Machine learning for international freight transportation management: A comprehensive review

Barua, Limon; Zou, Bo; Zhou, Yan

doi:10.1016/j.rtbm.2020.100453

Title: Machine learning for international freight transportation management: A comprehensive review

Journal Article · Sun Mar 01 00:00:00 EST 2020 · Research in Transportation Business & Management

DOI:https://doi.org/10.1016/j.rtbm.2020.100453· OSTI ID:1780682

Barua, Limon; Zou, Bo; Zhou, Yan

Machine learning (ML) offers a promising avenue for international freight transportation management (IFTM) given its capability to harness the power of data that have become increasingly available to freight transportation researchers and practitioners. This paper conducts a comprehensive investigation of the state-of-the-art in developing ML models for applications to different aspects of IFTM. We start by giving an overview of various fundamental ML methods. Then, how different ML methods have been employed, adapted, and applied to a multitude of subject areas in IFTM are discussed, including demand forecast, operation and asset maintenance, and vehicle trajectory and on-time performance prediction. The potential data sources that may be used to develop ML models are further examined. Subsequently, a synthesis of the exiting work is performed to identify the specific topics addressed in the existing research, ML methods used, the trends of research, and opportunities for further explorations. Four directions for future research are proposed in the end.

Cite

Export

Save

Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Office of Vehicle Technologies (VTO)

DOE Contract Number:: AC02-06CH11357

OSTI ID:: 1780682

Journal Information:: Research in Transportation Business & Management, Vol. 34, Issue Special Issue

Country of Publication:: United States

Language:: English

References (41)

Application of Artificial Neural Networks for Shortterm Prediction of Container Train Flows in Direction of China – Europe via Kazakhstan Abdirassilov, Zhomart; SŁAdkowski, Aleksander Transport Problems, Vol. 13, Issue 4 https://doi.org/10.20858/tp.2018.13.4.10	journal	January 2019
A review on applications of ANN and SVM for building electrical energy consumption forecasting Ahmad, A. S.; Hassan, M. Y.; Abdullah, M. P. Renewable and Sustainable Energy Reviews, Vol. 33 https://doi.org/10.1016/j.rser.2014.01.069	journal	May 2014
Which Method Is Better for Developing Freight Planning Models at Seaports—Neural Networks or Multiple Regression? Al-Deek, Haitham M. Transportation Research Record: Journal of the Transportation Research Board, Vol. 1763, Issue 1 https://doi.org/10.3141/1763-14	journal	January 2001
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data Ali, Iftikhar; Greifeneder, Felix; Stamenkovic, Jelena Remote Sensing, Vol. 7, Issue 12 https://doi.org/10.3390/rs71215841	journal	December 2015
A gradient boosting approach to understanding airport runway and taxiway pavement deterioration Barua, Limon; Zou, Bo; Noruzoliaee, Mohamadhossein International Journal of Pavement Engineering https://doi.org/10.1080/10298436.2020.1714616	journal	January 2020
An artificial neural network based decision support system for energy efficient ship operations Bal Beşikçi, E.; Arslan, O.; Turan, O. Computers & Operations Research, Vol. 66 https://doi.org/10.1016/j.cor.2015.04.004	journal	February 2016
Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks Chen, Shu-Chuan; Kuo, Shih-Yao; Chang, Kuo-Wei Transportation Planning and Technology, Vol. 35, Issue 3 https://doi.org/10.1080/03081060.2012.673272	journal	April 2012
Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback Cipollini, Francesca; Oneto, Luca; Coraddu, Andrea Reliability Engineering & System Safety, Vol. 177 https://doi.org/10.1016/j.ress.2018.04.015	journal	September 2018
Detecting flight trajectory anomalies and predicting diversions in freight transportation Di Ciccio, Claudio; van der Aa, Han; Cabanillas, Cristina Decision Support Systems, Vol. 88 https://doi.org/10.1016/j.dss.2016.05.004	journal	August 2016
Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data Du, Yuquan; Meng, Qiang; Wang, Shuaian Transportation Research Part B: Methodological, Vol. 122 https://doi.org/10.1016/j.trb.2019.02.004	journal	April 2019
Finding Structure in Time Elman, Jeffrey L. Cognitive Science, Vol. 14, Issue 2 https://doi.org/10.1207/s15516709cog1402_1	journal	March 1990
Prediction of arrival times and human resources allocation for container terminal Fancello, Gianfranco; Pani, Claudia; Pisano, Marco Maritime Economics & Logistics, Vol. 13, Issue 2 https://doi.org/10.1057/mel.2011.3	journal	May 2011
Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept Ferreiro, Susana; Arnaiz, Aitor; Sierra, Basilio Expert Systems with Applications, Vol. 39, Issue 7 https://doi.org/10.1016/j.eswa.2011.12.027	journal	June 2012
The Artificial Neural Networks to Obtain Port Planning Parameters García, Tomás Rodríguez; Cancelas, Nicoletta González; Soler-Flores, Francisco Procedia - Social and Behavioral Sciences, Vol. 162 https://doi.org/10.1016/j.sbspro.2014.12.197	journal	December 2014
Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports Gökkuş, Ümit; Yıldırım, Mehmet Sinan; Aydin, Metin Mutlu Discrete Dynamics in Nature and Society, Vol. 2017 https://doi.org/10.1155/2017/2984853	journal	January 2017
A Comparison of Traditional and Neural Networks Forecasting Techniques for Container Throughput at Bangkok Port Gosasang, Veerachai; Chandraprakaikul, Watcharavee; Kiattisin, Supaporn The Asian Journal of Shipping and Logistics, Vol. 27, Issue 3 https://doi.org/10.1016/S2092-5212(11)80022-2	journal	December 2011
Medical image analysis with artificial neural networks Jiang, J.; Trundle, P.; Ren, J. Computerized Medical Imaging and Graphics, Vol. 34, Issue 8 https://doi.org/10.1016/j.compmedimag.2010.07.003	journal	December 2010
Reinforcement Learning: A Survey Kaelbling, L. P.; Littman, M. L.; Moore, A. W. Journal of Artificial Intelligence Research, Vol. 4 https://doi.org/10.1613/jair.301	journal	January 1996
Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective Kang, John; Schwartz, Russell; Flickinger, John International Journal of Radiation OncologyBiologyPhysics, Vol. 93, Issue 5 https://doi.org/10.1016/j.ijrobp.2015.07.2286	journal	December 2015
Containership routing with time deadlines and simultaneous deliveries and pick-ups Karlaftis, Matthew G.; Kepaptsoglou, Konstantinos; Sambracos, Evangelos Transportation Research Part E: Logistics and Transportation Review, Vol. 45, Issue 1 https://doi.org/10.1016/j.tre.2008.05.001	journal	January 2009
Vessel Target Prediction Method and Dead Reckoning Position Based on SVR Seaway Model Kim, Joo-Sung INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS, Vol. 17, Issue 4 https://doi.org/10.5391/IJFIS.2017.17.4.279	journal	December 2017
Intelligent Prediction of Ship Maneuvering Lacki, Miroslaw TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 10, Issue 3 https://doi.org/10.12716/1001.10.03.17	journal	January 2016
Forecasts and Reliability Analysis of Port Cargo Throughput in Hong Kong Lam, William H. K.; Ng, Pan L. P.; Seabrooke, William Journal of Urban Planning and Development, Vol. 130, Issue 3 https://doi.org/10.1061/(ASCE)0733-9488(2004)130:3(133)	journal	September 2004
Calibrating microscopic traffic simulators using machine learning and particle swarm optimization Liu, Yanchen; Zou, Bo; Ni, Anning Transportation Letters https://doi.org/10.1080/19427867.2020.1728037	journal	February 2020
Improving the adaptability in automated vessel scheduling in container ports using intelligent software agents Lokuge, Prasanna; Alahakoon, Damminda European Journal of Operational Research, Vol. 177, Issue 3 https://doi.org/10.1016/j.ejor.2005.12.016	journal	March 2007
GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries Mo, Lili; Xie, Ling; Jiang, Xiaoyi Applied Soft Computing, Vol. 62 https://doi.org/10.1016/j.asoc.2017.10.033	journal	January 2018
A two-stage forecasting approach for short-term intermodal freight prediction Moscoso-López, José Antonio; Turias, Ignacio; Jiménez-Come, Maria Jesús International Transactions in Operational Research, Vol. 26, Issue 2 https://doi.org/10.1111/itor.12337	journal	September 2016
Short-term Forecasting of Intermodal Freight Using ANNs and SVR: Case of the Port of Algeciras Bay Moscoso-López, J. A.; Turias, I. J. Turias; Come, M. J. Transportation Research Procedia, Vol. 18 https://doi.org/10.1016/j.trpro.2016.12.015	journal	January 2016
A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting Niu, Mingfei; Hu, Yueyong; Sun, Shaolong Applied Mathematical Modelling, Vol. 57 https://doi.org/10.1016/j.apm.2018.01.014	journal	May 2018
Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN) Salleh, Nurul Haqimin Mohd; Riahi, Ramin; Yang, Zaili The Asian Journal of Shipping and Logistics, Vol. 33, Issue 2 https://doi.org/10.1016/j.ajsl.2017.06.007	journal	July 2017
Evaluation of Two Modeling Methods for Generating Heavy-Truck Trips at an Intermodal Facility by Using Vessel Freight Data Sarvareddy, Pradeep; Al-Deek, Haitham; Klodzinski, Jack Transportation Research Record: Journal of the Transportation Research Board, Vol. 1906, Issue 1 https://doi.org/10.1177/0361198105190600114	journal	January 2005
Using neural networks to predict component inspection requirements for aging aircraft Shyur, Huan-Jyh; Luxhoj, James T.; Williams, Trefor P. Computers & Industrial Engineering, Vol. 30, Issue 2 https://doi.org/10.1016/0360-8352(95)00170-0	journal	April 1996
Using artificial neural networks to predict container flows between the major ports of Asia Tsai, Feng-Ming; Huang, Linda J. W. International Journal of Production Research, Vol. 55, Issue 17 https://doi.org/10.1080/00207543.2015.1112046	journal	December 2015
Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes Tu, Jack V. Journal of Clinical Epidemiology, Vol. 49, Issue 11 https://doi.org/10.1016/S0895-4356(96)00002-9	journal	November 1996
A neural network approach to the vessel dispatching problem Vukadinovic´, Katarina; Teodorovic´, Dusˇan; Pavkovic´, Goran European Journal of Operational Research, Vol. 102, Issue 3 https://doi.org/10.1016/S0377-2217(96)00237-8	journal	November 1997
The Analysis for the Cargo Volume with Hybrid Discrete Wavelet Modeling Xiao, Yi; Wang, Shouyang; Xiao, Ming International Journal of Information Technology & Decision Making, Vol. 16, Issue 03 https://doi.org/10.1142/S0219622015500285	journal	April 2017
Maritime Traffic Probabilistic Forecasting Based on Vessels’ Waterway Patterns and Motion Behaviors Xiao, Zhe; Ponnambalam, Loganathan; Fu, Xiuju IEEE Transactions on Intelligent Transportation Systems, Vol. 18, Issue 11 https://doi.org/10.1109/TITS.2017.2681810	journal	November 2017
Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study Xie, Gang; Wang, Shouyang; Zhao, Yingxue Applied Soft Computing, Vol. 13, Issue 5 https://doi.org/10.1016/j.asoc.2013.02.002	journal	May 2013
Kernel-Based Machine Learning Models for Predicting Daily Truck Volume at Seaport Terminals Xie, Yuanchang; Huynh, Nathan Journal of Transportation Engineering, Vol. 136, Issue 12 https://doi.org/10.1061/(ASCE)TE.1943-5436.0000186	journal	December 2010
Ship arrival prediction and its value on daily container terminal operation Yu, Jingjing; Tang, Guolei; Song, Xiangqun Ocean Engineering, Vol. 157 https://doi.org/10.1016/j.oceaneng.2018.03.038	journal	June 2018
A chance-constrained stochastic approach to intermodal container routing problems Zhao, Yi; Liu, Ronghui; Zhang, Xi PLOS ONE, Vol. 13, Issue 2 https://doi.org/10.1371/journal.pone.0192275	journal	February 2018

Similar Records

Teaching freight mode choice models new tricks using interpretable machine learning methods

Journal Article · Wed Mar 13 00:00:00 EDT 2024 · Frontiers in Future Transportation · OSTI ID:1780682

Xu, Xiaodan; Yang, Hung-Chia; Jeong, Kyungsoo; +5 more

A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model

Journal Article · Fri Nov 18 00:00:00 EST 2022 · Sustainability (Basel) · OSTI ID:1780682

Lim, Hyeonsup; Uddin, Majbah; Liu, Yuandong; +2 more

Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport

Journal Article · Tue Jun 02 00:00:00 EDT 2020 · Transportation Research Record · OSTI ID:1780682

Jeong, Kyungsoo; Garikapati, Venu; Hou, Yi; +2 more

Related Subjects

Data sources
International freight transportation management
Literature review
Prediction
freight transportation
machine learning

Title: Machine learning for international freight transportation management: A comprehensive review

Citation Formats

References (41)

Similar Records

Related Subjects