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

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

Li, Na / Sheng, Haotian / Wang, Pingyao / Jia, Yulin / Yang, Zaili / Jin, Zhihong

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.