Modeling Categorized Truck Arrivals at Ports: Big Data for Traffic Prediction

Abstract

Accurate truck arrival prediction is complex but critical for container terminals. A deep learning model combining Gated Recurrent Unit (GRU) and Fully Connected Neural Network (FCNN), is proposed to predict daily truck arrivals using fusion technology. The model can efficiently analyze sequence and cross-section data sets. The new feature in the new model lies in that it, for the first time, incorporates the new parameters influencing traffic volumes such as the vessel-related information, arrival weekdays, and weather conditions into the long-time series of truck arrivals. Furthermore, truck arrivals are predicted in three groups based on their movement purposes: pick-up, delivery, and dual. it also contributes to the literature in a sense that the performance of the model is tested using real big data from a world-leading container port in Southern China. The results generate insightful managerial implications for guiding port traffic management in a generic manner. It reveals the relation of export container arrivals with the Container Yard (CY) closing time of a specific vessel. It is demonstrated the proposed model outperforms the currently available methods with an improved accuracy rate of prediction by 23.44% (dual), 32.09% (pick-up), and 26.99% (delivery), respectively. As a result, the model can better reflect reality compared to the existing ones in the literature. It is also evident that the 3-categorized prediction model can significantly help increase prediction accuracy in comparison with the 2-categorized methods used in practice.

Enhancing Safety and Efficiency in Automated Container Terminals: Route Planning for Hazardous Material AGV Using LSTM Neural Network and Deep Q-Network

“…The operational dynamics of this yard, where Automated Guided Vehicles (AGVs) and cranes diligently operate, are integral to the overall efficiency of the terminal's functions. The correlation between the yard's operational efficiency and the terminal's overall performance has been underscored in numerous studies (Gunawardhana et al, 2021;Li et al, 2021Li et al, , 2023Skaf et al, 2021;Tan et al, 2024;Xiang et al, 2022). Therefore, a substantial number of scholars have dedicated their efforts to enhancing work efficiency and striving for practicality (Wang et al, 2018;Zhao et al, 2020;Zhen et al, 2012;Zhu et al, 2012).…”

Harnessing the Power of Stacked GRU for Accurate Weather Predictions

“…GRU models have been widely used in various domains, including electric load forecasting [33], solar irradiance forecasting [34], precision agriculture [35], carbon dioxide concentration prediction [36], traffic prediction [37], landslide displacement prediction [38], wind speed and temperature forecasting [39], wildfire detection [40], and solar radiation prediction [41]. The advantages of GRU models in weather prediction include their simplicity and ease of implementation [33], ability to capture long-term dependencies in sequential data [34], improved performance with attention mechanisms [33], and computational efficiency compared to other recurrent neural network models [37]. GRU models have shown promising results in accuracy, prediction performance, and efficiency in various weather prediction tasks, making them a suitable choice for this analysis.…”

“…The expected benefits of using a Stacked GRU model for weather prediction using the Denpasar Weather Data include improved prediction accuracy, capturing long-term dependencies in weather patterns, and efficient computation. Stacked GRU models have been shown to outperform other models in various prediction tasks, such as uncertainty estimation [45], sentiment analysis [46], parking occupancy prediction [47], traffic prediction [37], precipitation forecasting [12], and disease prediction [48]. The advantages of using Stacked GRU models include their ability to handle long-term dependencies [26], capture complex patterns in sequential data [22], and improve prediction accuracy [42].…”