Vehicle Transit Time Prediction Based on Multi-model Fusion

doi:10.3969/j.issn.1006-2475.2019.02.012

Abstract

Abstract: The time that a vehicle passes through a certain road network is one of an important indicator to measure traffic congestion degree. In order to improve the prediction accuracy of vehicle transit time, it is necessary to consider not only the influence of data acquisition accuracy but also the choice of model. This paper proposes a method of the multi-model fusion to predict vehicle transit time, and it is found that the multi-model fusion has higher prediction accuracy. Vehicle traffic monitoring data of three intersections of a highway is empirical data. Comparing the multi-model fusion algorithm with a single model, it shows the application potential of multi-model fusion algorithm in the field of traffic congestion management.

Key words: multi-model fusion, vehicle transit time, traffic congestion

CLC Number:

TP391

LIU Yin-ping, MA Shao-hui. Vehicle Transit Time Prediction Based on Multi-model Fusion[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.02.012.

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