With the emergence of urban traffic brains, big data has been widely used in traffic management and transportation. Traffic big data has gradually become the basic resource of urban traffic management, and it has increasingly shown its pivotal role and status in traffic management.
One of the characteristics of traffic big data is that the content rich structure is complex and has the characteristics of “multi-source heterogeneityâ€, that is, the data source is diversified and the data structure is complicated. Any single source data cannot be called traffic big data, and traffic big data must conform to the "multi-source heterogeneous" characteristics.
Many companies are biased in understanding big traffic data. They think that the large amount of data is big data, ignoring the big data characteristics of many types of data. The author believes that only when it meets the "multiple types of data and large amount of data" can be regarded as a veritable name. Traffic big data.
The second characteristic of traffic big data is the correlation with the transportation industry. That is, traffic-related data can be regarded as traffic big data. It is not necessarily that traffic management data is traffic big data and can serve traffic management. Data can be considered as traffic big data.
In summary, the characteristics of the connotation of traffic big data are multi-source heterogeneous, and the characteristics of epitaxy are rich in correlation. If the traffic big data is classified, the author believes that it can be divided into four categories: “government dataâ€, “operation dataâ€, “Internet of Things data†and “Internet dataâ€:
First, government data
Government data refers to the data generated in government management. The government government data generation depends on the information construction of various government departments. That is to say, the data generated by the information systems of various government departments is the government data.
The data related to traffic management in government data can be subdivided according to the needs of traffic management work:
First, the traffic management data of Gong'an is mainly generated from the information system of Gong'anjiao police, including vehicle management, driver management, traffic violations, traffic accidents, etc., as well as police data, mission data, road data, equipment. Data, facility data, police data, equipment data, etc.
The second is the transportation data, mainly from the information system of the Transportation Committee or the Transportation Bureau, including passenger transportation data, freight data, logistics data, bus data, orbit data, transportation hub data, taxi data, highway data, bridge and tunnel data, Maintenance data, maintenance data, etc.
The third is planning data. It is mainly generated from the planning information system of the national land planning department, especially the “multi-integration†system construction carried out in various places in recent years, which realizes the integration of multi-sector planning and greatly promotes the application of planning data in the transportation field.
The fourth is meteorological data. It mainly produces information systems from the meteorological department, including meteorological satellites, high-air balloon, ground meteorological monitoring stations, etc., focusing on various weather forecast data such as rain, snow, fog, ice, and wind.
The fifth is the data of industrial and commercial corporations. It is mainly produced in the industrial and commercial information management system of the industrial and commercial administration department, with emphasis on passenger-related data such as passenger transportation, freight transportation, logistics, and auto repair shops in the system.
Six is ​​the city construction data. It is mainly generated from the information system of the housing construction department and the urban construction department, including the data involved in urban construction such as purchase data, second-hand housing data, construction site data, construction management data, and muck truck data.
Seven is the large event data. It is mainly generated from the information management system of the Tourism Commission, including ticket data, parking data, visitor data, and actor data.
Eight is the integrity data. Including untrustworthy businesses and personal data, nine is the traffic card data. The traffic card data is the card usage data issued by the transportation management department, including personnel information and usage information.
Second, operational data
Operational data refers to the data generated by the enterprises authorized by the state in the operation and production.
The first is telecommunications data. Including China Telecom, China Unicom, China Mobile and other telecom operators in the business activities generated by the installed data, user data, location data, call charge data.
The second is the railway passenger transport data. Railway passenger data is the data generated by the operation of the railway department, including ticket purchase data, train data, time data and delay data.
The third is civil aviation passenger data. Civil aviation passenger data is the data accumulated in the daily operations of various airlines, including passenger data, flight data, and delay data.
The fourth is insurance data. Insurance data is insurance-related business data accumulated in the daily business activities of insurance companies, including claims data and safety credit data.
The fifth is the charging pile data. The charging pile data is the new energy charging pile data constructed and operated by the State Grid Corporation, including the position data, quantity data and usage data of the new energy vehicle charging pile.
Third, the Internet of Things data
Internet of Things data is traffic-aware data that the government invests in or builds:
The first is the traffic flow. It includes video detection, coil detection, geomagnetic detection, etc. of microwave detection, ultrasonic detection and passive sensing detection technology of active sensing technology.
The second is car networking data. Including taxi data, muck truck data, bus operation data, heavy truck freight data, passenger data, etc.
The third is road environmental monitoring data, including icing, stagnant water, visibility, temperature, etc.
The fourth is the monitoring data of the motor vehicle number plate, that is, the data of the bayonet, including the number plate, model, color and other data of the passing vehicles.
The fifth is traffic video surveillance data, including traffic monitoring information at intersections, road sections, and high altitudes. The focus is on the formation of video big data accumulation based on video structuring.
Fourth, Internet data
IoT data refers to the data generated by the Internet of Things companies through operations.
The first is navigation data. Including the data generated by the map navigation system of Internet of Things companies such as Gao De, Tencent, Sina, and Sogou.
The second is the network car data. Including the data generated by Didi, Shenzhou, Shouqi, Yidao, Meituan, Cao Cao and other Internet network car companies.
The third is takeaway data. Including the data generated by three large-scale enterprises such as Hungry, Meituan Takeaway, BAIDU Takeaway, etc., including the operation of smaller regional enterprises such as stupid bear cooking, home food meeting, word of mouth takeaway, and zero line. data.
The fourth is the express data. Including China Post, SF, Jingdong, rookie, Yuantong, Shentong, Debon and other large-scale enterprise operation data, in addition there are a large number of small-scale companies.
The fifth is to share bicycle data. Including Mobai, ofo, and small blue cars, etc., from the current view, Mobike's operational data is more valuable, and other operations have major problems.
The sixth is the personnel location data. Location data generated by many Internet portals including WeChat, QQ, Sogou, Meituan, and headlines are more valuable for location data generated by WeChat and QQ social platforms.
Conclusion
The purpose of traffic big data classification research:
First, in order to clarify the channels and methods for big data acquisition, only by scientific and accurate classification can we know who the data is supplied to? It is also possible to clarify the method of data aggregation and sharing.
Second, in order to clarify the content of traffic big data applications, only on the basis of clear data classification and data content, we can know how to use this data for traffic management.
Traffic big data can only play the role of big data when the types are as complete as possible and the content is as rich as possible, in order to truly play the role of big data to provide support and services for traffic management.
In this article, the author only has a relatively shallow classification of traffic big data. Due to the limitations of the understanding of traffic big data, it is inevitable that there are biases in concepts and concepts.
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