Method for identifying traffic information in social media

Abstract

The invention discloses a method for identifying traffic information in social media. The method comprises the following steps of: 1, preprocessing social media data; 2, classifying the social media data; and 3, carrying out named entity identification on the social media data. According to the method, machine learning technologies such as a polynomial model, a conditional random field algorithm and the like are utilized to carry out classification and named entity identification on social media information, so as to extract information related to traffic from the social media and then extractlocations of incidents in the information. The method is capable of timely providing traffic public opinions and information such as situations, influence ranges, origins and the like of emergent traffic incidents for the traffic management department. In regions, the traffic information acquisition and construction of which are relatively weak, the method is capable of providing information supplementation for the traffic management.

Description

Technical field [0001] The invention relates to a method for identifying traffic information in social media. Background technique [0002] Traffic data collection methods based on floating car GPS trajectory, magnetic induction coil, video surveillance and other data have played an important role in the construction and development of intelligent transportation. The applications of these methods are as follows: First, based on floating car GPS data , It can obtain the information of the running speed and travel time of the road section, thereby judging the traffic situation of the road section, and improving the disadvantages of high investment and low accuracy of traditional traffic detection methods. Second, the induction data of the road coil can obtain the speed information of the vehicle. Third, the use of video processing technology can analyze traffic flow characteristics and improve the accuracy of vehicle recognition. [0003] The structure and characteristics of these ...

AI Technical Summary

Problems solved by technology

[0004] To sum up, most of the existing traffic information detection technologies rely on hardware, and its detection range is limited by the number and scope of hardware deployment, and the data of detection equipment is relatively mechanical, so it is difficult to capture multi-level and multi-dimensional traffic information

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