Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach

Muhammad Zahid, Yangzhou Chen, Arshad Jamal and Coulibaly Zie Mamadou
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Muhammad Zahid: College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
Yangzhou Chen: College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124, China
Arshad Jamal: Department of Civil Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
Coulibaly Zie Mamadou: Department of Artificial Intelligence and Management, Group Gema-Esi Business School/IA School, 61 bis rue des Peupliers, Boulogne-Billancourt, 92100 Paris, France

Sustainability, 2020, vol. 12, issue 2, 1-19

Abstract: Short-term traffic speed prediction is vital for proactive traffic control, and is one of the integral components of an intelligent transportation system (ITS). Accurate prediction of short-term travel speed has numerous applications for traffic monitoring, route planning, as well as helping to relieve traffic congestion. Previous studies have attempted to approach this problem using statistical and conventional artificial intelligence (AI) methods without accounting for influence of data collection time-horizons. However, statistical methods have received widespread criticism concerning prediction accuracy performance, while traditional AI approaches have too shallow architecture to capture non-linear stochastics variations in traffic flow. Hence, this study aims to explore prediction of short-term traffic speed at multiple time-ahead intervals using data collected from loop detectors. A fast forest quantile regression (FFQR) via hyperparameters optimization was introduced for predicting short-term traffic speed prediction. FFQR is an ensemble machine learning model that combines several regression trees to improve speed prediction accuracy. The accuracy of short-term traffic speed prediction was compared using the FFQR model at different data collection time-horizons. Prediction results demonstrated the adequacy and robustness of the proposed approach under different scenarios. It was concluded that prediction performance of FFQR was significantly enhanced and robust, particularly at time intervals larger than 5 min. The findings also revealed that speed prediction error (in terms of quantiles loss) ranged between 0.58 and 1.18.

Keywords: ITS; traffic simulation and modeling; travel speed prediction; fast forest quantile regression; Beijing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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