What are the applications of big data and data mining in the field of transportation?

Generally speaking, the core of the so-called wisdom exchange is large numbers.

According to, artificial intelligence and the Internet of Things, big data in a variety of sensors, cameras and even manual collection, big data for artificial intelligence to provide raw materials, for the control of Internet of Things equipment to provide decision-making basis, in general, big data for smart transportation in the following aspects of the role, perception - the entire city traffic conditions, such as vehicle information, road information, etc. to form a multi-dimensional perception optimization - traffic signal optimization, bus route optimization and so on.

Control – ramp control, special focus vehicle management.

Dispatching - intelligent adjustment according to traffic flow, emergency response service - real-time release of traffic information, convenient service.

The application of big data in the field of transportation can improve urban traffic congestion, improve road capacity, reduce the incidence of traffic accidents, etc., and the specific applications are as follows:

1.Traffic flow: By analyzing historical traffic flow data and real-time vehicle location and other information, it is possible to optimize traffic light control or road condition navigation prompts for future traffic flow.

2.Road network optimization: By collecting traffic data and map data, the entire city's road network is modeled and analyzed, bottlenecks and congestion points are identified, and optimization solutions are proposed, such as adding traffic lights, building new roads, and adjusting traffic management policies.

3.Smart Travel Bucket Trillion Service: Real-time traffic information and public transportation arrival time are displayed through mobile apps or public screens to provide citizens with a more intelligent way to travel and reduce traffic congestion.

4.Traffic accident prevention: By monitoring the driving speed and density of vehicles in various sections of the city, and using technologies such as machine learning algorithms, we look for subtle abnormal changes that may cause accidents, so as to detect and avoid traffic accidents early.

In short, the application of big data in the field of transportation provides a more accurate, efficient and scientific means for urban transportation management, so as to effectively solve the problem of urban transportation.

1. From single scattered to resource integration: Single-scattered includes scattered data, scattered technology, scattered business and scattered applications. After China's large-scale informatization construction, most departments related to transportation have built their own informatization systems and accumulated a large amount of data.

However, at present, too much data information only exists in the vertical business and single application of a single department, and there is a lack of open communication between departments, resulting in fragmentation of data resources and fragmentation of information, and the degree of data sharing is not high.

2. From data statistics to intelligent auxiliary decision-making: traffic big data has a large volume and a wide variety, including bayonet, road monitoring, electronic police, traffic signal control, traffic guidance information, vehicle driving management, traffic accidents, parking lots, operating vehicles, vehicles, stations, bus route networks, vehicle positioning and other data. However, in the face of such massive and complex data, most of the current background data processing still uses traditional statistical analysis methods, or through the comparison, accumulation, and percentage calculation of single dimensional data, a simple graphical model is generated to assist decision-making, or a simple model algorithm based on finite dimensional data is the key parameter of the key parameters.

3. From focusing on data processing to running through the whole life cycle of data: the construction of a complete big data platform, including data collection, storage, processing and display, the main challenges lie in the following aspects. The diversity of data types, the exponential growth of unstructured data:

In the process of intelligent transportation construction and operation, it is necessary to deal with structured data, as well as a large number of unstructured data such as monitoring and bayonet electro-hydraulic police; Traditional relational databases can only analyze and process determined data relationships, but cannot do anything about massive data, especially semi-structured and unstructured data.

Generally speaking, the core of the so-called intelligent transportation is big data, artificial intelligence and the Internet of Things, big data is the best in a variety of sensors, cameras and even manual collection, big data provides raw materials for artificial intelligence, and provides decision-making basis for the control of Internet of Things devices.

Optimization – traffic signal optimization, bus route optimization, etc.

Control - lead ramp control, special key vehicle management.

Dispatch – Intelligently adjust according to traffic flow to respond to emergencies.

Service - real-time release of traffic information, convenient service.

Intelligent transportation is the key to improving urban mobility. Therefore, timely and accurate acquisition of traffic data and construction of traffic data processing model are the prerequisites for the construction of intelligent transportation, and this problem can be solved by big data technology.

The overall framework of intelligent transportation mainly includes the physical perception layer, software application platform and the application of analysis and optimization management. Among them, the physical perception layer is mainly the perception and collection of traffic conditions and traffic data. The software application platform integrates and converts the information of each sensing terminal to support the construction of an application system for analysis, early warning and optimal management; The analysis and optimization management applications mainly include traffic planning, traffic monitoring, intelligent guidance, intelligent parking and other application systems.

The system uses advanced monitoring, intelligent identification and information technology means to increase the manageable space, time and scope, and continuously improve the breadth, depth and fineness of management. The whole system is composed of an integrated information application platform, a signal control system, a monitoring system, a smart bayonet system, an electronic police system, an information collection system, an information release system, etc. In order to achieve four goals:

Improve traffic capacity, reduce traffic accidents, crack down on violations, and travel information services.

In the process of building smart transportation in various cities, more and more data such as monitoring, bayonet electric alarm, road condition information, control information, operation information, GPS positioning information, RFID identification information and other data will be generated, and the amount of data generated every day can reach the PB level, and show exponential growth.