Predicting cycle-level traffic movements at signalized intersections using machine learning models. (March 2021)

• Record Type: Journal Article
• Authors: Mahmoud, Nada; Abdel-Aty, Mohamed; Cai, Qing; Yuan, Jinghui
• Is Part Of: Transportation research. Volume 124(2021)
• Publication Date: 2021-03
• Subjects: Cycle-level -- Deep learning -- Machine learning -- Traffic prediction -- Turning movement counts; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102930
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Predict cycle-level real-time through and left-turn movements at intersections. Compare between different machine learning prediction models. Predict movements at signalized intersections for different time horizons. Abstract: Predicting accurate traffic parameters is fundamental and cost-effective in providing traffic applications with required information. Many studies adopted various parametric and machine learning techniques to predict traffic parameters such as travel time, speed, and traffic volume. Machine learning techniques have achieved promising results in predicting traffic volume. However, the utilized data were mostly aggregated in 5, 10, or 15 min. This study attempts to bridge the research gap by predicting signal cycle-level through and left-turn movements in real-time at signalized intersections. The utilized data were limited to the upstream and downstream intersections at the corridor level. Aiming to achieve this objective, eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models were developed using datsets that contain variables from different number of utilized cycles (4, 6, and 8 cycles). The three models were evaluated by calculating Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results showed that the performance measures for the three models were close. Meanwhile. the GRU model using variables from six previous cycles outperformed the others. This modelling approach was followed to predict traffic movements for different time horizons (five cycles ahead). The performance measures values were close for the five predicted cycles. It is expected that the model could help in obtaining accurate traffic movement at intersections, which could be used for adjusting adaptive signal timing and improve signal and intersections' efficiency.