Applications of machine learning methods in port operations–A systematic literature review
Ports are pivotal nodes in supply chain and transportation networks, in which most of the
existing data remain underutilized. Machine learning methods are versatile tools to utilize …
existing data remain underutilized. Machine learning methods are versatile tools to utilize …
Machine learning for international freight transportation management: A comprehensive review
Abstract Machine learning (ML) offers a promising avenue for international freight
transportation management (IFTM) given its capability to harness the power of data that …
transportation management (IFTM) given its capability to harness the power of data that …
Statistical methods versus neural networks in transportation research: Differences, similarities and some insights
MG Karlaftis, EI Vlahogianni - Transportation Research Part C: Emerging …, 2011 - Elsevier
In the field of transportation, data analysis is probably the most important and widely used
research tool available. In the data analysis universe, there are two 'schools of thought'; the …
research tool available. In the data analysis universe, there are two 'schools of thought'; the …
Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting
In this paper, the number of goods subject to inspection at European Border Inspections
Post are predicted using a hybrid two-step procedure. A hybridization methodology based …
Post are predicted using a hybrid two-step procedure. A hybridization methodology based …
[HTML][HTML] Short-term prediction of outbound truck traffic from the exchange of information in logistics hubs: A case study for the port of Rotterdam
Short-term traffic prediction is an important component of traffic management systems.
Around logistics hubs such as seaports, truck flows can have a major impact on the …
Around logistics hubs such as seaports, truck flows can have a major impact on the …
From digitalization to data-driven decision making in container terminals
With the new opportunities emerging from the current wave of digitalization, terminal
planning and management need to be revisited by taking a data-driven perspective …
planning and management need to be revisited by taking a data-driven perspective …
Freight production of agricultural commodities in India using multiple linear regression and generalized additive modelling
S Dhulipala, GR Patil - Transport Policy, 2020 - Elsevier
Freight transportation has a key role in the economic competitiveness of any nation. India is
one of the fastest-growing nations in the world; its agricultural sector plays a vital role in …
one of the fastest-growing nations in the world; its agricultural sector plays a vital role in …
Modeling categorized truck arrivals at ports: Big data for traffic prediction
N Li, H Sheng, P Wang, Y Jia… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Accurate truck arrival prediction is complex but critical for container terminals. A deep
learning model combining Gated Recurrent Unit (GRU) and Fully Connected Neural …
learning model combining Gated Recurrent Unit (GRU) and Fully Connected Neural …
Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia
We propose a new approach to forecasting total port container throughput: to generate
forecasts based on each of the port's terminals and aggregate them into the total throughput …
forecasts based on each of the port's terminals and aggregate them into the total throughput …
A two‐stage forecasting approach for short‐term intermodal freight prediction
JA Moscoso‐López, I Turias… - International …, 2019 - Wiley Online Library
The forecasting of the freight transportation, especially the short‐term case, is an important
topic in the daily supply chain management. Intermodal freight transportation is subject to …
topic in the daily supply chain management. Intermodal freight transportation is subject to …