CN110675625A - Intelligent traffic big data method and system - Google Patents
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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Abstract
The invention discloses a smart traffic big data method and a system, comprising a vehicle-mounted terminal, a remote smart traffic data platform and a network transmission terminal, wherein the network transmission terminal is used for data transmission between the vehicle-mounted terminal and the remote smart traffic data platform, the vehicle-mounted terminal comprises a vehicle information acquisition module, a vehicle positioning data acquisition module, a driving safety data acquisition module and a data transmission module, the remote smart traffic data platform comprises a data receiving module, a big data analysis management module, an urban traffic space information model establishing module, an urban traffic supervision big data model establishing module, an urban traffic environment big data model establishing module and an urban traffic driving safety big data model establishing module, the occurrence of road traffic accidents is reduced by establishing an urban traffic driving safety big data model, thereby ensuring the smoothness of traffic, and intelligent traffic based on big data is realized.
Description
Technical Field
The invention relates to the field of intelligent traffic big data, in particular to an intelligent traffic big data method and system.
Background
In life, there are places where the traffic is inconvenient, such as late airplane, delay, super traffic jam and the like, which become common things in life. When the traffic never develops to the perfect state, people continuously put forward new requirements to improve the comfort level, and the big data of the traffic trip is analyzed and summarized to obtain the interconnection strength of different cities and the source of urban floating population so as to guide the external traffic construction of the cities; the relation between urban traffic phenomena and important events can be analyzed, and traffic pressure caused by the next emergency can be effectively prevented; the big data can vividly reflect the travel path and preference of residents, summarize the travel habits of the residents, thereby providing reference for a third-party service platform and accelerating the transformation and the upgrade of the transportation from the traditional industry to the modern service industry;
chinese patent (CN106920392A) discloses a always intelligent traffic big data system, through the application scene demand based on urban intelligent traffic, combine the professional application integration of degree of depth, establish the big data model of intelligent traffic, realize the coordination and integration of people, car, way three, improve urban traffic transportation efficiency, traffic management efficiency and quality, automatic intelligent level, but in prior art and the above-mentioned technique to the big data integration of intelligent traffic in-process, mainly to the real-time road conditions under specific road conditions information and the traffic control, it is not enough to the safe vigilance of driving of driver self, make the emergence of traffic accident easily caused by the untimely driving action of driver.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides an intelligent traffic big data method and system.
The technical problem to be solved by the invention is as follows:
the urban traffic driving safety big data model is established by utilizing the vehicle driving information and the driving state information of the driver, the driving behavior of the driver is analyzed based on the five information of the vehicle driving time interval, the driving time length of the driver, the vehicle driving speed, the vehicle driving stability and the driving concentration of the driver, a complete comprehensive score is carried out on the driver, and the driving behavior of the driver is alerted based on the comprehensive score, so that the occurrence of road traffic accidents is reduced, the traffic smoothness is ensured, and the technical problem that the traffic accidents are easily caused by the mishandling driving behavior of the driver due to insufficient driving safety alerting of the driver in the using process of the existing intelligent traffic big data system is solved.
The purpose of the invention can be realized by the following technical scheme:
the intelligent traffic big data system comprises a vehicle-mounted terminal, a remote intelligent traffic data platform and a network transmission terminal;
the network transmission terminal is used for data transmission between the vehicle-mounted terminal and the remote intelligent traffic data platform;
the vehicle-mounted terminal comprises a vehicle information acquisition module, a vehicle positioning data acquisition module, a driving safety data acquisition module and a data transmission module;
the remote intelligent traffic data platform comprises a data receiving module, a big data analysis management module, an urban traffic space information model building module, an urban traffic supervision big data model building module, an urban traffic environment big data model building module and an urban traffic driving safety big data model building module;
the driving safety data acquisition module is used for acquiring vehicle running information and driver running state information in the running process of the vehicle and transmitting the vehicle running information and the driver running state information to the data transmission module; the data transmission module is used for transmitting the vehicle information, the current positioning data, the vehicle running information and the driver running state information to the data receiving module;
the data receiving module is used for receiving the vehicle information, the current positioning data, the vehicle running information and the driver running state information which are transmitted by the data transmitting module;
the urban traffic spatial information model building module is used for collecting BIM data of urban geographic information and a building information model and building an urban traffic spatial information model according to the BIM data of the geographic information and the building information model, the urban traffic supervision big data model building module is used for building an urban traffic supervision system model according to urban traffic management rules, a traffic temporary control system and traffic operation monitoring information, and the urban traffic environment big data model building module is used for building a vehicle traffic operation environment model according to urban meteorological hydrological environment data, geological disasters and emergency disaster information;
the urban traffic driving safety big data model building module automatically obtains vehicle driving information and driver driving state information in the data receiving module, and builds an urban traffic driving safety model according to the vehicle driving information and the driver driving state information;
the big data analysis management module is used for analyzing, managing and storing the received data, carrying out matching correlation analysis on the received data and an urban traffic space information model, an urban traffic supervision system model and a vehicle traffic operation environment model, and obtaining topological analysis results of overlapping, approaching, entering and collision of vehicles and geographic space under a traffic constraint environment along with time, and the big data analysis management module is also used for grading the urban traffic driving safety model.
Further, the vehicle information includes:
driver basic information including: the driver name, the identification number and the contact way;
vehicle basic information including: vehicle type, license plate number;
vehicle state information: whether the vehicle is running, whether the vehicle is driven into a parking space, and whether collision and fire happen.
Further, the vehicle travel information and the driver travel state information include: the driving time interval of the vehicle, the driving time length of the driver, the driving speed of the vehicle, the driving stability of the vehicle and the driving concentration of the driver.
Further, the specific establishing method of the urban traffic driving safety big data model establishing module is as follows:
s1: scoring the vehicle driving time period, analyzing according to historical accident of trip, obtaining the risk rate of road accidents occurring in different time periods, and converting the risk rate into a score;
s2: scoring the driving time of the driver, recording the driving time of the driver, and scoring the driving time required by the driver and real-time scoring the driving time according to a traffic law by taking the fatigue driving time as a reference;
s3: the method comprises the steps of vehicle running speed grading, wherein a driver road is cut into sections, the average running speed of each section of other vehicle ways is calculated, the vehicle running speed is compared with the average running speed to obtain the relative running speed of the vehicle in the whole section, and grading is carried out on the basis of the vehicle relative running speed;
s4: grading the running stability of the vehicle, recording event data of rapid acceleration and rapid deceleration in the running process of the vehicle by using intelligent vehicle-mounted equipment, analyzing the rapid acceleration and rapid deceleration behaviors of a vehicle owner in the running process of the vehicle, and grading based on the behavior analysis result;
s5: the method comprises the following steps of scoring the driving concentration degree of a driver, collecting and analyzing the behaviors of calling and sending short messages existing in the driving process of a vehicle owner by utilizing video analysis equipment and video collection equipment, and scoring based on an analysis result;
s6: and establishing a big data model of urban traffic driving safety, and taking an average value of all the scores as a score for the vehicle owner so as to remind the vehicle owner of driving behaviors.
Further, the risk rate in S1 is calculated from data obtained from big data, that is, the road accident occurring in a certain time period is divided by the traffic accident occurring in a day, the risk rate is recorded as a, and a score a is obtained by 100 × a;
in the step S2, the driver duration score is obtained through the actual vehicle running time recorded by the intelligent vehicle-mounted device or the required running time obtained through the big data, the time is recorded as B, and a score B is obtained through 100- (B/4 x 100);
the relative driving speed of the vehicle in the S3 is obtained through the driving speed of the vehicle recorded by the intelligent vehicle-mounted equipment and is recorded as k, the average driving speed of each area is obtained through big data and is recorded as C, and the score C is obtained through 100- | k-C |;
the judgment of the rapid acceleration and the rapid deceleration in the S4 is mainly that the number of times of the rapid acceleration and the rapid deceleration in the single vehicle running process is recorded as D by judging whether the acceleration or the deceleration exceeds a preset value k within a preset time, the score is set as D {0, D1, D2, 100}, when D is more than or equal to 0 and less than or equal to 1, the score is 100, when D is more than or equal to 2 and less than or equal to 3, the score is D1, when D is more than or equal to 3 and less than or equal to 4, the score is D2, and when D is more than 4, the score is 0;
when the time judgment of the call making and short message sending behaviors in the S5 exceeds a preset value i, recording the behaviors as one-time call making and short message sending behaviors, and when the recording times exceed 2, marking the behaviors as 0, when the recording times are 0, marking the behaviors as 100, and when the recording times are 1, marking the behaviors as E;
and when the total score in the S6 is lower than 90, the vehicle owner is alerted through the intelligent vehicle-mounted equipment.
Further, the intelligent traffic big data method comprises the following steps:
s1: collecting BIM data of urban geographic information and a building information model, and constructing an urban traffic space information model according to the BIM data of the geographic information and the building information model; constructing an urban traffic supervision big data model based on urban traffic management rules, a traffic temporary control system and traffic operation monitoring information; constructing an urban traffic environment big data model based on urban meteorological hydrological environment data, geological disasters and emergency disaster information, and constructing an urban traffic driving safety big data model based on vehicle running information;
s2: the method comprises the steps of obtaining vehicle information, transmitting position information collected by a vehicle positioning data collection module to a remote intelligent traffic data platform through a data transmission module, and meanwhile collecting vehicle running information and driver running state information in the vehicle running process according to a driving safety data collection module, so that a city traffic driving safety big data model is perfected;
s3: the received data are analyzed, managed and stored through a big data analysis management module, and are matched with an urban traffic space information model, an urban traffic supervision system model, a vehicle traffic operation environment model and an urban traffic driving safety model for correlation analysis, so that an optimal vehicle safety driving analysis result is obtained, and guidance optimization, traffic scheduling and control, guidance of temporary parking, automatic identification of vehicle illegal behaviors, emergency treatment of traffic accidents, vigilance and guarantee of driving safety and intelligent service in the driving process are provided.
The invention has the beneficial effects that:
the urban traffic driving safety big data model is established by utilizing the vehicle driving information and the driving state information of the driver, the driving behavior of the driver is analyzed based on the five information of the vehicle driving time interval, the driving time length of the driver, the vehicle driving speed, the vehicle driving stability and the driving concentration of the driver, a complete comprehensive score is carried out on the driver, and the driving behavior of the driver is alerted based on the comprehensive score, so that the occurrence of road traffic accidents is reduced, the traffic smoothness is ensured, and the intelligent traffic based on the big data is realized.
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The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic of the process of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present embodiment provides an intelligent traffic big data system, which includes a vehicle-mounted terminal, a remote intelligent traffic data platform, and a network transmission terminal;
the network transmission terminal is used for data transmission between the vehicle-mounted terminal and the remote intelligent traffic data platform;
the vehicle-mounted terminal comprises a vehicle information acquisition module, a vehicle positioning data acquisition module, a driving safety data acquisition module and a data transmission module;
the remote intelligent traffic data platform comprises a data receiving module, a big data analysis management module, an urban traffic space information model building module, an urban traffic supervision big data model building module, an urban traffic environment big data model building module and an urban traffic driving safety big data model building module;
the driving safety data acquisition module is used for acquiring vehicle running information and driver running state information in the running process of the vehicle and transmitting the vehicle running information and the driver running state information to the data transmission module; the data transmission module is used for transmitting the vehicle information, the current positioning data, the vehicle running information and the driver running state information to the data receiving module;
the data receiving module is used for receiving the vehicle information, the current positioning data, the vehicle running information and the driver running state information which are transmitted by the data transmitting module;
the urban traffic spatial information model building module is used for collecting BIM data of urban geographic information and a building information model and building an urban traffic spatial information model according to the BIM data of the geographic information and the building information model, the urban traffic supervision big data model building module is used for building an urban traffic supervision system model according to urban traffic management rules, a traffic temporary control system and traffic operation monitoring information, and the urban traffic environment big data model building module is used for building a vehicle traffic operation environment model according to urban meteorological hydrological environment data, geological disasters and emergency disaster information;
the urban traffic driving safety big data model building module automatically obtains vehicle driving information and driver driving state information in the data receiving module, and builds an urban traffic driving safety model according to the vehicle driving information and the driver driving state information;
the big data analysis management module is used for analyzing, managing and storing the received data, carrying out matching correlation analysis on the received data and an urban traffic space information model, an urban traffic supervision system model and a vehicle traffic operation environment model, and obtaining topological analysis results of overlapping, approaching, entering and collision of vehicles and geographic space under a traffic constraint environment along with time, and the big data analysis management module is also used for grading the urban traffic driving safety model.
The vehicle information includes:
driver basic information including: the driver name, the identification number and the contact way;
vehicle basic information including: vehicle type, license plate number;
vehicle state information: whether the vehicle is running, whether the vehicle is driven into a parking space, and whether collision and fire happen.
The vehicle travel information and the driver travel state information include: the driving time interval of the vehicle, the driving time length of the driver, the driving speed of the vehicle, the driving stability of the vehicle and the driving concentration of the driver.
The specific establishing method of the urban traffic driving safety big data model establishing module comprises the following steps:
s1: scoring the vehicle driving time period, analyzing according to historical accident of trip, obtaining the risk rate of road accidents occurring in different time periods, and converting the risk rate into a score;
s2: scoring the driving time of the driver, recording the driving time of the driver, and scoring the driving time required by the driver and real-time scoring the driving time according to a traffic law by taking the fatigue driving time as a reference;
s3: the method comprises the steps of vehicle running speed grading, wherein a driver road is cut into sections, the average running speed of each section of other vehicle ways is calculated, the vehicle running speed is compared with the average running speed to obtain the relative running speed of the vehicle in the whole section, and grading is carried out on the basis of the vehicle relative running speed;
s4: grading the running stability of the vehicle, recording event data of rapid acceleration and rapid deceleration in the running process of the vehicle by using intelligent vehicle-mounted equipment, analyzing the rapid acceleration and rapid deceleration behaviors of a vehicle owner in the running process of the vehicle, and grading based on the behavior analysis result;
s5: the method comprises the following steps of scoring the driving concentration degree of a driver, collecting and analyzing the behaviors of calling and sending short messages existing in the driving process of a vehicle owner by utilizing video analysis equipment and video collection equipment, and scoring based on an analysis result;
s6: and establishing a big data model of urban traffic driving safety, and taking an average value of all the scores as a score for the vehicle owner so as to remind the vehicle owner of driving behaviors.
Calculating the risk rate in S1 according to data obtained by big data, namely dividing road accidents occurring in a certain time period by traffic accidents occurring in one day, recording the risk rate as a, and obtaining a score A through 100 × a;
in the S2, the driver duration score is obtained through the actual vehicle running time recorded by the intelligent vehicle-mounted equipment or the required running time obtained through big data, the time is recorded as B, and a score B is obtained through 100- (B/4 x 100);
the relative driving speed of the vehicle in the S3 is obtained through the driving speed of the vehicle recorded by the intelligent vehicle-mounted equipment and is recorded as k, the average driving speed of each area is obtained through big data and is recorded as C, and the score C is obtained through 100- | k-C |;
in the S4, the judgment of the rapid acceleration and the rapid deceleration is mainly that whether the acceleration or the deceleration exceeds a preset value k within a preset time, the times of the rapid acceleration and the rapid deceleration in the single vehicle running process are recorded as D, the scores are set as D {0, D1, D2, 100}, when D is more than or equal to 0 and less than or equal to 1, the score is 100, when D is more than or equal to 2 and less than or equal to 3, the score is D1, when D is more than or equal to 3 and less than or equal to 4, the score is D2, and when D is more than 4, the score is 0;
when the time of the call making and short message sending behaviors is judged to exceed a preset value i in the S5, the behaviors are recorded as one-time call making and short message sending behaviors, when the recording times exceed 2, the score is 0, when the recording times are 0, the score is 100, and when the recording times are 1, the score is E;
and when the total score is lower than 90 in the S6, the vehicle owner is alerted through the intelligent vehicle-mounted equipment.
The intelligent traffic big data method comprises the following steps:
s1: collecting BIM data of urban geographic information and a building information model, and constructing an urban traffic space information model according to the BIM data of the geographic information and the building information model; constructing an urban traffic supervision big data model based on urban traffic management rules, a traffic temporary control system and traffic operation monitoring information; constructing an urban traffic environment big data model based on urban meteorological hydrological environment data, geological disasters and emergency disaster information, and constructing an urban traffic driving safety big data model based on vehicle running information;
s2: the method comprises the steps of obtaining vehicle information, transmitting position information collected by a vehicle positioning data collection module to a remote intelligent traffic data platform through a data transmission module, and meanwhile collecting vehicle running information and driver running state information in the vehicle running process according to a driving safety data collection module, so that a city traffic driving safety big data model is perfected;
s3: the received data are analyzed, managed and stored through a big data analysis management module, and are matched with an urban traffic space information model, an urban traffic supervision system model, a vehicle traffic operation environment model and an urban traffic driving safety model for correlation analysis, so that an optimal vehicle safety driving analysis result is obtained, and guidance optimization, traffic scheduling and control, guidance of temporary parking, automatic identification of vehicle illegal behaviors, emergency treatment of traffic accidents, vigilance and guarantee of driving safety and intelligent service in the driving process are provided.
The urban traffic driving safety big data model is established by utilizing the vehicle driving information and the driving state information of the driver, the driving behavior of the driver is analyzed based on the five information of the vehicle driving time interval, the driving time length of the driver, the vehicle driving speed, the vehicle driving stability and the driving concentration of the driver, a complete comprehensive score is carried out on the driver, and the driving behavior of the driver is alerted based on the comprehensive score, so that the occurrence of road traffic accidents is reduced, the traffic smoothness is ensured, and the intelligent traffic based on the big data is realized.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. The intelligent traffic big data system is characterized by comprising a vehicle-mounted terminal, a remote intelligent traffic data platform and a network transmission terminal;
the network transmission terminal is used for data transmission between the vehicle-mounted terminal and the remote intelligent traffic data platform;
the vehicle-mounted terminal comprises a vehicle information acquisition module, a vehicle positioning data acquisition module, a driving safety data acquisition module and a data transmission module;
the remote intelligent traffic data platform comprises a data receiving module, a big data analysis management module, an urban traffic space information model building module, an urban traffic supervision big data model building module, an urban traffic environment big data model building module and an urban traffic driving safety big data model building module;
the driving safety data acquisition module is used for acquiring vehicle running information and driver running state information in the running process of the vehicle and transmitting the vehicle running information and the driver running state information to the data transmission module; the data transmission module is used for transmitting the vehicle information, the current positioning data, the vehicle running information and the driver running state information to the data receiving module;
the data receiving module is used for receiving the vehicle information, the current positioning data, the vehicle running information and the driver running state information which are transmitted by the data transmitting module;
the urban traffic spatial information model building module is used for collecting BIM data of urban geographic information and a building information model and building an urban traffic spatial information model according to the BIM data of the geographic information and the building information model, the urban traffic supervision big data model building module is used for building an urban traffic supervision system model according to urban traffic management rules, a traffic temporary control system and traffic operation monitoring information, and the urban traffic environment big data model building module is used for building a vehicle traffic operation environment model according to urban meteorological hydrological environment data, geological disasters and emergency disaster information;
the urban traffic driving safety big data model building module automatically obtains vehicle driving information and driver driving state information in the data receiving module, and builds an urban traffic driving safety model according to the vehicle driving information and the driver driving state information;
the big data analysis management module is used for analyzing, managing and storing the received data, carrying out matching correlation analysis on the received data and an urban traffic space information model, an urban traffic supervision system model and a vehicle traffic operation environment model, and obtaining topological analysis results of overlapping, approaching, entering and collision of vehicles and geographic space under a traffic constraint environment along with time, and the big data analysis management module is also used for grading the urban traffic driving safety model.
2. The intelligent traffic big data system of claim 1, wherein the vehicle information comprises:
driver basic information including: the driver name, the identification number and the contact way;
vehicle basic information including: vehicle type, license plate number;
vehicle state information: whether the vehicle is running, whether the vehicle is driven into a parking space, and whether collision and fire happen.
3. The intelligent traffic big data system of claim 1, wherein the vehicle driving information and the driver driving state information comprise: the driving time interval of the vehicle, the driving time length of the driver, the driving speed of the vehicle, the driving stability of the vehicle and the driving concentration of the driver.
4. The intelligent traffic big data system according to claim 1, wherein the specific building method of the urban traffic driving safety big data model building module is as follows:
s1: scoring the vehicle driving time period, analyzing according to historical accident of trip, obtaining the risk rate of road accidents occurring in different time periods, and converting the risk rate into a score;
s2: scoring the driving time length of a driver, recording the driving time length of the driver, setting reference time, scoring the driving time length required by the driver and scoring the driving time length in real time;
s3: the method comprises the steps of vehicle running speed grading, wherein a driver road is cut into sections, the average running speed of each section of other vehicle ways is calculated, the vehicle running speed is compared with the average running speed to obtain the relative running speed of the vehicle in the whole section, and grading is carried out on the basis of the vehicle relative running speed;
s4: grading the running stability of the vehicle, recording event data of rapid acceleration and rapid deceleration in the running process of the vehicle by using intelligent vehicle-mounted equipment, analyzing the rapid acceleration and rapid deceleration behaviors of a vehicle owner in the running process of the vehicle, and grading based on the behavior analysis result;
s5: the method comprises the following steps of scoring the driving concentration degree of a driver, collecting and analyzing the behaviors of calling and sending short messages existing in the driving process of a vehicle owner by utilizing video analysis equipment and video collection equipment, and scoring based on an analysis result;
s6: and establishing a big data model of urban traffic driving safety, and taking an average value of all the scores as a score for the vehicle owner so as to remind the vehicle owner of driving behaviors.
5. The intelligent traffic big data system according to claim 4, wherein the risk ratio in S1 is calculated from data obtained from big data, that is, the time of day is divided into 24 time segments in units of hours, the road accident occurred in each time segment is divided by the traffic accident occurred in each day, the risk ratio is recorded as a, and the score a is obtained by 100 a;
in the step S2, the driver duration score is obtained through the actual vehicle running time recorded by the intelligent vehicle-mounted device or the required running time obtained through the big data, the time is recorded as B, and a score B is obtained through 100- (B/4 x 100);
the relative driving speed of the vehicle in the S3 is obtained through the driving speed of the vehicle recorded by the intelligent vehicle-mounted equipment and is recorded as k, the average driving speed of each area is obtained through big data and is recorded as C, and the score C is obtained through 100- | k-C |;
the judgment of the rapid acceleration and the rapid deceleration in the S4 is mainly that the number of times of the rapid acceleration and the rapid deceleration in the single vehicle running process is recorded as D by judging whether the acceleration or the deceleration exceeds a preset value k within a preset time, the score is set as D {0, D1, D2, 100}, when D is more than or equal to 0 and less than or equal to 1, the score is 100, when D is more than or equal to 2 and less than or equal to 3, the score is D1, when D is more than or equal to 3 and less than or equal to 4, the score is D2, and when D is more than 4, the score is 0;
when the time judgment of the call making and short message sending behaviors in the S5 exceeds a preset value i, recording the behaviors as one-time call making and short message sending behaviors, and when the recording times exceed 2, marking the behaviors as 0, when the recording times are 0, marking the behaviors as 100, and when the recording times are 1, marking the behaviors as E;
and when the total score in the S6 is lower than 90, the vehicle owner is alerted through the intelligent vehicle-mounted equipment.
6. The intelligent traffic big data method according to claim 1, comprising the steps of:
s1: collecting BIM data of urban geographic information and a building information model, and constructing an urban traffic space information model according to the BIM data of the geographic information and the building information model; constructing an urban traffic supervision big data model based on urban traffic management rules, a traffic temporary control system and traffic operation monitoring information; constructing an urban traffic environment big data model based on urban meteorological hydrological environment data, geological disasters and emergency disaster information, and constructing an urban traffic driving safety big data model based on vehicle running information;
s2: the method comprises the steps of obtaining vehicle information, transmitting position information collected by a vehicle positioning data collection module to a remote intelligent traffic data platform through a data transmission module, and meanwhile collecting vehicle running information and driver running state information in the vehicle running process according to a driving safety data collection module, so that a city traffic driving safety big data model is perfected;
s3: the received data are analyzed, managed and stored through a big data analysis management module, and are matched and associated with an urban traffic space information model, an urban traffic supervision system model, a vehicle traffic operation environment model and an urban traffic driving safety model for analysis, so that an optimal vehicle safety driving analysis result is obtained.
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| CN111091312A (en) * | 2020-03-25 | 2020-05-01 | 江苏广宇科技产业发展有限公司 | Intelligent traffic management and evaluation system based on BIM |
| CN112598903A (en) * | 2020-12-07 | 2021-04-02 | 博康智能信息技术有限公司 | Urban traffic value exchange system |
| CN113990069A (en) * | 2021-10-28 | 2022-01-28 | 陕西省君凯电子科技有限公司 | Urban traffic management method and system based on satellite linkage technology |
| CN114299756A (en) * | 2021-12-29 | 2022-04-08 | 盐城工学院 | Bus intelligent safety management system based on Internet of things and big data analysis |
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