Traffic parallel method with mapping between microblog public sentiments and city road conditions

Abstract

The invention provides a traffic parallel method with mapping between microblog public sentiments and city road conditions. The method comprises steps of timely collecting and acquiring microblog message data related to specific city traffic information, and conducting four sub-steps for each acquired microblog message: (a) indicating original attributes of the microblog and mapping attributes in a formal way, (b) setting type attributes according to traffic topic classification data clusters, (c) extracting geographical position information according to city road data clusters and extracting specific topics feedback in the microblog according to traffic feedback information, and (d) calculating related index of each microblog according to the extracted basic attribute information. The index comprises credibility, affection degree and importance. Traffic information of the microblogs can be quickly classified and positioned; visualized display and precautions can be achieved for traffic accidents, traffic jams and feedback information based on internet information; and assistant decision-making performance is provided for city traffic management, planning, emergency plan and problem solutions.

Description

technical field [0001] The invention relates to the technical field of Internet data processing, and in particular relates to a traffic parallel method for mapping microblog public opinion and urban road conditions. Background technique [0002] At present, the main traffic data collection technologies include fixed sensor collection, floating vehicle collection and mobile perception collection. Fixed sensor technology uses sensor nodes installed on the road or paved in the road to form a sensor network to collect traffic data at fixed points, but generally there is dependence on the installation site and environment, and it faces huge initial investment, short life cycle, and high maintenance costs. question. Floating car technology mainly uses floating cars (taxis, bicycles, etc.) traveling in the road network to collect GPS estimation data when they are driving and upload them to the server. However, there are also problems such as large investment, lack of data of othe...

AI Technical Summary

Problems solved by technology

The two technologies also have problems such as low network positioning accuracy, unfixed collection frequency, and increased user perception burden caused by artificial triggering and monitoring.

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