Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

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

Li, N, Sheng, H, Wang, P, Jia, Y, Yang, Z and Jin, Z (2022) Modeling Categorized Truck Arrivals at Ports: Big Data for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems, 24 (3). pp. 2772-2788. ISSN 1524-9050

Modeling Categorized Truck Arrivals at Ports Big Data for Traffic Prediction.pdf - Accepted Version

Download (2MB) | Preview


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.

Item Type: Article
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: 0801 Artificial Intelligence and Image Processing; 0905 Civil Engineering; 1507 Transportation and Freight Services; Logistics & Transportation
Subjects: H Social Sciences > HE Transportation and Communications
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
SWORD Depositor: A Symplectic
Date Deposited: 21 Dec 2022 15:14
Last Modified: 15 Mar 2023 12:15
DOI or ID number: 10.1109/TITS.2022.3219882
URI: https://researchonline.ljmu.ac.uk/id/eprint/18494
View Item View Item