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题名Long-Term Traffic Forecast Using Neural Network and Seasonal Autoregressive Integrated Moving Average: Case of a Container Port
作者
发表日期2022-08-01
发表期刊Transportation Research Record
ISSN/eISSN0361-1981
卷号2676期号:8页码:236-252
摘要

Long-term insight into maritime traffic is critical for port authorities, logistics companies, and port operators to proactively formulate suitable policies, develop strategic plans, allocate budget, and preserve and improve competitiveness. Forecasting freight rate is a spotlight in port traffic literature, but relatively little research has been directed at forecasting long-term vessel traffic trends. Based on forecast long-term freight rate input provided by the recent 10-year strategic planning of the port of Rajaee, the largest port of Iran, the paper implements seasonal autoregressive integrated moving average (SARIMA) and neural network (NN) models to forecast its container vessel traffic between 2020 and 2025. A database consisting of monthly container traffic data for this port from 1999 to 2019 is utilized. The comparison between the two forecasting models is fulfilled by benchmarking the naïve method. The results reveal the superiority of the NN model over SARIMA in this practice. Considering NN model outputs, the port should expect a significant increase in Panamax and Over-Panamax vessels in the future, and, if not timely addressed, this would result in a systemic queue in the port of Rajaee. That said, the approach can be implemented in port planning and design to avoid under-or over-estimations in such capital-intensive projects.

关键词artificial intelligence artificial intelligence and advanced computing applications container data analytics data and data science freight transportation data logistics machine learning (artificial intelligence) marine marine transportation (water transportation) neural networks port ports and channels vessels
DOI10.1177/03611981221083311
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收录类别SCIE
语种英语English
WOS研究方向Engineering ; Transportation
WOS类目Engineering, Civil ; Transportation ; Transportation Science & Technology
WOS记录号WOS:000773783500001
Scopus入藏号2-s2.0-85136168265
引用统计
被引频次:13[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/9806
专题工商管理学院
通讯作者Gargari, Negar Sadeghi
作者单位
1.Cork University Business School,University College Cork,Ireland
2.Jacobs,Toronto,Canada
3.Department of Civil and Environmental Engineering,Tarbiat Modares University,Tehran,Iran
4.Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,China
5.St. John’s College,University of Manitoba,Winnipeg,Canada
推荐引用方式
GB/T 7714
Gargari, Negar Sadeghi,Panahi, Roozbeh,Akbari, Hassanet al. Long-Term Traffic Forecast Using Neural Network and Seasonal Autoregressive Integrated Moving Average: Case of a Container Port[J]. Transportation Research Record, 2022, 2676(8): 236-252.
APA Gargari, Negar Sadeghi, Panahi, Roozbeh, Akbari, Hassan, & Ng, Adolf K.Y. (2022). Long-Term Traffic Forecast Using Neural Network and Seasonal Autoregressive Integrated Moving Average: Case of a Container Port. Transportation Research Record, 2676(8), 236-252.
MLA Gargari, Negar Sadeghi,et al."Long-Term Traffic Forecast Using Neural Network and Seasonal Autoregressive Integrated Moving Average: Case of a Container Port". Transportation Research Record 2676.8(2022): 236-252.
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