Short-term traffic flow prediction method based on Spark platform

Abstract

The invention provides a short-term traffic flow prediction method based on a Spark platform. A parallel KNN algorithm is applied to the field of short-term traffic flow prediction. Compared with the conventional KNN algorithm based on single-computer computing, the problems of small system storage capacity and slow computing speed in performing data computing on the single physical computer can be solved by the method, and the problem of low neighbor matching efficiency in the neighbor search process of the KNN algorithm can also be solved by the method. According to the method, the computing efficiency of the algorithm can be enhanced under the premise of guaranteeing prediction precision, the practicality of the KNN prediction algorithm can be effectively improved and the system has great extensibility and speed-up ratio. The method also has reference meaning for other applications requiring large-scale data processing.

Description

technical field [0001] The invention belongs to the field of cloud computing and data mining, and in particular relates to a short-term traffic flow prediction method based on a Spark platform. Background technique [0002] Short-term traffic flow prediction refers to the prediction of the traffic flow of a specific road segment in the next few minutes. Short-term traffic flow prediction is an important way to solve urban traffic congestion, and with the increase of the number of cars in the city, the amount of traffic flow data increases sharply. Single-machine short-term traffic flow prediction based on location data faces problems such as large data volume, difficulty in storage, and long calculation time. The cloud computing platform has strong technical advantages in massive data storage and large-scale parallel real-time processing, which can effectively improve the computational efficiency of short-term traffic flow forecasting under the premise of ensuring the forec...

AI Technical Summary

Problems solved by technology

For short-term traffic flow prediction, with the double increase of traffic data volume, the traditional stand-alone data mining mode has been difficult to meet the practical application requirements

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