Next Article in Journal
Resilience and Vulnerability of Public Transportation Fare Systems: The Case of the City of Rio De Janeiro, Brazil
Next Article in Special Issue
An Analysis of the Interactions between Adjustment Factors of Saturation Flow Rates at Signalized Intersections
Previous Article in Journal
A Prototype that Fuses Virtual Reality, Robots, and Social Networks to Create a New Cyber–Physical–Social Eco-Society System for Cultural Heritage
Previous Article in Special Issue
Effect of the Parking Lane Configuration on Vehicle Speeds in Home Zones in Poland
Open AccessArticle

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

1
College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
2
College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124, China
3
Department of Civil Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
4
Department of Artificial Intelligence and Management, Group Gema-Esi Business School/IA School, 61 bis rue des Peupliers, Boulogne-Billancourt, 92100 Paris, France
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(2), 646; https://doi.org/10.3390/su12020646
Received: 11 December 2019 / Revised: 9 January 2020 / Accepted: 10 January 2020 / Published: 16 January 2020
(This article belongs to the Special Issue Road Traffic Engineering and Sustainable Transportation)
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. View Full-Text
Keywords: ITS; traffic simulation and modeling; travel speed prediction; fast forest quantile regression; Beijing ITS; traffic simulation and modeling; travel speed prediction; fast forest quantile regression; Beijing
Show Figures

Figure 1

MDPI and ACS Style

Zahid, M.; Chen, Y.; Jamal, A.; Mamadou, C.Z. Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach. Sustainability 2020, 12, 646.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop