Travel time prediction utilizing hybrid deep learning models
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Journal ArticleDate:
2023Access:
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Dhivya Bharathi, Juan Manuel González Sopeña, Siobhan Clarke, Bidisha Ghosh, Travel time prediction utilizing hybrid deep learning models, Transportation Research Record: Journal of the Transportation Research Board, 2023Download Item:
Abstract:
Travel time prediction is vital to the development and maintainence of advanced intelligent transportation system technologies. The travel time on a road segment is dependent on various factors like dynamic traffic demands, incidents, weather conditions, and geometric factors. However, uncertainties associated with prediction performance consistency may reduce the
effectiveness of such systems. To tackle these challenges, this paper proposes a hybrid deep learning algorithm-based methodology by integrating variational mode decomposition, multivariate long short-term memory, and quantile regression to predict estimates of travel time ranges instead of single-point predictions. Travel time data collected from loop detectors on
motorways near the city of Dublin, Republic of Ireland were modeled. The proposed method was evaluated using various
design scenarios and was found to perform efficiently in comparison with conventional deep learning algorithms.
Author's Homepage:
http://people.tcd.ie/bhaskardhttp://people.tcd.ie/bghosh
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PUBLISHED
Author: Bhaskaran, Dhivyabharathi; Ghosh, Bidisha
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Journal ArticleSeries/Report no:
Transportation Research Record: Journal of the Transportation Research Board;Availability:
Full text availableKeywords:
data and data science, advanced traffic management systems, intelligent transportation systems, Traffic Prediction, traveler information systemsSubject (TCD):
Digital Engagement , Smart & Sustainable Planet , Transportation EngineeringDOI:
https://doi.org/10.1177/0361198123118296Source URI:
https://trafficdata.tii.ie/publicmultinodemap.aspMetadata
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