Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network

Short-term traffic speed prediction plays an important role in the field of Intelligent Transportation Systems (ITS). Usually, traffic speed forecasting can be divided into single-step-ahead and multi-step-ahead. Compared with the single-step method, multi-step prediction can provide more future tra...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2021-05, Vol.11 (10), p.4423
Main Authors: Feng, Bin, Xu, Jianmin, Zhang, Yonggang, Lin, Yongjie
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Correlation analysis
Datasets
Decision making
Deep learning
direct strategy
ensemble learning
Forecasting
Fuzzy logic
Intelligent transportation systems
Machine learning
Neural networks
Optimization
Predictions
speed detrending
Support vector machines
Time series
Traffic congestion
Traffic flow
Traffic models
Traffic speed
traffic speed multi-step prediction
Transportation networks
Travel
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Short-term traffic speed prediction plays an important role in the field of Intelligent Transportation Systems (ITS). Usually, traffic speed forecasting can be divided into single-step-ahead and multi-step-ahead. Compared with the single-step method, multi-step prediction can provide more future traffic condition to road traffic participants for guidance decision-making. This paper proposes a multi-step traffic speed forecasting by using ensemble learning model with traffic speed detrending algorithm. Firstly, the correlation analysis is conducted to determine the representative features by considering the spatial and temporal characteristics of traffic speed. Then, the traffic speed time series is split into a trend set and a residual set via a detrending algorithm. Thirdly, a multi-step residual prediction with direct strategy is formulated by the ensemble learning model of stacking integrating support vector machine (SVM), CATBOOST, and K-nearest neighbor (KNN). Finally, the forecasting traffic speed can be reached by adding predicted residual part to the trend one. In tests that used field data from Zhongshan, China, the experimental results indicate that the proposed model outperforms the benchmark ones like SVM, CATBOOST, KNN, and BAGGING.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11104423