A travel time prediction method based on multi-modal data fusion and multi-model integration

Abstract

The invention discloses a travel time prediction method based on multi-modal data fusion and multi-model integration. The travel time prediction method comprises a multi-modal data preprocessing module which extracts taxi travel data from taxi GPS track data according to the passenger carrying state; a multi-modal data analysis, feature extraction and feature fusion module which is used for extracting corresponding feature sub-vectors from the fields of taxi track data, weather data, driver portrait data and the like and completing feature splicing; and a multi-model integration module which is used for respectively establishing a gradient improvement decision tree model and a deep neural network model, and integrating prediction results of the models by using the decision tree model. According to the travel time prediction method, by fusing the multi-modal data such as taxi track data, weather data and driver portrait data, the factors influencing travel time are fully extracted and mined, and an integrated model based on a decision tree is established, so that higher travel time prediction accuracy is obtained at lower calculation cost.

Description

technical field [0001] The invention relates to a travel time prediction method based on multi-modal data fusion and multi-model integration, and belongs to the technical field of intelligent traffic information processing. Background technique [0002] Travel time prediction is a critical, complex, and challenging problem in Intelligent Traffic Systems (ITS) and location-based services. Traffic regulators can indirectly understand changes in urban flow through travel time. Real-time travel time prediction and prompts can also alleviate traffic congestion to a certain extent. Travel time estimation provides effective decision support for traffic flow control in ITS. Travel time prediction is also an important module of map navigation and travel service software, such as Baidu Maps, Didi Chuxing, etc. People can reasonably arrange and plan their travel activities through travel time estimation. [0003] Current travel time prediction methods can be divided into two types: se...

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: There are many complex situations in the travel time prediction problem. In order to overcome the deficiencies in the prior art, the present invention provides a travel time prediction method based on multi-modal data fusion and multi-model integration to solve the problem of arbitrary origin and destination in cities. The problem that it is difficult to obtain accurate prediction results for the travel time of the point itinerary

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