Shulou(Shulou.com)06/03 Report--

With the emergence of urban traffic brain, big data has been widely used in the fields of traffic management and transportation. Big data has gradually become the basic resource of urban traffic management, and is increasingly showing its key role and position in traffic management. Many companies are biased in their understanding of traffic data. They think that big data is big data, ignoring many characteristics of big data. The author believes that only by meeting the requirements of "big data has a lot of data and a large amount of data" can it be regarded as real traffic data.

The second characteristic of traffic big data is its correlation with the transportation industry, that is, traffic-related data can be regarded as traffic big data, and the data generated by traffic management is not necessarily traffic big data. Data that can serve traffic management can be regarded as traffic big data.

To sum up, the connotation of Traffic big data is multi-source heterogeneity, and its denotation has rich correlation. Traffic data can be classified into four categories: government data, operation data, Internet of things data and Internet data.

I. Government data

Government data refers to the data generated in government management. The generation of government data depends on the information construction of various government departments. In other words, the data generated by the information systems of various government departments are government data. According to the needs of traffic management, the government data related to traffic management can be subdivided into the following parts:

1. The public security traffic management data mainly comes from the public security traffic police information system, including traffic management, driver management, traffic violations, traffic accidents and other business data. it also includes police data, publicity data, road data, equipment data, facility data, police data, equipment. Data and so on. 2. The transportation data mainly comes from the information system of the Transport Committee or the Transport Bureau, including passenger data, freight data, logistics data, public transport data, track data, transportation hub data, taxi data, road data, bridge and tunnel data, maintenance data, maintenance data and so on. 3. Planning data. The planning information system mainly comes from the land planning department, especially the "multi-integration" system built in recent years, which realizes the integration of multi-sector planning and greatly promotes the application of planning data in the field of transportation. 4. Meteorological data. It mainly comes from the information systems of meteorological departments, including meteorological satellites, high-altitude meteorological balloons, ground meteorological monitoring stations and so on. The key point is the forecast data of disastrous weather such as rain, snow, fog, ice, wind and so on. 5. Data of industrial and commercial legal persons. It mainly comes from the industrial and commercial information management system of × × department. Focus on the data of transportation-related enterprises as legal persons, such as passenger transport, freight transport, logistics, auto repair shop and so on. 6. Urban construction data. Mainly from the information system of housing and urban construction departments, including housing purchase, second-hand housing, construction site, construction management, dredger and other data. 7. Large-scale activity data. Mainly from the tourism committee department of the information management system, including ticket data, parking data, VIP data, actor data and so on. 8. Complete data. Including business and personal data dishonesty, the ninth is traffic card data. Traffic card data is the card usage data issued by the traffic management department, including personnel information, usage information and so on.

2. Operation data

Operational data refers to the data generated in the operation and production activities of enterprises authorized by the state.

1. Telecom data. Including China Telecom, China Unicom, China Mobile and other telecom operators in the business activities generated by the installation data, user data, location data, telephone bill data. 2. Railway passenger transport data. The railway passenger transport data are generated by the operation of the railway department, including ticket purchase data, train number data, time data and delay data. 3. Civil aviation passenger data. Civil aviation passenger transport data is the data accumulated by airlines in their daily business activities, including passenger data, flight data, delay data and so on. 4. Insurance data. Insurance data are insurance-related business data accumulated by insurance companies in their daily business activities, including claim data, safety credit data and so on. 5. Charging pile data. The charging pile data is the new energy charging pile data built and operated by the State Grid Corporation, including the location data, quantity data and utilization data of the new energy vehicle charging pile.

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III. Internet of things data

Internet of things data is traffic perception data invested or authorized by the government.

1. Traffic flow. It includes microwave detection, ultrasonic detection and video detection, coil detection and geomagnetic detection. 2. Vehicle network data. Including taxi data, sand truck data, bus operation data, heavy truck freight data, passenger data and so on. 3. Road environment monitoring data, including ice, water, visibility, temperature and so on. 4. License plate monitoring data, that is, card data, including license plate, model, color data of passing vehicles. 5. Traffic video surveillance data, including traffic monitoring information at intersections, road sections and high altitude areas, mainly focus on the formation of large-scale video data accumulation based on video structure.

IV. Internet data

Internet of things data refers to the data generated by Internet of things companies through their operations.

1. Navigation data. Including Gao de, Baidu, Tencent, Sina, Sogou and other Internet of things companies map navigation system generated data. 2. Data of online car reservation. Including data generated by Internet car companies such as Didi, Shenzhou, first Automobile, Yidao, Meituan, Cao Cao and so on. 3. Take out the data. Including hunger, beauty group takeout, Baidu takeout and other three large enterprises data, including silly bear cooking, home cooking, word-of-mouth takeout, zero line and other small area business data. 4. Fast data. Including China Post, Shunfeng, JD.com, Rookie, Yuantong, Shentong, Debang and other large enterprise operation data, there are also a large number of small enterprises. 5. Share bicycle data. Including MOBAI, OFO, small Blue car and other enterprises, from the current point of view, the operation data of MOBAI is more valuable, other operations have big problems.

Only when the type of traffic data is as complete as possible and the content is as rich as possible, can we give full play to the role of big data, really play the role of big data, and provide support and services for traffic management. In fact, this is not only reflected in the traffic data, any data volume talk results are self-deceiving, only with a lot of prepared data to support the results are the closest to the reality, this is the charm of big data, we make every idea reasonable.

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