Real-time traffic condition acquisition method based on mobile intelligent terminal

Abstract

The invention discloses a real-time traffic condition acquisition method based on a mobile intelligent terminal. The mobile intelligent terminal identifies the traffic condition according to sensor information, traces a running route in combination with a road database in real time and calculates the running time according to a traced result of the running route and uploads the real-time road condition information to a server; and the server analyses massive received real-time traffic condition information, removes repeated data, saves the result into a traffic condition database and estimates the real-time traffic condition for a minority of road sections in short of uploaded data according to the traffic condition data in the traffic condition database by the compressive sensing technology, to collect the real-time traffic condition of the whole urban traffic network. According to the invention, the mobile intelligent terminal can be used for collecting the real-time traffic condition information so as to obtain massive traffic condition data. The method has the characteristics of low cost, high accuracy, high data volume, good real-time property, high network coverage, good expansibility and the like, and has a quite wide application prospect.

Description

technical field [0001] The invention relates to a method for collecting traffic data in intelligent transportation, in particular to a method for collecting traffic road conditions based on a mobile intelligent terminal that requires high data accuracy and real-time performance. Background technique [0002] The increase in the number of vehicles has brought a series of problems such as traffic congestion, traffic accidents, and traffic pollution to the city. The intelligent transportation system is proposed to effectively solve the above problems. The most basic and critical issue in the development of intelligent transportation systems is how to collect real-time dynamic data of road traffic, so that the upper-level systems and modules of intelligent transportation can process, analyze and predict data, and realize real-time regulation of traffic information. There are many existing traffic data collection systems, and the present invention focuses on a real-time traffic ...

AI Technical Summary

Problems solved by technology

[0003] There are two relatively mature real-time traffic data collection methods: the first is the sensor-based real-time traffic data collection method, which includes active monitoring methods and passive detection methods. The sensors include piezoelectric sensors, buried coil detectors, microwave radars, Ultrasound, camera, etc. can collect and calculate road condition information including vehicle speed, traffic flow, lane occupancy rate, vehicle category information, etc. Although the principles and applicable occasions of various sensors are different, most of them are dependent on the installation site and environment. And it faces problems such as huge initial investment, high maintenance cost, small coverage, low data accuracy, and poor real-time performance. For taxis and buses equipped with GPS and wireless communication modules, when the floating vehicle is driving on the road, it periodically collects its own GPS data and sends it to the server, and the server estimates the driving route of the floating vehicle through map matching technology , and then get the travel time of the floating vehicle, the travel speed and the average speed of the road it passes

Compared with the first method, although this method has improved in cost and coverage, and the equipment is easy to maintain and will not be disturbed by other irrelevant factors such as weather and pedestrians, it still requires a large one-time investment. Accuracy is also limited by map matching techniques

Considering the communication cost, the collection frequency of GPS data of floating vehicles is often low, usually around 15 minutes, and the server can only use GPS data to screen and filter invalid data due to the lack of traffic context information, which makes data preprocessing difficult. It is very difficult to match with the map, which reduces the real-time performance of the data while increasing the burden on the server

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