Multi-dimensional road network data integrated supervision system and method based on traffic service data

Abstract

The invention discloses a multi-dimensional road network data integrated supervision system and method based on traffic service data, and relates to the technical field of multi-dimensional road network data supervision, and the method comprises the steps: constructing a road network supervision cloud platform, obtaining the historical vehicle passing record of a road section, obtaining the historical vehicle passing rate data from the historical vehicle passing record, and carrying out the management of the historical vehicle passing rate data. Performing traffic abnormality evaluation on road sections in the city; analyzing the abnormal influence degree of the traffic flow on the traffic of the characteristic road section to obtain characteristic vehicle traffic data; acquiring historical traffic data of historical public transport vehicles in the city, constructing a vehicle traffic prediction model in combination with the historical vehicle traffic data of the feature road section, and predicting the vehicle traffic condition of the feature road section in the current period; and acquiring passing data of public transport vehicles in the city in the current period, and performing intelligent supervision on the traffic of the feature road section in combination with the feature vehicle passing data of the feature road section.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-dimensional road network data supervision, and in particular to a multi-dimensional road network data integrated supervision system and method based on traffic business data. Background Art

[0002] Traffic business data refers to various types of data related to the transportation system. These data can be used to analyze, manage and optimize transportation. The road network refers to a network system composed of various types of interconnected transportation roads. It is an important part of transportation. Based on traffic business data, road network data can be better supervised.

[0003] The traffic network in the city is composed of different roads connected to each other. The widths, road conditions and vehicle traffic requirements of different roads are all different. Public transportation vehicles and private cars need to pass through the urban traffic network. The traffic requirements of these vehicles are different. For example, public transportation vehicles basically pass through according to pre-set routes within fixed times, while private cars do not have this feature in terms of travel time and routes. However, in actual traffic data supervision, urban traffic is often supervised simply based on vehicle flow, but the impact of different roads and vehicles on traffic is rarely considered. This makes the prediction and analysis of traffic conditions in the city inaccurate, and will not only cause traffic congestion, but may also lead to traffic safety accidents. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-dimensional road network data integrated supervision system and method based on traffic business data to solve the problems raised in the prior art.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a multi-dimensional road network data integrated supervision method based on traffic business data, the method comprising: Step S100: construct a road network supervision cloud platform, obtain historical traffic accident records of road sections in the city, obtain historical vehicle traffic records of road sections, obtain historical vehicle traffic speed data from historical vehicle traffic records, conduct traffic anomaly assessment on road sections in the city, and obtain characteristic road sections; Step S200: Obtain historical vehicle traffic records in the characteristic road section, obtain historical vehicle traffic data from the historical vehicle traffic records, analyze the abnormal impact of vehicle flow on the traffic of the characteristic road section, and obtain characteristic vehicle traffic data; Step S300: Obtain historical traffic data of the characteristic road section, obtain historical traffic data of public transportation vehicles in the city, and build a vehicle traffic prediction model based on the historical vehicle traffic data of the characteristic road section to predict the vehicle traffic conditions of the characteristic road section in the current cycle to obtain vehicle traffic prediction data; Step S400: Obtain vehicle traffic prediction data for characteristic road sections in the current cycle, obtain traffic data of public transportation vehicles in the city in the current cycle, and combine the characteristic vehicle traffic data of characteristic road sections to perform intelligent supervision on the traffic of characteristic road sections.

[0006] Furthermore, step S100 includes: Step S101: Obtain historical traffic accident records of road sections within the city, and extract historical traffic accident data from the historical traffic accident records, wherein the historical traffic accident data includes the total number of vehicles involved in traffic accidents in the historical traffic accident records; Step S102: vehicles passing through the road section are collected every historical period to obtain historical vehicle traffic records of the road section, and historical vehicle traffic rate data are extracted from the historical vehicle traffic records. The historical vehicle traffic rate data includes an average rate of vehicles passing through the road section in the historical period; Step S103: evaluating the traffic anomaly of each road section in the city, wherein the specific evaluation process of the traffic anomaly of the ath road section in the city includes: Obtain several historical traffic accident records of the a-th road section in each historical period, and calculate the traffic accident characteristic value of the a-th road section in each historical period, where the traffic accident characteristic value P of the a-th road section in the b-th historical period is a,b : , Where j represents the total number of historical traffic accident records of the a-th road section in the b-th historical period; M a b,x It is expressed as the total number of vehicles involved in traffic accidents in the xth historical traffic accident record in the bth historical period on the ath road section; Get the maximum value P of the traffic accident characteristic value of each road section in each historical period max , minimum value P min ; Step S104: Obtain the preset maximum vehicle speed limit v for the ath road section a , calculate the traffic anomaly score E of the a-th road segment a : , Among them, P a,i It is represented as the traffic accident characteristic value of the a-th road section in the i-th historical period; v a,i It is represented as the average speed of vehicles passing through the a-th road section in the i-th historical period; n is represented as the total number of each historical period; β1 is represented as the preset first characteristic proportional coefficient; β2 is represented as the preset second characteristic proportional coefficient; β1+β2=1; Step S105: When the traffic anomaly score E a If the traffic anomaly score is greater than the preset traffic anomaly score threshold, the traffic anomaly of the a-th road section is determined and the a-th road section is recorded as a characteristic road section.

[0007] Further, step S200 includes: Step S201: Obtain historical vehicle traffic records of a characteristic road section, and calculate characteristic traffic congestion values ​​of a number of historical vehicle traffic records, wherein the characteristic traffic congestion value F of the cth historical vehicle traffic record of the characteristic road section is c : , Where v' represents the maximum speed limit of the vehicle on the characteristic road section; v' c It is expressed as the average speed of the passing vehicles in the cth historical vehicle passing record; Step S202: When the characteristic traffic congestion value F c When the value is greater than a preset characteristic traffic congestion threshold, it is determined that there is vehicle congestion on the historical characteristic road section in the c-th historical vehicle traffic record, and the c-th historical vehicle traffic record is recorded as the characteristic historical vehicle traffic record of the characteristic road section; Step S203: Obtain historical vehicle traffic data from historical vehicle traffic records, the historical vehicle traffic data being the total number of vehicles passing through the historical vehicle traffic records, obtaining a number of characteristic historical vehicle traffic records in the characteristic road section, and obtaining a minimum value M of the total number of vehicles passing through the characteristic road section in the number of characteristic historical vehicle traffic records. min , obtain the maximum value M' of the total number of vehicles passing through several historical vehicle traffic records in the characteristic road section max , when the maximum value M' max Greater than the minimum value M min , the maximum value M' max As the characteristic vehicle threshold M' of the characteristic road section; On the contrary, when the maximum value M' max Less than or equal to the minimum value M min , the minimum value M min As the characteristic vehicle threshold M' of the characteristic road section, the characteristic vehicle traffic data of the characteristic road section is obtained.

[0008] Furthermore, step S300 includes: Step S301: acquiring historical traffic data of characteristic road sections in each historical period, wherein the historical traffic data includes data corresponding to several traffic characteristic indicators of the characteristic road sections; Step S302: Acquire historical traffic data of historical public transport vehicles in the city, the historical traffic data including the routes of historical public transport vehicles in the historical period, acquire historical vehicle traffic data of characteristic sections, and when the routes of historical public transport vehicles include characteristic sections, record the historical public transport vehicles as marked historical public transport vehicles of the characteristic sections; Step S303: Obtain historical vehicle traffic data of the characteristic road section, obtain the total number of vehicles passing through the characteristic road section in each historical period from the historical vehicle traffic data, and calculate the characteristic value of vehicles passing through the characteristic road section in each historical period, wherein the characteristic value K of vehicles passing through the characteristic road section in the gth historical period is g =M g -γ g , M g It is represented by the total number of vehicles passing through the characteristic road section in the gth historical period, γ g It is represented by the total number of historical public transport vehicles that have passed through the characteristic road section in the gth historical period; Step S304: constructing a vehicle traffic prediction model, taking several traffic characteristic indicators of the characteristic road section as input data of the vehicle traffic prediction model, taking characteristic values ​​of vehicles passing through the characteristic road section as target data, dividing historical traffic data in each historical period of the characteristic road section and characteristic values ​​of vehicles passing through into a training set and a test set, and training and testing the vehicle traffic prediction model; Step S305: Obtain the average value Y of the characteristic values ​​of vehicles passing through each historical period of the characteristic road section △ , obtain the vehicle traffic prediction model, predict the characteristic values ​​of vehicles passing through the characteristic road section in several historical periods, and calculate the model prediction score R of the vehicle traffic prediction model: , Among them, Y z It is represented as a characteristic road section, the characteristic value of the vehicles passing through in the zth historical cycle among several historical cycles; Y △,z It is represented by the vehicle traffic prediction model, which is the predicted value of the characteristic value of the passing vehicles in the zth historical period of the characteristic road section; q is represented by the total number of several historical periods; Step S306: When the model prediction score of the vehicle traffic prediction model is greater than the preset model prediction score, the vehicle traffic prediction model is determined to have prediction capability, data corresponding to several traffic characteristic indicators of the characteristic road section in the current cycle are obtained, and the vehicle traffic prediction model is used to predict the vehicle traffic condition of the characteristic road section in the current cycle to obtain vehicle traffic prediction data; In the above steps, when constructing the vehicle traffic prediction model, the target data of the model is processed, that is, public transportation vehicles are removed from the vehicles passing through the characteristic road section. This is because in real life, public transportation vehicles have fixed travel times and routes, which generally do not change due to the conditions of the road section. Therefore, using the characteristic values ​​of vehicles passing through the road section as target data can more accurately predict the traffic conditions of the road section, thereby making the formulated management strategy more scientific and effective.

[0009] Furthermore, step S400 includes: Step S401: obtaining vehicle traffic prediction data of a characteristic road section in a current cycle, and obtaining a predicted value of a characteristic value of a vehicle passing through the characteristic road section in the current cycle from the vehicle traffic prediction data; Step S402: Acquire the traffic data of the public transportation vehicles in the current cycle, the traffic data includes the travel time and the travel route of the public transportation vehicles. When the travel route of the public transportation vehicles includes a characteristic section, and the travel time of the public transportation vehicles in the characteristic section, within the current cycle, the public transportation vehicles are recorded as the characteristic section, and a number of characteristic public transportation vehicles in the current cycle; Step S403: Acquire the total number of characteristic public transport vehicles in the characteristic road section in the current cycle. When the predicted value of the characteristic value of the passing vehicle in the characteristic road section in the current cycle and the sum of the total number of characteristic public transport vehicles are less than the characteristic vehicle threshold in the characteristic vehicle passing data in the characteristic road section, it is determined that the traffic on the characteristic road section in the current cycle is normal; Step S404: When the total number of characteristic road sections in the current cycle is greater than the characteristic vehicle threshold, the traffic abnormality of the characteristic road sections in the current cycle is determined, and the vehicles containing the characteristic road sections in the navigation routes within the city in the current cycle are obtained. A prompt is sent to the user of the vehicle through the navigation system, and a navigation route update recommendation is made to intelligently supervise the traffic on the characteristic road sections.

[0010] In order to better implement the above steps, a multi-dimensional road network data integrated supervision system based on traffic business data is proposed. The system includes a characteristic road section module, a characteristic vehicle traffic data module, a vehicle traffic prediction data module, and an intelligent supervision module; The characteristic road section module is used to evaluate the traffic anomaly of the road sections in the city and obtain the characteristic road sections; The characteristic vehicle traffic data module is used to analyze the abnormal impact of vehicle flow on the traffic of characteristic road sections and obtain characteristic vehicle traffic data; The vehicle traffic prediction data module is used to obtain the historical traffic data of the characteristic road section, obtain the historical traffic data of the historical public transportation vehicles in the city, build a vehicle traffic prediction model, predict the vehicle traffic conditions of the characteristic road section in the current cycle, and obtain vehicle traffic prediction data; The intelligent supervision module is used to obtain the traffic data of public transportation vehicles in the city, and to carry out intelligent supervision of the traffic on the characteristic sections in combination with the traffic data of characteristic vehicles on the characteristic sections.

[0011] Furthermore, the characteristic road section module includes a traffic anomaly scoring unit and a characteristic road section unit; The traffic anomaly scoring unit is used to calculate the traffic accident characteristic values ​​of the road section in each historical period, and calculate the traffic anomaly score of the road section based on the traffic accident characteristic values; The characteristic road section unit is used to determine the traffic anomaly of the road section according to the traffic anomaly score to obtain the characteristic road section.

[0012] Furthermore, the characteristic vehicle traffic data module includes a characteristic traffic congestion value unit and a characteristic vehicle traffic data unit; A characteristic traffic congestion value unit is used to obtain historical vehicle traffic records of characteristic road sections and calculate characteristic traffic congestion values ​​of the historical vehicle traffic records; The characteristic vehicle traffic data unit is used to determine the historical vehicle traffic records according to the characteristic traffic congestion value, and obtain the characteristic vehicle threshold of the characteristic road section to obtain the characteristic vehicle traffic data of the characteristic road section.

[0013] Further, the vehicle traffic prediction data module includes a model building unit and a vehicle traffic prediction data unit; A model building unit is used to obtain historical traffic data of characteristic road sections, obtain historical traffic data of historical public transportation vehicles in the city, and build a vehicle traffic prediction model in combination with the historical vehicle traffic data of characteristic road sections; The vehicle traffic prediction data unit is used to obtain data corresponding to several traffic characteristic indicators of the characteristic road section in the current cycle, use the vehicle traffic prediction model to predict the vehicle traffic conditions of the characteristic road section in the current cycle, and obtain vehicle traffic prediction data.

[0014] Further, the intelligent supervision module includes an intelligent supervision unit; The intelligent supervision unit is used to determine traffic anomalies on characteristic road sections, obtain vehicles whose navigation routes within the city in the current period contain characteristic road sections, send prompts to vehicle users through the navigation system, make navigation route update recommendations, and perform intelligent supervision on traffic on characteristic road sections.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention realizes the integrated supervision of multi-dimensional road network data, firstly, the traffic anomaly of each road section is determined based on the vehicle congestion and traffic accident occurrence of each road section in the historical period, and the road sections with frequent accidents and congestion are obtained, and the actual conditions of different road sections are different are taken into consideration, and the capacity of different road sections for passing vehicles is analyzed, and a prediction model is constructed to predict the passing vehicles of the road section in the current period, and the influence of public transportation vehicles on the traffic conditions of the road section is taken into consideration, and then the passing vehicles of different road sections in the city are managed, thereby greatly reducing the occurrence of traffic congestion in urban roads, and also reducing the risk of traffic accidents to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a method flow chart of the multi-dimensional road network data integrated supervision system and method based on traffic business data of the present invention; Figure 2 It is a module schematic diagram of the multi-dimensional road network data integrated supervision system and method based on traffic business data of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, a multi-dimensional road network data integrated supervision method based on traffic business data, the method comprising: Step S100: construct a road network supervision cloud platform, obtain historical traffic accident records of road sections in the city, obtain historical vehicle traffic records of road sections, obtain historical vehicle traffic speed data from historical vehicle traffic records, conduct traffic anomaly assessment on road sections in the city, and obtain characteristic road sections; Wherein, step S100 includes: Step S101: Obtain historical traffic accident records of road sections within the city, and extract historical traffic accident data from the historical traffic accident records, wherein the historical traffic accident data includes the total number of vehicles involved in traffic accidents in the historical traffic accident records; Step S102: vehicles passing through the road section are collected every historical period to obtain historical vehicle traffic records of the road section, and historical vehicle traffic rate data are extracted from the historical vehicle traffic records. The historical vehicle traffic rate data includes an average rate of vehicles passing through the road section in the historical period; Step S103: evaluating the traffic anomaly of each road section in the city, wherein the specific evaluation process of the traffic anomaly of the ath road section in the city includes: Obtain several historical traffic accident records of the a-th road section in each historical period, and calculate the traffic accident characteristic value of the a-th road section in each historical period, where the traffic accident characteristic value P of the a-th road section in the b-th historical period is a,b : , Where j represents the total number of historical traffic accident records of the a-th road section in the b-th historical period; M a b,x It is expressed as the total number of vehicles involved in traffic accidents in the xth historical traffic accident record in the bth historical period on the ath road section; For example, the total number of historical traffic accident records in the third historical period of the first road section is j, which is 3; the total number of vehicles involved in traffic accidents in the first historical traffic accident record in the third historical period of the first road section is M. 1 3,1 =4; the total number of vehicles involved in traffic accidents in the second historical traffic accident record of the first road section in the third historical cycle is M 1 3,2 =5; the total number of vehicles involved in traffic accidents in the third historical traffic accident record in the third historical cycle for the first road section is M 1 3,3 is 7; Calculate the traffic accident characteristic value P of the first road section in the third historical period 1,3 : , Get the maximum value P of the traffic accident characteristic value of each road section in each historical period max , minimum value P min ; Step S104: Obtain the preset maximum vehicle speed limit v for the ath road section a , calculate the traffic anomaly score E of the a-th road segment a : , Among them, P a,i It is represented as the traffic accident characteristic value of the a-th road section in the i-th historical period; va,i It is represented as the average speed of vehicles passing through the a-th road section in the i-th historical period; n is represented as the total number of each historical period; β1 is represented as the preset first characteristic proportional coefficient; β2 is represented as the preset second characteristic proportional coefficient; β1+β2=1; Step S105: When the traffic anomaly score E a If the traffic anomaly score is greater than the preset traffic anomaly score threshold, the traffic anomaly of the a-th road section is determined and the a-th road section is recorded as a characteristic road section; Step S200: Obtain historical vehicle traffic records in the characteristic road section, obtain historical vehicle traffic data from the historical vehicle traffic records, analyze the abnormal impact of vehicle flow on the traffic of the characteristic road section, and obtain characteristic vehicle traffic data; Wherein, step S200 includes: Step S201: Obtain historical vehicle traffic records of a characteristic road section, and calculate characteristic traffic congestion values ​​of a number of historical vehicle traffic records, wherein the characteristic traffic congestion value F of the cth historical vehicle traffic record of the characteristic road section is c : , Where v' represents the maximum speed limit of the vehicle on the characteristic road section; v' c It is expressed as the average speed of the passing vehicles in the cth historical vehicle passing record; For example, the maximum speed limit v' of the characteristic road section is 60km / h; the average speed v'2 of the passing vehicles in the second historical vehicle traffic record is 20km / h; Calculate the characteristic traffic congestion value F2 of the second historical vehicle traffic record of the characteristic road section: , Step S202: When the characteristic traffic congestion value F c When the value is greater than a preset characteristic traffic congestion threshold, it is determined that there is vehicle congestion on the historical characteristic road section in the c-th historical vehicle traffic record, and the c-th historical vehicle traffic record is recorded as the characteristic historical vehicle traffic record of the characteristic road section; Step S203: Obtain historical vehicle traffic data from historical vehicle traffic records, the historical vehicle traffic data being the total number of vehicles passing through the historical vehicle traffic records, obtaining a number of characteristic historical vehicle traffic records in the characteristic road section, and obtaining a minimum value M of the total number of vehicles passing through the characteristic road section in the number of characteristic historical vehicle traffic records. min , obtain the maximum value M' of the total number of vehicles passing through several historical vehicle traffic records in the characteristic road section max , when the maximum value M' max Greater than the minimum value M min , the maximum value M' maxAs the characteristic vehicle threshold M' of the characteristic road section; On the contrary, when the maximum value M' max Less than or equal to the minimum value M min , the minimum value M min As the characteristic vehicle threshold M' of the characteristic road section, the characteristic vehicle traffic data of the characteristic road section is obtained; Step S300: Obtain historical traffic data of the characteristic road section, obtain historical traffic data of public transportation vehicles in the city, and build a vehicle traffic prediction model based on the historical vehicle traffic data of the characteristic road section to predict the vehicle traffic conditions of the characteristic road section in the current cycle to obtain vehicle traffic prediction data; Wherein, step S300 includes: Step S301: acquiring historical traffic data of characteristic road sections in each historical period, wherein the historical traffic data includes data corresponding to several traffic characteristic indicators of the characteristic road sections; For example, several traffic characteristic indicators include time, temperature, weather, precipitation, etc.; Step S302: Acquire historical traffic data of historical public transport vehicles in the city, the historical traffic data including the routes of historical public transport vehicles in the historical period, acquire historical vehicle traffic data of characteristic sections, and when the routes of historical public transport vehicles include characteristic sections, record the historical public transport vehicles as marked historical public transport vehicles of the characteristic sections; Step S303: Obtain historical vehicle traffic data of the characteristic road section, obtain the total number of vehicles passing through the characteristic road section in each historical period from the historical vehicle traffic data, and calculate the characteristic value of vehicles passing through the characteristic road section in each historical period, wherein the characteristic value K of vehicles passing through the characteristic road section in the gth historical period is g =M g -γ g , M g It is represented by the total number of vehicles passing through the characteristic road section in the gth historical period, γ g It is represented by the total number of historical public transport vehicles that have passed through the characteristic road section in the gth historical period; Step S304: constructing a vehicle traffic prediction model, taking several traffic characteristic indicators of the characteristic road section as input data of the vehicle traffic prediction model, taking characteristic values ​​of vehicles passing through the characteristic road section as target data, dividing historical traffic data in each historical period of the characteristic road section and characteristic values ​​of vehicles passing through into a training set and a test set, and training and testing the vehicle traffic prediction model; Step S305: Obtain the average value Y of the characteristic values ​​of vehicles passing through each historical period of the characteristic road section △, obtain the vehicle traffic prediction model, predict the characteristic values ​​of vehicles passing through the characteristic road section in several historical periods, and calculate the model prediction score R of the vehicle traffic prediction model: , Among them, Y z It is represented as a characteristic road section, the characteristic value of the vehicles passing through in the zth historical cycle among several historical cycles; Y △,z It is represented by the vehicle traffic prediction model, which is the predicted value of the characteristic value of the passing vehicles in the zth historical period of the characteristic road section; q is represented by the total number of several historical periods; Step S306: When the model prediction score of the vehicle traffic prediction model is greater than the preset model prediction score, the vehicle traffic prediction model is determined to have prediction capability, data corresponding to several traffic characteristic indicators of the characteristic road section in the current cycle are obtained, and the vehicle traffic prediction model is used to predict the vehicle traffic condition of the characteristic road section in the current cycle to obtain vehicle traffic prediction data; Step S400: obtaining vehicle traffic prediction data of characteristic road sections in the current cycle, obtaining traffic data of public transportation vehicles in the city in the current cycle, and combining the characteristic vehicle traffic data of characteristic road sections to perform intelligent supervision on the traffic of characteristic road sections; Wherein, step S400 includes: Step S401: obtaining vehicle traffic prediction data of a characteristic road section in a current cycle, and obtaining a predicted value of a characteristic value of a vehicle passing through the characteristic road section in the current cycle from the vehicle traffic prediction data; Step S402: Acquire the traffic data of the public transportation vehicles in the current cycle, the traffic data includes the travel time and the travel route of the public transportation vehicles. When the travel route of the public transportation vehicles includes a characteristic section, and the travel time of the public transportation vehicles in the characteristic section, within the current cycle, the public transportation vehicles are recorded as the characteristic section, and a number of characteristic public transportation vehicles in the current cycle; Step S403: Acquire the total number of characteristic public transport vehicles in the characteristic road section in the current cycle. When the predicted value of the characteristic value of the passing vehicle in the characteristic road section in the current cycle and the sum of the total number of characteristic public transport vehicles are less than the characteristic vehicle threshold in the characteristic vehicle passing data in the characteristic road section, it is determined that the traffic on the characteristic road section in the current cycle is normal; Step S404: when the total number of characteristic road sections in the current cycle is greater than the characteristic vehicle threshold, it is determined that the traffic on the characteristic road section in the current cycle is abnormal, and vehicles whose navigation routes in the city in the current cycle contain the characteristic road section are obtained, a prompt is sent to the user of the vehicle through the navigation system, and a navigation route update recommendation is made, so as to perform intelligent supervision on the traffic on the characteristic road section; In order to better implement the above steps, a multi-dimensional road network data integrated supervision system based on traffic business data is proposed. The system includes a characteristic road section module, a characteristic vehicle traffic data module, a vehicle traffic prediction data module, and an intelligent supervision module; The characteristic road section module is used to evaluate the traffic anomaly of the road sections in the city and obtain the characteristic road sections; The characteristic vehicle traffic data module is used to analyze the abnormal impact of vehicle flow on the traffic of characteristic road sections and obtain characteristic vehicle traffic data; The vehicle traffic prediction data module is used to obtain the historical traffic data of the characteristic road section, obtain the historical traffic data of the historical public transportation vehicles in the city, build a vehicle traffic prediction model, predict the vehicle traffic conditions of the characteristic road section in the current cycle, and obtain vehicle traffic prediction data; The intelligent supervision module is used to obtain the traffic data of public transportation vehicles in the city, and to carry out intelligent supervision of the traffic on the characteristic road sections by combining the traffic data of characteristic vehicles on the characteristic road sections; Among them, the characteristic road section module includes a traffic anomaly scoring unit and a characteristic road section unit; The traffic anomaly scoring unit is used to calculate the traffic accident characteristic values ​​of the road section in each historical period, and calculate the traffic anomaly score of the road section based on the traffic accident characteristic values; A characteristic road section unit is used to determine traffic anomalies on a road section according to traffic anomaly scores to obtain characteristic road sections; Among them, the characteristic vehicle traffic data module includes a characteristic traffic congestion value unit and a characteristic vehicle traffic data unit; A characteristic traffic congestion value unit is used to obtain historical vehicle traffic records of characteristic road sections and calculate characteristic traffic congestion values ​​of the historical vehicle traffic records; The characteristic vehicle traffic data unit is used to determine the historical vehicle traffic records according to the characteristic traffic congestion value, and obtain the characteristic vehicle threshold of the characteristic road section to obtain the characteristic vehicle traffic data of the characteristic road section; Among them, the vehicle traffic prediction data module includes a model building unit and a vehicle traffic prediction data unit; A model building unit is used to obtain historical traffic data of characteristic road sections, obtain historical traffic data of historical public transportation vehicles in the city, and build a vehicle traffic prediction model in combination with the historical vehicle traffic data of characteristic road sections; The vehicle traffic prediction data unit is used to obtain data corresponding to several traffic characteristic indicators of the characteristic road section in the current cycle, and use the vehicle traffic prediction model to predict the vehicle traffic conditions of the characteristic road section in the current cycle to obtain vehicle traffic prediction data; Among them, the intelligent supervision module includes an intelligent supervision unit; The intelligent supervision unit is used to determine traffic anomalies on characteristic road sections, obtain vehicles whose navigation routes within the city in the current period contain characteristic road sections, send prompts to vehicle users through the navigation system, make navigation route update recommendations, and perform intelligent supervision on traffic on characteristic road sections.

[0019] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A multi-dimensional road network data integrated supervision method based on traffic business data, characterized in that: The method comprises: Step S100: constructing a road network supervision cloud platform, obtaining historical traffic accident records of road sections in the city, obtaining historical vehicle traffic records of the road sections, obtaining historical vehicle traffic rate data from the historical vehicle traffic records, performing traffic anomaly assessment on the road sections in the city, and obtaining characteristic road sections; Step S200: obtaining historical vehicle traffic records in the characteristic road section, obtaining historical vehicle traffic data from the historical vehicle traffic records, analyzing the abnormal impact of vehicle flow on the traffic of the characteristic road section, and obtaining characteristic vehicle traffic data; Step S300: acquiring historical traffic data of the characteristic road section, acquiring historical traffic data of public transportation vehicles in the city, and building a vehicle traffic prediction model in combination with the historical vehicle traffic data of the characteristic road section, predicting the vehicle traffic conditions of the characteristic road section in the current cycle, and obtaining vehicle traffic prediction data; Step S400: Obtain vehicle traffic prediction data for the characteristic road section in the current cycle, obtain traffic data of public transportation vehicles in the city in the current cycle, and combine the characteristic vehicle traffic data of the characteristic road section to intelligently supervise the traffic of the characteristic road section.

2. The multi-dimensional road network data integrated supervision method based on traffic business data according to claim 1 is characterized in that: The step S100 includes: Step S101: acquiring historical traffic accident records of road sections in a city, and extracting historical traffic accident data from the historical traffic accident records, wherein the historical traffic accident data includes the total number of vehicles involved in traffic accidents in the historical traffic accident records; Step S102: collecting vehicles passing through the road section every historical period to obtain historical vehicle traffic records of the road section, and extracting historical vehicle traffic rate data from the historical vehicle traffic records, wherein the historical vehicle traffic rate data includes an average rate of vehicles passing through the road section in the historical period; Step S103: evaluating the traffic anomaly of each road section in the city, wherein the specific evaluation process of the traffic anomaly of the ath road section in the city includes: Obtain several historical traffic accident records of the a-th road section in each historical period, and calculate the traffic accident characteristic value of the a-th road section in each historical period, wherein the traffic accident characteristic value P of the a-th road section in the b-th historical period is a,b : ， Wherein, j represents the total number of historical traffic accident records of the a-th road section in the b-th historical period; M a b,x It is represented by the total number of vehicles involved in traffic accidents in the xth historical traffic accident record in the bth historical period on the ath road section; Obtain the maximum value P of the traffic accident characteristic value of each road section in each historical period max , minimum value P min ; Step S104: Obtain the preset maximum vehicle speed limit v of the ath road section a , calculate the traffic anomaly score E of the a-th road segment a : ， Among them, P a,i It is represented as the traffic accident characteristic value of the a-th road section in the i-th historical period; v a,i It is represented as the average speed of vehicles passing through the a-th road section in the i-th historical period; n is represented as the total number of the historical periods; β1 is represented as the preset first characteristic proportional coefficient; β2 is represented as the preset second characteristic proportional coefficient; β1+β2=1; Step S105: When the traffic anomaly score E a If the traffic anomaly score is greater than a preset traffic anomaly score threshold, the a-th road section is determined to have traffic anomaly, and the a-th road section is recorded as a characteristic road section.

3. The multi-dimensional road network data integrated supervision method based on traffic business data according to claim 2 is characterized in that: The step S200 includes: Step S201: Obtain the historical vehicle traffic records of the characteristic road section, and calculate the characteristic traffic congestion values ​​of the plurality of historical vehicle traffic records, wherein the characteristic traffic congestion value F of the cth historical vehicle traffic record of the characteristic road section is c : ， Wherein, v' represents the maximum speed limit of the vehicle on the characteristic road section; v' c is represented by the average speed of the passing vehicles of the cth historical vehicle passing record; Step S202: When the characteristic traffic congestion value F c When the value is greater than a preset characteristic traffic congestion threshold, it is determined that there is vehicle congestion on the historical characteristic road section in the c-th historical vehicle traffic record, and the c-th historical vehicle traffic record is recorded as the characteristic historical vehicle traffic record of the characteristic road section; Step S203: Obtain historical vehicle traffic data from the historical vehicle traffic records, the historical vehicle traffic data being the total number of vehicles passing through the historical vehicle traffic records, obtaining a number of characteristic historical vehicle traffic records in the characteristic road section, and obtaining the minimum value M of the total number of vehicles passing through the characteristic road section in the characteristic historical vehicle traffic records. min , obtain the maximum value M' of the total number of vehicles passing through several historical vehicle traffic records in the characteristic road section max , when the maximum value M' max Greater than the minimum value M min , the maximum value M' max As the characteristic vehicle threshold M' of the characteristic road section; On the contrary, when the maximum value M' max Less than or equal to the minimum value M min , the minimum value M min As the characteristic vehicle threshold M' of the characteristic road section, characteristic vehicle traffic data of the characteristic road section is obtained.

4. The multi-dimensional road network data integrated supervision method based on traffic business data according to claim 3 is characterized in that: The step S300 includes: Step S301: acquiring historical traffic data of the characteristic road section in each historical period, wherein the historical traffic data includes data corresponding to several traffic characteristic indicators of the characteristic road section; Step S302: Acquire historical traffic data of historical public transport vehicles in the city, the historical traffic data including the routes of the historical public transport vehicles in the historical period, acquire historical vehicle traffic data of the characteristic road section, and when the route of the historical public transport vehicle includes the characteristic road section, record the historical public transport vehicle as a marked historical public transport vehicle of the characteristic road section; Step S303: Obtain historical vehicle traffic data of the characteristic road section, obtain the total number of vehicles passing through the characteristic road section in each historical period from the historical vehicle traffic data, and calculate the characteristic value of vehicles passing through the characteristic road section in each historical period, wherein the characteristic value K of vehicles passing through the characteristic road section in the gth historical period is g =M g -γ g , M g It is represented by the total number of vehicles passing through the characteristic road section in the gth historical period, γ g It is represented by the total number of historical public transport vehicles that have passed through the characteristic road section in the g-th historical period; Step S304: constructing a vehicle traffic prediction model, taking several traffic characteristic indicators of the characteristic road section as input data of the vehicle traffic prediction model, taking characteristic values ​​of vehicles passing through the characteristic road section as target data, dividing historical traffic data in each historical period of the characteristic road section and characteristic values ​​of vehicles passing through into a training set and a test set, and training and testing the vehicle traffic prediction model; Step S305: Obtain the average characteristic value Y of vehicles passing through the characteristic road section in each historical period △ , obtain a vehicle traffic prediction model, and calculate the model prediction score R of the vehicle traffic prediction model based on the predicted values ​​of the characteristic values ​​of vehicles passing through the characteristic road section in several historical periods: ， Among them, Y z Y is represented as the characteristic value of the vehicles passing through the characteristic road section in the zth historical cycle among the several historical cycles; △,z It is represented by the vehicle traffic prediction model, which is the predicted value of the characteristic value of the passing vehicles in the zth historical period of the characteristic road section; q is represented by the total number of the several historical periods; Step S306: When the model prediction score of the vehicle traffic prediction model is greater than the preset model prediction score, the vehicle traffic prediction model is determined to have prediction capability, and data corresponding to several traffic characteristic indicators of the characteristic road section in the current cycle are obtained. The vehicle traffic prediction model is used to predict the vehicle traffic conditions of the characteristic road section in the current cycle to obtain vehicle traffic prediction data.

5. The multi-dimensional road network data integrated supervision method based on traffic business data according to claim 4 is characterized in that: The step S400 includes: Step S401: obtaining vehicle traffic prediction data of the characteristic road section in the current cycle, and obtaining a prediction value of a characteristic value of vehicles passing through the characteristic road section in the current cycle from the vehicle traffic prediction data; Step S402: Acquire the travel data of the public transportation vehicles in the current cycle, the travel data including the travel time and travel route of the public transportation vehicles. When the travel route of the public transportation vehicles includes the characteristic road section, and the travel time of the public transportation vehicles in the characteristic road section, within the current cycle, the public transportation vehicles are recorded as the characteristic road section, and a number of characteristic public transportation vehicles in the current cycle; Step S403: Obtain the total number of each characteristic public transportation vehicle in the current cycle of the characteristic road section. When the predicted value of the characteristic value of the passing vehicle of the characteristic road section in the current cycle and the sum of the total number of the characteristic public transportation vehicles are less than the characteristic vehicle threshold in the characteristic vehicle passing data of the characteristic road section, it is determined that the traffic of the characteristic road section in the current cycle is normal; Step S404: When the total number of the characteristic road sections in the current cycle is greater than the characteristic vehicle threshold, the traffic abnormality of the characteristic road sections in the current cycle is determined, and the vehicles containing the characteristic road sections in the navigation routes within the city in the current cycle are obtained, a prompt is sent to the user of the vehicle through the navigation system, and a navigation route update recommendation is made, so as to intelligently supervise the traffic of the characteristic road sections.

6. A multi-dimensional road network data integrated supervision system based on traffic business data, used to execute the multi-dimensional road network data integrated supervision method based on traffic business data according to any one of claims 1 to 5, characterized in that: The system includes a characteristic road section module, a characteristic vehicle traffic data module, a vehicle traffic prediction data module, and an intelligent supervision module; The characteristic road section module is used to evaluate the traffic anomaly of the road sections in the city to obtain characteristic road sections; The characteristic vehicle traffic data module is used to analyze the abnormal impact of vehicle flow on the traffic of the characteristic road section to obtain characteristic vehicle traffic data; The vehicle traffic prediction data module is used to obtain the historical traffic data of the characteristic road section, obtain the historical traffic data of the historical public transportation vehicles in the city, build a vehicle traffic prediction model, predict the vehicle traffic conditions of the characteristic road section in the current cycle, and obtain vehicle traffic prediction data; The intelligent supervision module is used to acquire the traffic data of public transportation vehicles in the city, and to perform intelligent supervision on the traffic of the characteristic road section in combination with the characteristic vehicle traffic data of the characteristic road section.

7. The multi-dimensional road network data integrated supervision system based on traffic business data according to claim 6 is characterized in that: The characteristic road section module includes a traffic anomaly scoring unit and a characteristic road section unit; The traffic anomaly scoring unit is used to calculate the traffic accident characteristic value of the road section in each historical period, and calculate the traffic anomaly score of the road section according to the traffic accident characteristic value; The characteristic road section unit is used to determine the traffic anomaly of the road section according to the traffic anomaly score to obtain the characteristic road section.

8. The multi-dimensional road network data integrated supervision system based on traffic business data according to claim 6 is characterized in that: The characteristic vehicle traffic data module includes a characteristic traffic congestion value unit and a characteristic vehicle traffic data unit; The characteristic traffic congestion value unit is used to obtain the historical vehicle traffic records of the characteristic road section and calculate the characteristic traffic congestion value of the historical vehicle traffic records; The characteristic vehicle traffic data unit is used to determine the historical vehicle traffic records according to the characteristic traffic congestion value, and to obtain the characteristic vehicle threshold of the characteristic road section to obtain the characteristic vehicle traffic data of the characteristic road section.

9. The multi-dimensional road network data integrated supervision system based on traffic business data according to claim 6 is characterized in that: The vehicle traffic prediction data module includes a model building unit and a vehicle traffic prediction data unit; The model building unit is used to obtain the historical traffic data of the characteristic road section, obtain the historical traffic data of the historical public transportation vehicles in the city, and build a vehicle traffic prediction model in combination with the historical vehicle traffic data of the characteristic road section; The vehicle traffic prediction data unit is used to obtain data corresponding to several traffic characteristic indicators of the characteristic road section in the current cycle, and use the vehicle traffic prediction model to predict the vehicle traffic conditions of the characteristic road section in the current cycle to obtain vehicle traffic prediction data.

10. The multi-dimensional road network data integrated supervision system based on traffic business data according to claim 6 is characterized in that: The intelligent supervision module includes an intelligent supervision unit; The intelligent supervision unit is used to determine traffic anomalies on the characteristic road section, obtain vehicles whose navigation routes in the city within the current period contain the characteristic road section, send prompts to users of the vehicles through the navigation system, make navigation route update recommendations, and intelligently supervise the traffic on the characteristic road section.

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