Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

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.

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A Short-term Traffic Speed Prediction Model Based on LSTM Networks

International Journal of Intelligent Transportation Systems Research ◽

10.1007/s13177-021-00260-7 ◽

2021 ◽

Author(s):

Yu-Ling Hsueh ◽

Yu-Ren Yang

Keyword(s):

Prediction Model ◽

Short Term ◽

Model Based ◽

Speed Prediction ◽

Traffic Speed

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D-LSTM: Short-Term Road Traffic Speed Prediction Model Based on GPS Positioning Data

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2020.3030546 ◽

2020 ◽

pp. 1-10

Author(s):

Xianwei Meng ◽

Hao Fu ◽

Liqun Peng ◽

Guiquan Liu ◽

Yang Yu ◽

...

Keyword(s):

Prediction Model ◽

Road Traffic ◽

Short Term ◽

Gps Positioning ◽

Model Based ◽

Speed Prediction ◽

Traffic Speed

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A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework

Transportmetrica B Transport Dynamics ◽

10.1080/21680566.2015.1060582 ◽

2015 ◽

Vol 4 (3) ◽

pp. 159-186 ◽

Cited By ~ 20

Author(s):

Haizhong Wang ◽

Lu Liu ◽

Shangjia Dong ◽

Zhen Qian ◽

Heng Wei

Keyword(s):

Prediction Model ◽

Work Zone ◽

Short Term ◽

Vehicle Type ◽

Speed Prediction ◽

Traffic Speed

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Data Collection Time Intervals for Stochastic Short-Term Traffic Flow Forecasting

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2024-03 ◽

2007 ◽

Vol 2024 (1) ◽

pp. 18-26 ◽

Cited By ~ 26

Author(s):

Jianhua Guo ◽

Billy M. Williams ◽

Brian L. Smith

Keyword(s):

Data Collection ◽

Traffic Flow ◽

Short Term ◽

Time Intervals ◽

Traffic Flow Forecasting ◽

Collection Time

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An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

Wind Engineering ◽

10.1177/0309524x19849843 ◽

2019 ◽

Vol 44 (3) ◽

pp. 266-281 ◽

Cited By ~ 1

Author(s):

Zhongda Tian ◽

Yi Ren ◽

Gang Wang

Keyword(s):

Support Vector Machine ◽

Wind Speed ◽

Prediction Model ◽

Least Squares ◽

Support Vector ◽

Short Term ◽

Backtracking Search ◽

Wind Speed Prediction ◽

Search Optimization ◽

Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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