Travel Time Modelling of Urban Roads By Application of Coyote Optimization-based Machine Learning Method

Document Type : Research Article

Authors

1 Transportation Engineering, Department of Civil Engineering, Amirkabir University of Technology, Tehran, Iran

2 Amir Kabir University of Technology

Abstract

Travel time prediction as an essential issue has been scrutinized in recent decades. To this end, various techniques are applied to estimate travel duration in dynamic networks and intelligent transportation systems. Accordingly, in this investigation, the prediction of travel time is considered by machine learning techniques. Initially, the experimental test is planned, and the travel time effective parameters are spotted. Subsequently, with the assistance of the floating car method, and My-tracks application, the data are collected in six elected roads. After data preparation, stop delay, grades, and the number of the lane are determined as the most effective travel time criteria. In this study, a novel machine learning technique based on the coyote optimization algorithm is introduced, and its precision is compared with five conventional regression models. Drawing on results, the accuracy of the coyote optimization algorithm-based machine learning technique is more than that of other prediction methods. The coefficient of determination of the introduced machine learning technique for training and testing data is equal to 0.746 and 0.724, respectively. Furthermore, coyote optimization algorithm-based machine learning estimates 73% of testing data with an error of fewer than 20 seconds.

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Main Subjects

References

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Amirkabir Journal of Civil Engineering
Volume 53, Issue 9
December 2021
Pages 3649-3664

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