Road congestion monitoring method based on mobile phone communication big data

Abstract

The invention provides a road congestion monitoring method based on mobile phone communication big data. The method comprises the following steps of: S1, carrying out real-time communication data processing of a mobile phone, and obtaining a 5-minute position snapshot graph of a user, S2, constructing a road network model, and mapping cellular communication base stations and roads to obtain a basestation road point matching table, S3, user path trajectory fitting and road section speed calculation, and S4, visualizing the road congestion, and monitoring the road congestion. Fitting of the cellular base station and the road is realized; based on the mobile phone communication big data and according to a kafka + hadoop + redis + spark streaming big data real-time stream processing technology, all-weather all-road-section monitoring of the road congestion condition is achieved, display and automatic early warning are carried out with the assistance of a visual interface, query and analysis of historical conditions can be achieved, and compared with a traditional manual inspection or fixed-point equipment monitoring mode, the cost and the technical implementation difficulty are greatly reduced.

Description

technical field [0001] The invention relates to the field of road traffic congestion monitoring, and more particularly, to a road congestion monitoring method based on mobile phone communication big data. Background technique [0002] Urban road traffic provides travel for people's work, life, cultural and entertainment activities, and is responsible for the accessibility of passenger flow and logistics in various regions of the city, and the connection and circulation of the city to the traffic outside the city. With the rapid development of social economy and the continuous improvement of people's living standards, urban roads are getting wider and wider, but at the same time, the number of motor vehicles and the population of urban agglomeration are also increasing. Road traffic congestion will not only bring a lot of inconvenience to people's daily life and work, but also restrict economic growth, accelerate the deterioration of the urban environment, and bring a lot of ...

AI Technical Summary

Problems solved by technology

With the rapid development of social economy and the continuous improvement of people's living standards, urban roads are getting wider and wider, but at the same time, the number of motor vehicles and the population of urban agglomeration are also increasing. Road traffic congestion will not only bring a lot of inconvenience to people's daily life and work, but also restrict economic growth, accelerate the deterioration of the urban environment, and bring a lot of trouble to people

[0003] At present, traffic control departments mainly rely on manual inspections and fixed-point equipment monitoring to monitor road congestion. These methods have high costs, limited road coverage, passive monitoring, and low levels of automation and intelligence. Defects such as difficulty in data recording and historical analysis

The existing manual inspection method can only monitor the traffic operation of the inspection section at the time of inspection, and cannot complete the inspection of all road sections around the clock. It is difficult to record and save the data of manual inspection, and the labor cost is high. The efficiency is low; and the existing fixed-point equipment monitoring method can only monitor the traffic conditions in the vicinity of the fixed-point equipment. It is a passive monitoring, the information is incomplete, and the automatic and intelligent active monitoring has not been realized. The operation of the road requires a lot of investment in equipment and construction manpower, and inevitably involves coordination between equipment from different manufacturers and different systems. In addition, the processing and storage of video data also requires more capital and technical investment.

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