Traffic text data-based speed prediction and journey planning method

Abstract

The invention belongs to the field of data mining, data analysis and deep learning, and provides a method for using traffic text data for speed prediction and travel planning. Firstly, collecting traffic text information data published by an internet social media platform in time; converting the unstructured traffic text data into structured information data through text classification, word segmentation and entity naming recognition methods, and storing the structured information data into a file; carrying out data fusion on the traffic flow speed data and the vectorized traffic text data; constructing and performing traffic flow speed data prediction analysis through an LSTM deep learning network model, and finally designing a dynamic path planning method based on a prediction analysis result through a traffic flow speed data result of the prediction analysis in combination with the content of a traffic event text in a corresponding time period. According to the invention, a more accurate and reliable path planning and guiding function can be provided for a user, an urban traffic management department is helped to quickly analyze the traffic situation, and urban traffic is effectively managed.

Description

technical field [0001] The invention belongs to the fields of data mining, data analysis and deep learning, and in particular relates to a method for using traffic text data in speed prediction and travel planning. Background technique [0002] With the promotion of urbanization and the development of modernization, urban transportation has become an indispensable part of our life, which is related to the development of urban economy and the construction of smart cities. While urban traffic is more convenient, a series of traffic problems have also emerged. For example, in order to alleviate the continuously growing traffic pressure in cities, many cities are widening and building new roads. However, urban road traffic resources are limited, and it is difficult to solve congestion. The key issue is not to build more roads, but how to make rational use of limited traffic resources to improve the operational efficiency of urban road networks. A large amount of traffic data is...

AI Technical Summary

Problems solved by technology

At present, most of the existing path planning systems are still static path planning methods or existing dynamic path planning methods, but they are often planned based on real-time road conditions, and lack of consideration of factors that can be foreseen in a short period of time

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