Main applications of intelligent transportation system

Intelligent transportation system (ITS) is the predecessor of intelligent transportation system (ITS), which was proposed by the United States in the early 1990s.

In 2009, IBM put forward the concept of intelligent transportation. Intelligent transportation is based on intelligent transportation and integrates high-tech IT technologies such as Internet of things, cloud computing, big data, mobile Internet, etc., and collects traffic information through high-tech to provide traffic information service under real-time traffic data. A large number of data processing technologies such as data model and data mining are used to realize the systematicness, real-time, interactive information exchange and extensive service of intelligent transportation.

Intelligent transportation system mainly solves the application requirements in four aspects

·Real time traffic monitoring. Know where the traffic accident happened, where the traffic congestion, which road is the most unblocked, and provide it to drivers and traffic management personnel with the fastest speed;

·Public vehicle management. The two-way communication between drivers and dispatching management center is realized to improve the operation efficiency of commercial vehicles, buses and taxis;

·Travel information service. Through multi-media and multi-terminal, it can provide comprehensive traffic information to travelers;

·Vehicle auxiliary control. Using real-time data to assist the driver or replace the driver’s autopilot.

Data is the foundation and lifeblood of intelligent transportation. Any of the above applications are based on the real-time acquisition and analysis of massive data. Location information, traffic flow, speed, occupancy, queue length, travel time, interval speed are the most important traffic data.

The big data platform of the Internet of things collects and stores massive traffic data, at the same time, it conducts deep-seated data mining on the associated user information and location information, and finds the useful value hidden in the data. For example, the user behavior trajectory model organized by user ID and timeline actually records the user’s activities in the real world, which reflects the individual’s intention, preference and behavior mode to a certain extent. Mastering these is very helpful for intelligent transportation system to provide personalized travel information push service.