorten the green light time on the corresponding road section by a first time period.

[0098] Specifically, the normalization of weather data includes: collecting data on factors such as rainfall, fog, snow, and geological disasters, and organizing the data of each factor into a corresponding data set, determining the value range for each weather factor, and then using the normalization formula to convert the actual value of each factor into a normalized value between 0 and 1. The normalization formula is as follows:

[0099]

[0100] Then, the mean and standard deviation of each weather factor are calculated, and the value of each weather factor is standardized to a value with a mean of 0 and a standard deviation of 1 using a standardized formula. The standardized formula is as follows:

[0101]

[0102] Next, the weight of each weather factor is determined based on the degree of its impact on traffic and other aspects. Finally, each normalized weather factor value is multiplied by its corresponding weight, and then the sum is obtained to obtain a comprehensive value. Different weather levels are divided according to the range of the comprehensive value. In this embodiment, the weather levels are divided into three types: A, B, and C.

[0103] Different weather factors have different dimensions and value ranges. Normalization processing unifies these data to the same scale, making different factors comparable. It can also fully reflect the overall weather conditions and provide a basis for decision-making for traffic management departments.

[0104] Furthermore, a corresponding matching strategy is determined based on the vehicle data and the 3D road data, including:

[0105] (1) Segment the road length in the 3D road data to obtain the target road segment;

[0106] (2) Calculate the average speed of all vehicles that completely pass through the target road section within a time period based on the vehicle data, where the calculation formula used is:

[0107]

[0108] Among them, V1 represents the average speed of vehicle 1 on the road section, V2 represents the average speed of vehicle 2 on the road section, V3 represents the average speed of vehicle 3 on the road section, and so on, VN represents the average speed of vehicle N on the road section.

[0109] (3) Based on the average speed, the speed data is calculated, where the calculation formula used is:

[0110]

[0111] Among them, AV represents the average speed, and DS represents the speed limit of the road;

[0112] (4) Based on the speed data, congestion data is calculated, where the calculation formula used is:

[0113]

[0114] Congestion data = speed data * congestion index

[0115] Among them, VF represents the traffic volume, RC represents the road capacity;

[0116] (5) Determine the corresponding matching strategy based on the congestion data.

[0117] Specifically, the entire road in the 3D road data is divided into several sections, and one of the sections is selected as the target section. Then, the average speed AV of all vehicles that completely pass through the section in a certain period of time is calculated. The calculation formula used is as follows:

[0118]

[0119] Among them, V1 represents the average speed of vehicle 1 on the road section, V2 represents the average speed of vehicle 2 on the road section, V3 represents the average speed of vehicle 3 on the road section, and so on, VN represents the average speed of vehicle N on the road section.

[0120] Then, the speed data and congestion data are calculated in turn by the following formulas:

[0121]

[0122]

[0123] Congestion data = speed data * congestion index

[0124] Among them, AV represents the average speed, DS represents the speed limit of the road, VF represents the traffic flow, and RC represents the road capacity.

[0125] Furthermore, according to the congestion data, a corresponding matching strategy is determined, including:

[0126] The congestion data is processed to obtain the road congestion level, which includes smooth traffic, slow traffic, moderate congestion and severe congestion;

[0127] When the road congestion level is smooth, the matching strategy is determined to shorten the green light time on the corresponding road section by a first time period;

[0128] When the road congestion level is moderate congestion, the matching strategy is determined to extend the green light time on the corresponding road section by a first time period;

[0129] When the road congestion level is severe congestion, the matching strategy is determined to extend the green light time on the corresponding road section by a second time period;

[0130] The first time period is shorter than the second time period.

[0131] Specifically, the congestion data is processed to obtain the road congestion level, and its specific contents include: according to relevant traffic management regulations, corresponding congestion thresholds are set in different sections, such as the thresholds can be set as: smooth: congestion data ≤ 1.0; slow: 1.0 < congestion data ≤ 2.0; moderate congestion: 2.0 < congestion data ≤ 2.5; severe congestion: congestion data > 2.5; then the calculated congestion data is substituted into the set threshold for comparison to obtain the road congestion level. When the road congestion level is smooth, the matching strategy is determined to shorten the green light time on the corresponding section by a first time period; when the road congestion level is moderate congestion, the matching strategy is determined to extend the green light time on the corresponding section by a first time period; when the road congestion level is severe congestion, the matching strategy is determined to extend the green light time on the corresponding section by a second time period, wherein the first time period is less than the second time period, and both the first time period and the second time period can be set according to actual conditions, and are not fixed time periods.

[0132] Furthermore, the accident data, vehicle data, weather data and 3D road data are processed and combined to obtain real-time traffic status information, including:

[0133] (1) Based on congestion data, road length, and road speed limit, the user's estimated arrival time is calculated using a formula;

[0134] The formula is:

[0135] SAF = 1-exp(-k*congestion data)

[0136] AS=SL*SAF

[0137]

[0138] ATA=WT1+WT2+WT3+...+WTn

[0139] ETA=CT+ETT+ATA

[0140] Wherein, k is the adjustment factor coefficient, AS is the actual driving speed of the vehicle, ETT is the estimated driving time of the vehicle, ATA is the additional driving time of the vehicle, ETA is the estimated arrival time of the user, WT1 is the waiting time of the vehicle at traffic light 1, WT2 is the waiting time of the vehicle at traffic light 2, WT3 is the waiting time of the vehicle at traffic light 3, and so on, WTn is the waiting time of the vehicle at traffic light n; CT is the current time;

[0141] (2) Obtaining road congestion level;

[0142] (3) Displaying the real-time traffic status information including the user's estimated arrival time and the road congestion level on the first user terminal.

[0143] Specifically, the road length and the road speed limit are obtained, and then the vehicle speed is adjusted according to the congestion data, and the speed adjustment factor SAF is calculated. Then, the actual driving speed AS is calculated based on the speed adjustment factor, and then the estimated driving time ETT is calculated based on the actual driving speed AS. Then, the user's estimated arrival time is corrected according to additional factors, that is, the additional time ATA is calculated. Finally, the user's estimated arrival time EAT is calculated comprehensively, where the specific calculation process is as follows:

[0144] SAF = 1-exp(-k*congestion data)

[0145] AS=SL*SAF

[0146]

[0147] ATA=WT1+WT2+WT3+...+WTn

[0148] ETA=CT+ETT+ATA

[0149] Among them, k is the adjustment factor coefficient, AS is the actual driving speed of the vehicle, ETT is the estimated driving time of the vehicle, ATA is the additional driving time of the vehicle, EAT is the estimated arrival time of the user, WT1 represents the waiting time of traffic light 1 of the vehicle, WT2 is the waiting time of traffic light 2 of the vehicle, WT3 is the waiting time of traffic light 3 of the vehicle, and so on, WTn is the waiting time of traffic light n of the vehicle; CT represents the current time.

[0150] Next, the road congestion level is obtained, and the obtained road congestion level and the calculated estimated arrival time of the user are displayed on the first user terminal.

[0151] Furthermore, the matching strategy is adjusted according to the command and dispatch information, including:

[0152] (1) When the command and dispatch information is in a processing state, the matching strategy is determined to extend the green light time on the corresponding road section by a third time period;

[0153] (2) When the command and dispatch information is in a non-processing state, the matching strategy is determined to shorten the green light time on the corresponding road section by a fourth time period.

[0154] Specifically, the command and dispatch information is adjusted and confirmed by the traffic management personnel, that is, in addition to some preset instructions, the command and dispatch information can also be modified by the traffic management personnel according to the actual situation. In one embodiment, the command and dispatch information includes two states: processing state and non-processing state. When the command and dispatch information is in the processing state, the matching strategy is determined to extend the green light time on the corresponding road section by a third time period; when the command and dispatch information is in the non-processing state, the matching strategy is determined to shorten the green light time on the corresponding road section by a fourth time period. Among them, the third time period and the fourth time period are not fixed values ​​and can be adjusted by the traffic management personnel.

[0155] Furthermore, the first user terminal is further used for:

[0156] (1) Obtain user terminal information;

[0157] (2) judging the user terminal information to obtain a first judgment result, where the first judgment result is used to indicate that the user of the first user terminal is a common user or a special user;

[0158] (3) When the first judgment result indicates that the user of the first user terminal is a common user, the use authority of the corresponding user terminal is restricted.

[0159] Specifically, since the user of the second user terminal is a traffic management personnel, the usage rights of the second user terminal are wider than those of the first user terminal, wherein the specific usage rights include querying the detailed driving track of a specific vehicle; querying the personal violation record of a specific driver; conducting in-depth analysis of traffic flow in some areas and generating detailed reports; and obtaining real-time monitoring data of sensitive traffic areas. In order to enable traffic management personnel to use relevant rights to perform traffic management work when they are out, the users of the first user terminal are divided into ordinary users and special users. When the user of the first user terminal is determined to be an ordinary user, the usage rights of the corresponding user terminal are restricted to prevent ordinary users from excessively interfering with or abusing the traffic management system, thereby ensuring the stable operation and efficient service of the system.

[0160] Second, as Figure 2 As shown, a method for traffic service management based on big data is provided, including:

[0161] S201: Acquire accident data, vehicle data, weather data, and 3D road data;

[0162] S202: Determine a corresponding matching strategy according to at least one of the vehicle data, the weather data, and the 3D road data;

[0163] S203: Processing and combining the accident data, vehicle data, weather data and 3D road data to obtain real-time traffic status information, where the traffic status information includes a road congestion level and an estimated arrival time of the user;

[0164] S204: Generate a display interface according to the accident data. The display interface is also used to receive command and dispatch information. The command and dispatch information is used to adjust the matching strategy.

[0165] Further, the data collection terminal includes a traffic data collection submodule, a weather data acquisition submodule and a road information collection submodule;

[0166] The traffic data collection submodule is used to collect current accident data and vehicle data, and transmit the accident data and vehicle data to the server terminal, wherein the accident data includes the accident handling time and the accident location;

[0167] The weather data acquisition submodule is used to obtain weather data;

[0168] The road information acquisition submodule is used to obtain the road information of the target area and perform 3D modeling based on the road information to obtain 3D road data. The road information includes road length and road speed limit.

[0169] Further, the server includes a data receiving submodule, a data verification and cleaning submodule, and a data processing submodule;

[0170] The data receiving submodule is used to receive vehicle data, weather data and 3D road data;

[0171] The data verification and cleaning submodule is used to verify and clean the acquired vehicle data, weather data and 3D road data to remove outliers and erroneous data;

[0172] The data processing submodule is used to determine a corresponding matching strategy according to at least one of the vehicle data, weather data and 3D road data.

[0173] Furthermore, according to the weather data, a corresponding matching strategy is determined, including:

[0174] Normalize the weather data to obtain the weather level corresponding to the weather data, and the weather levels include A, B and C;

[0175] Among them, the normalization formula is:

[0176]

[0177]

[0178] When the weather level is C, the matching strategy is determined to extend the green light time on the corresponding road section by the first time period;

[0179] When the weather level is A, the matching strategy is determined to shorten the green light time on the corresponding road section by a first time period.

[0180] Furthermore, a corresponding matching strategy is determined based on the vehicle data and the 3D road data, including:

[0181] (1) Segment the road length in the 3D road data to obtain the target road segment;

[0182] (2) Calculate the average speed of all vehicles that completely pass through the target road section within a time period based on the vehicle data, where the calculation formula used is:

[0183]

[0184] Wherein, V1 represents the average speed of vehicle 1 on the road section, V2 represents the average speed of vehicle 2 on the road section, V3 represents the average speed of vehicle 3 on the road section, and so on, VN represents the average speed of vehicle N on the road section;

[0185] (3) Based on the average speed, the speed data is calculated, where the calculation formula used is:

[0186]

[0187] Among them, AV represents the average speed, and DS represents the speed limit of the road;

[0188] (4) Based on the speed data, congestion data is calculated, where the calculation formula used is:

[0189]

[0190] Congestion data = speed data * congestion index

[0191] Among them, VF represents the traffic volume, RC represents the road capacity;

[0192] (5) Determine the corresponding matching strategy based on the congestion data.

[0193] Furthermore, according to the congestion data, a corresponding matching strategy is determined, including:

[0194] The congestion data is processed to obtain the road congestion level, which includes smooth traffic, slow traffic, moderate congestion and severe congestion;

[0195] When the road congestion level is smooth, the matching strategy is determined to shorten the green light time on the corresponding road section by a first time period;

[0196] When the road congestion level is moderate congestion, the matching strategy is determined to extend the green light time on the corresponding road section by a first time period;

[0197] When the road congestion level is severe congestion, the matching strategy is determined to extend the green light time on the corresponding road section by a second time period;

[0198] The first time period is shorter than the second time period.

[0199] Furthermore, the accident data, vehicle data, weather data and 3D road data are processed and combined to obtain real-time traffic status information, including:

[0200] (1) Based on congestion data, road length, and road speed limit, the user's estimated arrival time is calculated using a formula;

[0201] The formula is:

[0202] SAF = 1-exp(-k*congestion data)

[0203] AS=SL*SAF

[0204]

[0205] ATA=WT1+WT2+WT3+...+WTn

[0206] ETA=CT+ETT+ATA

[0207] Wherein, k is the adjustment factor coefficient, AS is the actual driving speed of the vehicle, ETT is the estimated driving time of the vehicle, ATA is the additional driving time of the vehicle, ETA is the estimated arrival time of the user, WT1 is the waiting time of the vehicle at traffic light 1, WT2 is the waiting time of the vehicle at traffic light 2, WT3 is the waiting time of the vehicle at traffic light 3, and so on, WTn is the waiting time of the vehicle at traffic light n; CT is the current time;

[0208] (2) Obtaining road congestion level;

[0209] (3) Displaying the real-time traffic status information including the user's estimated arrival time and the road congestion level on the first user terminal.

[0210] Furthermore, the matching strategy is adjusted according to the command and dispatch information, including:

[0211] When the command and dispatch information is in a processing state, the matching strategy is determined to extend the green light time on the corresponding road section by a third time period;

[0212] When the command and dispatch information is in a non-processing state, the matching strategy is determined to shorten the green light time on the corresponding road section by a fourth time period.

[0213] Furthermore, the first user terminal is further used for:

[0214] Obtain user terminal information;

[0215] The user terminal information is judged to obtain a first judgment result, where the first judgment result is used to indicate that the user of the first user terminal is a common user or a special user;

[0216] When the first judgment result indicates that the user of the first user terminal is a common user, the use authority of the corresponding user terminal is restricted.

[0217] The technical solution provided by this application includes at least the following beneficial effects: improving traffic safety and efficiency, reducing the accident rate, optimizing traffic resource allocation and helping traffic management personnel to quickly handle accidents, and providing route planning for travelers, achieving comprehensive and accurate decision-making, real-time control of traffic status, information sharing, and improving traffic safety and efficiency.

[0218] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0219] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A traffic service management system based on big data, characterized in that: The system includes a data acquisition terminal, a server, a first user terminal and a second user terminal; The data acquisition terminal is used to obtain accident data, vehicle data, weather data and 3D road data; The server is used to determine a corresponding matching strategy according to at least one of the vehicle data, the weather data and the 3D road data, wherein the matching strategy is used to characterize and adjust the green light time in the road; The first user terminal is used to process and combine the accident data, the vehicle data, the weather data and the 3D road data to obtain real-time traffic status information, wherein the traffic status information includes a road congestion level and an estimated arrival time of a user; The second user terminal is used to obtain accident data and generate a display interface according to the accident data. The display interface is also used to receive command and dispatch information and send the command and dispatch information to the server so that the server adjusts the matching strategy according to the command and dispatch information.

2. A traffic service management system based on big data according to claim 1, characterized in that: The data collection terminal includes a traffic data collection submodule, a weather data acquisition submodule and a road information collection submodule; The traffic data collection submodule is used to collect the current accident data and vehicle data, and transmit the accident data and vehicle data to the server terminal, wherein the accident data includes the accident handling time and the accident occurrence location; The weather data acquisition submodule is used to acquire the weather data; The road information acquisition submodule is used to obtain road information of a target area, and perform 3D modeling according to the road information to obtain the 3D road data, wherein the road information includes road length and road speed limit value.

3. A traffic service management system based on big data according to claim 1, characterized in that: The server includes a data receiving submodule, a data verification and cleaning submodule, and a data processing submodule; The data receiving submodule is used to receive the vehicle data, the weather data and the 3D road data; The data verification and cleaning submodule is used to verify and clean the acquired vehicle data, weather data and 3D road data to remove abnormal values ​​and erroneous data; The data processing submodule is used to determine a corresponding matching strategy according to at least one of the vehicle data, the weather data and the 3D road data.

4. A traffic service management system based on big data according to claim 3, characterized in that: Determining a corresponding matching strategy based on weather data includes: Normalizing the weather data to obtain weather grades corresponding to the weather data, where the weather grades include A, B and C; Among them, the normalization formula is: When the weather level is C, the matching strategy is determined to extend the green light time on the corresponding road section by a first time period; When the weather level is A, the matching strategy is determined to shorten the green light time on the corresponding road section by a first time period.

5. A traffic service management system based on big data according to claim 4, characterized in that: Determining a corresponding matching strategy based on the vehicle data and the 3D road data includes: Segmenting the road length in the 3D road data to obtain a target road segment; The average speed of all vehicles that completely pass through the target road section within a time period is calculated based on the vehicle data, wherein the calculation formula used is: Wherein, V1 represents the average speed of vehicle 1 on the road section, V2 represents the average speed of vehicle 2 on the road section, V3 represents the average speed of vehicle 3 on the road section, and so on, VN represents the average speed of vehicle N on the road section; Based on the average speed, the speed data is calculated, wherein the calculation formula used is: Among them, AV represents the average speed, and DS represents the speed limit of the road; Based on the speed data, congestion data is calculated, wherein the calculation formula used is: Congestion data = speed data * congestion index Among them, VF represents the traffic volume, RC represents the road capacity; A corresponding matching strategy is determined according to the congestion data.

6. A traffic service management system based on big data according to claim 5, characterized in that: Determining a corresponding matching strategy according to the congestion data includes: Processing the congestion data to obtain a road congestion level, wherein the road congestion level includes smooth traffic, slow traffic, moderate congestion and severe congestion; When the road congestion level is smooth, the matching strategy is determined to shorten the green light time on the corresponding road section by a first time period; When the road congestion level is moderate congestion, the matching strategy is determined to extend the green light time on the corresponding road section by a first time period; When the road congestion level is severe congestion, the matching strategy is determined to extend the green light time on the corresponding road section by a second time period; The first time period is shorter than the second time period.

7. A traffic service management system based on big data according to claim 1, characterized in that: The processing and combining of the accident data, the vehicle data, the weather data and the 3D road data to obtain real-time traffic status information includes: Based on the congestion data, the road length and the road speed limit value, the estimated arrival time of the user is calculated by a formula; Wherein, the formula is: SAF = 1-exp(-k*congestion data) AS=SL*SAF ATA=WT1+WT2+WT3+...+WTn ETA=CT+ETT+ATA Wherein, k is the adjustment factor coefficient, AS is the actual driving speed of the vehicle, ETT is the estimated driving time of the vehicle, ATA is the additional driving time of the vehicle, ETA is the estimated arrival time of the user, WT1 is the waiting time of the vehicle at traffic light 1, WT2 is the waiting time of the vehicle at traffic light 2, WT3 is the waiting time of the vehicle at traffic light 3, and so on, WTn is the waiting time of the vehicle at traffic light n; CT is the current time; Obtaining the road congestion level; The real-time traffic status information including the user's estimated arrival time and the road congestion level is displayed on the first user terminal.

8. A traffic service management system based on big data according to claim 1, characterized in that: The adjusting the matching strategy according to the command and dispatch information includes: When the command and dispatch information is in a processing state, the matching strategy is determined to extend the green light time on the corresponding road section by a third time period; When the command and dispatch information is in a non-processing state, the matching strategy is determined to shorten the green light time on the corresponding road section by a fourth time period.

9. A traffic service management system based on big data according to claim 1, characterized in that: The first user terminal is further configured to: Obtain user terminal information; The user terminal information is judged to obtain a first judgment result, where the first judgment result is used to indicate that the user of the first user terminal is a common user or a special user; When the first judgment result indicates that the user of the first user terminal is a common user, the use authority of the corresponding user terminal is restricted.

10. A method for traffic service management based on big data, characterized in that: include: Obtain accident data, vehicle data, weather data and 3D road data; Determining a corresponding matching strategy according to at least one of the vehicle data, the weather data, and the 3D road data; Processing and combining the accident data, the vehicle data, the weather data and the 3D road data to obtain real-time traffic status information, wherein the traffic status information includes a road congestion level and an estimated arrival time of a user; A display interface is generated according to the accident data, and the display interface is also used to receive command and dispatch information, and the command and dispatch information is used to adjust the matching strategy.

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