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.

在为交通应用程序提供所需信息时，预测准确的交通参数是基本且具有成本效益的。许多研究采用各种参数和机器学习技术来预测交通参数，例如行驶时间，速度和交通量。机器学习技术在预测流量方面取得了可喜的成果。但是，利用的数据大多数是在5、10或15分钟内汇总的。本研究试图通过实时预测信号交叉口的信号通过和左转运动的周期水平来弥合研究差距。所使用的数据仅限于走廊水平的上游和下游交叉口。为了实现这一目标，eXtreme Gradient Boosting（XGBoost），使用数据集开发了长期短期记忆（LSTM）和门控循环单元（GRU）模型，这些数据集包含来自不同使用周期数（4、6和8个周期）的变量。通过计算平均绝对误差（MAE）和均方根误差（RMSE）评估了这三个模型。结果表明，这三个模型的性能指标接近。与此同时。使用前六个周期的变量的GRU模型优于其他周期。遵循这种建模方法来预测不同时间范围（未来五个周期）的交通流量。在五个预测周期中，绩效指标值接近。期望该模型可以帮助获得十字路口的准确交通移动，这可以用于调整自适应信号时序并提高信号和十字路口的效率。