Machine learning for international freight transportation management: A comprehensive review

Research in Transportation Business & Management xxx (xxxx) xxxx

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Research in Transportation Business & Management journal homepage: www.elsevier.com/locate/rtbm

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

Limon Baruaa, Bo Zoua, , Yan Zhoub a b

Department of Civil and Materials Engineering, University of Illinois at Chicago, United States Argonne National Laboratory, United States

A R T I C LE I N FO

A B S T R A C T

Keywords: Machine learning International freight transportation management Literature review Data sources Prediction

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

1. Introduction International freight transportation pertains to the physical process of moving goods between countries by ship, air, rail, truck, pipeline, or intermodal. International freight transportation can involve a multitude of stakeholders including one or multiple shippers, carriers, forwarders, third-party logistics services, and customs of two or more countries for each movement. Compared to domestic goods movement, international freight transportation is characterized by large volumes (e.g., carried by containerships of over 10,000 TEU capacity) and longer distance (e.g., intercontinental), use of large vehicles (e.g., ocean-going ships) and infrastructure (e.g., seaports), and border-crossing checking, all of which contribute to high complexity for management of international freight transportation. Due to the complexity, it is more difficult to clearly characterize the relationships among the variety of inputs, outputs, and decisions in an analytical fashion for international freight transportation systems. Consequently, research problems pertaining to the management of international freight transportation—from demand forecasting to operation planning and from asset maintenance to ontime performance prediction—are more challenging to tackle. On the other hand, as the world has been increasingly interconnected with continued globalization, the importance of international freight transportation has become more evident and profound over time. This highlights the need for more efficient management of ⁎

international freight transportation through innovative research. With the proliferation of Big Data for international freight transportation and recent strides made in machine learning (ML), the capability to conduct data-driven research has been renewed. A large number of ML-based studies have appeared in the literature—particularly with a diversified use of various ML methods in the past decade—to investigate a range of issues in international freight transportation management (IFTM). In general, ML-based methods can be used to describe the current status of a system (descriptive), predict future state and values of the system (predictive), or recommend actions to maintain or improve system functionality (prescriptive), often in a better way than traditional approaches. Traditional approaches to studying IFTM largely fall into two categories: statistical modeling and operations research (OR). Compared to the traditional approaches, ML-based modeling has three advantages. First, traditional approaches rely on a priori assumption about the relationship between system inputs and outputs which are less than 100% accurate and often simplified. This leads to biased description and prediction of the system, and suboptimal decision-making. ML modeling avoids this because no a priori assumptions are required. Instead, ML lets the machine train itself to learn. Second, the large and complex operational environment of IFTM means that data often have high dimensions. Consequently, statistical modeling can encounter multicollinearity with which the validity of statistical models can be

Corresponding author. E-mail address: [email protected] (B. Zou).

https://doi.org/10.1016/j.rtbm.2020.100453 Received 30 July 2019; Received in revised form 20 November 2019; Accepted 8 February 2020 2210-5395/ © 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: Limon Barua, Bo Zou and Yan Zhou, Research in Transportation Business & Management, https://doi.org/10.1016/j.rtbm.2020.100453

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established, output for a new set of data can be predicted using the relationship. The remaining of the subsection reviews some classic supervised learning models: linear regression, k-nearest neighbors, artificial neural networks (ANN), support vector machines (SVM), and decision tree. We also review ensemble learning models including random forest, gradient boosting, and Bayesian networks, where a collection of single ML models are trained and combined for prediction.

compromised. For example, if multicollinearity is present in linear regression, small changes in the data may cause wild swings in the coefficient estimates. Different from this, many ML methods are quite robust to multicollinearity (Caraviello, Patel, & Pai, 2018; Kang, Schwartz, Flickinger, & Beriwal, 2015). Third, given the size and complexity of IFTM problems, the OR approach requires large-scale models which are computationally expensive to solve restraining OR models from being used in dynamic decision-making environments. Adopting a different, model-free philosophy, ML-based optimization needs a very small amount of solution time while yielding high-quality solutions (Nazari, Oroojlooy, Snyder, & Takác, 2018). Most of the computation time is actually spent on model training prior to optimization. These advantages of ML suggest that a refreshed view should be taken to understand how we leverage the strengths of ML to further IFTM research. While the IFTM literature has witnessed a number of ML-based studies, there remains a lack of review and synthesis of the exiting work to identify the specific topics addressed, ML methods used, research trends, and opportunities for further explorations. This paper attempts to fill this gap. We offer a comprehensive overview of ML-based modeling of IFTM. Answers to several research questions are sought in the review including: 1) What are the main problems in IFTM that have been investigated using ML? 2) Which ML methods have been used and for what problems? 3) Is there a taxonomy for ML in the IFTM context? If not, can we propose one? 4) What is the overall trend in the literature? 5) What ML methods have not been used and have potentials, especially vis-à-vis traditional approaches? 6) What are the data sources that may be considered for ML-based modeling? To facilitate reading by a broader audience, especially readers who have less familiarity with ML, a brief overview of different ML methods is accompanied to exhibit the fundamental ideas behind the ML methods. The remainder of the paper is organized as follows. Section 2 presents an overview of various fundamental ML methods. Section 3 reviews how different ML methods were considered in different application areas for IFTM. The data sources related to IFTM for developing ML models are discussed in Section 4. Section 5 synthesizes the findings from the review with discussions on the potential of ML for optimization as an alternative or in conjunction with the OR approach. Four directions for further explorations are proposed in Section 6.

2.1.1. Classic supervised learning Two simplest supervised ML methods are regression and k-nearest neighbors. Linear regression models make assumptions about the model structure, e.g., the error term and the relationship of output with inputs to predict the output. Different from linear regression, k-nearest neighbors is a non-parameter learning algorithm. The concept is that when a new data point x comes in, the k data points closest to x in the training data are identified. The prediction is based on the majority label for classification and the average label for regression. A third, very popular supervised ML method is ANN, which emulates the logic and behavioral characteristics of animal neural networks in information processing. The central idea is extracting linear combinations of features and then modeling output as a nonlinear function of the features (Hastie, Tibshirani, & Friedman, 2009). The information processing is performed by adjusting the interconnections of nodes in the neural network with the ability to self-learn and self-adapt (Liu, Zou, Ni, Gao, & Zhang, 2020). ANN can train very complex models, but often requires a large amount of data for training (Jiang, Trundle, & Ren, 2010). SVM is another widely used ML method mainly for classification. SVM constructs linear boundaries in a transformed version of the feature space such that the distance between boundaries of different classes is maximized. SVM is capable of handling a large number of features and tackling the issue of overfitting (Howley, Madden, O'Connell, & Ryder, 2005). Decision tree is an acyclic graph that organizes a dataset in smaller homogeneous subsets. In a tree, each node corresponds to a pairwise comparison on a feature. Each branch represents the outcome of the comparison. Conceptually simple, the prediction can be performed very fast using a decision tree. 2.1.2. Ensemble learning The ML methods above pertain to training a single model. To improve prediction accuracy and robustness, ensemble learning has also been developed with the idea of building a composite model based on a collection of single models. Ensemble learning can be broken into two tasks: developing a population of base models from the training data, and combing the base models to form the composition predictor (Hastie et al., 2009). Two ensembling methods exist: bagging and boosting. Bagging is a parallel ensemble technique to decrease variance by creating several models independently each from a subset of data from the training dataset. Boosting is a sequential ensemble to decrease the prediction error by creating sequential models in which a later model focuses on mistakes of the preceding model. Random forest and gradient boosting machine are the most common bagging- and boostingbased ensemble learning models. In addition, Bayesian networks can also be viewed as an ensemble learning method.

2. Overview of machine learning methods Generally speaking, ML teaches computers to do what comes naturally to humans and animals: learn from experience. ML uses computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The data used for ML consist of a set of variables (also known as features). ML can be classified in three categories: supervised learning, unsupervised learning, and reinforcement learning. Below we provide an overview of the basic ML methods under the three categories. Given the focus of the paper, the overview is intended to describe briefly the fundamental ideas of ML methods, but by no means an exhaustive list of all ML methods that appear in the IFTM literature reviewed in Section 3. Nonetheless, the more advanced ML methods that appear in the literature are not irrelevant to the overview here. Indeed, those models are developed from the basic models discussed in this section.

2.2. Unsupervised learning In unsupervised learning, unlabeled data are used to discover the hidden patterns in the data. Principal component analysis (PCA) and clustering are two most widely used techniques. PCA is a dimensionality reduction technique which transforms a number of correlated variables into a smaller number of uncorrelated principal components, each a sequence of linear projections of the original data. PCA provides a way to extract and synthesize features, and is used in both exploratory data analysis and prediction. Clustering partitions a dataset into groups (clusters) so that observations in each group are similar but more

2.1. Supervised learning Supervised learning builds models to predict output using known input-output data for model training. The known input-output data are also called labeled data, which are a group of samples with specific tags providing identifications of each sample. Data samples without such tags are called non-labeled data. For example, a labeled sample can be a shipping record with a label to identify the mode of shipment. Once trained, i.e., the relationship between the output and the inputs is 2

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developed by Tsai and Huang (2017) to predict container flows between major Asian ports. ANN has also been used jointly with other techniques. Gökkuş, Yıldırım, and Aydin (2017) applied ANN with artificial bee colony (ANN-ABC) and with Levenberg-Marquardt algorithms (ANN-LM) to forecast annual container traffic for the Istanbul, Izmir, and Mersin ports in Turkey. By comparing the models with nonlinear regression and least square support vector machine (LSSVM), it was found that LSSVM, ANN-ABC, and ANN-LM all performed better than multivariate nonlinear regression. Besides ANN, SVM is also commonly used for port demand forecast, especially when the input variables involve time series. Wu and Pan (2010) predicted total container volume by SVM in Jiujiang port of China using cargo volume data from 2001 to 2009. Mak and Yang (2007) used approximate least square support vector machine (ALSSVM), a modified version of SVM, to predict container throughput at the port of Hong Kong using time series data from January 1995 to October 2006. The performance of ALSSVM was compared with SVM, LSSVM, and ANN. The authors found that ALSSVM outperformed all other models. The literature also sees hybrid models to improve the forecast accuracy. Liu, Ji, Ye, and Geng (2007) developed a hybrid model based on historical container throughputs at the port of Shanghai with two prediction models—a grey prediction model and a cubic polynomial curve prediction model. The two models were mixed by a radial basis function neural network. The combined forecast generated more accurate predictions than each model alone. Zhang and Cui (2011) combined the cubic exponential smoothing method with a grey model by the Elman neural network (Elman, 1990) to predict container throughput for the port of Shanghai. Xie, Wang, Zhao, and Lai (2013) developed three hybrid models by mixing SVM with seasonal autoregressive integrated moving average (SARIMA), seasonal decomposition, and classical decomposition to forecast container throughput of Shanghai and Shenzhen ports. All three hybrid models outperformed models with a single technique. Xiao, Wang, Xiao, Xiao, and Hu (2017) developed another hybrid forecasting model which combines maximum overlap discrete wavelet transform of time series data with ANN, to predict container throughput for Tianjin and Shanghai ports. It was again found that the hybrid model was better than individual component models. The interest in hybrid models has become even more avid in the past couple of years. Niu, Hu, Sun, and Liu (2018) combined variational mode decomposition (VMD) algorithm, ARIMA model, hybridizing grey wolf optimization (HGWO), and SVM to improve the accuracy of container throughput forecast of Singapore and Shanghai ports. A model integrating SARIMA with ANN, SVM, and genetic algorithm was proposed by Mo et al. (2018) to predict the monthly container throughput of the ports of Xiamen and Shanghai. In the integrated model, SARIMA was used to predict the linear part of the time series, while ANN, SVM, and genetic algorithm were used for nonlinear part. Most recently, Milenković, Milosavljevic, Bojović, and Val (2017) considered combining ANN with simulated annealing and genetic algorithm to predict container flow for the port of Barcelona using time series data from January 2010 to December 2016. The hybrid model showed its superiority over SARIMA.

different across groups. By analyzing data in each group, clusters can be labeled. One consequent use of clustering is to improve the accuracy of supervised ML by adding the cluster labels as new features (Fradkin, 2006). 2.3. Reinforcement learning Reinforcement learning enables an agent to learn by trial-and-error using feedback from its actions and experiences (Kaelbling, Littman, & Moore, 1996). The objective of reinforcement learning is to maximize a predefined cumulative award. After an action is performed, the agent receives a reward which describes the immediate return sent from the environment associated with the action. Based on the reward and the current state, the agent decides the best action to take. Unlike supervised learning in which training data have the answer while being trained, there is no answer in reinforcement learning. Instead, the agent decides what to do to perform a given task. Popular reinforcement learning techniques include Q-Learning, state-action-reward-state-action (SARSA), and deep Q networks (DQNs). 3. Machine learning research for international freight transportation management This section reviews existing ML research for IFTM. The organization of the review is based on application areas, rather than specific ML methods used, for the following reasons. First, as can be seen in the synthesis in Section 5, the frequency of different ML methods being used varies significantly in the literature. Second, in our view IFTM is an applied field with respect to ML use. Potential readers of the paper may have keen interest in the use of ML for a particular IFTM topic rather than a specific ML method. Considering these, the following discussions of ML research for IFTM are by application area. In subsection 3.1, the focus is on demand forecast which is further divided by maritime, air, and intermodal freight. Subsection 3.2 is dedicated to operation-related problems including vessel and terminal operations and maintenance of transportation assets. Subsection 3.3 examines vehicle trajectories and related on-time performance predictions. The methodology for conducting the literature review follows three steps. First, six online databases (IEEE Xplore, ACM digital library, ScienceDirect, Web of Science, Ei Compendex, and Google Scholar) were searched to collect relevant journal and conference papers. In the search, the keyword “international freight” was used in combination with one of the other eight keywords: “demand forecast”, “operational,” “management”, “asset”, “trajectory”, “machine learning”, “big data”, and “data analytics”. For each paper resulting from the search, we scanned its title to get a sense of whether the paper is about ML for IFTM and retained papers that are. In the second step, we looked into the papers from the first step to identify references in those papers that appear relevant to ML for IFTM. In the third step, the pool of papers collected from the first two steps were further scrutinized. Only those truly focused on ML for IFTM were kept. After performing the three steps, we ended up including 61 papers for our detailed review below. 3.1. Demand forecast

3.1.2. Air cargo In predicting air cargo demand, ANN has been again the most popular ML method. The demand for air cargo highly correlates with the macroeconomy. Thus, it is not surprising that most ANN models used economic factors as inputs. Chen, Kuo, Chang, and Wang (2012) represents the first attempt to apply ANN to forecast air cargo demand from Japan to Taiwan. Loaiza, Solano, Simancas, and Ojito (2017) developed ANN models to predict air cargo demand for different products in Colombia. Baxter and Srisaeng (2018) applied ANN to predict Australia's annual export by air. Apart from ANN, Sulistyowati, Kuswanto, and Astuti (2018) developed hybrid models that combine statistical (linear modeling with time series regression and

3.1.1. Maritime freight ANN is the most popular ML method for predicting shipping demand for seaports. The use of ANN in this area can date back to Wei and Yang (1999) who built an ANN model to forecast the throughput of transshipment containers at Kaohsiung port, Taiwan. Later Lam, Ng, Seabrooke, and Hui (2004) developed ANN models to forecast 37 types of freight movements for the port of Hong Kong. The authors compared the ANN models with linear regression and found that forecasting results are more accurate using ANN models. Gosasang, Chandraprakaikul, and Kiattisin (2011) applied ANN to predict container throughput at the port of Bangkok. Another ANN model was 3

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Ölçer (2016) applied ANN to predict vessel fuel consumption, which was subsequently used for ship operation decision support. More recently, Du, Meng, Wang, and Kuang (2019) developed an ANN model to quantify the influence of sailing speed, displacement, trim, and weather and sea conditions on vessel fuel consumption rate. The ANN model was then used in the optimization of sailing speed and trim.

autoregressive integrated moving average with exogenous factors) and ML (ANN and SVM) models using time series data. The authors found that ANN in hybrid models had better performance than SVM in hybrid models. 3.1.3. Intermodal freight Intermodal is an important part of international freight transportation. One of the first studies on intermodal freight demand forecast using ML was by Bilegan, Crainic, and Gendreau (2008), who employed ANN to forecast freight demand at intermodal terminals. The results were validated using data from a Canadian port. Wu and Liu (2015) integrated ANN with grey modeling to forecast capacity growth of searail intermodal container transportation at the port of Ningbo, China. A most recent ANN model proposed by Abdirassilov and Sładkowski (2018) used time series data to predict short-term cargo train flows in the China-Europe transit corridor. ML models were also developed to predict the demand of Ro-Ro (roll-on/roll-off) vessels which carry wheeled cargo such as cars, trucks, semi-trailer trucks, trailers, and railroad cars. Moscoso-López, Turias, Come, Ruiz-Aguilar, and Cerbán (2016) developed ANN and SVM models to forecast Ro-Ro vegetable freight at the port of Algeciras Bay using time series data from 2000 to 2007. The authors found that SVM performed better than ANN. The same dataset was recently used by Moscoso-López, Turias, Jiménez-Come, Ruiz-Aguilar, and Cerbán (2019) to develop a two-stage Bayesian regularization neural networks (BRNN) to offer reliable prediction of fresh freight weight on Ro-Ro transportation with 7- and 14-day look-ahead. The forecast consists of two stages. In the first stage, prediction was carried out using different BRNN models (which differ in network configurations, inputs, and parameters), again with 7- and 14-day look-ahead. In the second stage, an ensemble framework of the best BRNN models was used to enhance the first stage forecast. Table 1 summarizes the above-reviewed studies in terms of application area, ML method, location, input, output, and prediction type.

3.2.2. Terminal operations A few studies looked into ML applications in support of terminal operations. Jin, Liu, and Gao (2004) presented a two-phase fuzzy-based ANN to forecast the container operation quantity in the first phase. Based on the forecast, the operation rule and stack height were determined to regulate container yard operations. Linn, Liu, Wan, and Zhang (2013) proposed an ANN-based approach to predict quay crane rate which is an important performance indicator for terminal operations. The authors argued that the proposed models might be used to suggest remedial actions if terminal operations were affected by security measures or incidents. A long short-term memory (LSTM) recurrent neural network (RNN) was developed by Gao, Chang, Chen, and Fang (2018) to predict daily container volumes entering a storage yard, which has significant impact on terminal operation planning. 3.2.3. Asset maintenance ML has been quite frequently adopted to support condition-based maintenance of international freight transportation assets. More specifically, ML was used to predict the actual conditions of assets to decide what maintenance actions to be performed. Palmé, Breuhaus, Assadi, Klein, and Kim (2011) used ANN to improve online monitoring of a gas turbine which is the main engine to propel ships, by detecting trends in the measured data and changes in the health status of the engine. Coraddu et al. (2015) generated a dataset from an accurate simulator of a naval vessel gas turbine propulsion plant and applied regularized kernel least square and SVM to predict propulsion equipment conditions to support condition-based maintenance. It was found that SVM performed better than regularized kernel least square in forecasting the wear of gas turbine. Cipollini, Oneto, Coraddu, Murphy, and Anguita (2018) extended the work of Coraddu et al. (2015) with a real dataset and applied deep neural networks, random forest, SVM, regularized kernel least square, Bayesian Methods, and k-nearest neighbors. Deep neural networks showed the best performances. Gkerekos, Lazakis, and Theotokatos (2016) applied SVM to monitor marine machinery conditions based on vibration measurements, acoustic measurements, oil/ debris analysis, corrosion (thickness) measurements, thermography, and motor current signature analysis. Besides vessels, ML has been applied to aircraft assets as well. Shyur, Luxhoj, and Williams (1996) employed ANN to predict component inspection requirements for aging aircraft to aid safety performance analysis. The approach could help inspectors identify potential problem areas and predict when the number of reported problems would exceed an expected level. Yuan, Wu, and Lin (2016) proposed an LSTM-based neural network to estimate the remaining useful life of aero engines in complicated operations, hybrid faults, and strong noises. Ferreiro, Arnaiz, Sierra, and Irigoien (2012) employed Bayesian networks to predict brake wear on the plane to minimize the cost of maintenance and increase aircraft/fleet operability. Another Bayesian network model was developed by Wei, Jinsong, Jun, and Hao (2013) to estimate the remaining useful life of an aerial system. The diagnostic experiments showed that the Bayesian network model could effectively isolate and track faults in the system. Similar to Table 1, the above-reviewed ML studies for operation and asset maintenance are summarized in Table 2.

3.2. Operation and asset maintenance 3.2.1. Vessel operations The literature sees three main applications of ML to vessel operations: assignment, routing, and speed determination. For assignment, Vukadinovic, Teodorovic, and Pavkovic (1997) made the earliest effort to use ANN to cope with assigning barges to pusher tugs when planned schedules encounter disturbances. Traditionally, whenever a disturbance occurs, vessel dispatchers need to respond immediately which increases their stress levels. The use of ANN can decrease the workload of vessel dispatchers while improving the quality of assignment solutions. Jiang and Zhu (2011) improved the algorithm of Vukadinovic et al. (1997) by using backpropagation and a radial basis function in ANN. The authors found that backpropagation ANN can easily get stuck in local optimum, which can be mitigated by using the radial basis function. Lokuge and Alahakoon (2007) used ANN to assign vessels to berths. The methodology was compared with the existing assignment system for the Jaya container terminal at the port of Colombo in Sri Lanka. The authors concluded that their methodology outperformed the existing system in terms of berth productivity and vessel waiting time. For vessel routing, Spiliopoulos et al. (2017) used four clustering techniques: connectivity-based clustering, centroid-based clustering, distribution-based clustering, and density-based clustering to identify major maritime trade routes. A large volume of automatic vessel identification data was used to understand the patterns of global maritime trade. ML has also been used for intermodal container routing. Zhao, Liu, Zhang, and Whiteing (2018) developed a hybrid heuristic algorithm which embeds ANN into Monte-Carlo simulation and genetic algorithm to select sea-rail container routes to minimize total transportation cost. In terms of vessel speed determination, Beşikçi, Arslan, Turan, and

3.3. Trajectory and on-time performance prediction 3.3.1. Trajectory prediction In predicting vessel trajectories, ANN is again the most popular ML 4

SVM and ANN ANN SVM ANN and linear regression ANN SVM

ANN SVM ANN

ANN

Mak and Yang (2007) Liu et al. (2007) Wu and Pan (2010) Gosasang et al. (2011)

Gökkuş et al. (2017)

Xiao, Wang, et al. (2017)

Tsai and Huang (2017)

5

Intermodal

Airport

ANN

Lam et al. (2004)

Port of Algeciras Bay China to Europe Port of Algeciras Bay

ANN and SVM ANN

ANN

ANN and SVM

ANN

BRNN

Baxter and Srisaeng (2018)

Sulistyowati et al. (2018) Bilegan et al. (2008)

Wu and Liu (2015)

Moscoso-López et al. (2016) Abdirassilov and Sładkowski (2018) Moscoso-López et al. (2019)

Ningbo port

Indonesian airports Canadian port

Australia

Colombia

ANN and linear regression ANN

Japan to Taiwan

Loaiza et al. (2017)

ANN

Milenković et al. (2017)

ANN

ANN and SVM

Mo et al. (2018)

Singapore and Shanghai ports Xiamen and Shanghai ports Barcelona port

Shanghai port Shanghai and Shenzhen ports Istanbul, Izmir, and Mersin ports Tianjin and Shanghai ports Major Asian ports

Hong Kong port Shanghai port Jiujiang port Bangkok port

Hong Kong port

Kaohsiung port

Location

Chen et al. (2012)

SVM

Niu et al. (2018)

Zhang and Cui (2011) Xie et al. (2013)

ANN

Wei and Yang (1999)

Seaport

ML method

Reference

Application area

Table 1 Summary of ML studies for demand forecast.

Ro-Ro vegetable freight data of previous days

Cargo train flows data of previous day

World merchandise exports, world population growth, world jet fuel prices, world air cargo yields, outbound flights from Australia, and Australian/United States dollar exchange rate Air passenger and cargo volume data of previous month Ship arrival, quantities of containers for particular destinations, historical arrival quantities Container throughput, GDP, total goods sent by rail, foreign trade import and export volume Ro-Ro vegetable freight data of previous days

Daily Ro-Ro vegetable freight volume

Value

Value

Value

Value

Value Value

Value

Air cargo demand

Air passenger and cargo volume Freight demand at intermodal terminals Intermodal container transportation capacity Daily Ro-Ro vegetable freight volume Short-term cargo train flows

Value

Value

Value

Value

Value

Value

Value

Value

Value Value

value Value Value Value

Value

Value

Prediction type

Air cargo demand

Monthly loaded, unloaded and empty container flows Air cargo demand

loaded, unloaded and empty container flows of previous month Population, employment rates, incomes per capita, GDP, gross national product, economic growth rates Rate charged for the carriage of cargo, GDP, types of products and area that were used to produce the products

Container throughput

Container throughput

Port-to-port container volume

Container throughput

Container traffic

Container throughput Container throughput

Freight movements by commodity type Container throughput Container throughput Container throughput Container throughput

Container throughput

Output

Container throughput data of previous month

GDP, interest rates, the value of export and import trade, economic growth rate, per capita GDP and industrial Production Index Container throughput data of previous year

GDP, world GDP, population, inflation rate, interest rate, fuel price, total exports, and imports Container throughput data of previous month

Container throughput data of previous year Container throughput data of previous year

GDP, economy growth rate, industry index of product, average national income, wholesale price index, and gross national product Population, trade values of imports and exports, electricity demand, GDP, expenditure on building and construction Container throughput data of previous year Container throughput data of previous year Container throughput data of previous year GDP, world GDP, exchange rate, population, inflation rate, interest rate and fuel price

Input

L. Barua, et al.

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6

Asset maintenance

Terminal operations

LSTM ANN Regularized kernel least square, SVM

Deep neural networks, random forest, SVM, regularized kernel least square, Bayesian Methods, and k-nearest neighbors SVM

ANN

LSTM Bayesian networks

Bayesian networks

Gao et al. (2018) Palmé et al. (2011) Coraddu et al. (2015)

Cipollini et al. (2018)

Shyur et al. (1996)

Yuan et al. (2016) Ferreiro et al. (2012)

Wei et al. (2013)

Gkerekos et al. (2016)

ANN

ANN

Du et al. (2019)

Linn et al. (2013)

ANN

Beşikçi et al. (2016)

Fuzzy-based ANN

ANN

Jin et al. (2004)

Clustering

Spiliopoulos et al. (2017) Zhao et al. (2018)

Hong Kong

ANN with radial basis function

Chinese airport

Simulated data Airports of UK

Airports of USA

Machinery data collected from experiments

Vessel route across the globe

Chinese port Simulated data Simulated data

Hong Kong

Vessel trade routes across the globe Intermodal sea-rail network of China to Singapore Vessel route across the globe Vessel route across the globe Simulated data

Port of Colombo

Hong Kong

Location

ANN

ANN

Vukadinovic et al. (1997) Jiang and Zhu (2011)

Vessel operations

ML method

Lokuge and Alahakoon (2007)

Reference

Application area

Table 2 Summary of ML studies for operation and asset maintenance.

Previous conditions of aero engines Aircraft weight, landing velocity, brake operation during landing, flap position and initial brake temperature Previous conditions of an aerial system

Vibration measurements, acoustic measurements, oil/debris Analysis, corrosion measurements, thermography, motor current signature analysis, and performance monitoring Age, estimated flying hours, estimated landing time

Previous conditions of propulsion equipment

Estimated workload, number of cranes and truck, container density, distance between berth and storage block Container volume of previous year Health status of previous three months Previous conditions of propulsion equipment

Number of quayside cranes in different hours

Vessel speed, revolutions per minute, mean draft, trim, cargo quantity, wind and sea effects Sailing speed, displacement, trim, and weather and sea conditions

Container demand, candidate routes of each shipment, cost and capacity of each route, start and delivery time of each shipment

Desired number of barges for each tug, deviation between release time of a barge and a tug Desired number of barges for each tug, deviation between release time of a barge and a tug Number of trucks available in the berth, average crane productivity, number of cranes allocated for the vessel, skill level of staffs and operational delay. Locations of vessels

Input

Remaining useful life

Component inspection requirements Remaining useful life Wear of brake on plan

Conditions of marine machinery

Daily container volume Health status of engines Conditions of propulsion equipment Conditions of propulsion equipment

Vessel fuel consumption Vessel fuel consumption rate Container operation quantity Quay crane rate

Identify major vessel trade routes Best routes

Assignment of barges to tugs Assignment of barges to tugs Assignment of vessels to berths

Output

Value

Value State

Value

State

State

Value State State

Value

Value

Value

Value

Action

Action

Action

Action

Action

Prediction type

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3.4. Other applications

method. Gan, Liang, Li, Deng, and Cheng (2016) trained an ANN to determine clusters of ship trajectory data along with ship speed, loading capacity, weight, maximum power, and water level. The trained ANN model was then used to predict future traffic with trajectory clusters. Daranda (2016) proposed a turning point-based path prediction method which first identifies ship turning points with the DBSCAN-based clustering algorithm and then trains an ANN which takes ship type, speed, course, length, and position as inputs to predict the next ship turning points. Łącki (2016) developed an ANN model using ship course, angular velocity, speed, position, angle, velocity of current, and angle and velocity of wind to predict future vessel position and state of the environment. Apart from ANN, Qi and Zheng (2016) developed a model to predict vessel trajectories based on clustering and SVM. A spatial clustering algorithm was used to group historical trajectories of vessels. SVM was used to train the classifiers generated by clustering. Kim (2017) proposed an SVM model to predict ship position, speed, and course in harbor areas using ship trajectory data. Xiao, Ponnambalam, Fu, and Zhang (2017) employed a lattice-based DBSCAN clustering algorithm to extract ship traffic patterns from ocean-going ship trajectories data. The clustering results were then used to predict maritime traffic 5, 30, and 60 min ahead. ML has been also used for flight trajectory prediction. The flights can be for both freight and passenger transportation. Jesse, Liu, Smart, and Brown (2008) used k-means clustering to group flights and identify anomalies during the flight approach phase. Focusing on air cargo, Di Ciccio, Van der Aa, Cabanillas, Mendling, and Prescher (2016) developed an automated prediction model to detect flight diversions based on flight track updates (position, speed, altitude, intended destination, etc.). Shi, Xu, Pan, Yan, and Zhang (2018) used LSTM-based ANN model for flight trajectory prediction and compared the results with those from a Markov model and a weighted Markov model. Both 3-D (time stamp, latitude, and longitude) and 4-D (time stamp, latitude, longitude, and altitude) trajectories were predicted. The authors concluded that LSTM-based ANN outperformed the Markov models.

Besides the applications areas discussed in subsections 3.1–3.3, a few other uses with ML are also worth noticing. First is vehicle trip generation. Al-Deek (2001) introduced an ANN model to predict truck traffic moving inbound and outbound of at the ports of Miami and Jacksonville in Florida. The number of exported containers, exported commodity type, exported commodity tonnage, the number of imported containers, imported commodity type, and imported commodity tonnage were considered as inputs. The author compared ANN with linear regression and concluded that ANN produced more accurate predictions. Sarvareddy, Al-Deek, Klodzinski, and Anagnostopoulos (2005) investigated backpropagation ANN and fully recurrent neural network for predicting heavy-truck trips at the port of Canaveral, Florida. It was found that backpropagation ANN performed better when the dataset is not large. Xie and Huynh (2010) developed two kernel-based supervised machine learning models (Gaussian process and SVM) to predict truck trips generated by import and export activities using terminal operation records at the port of Houston in Texas. The two models performed equally well and compared favorably to multilayer feedforward neural networks. The usefulness of ML has been demonstrated in behavioral analysis and facility planning as well. Mohri and Haghshenas (2017) developed a decision tree to identify the most important features affecting the choice between containerized and non-containerized transportation. The input variables included transportation cost, shipping distance, origin-destination, commodity type, weight, and value, among others. The costs of containerized transportation, whether the destination is a port, shipping distance, perishability of the commodity, and value of exports at the destination were found the most important inputs. In the context of container terminal planning, García, Cancelas, and SolerFlores (2014) employed ANN to predict traffic growth and consequently facility and equipment requirements. Dock length, terminal surface, and the number of dock cranes were used as inputs. 4. Data sources for machine learning The development and application of ML models depend critically on the availability of data. Yet data collection is not a trivial task given that there does not exist a single data inventory housing all records of international freight transportation activities. Adequate attention must be paid to identifying the right places to collect the needed data. In this section, we review some data sources that may be considered in the development of ML models for IFTM. For maritime shipping, the Automatic Identification System (AIS) is an excellent data source for modeling vessel routing and trajectory prediction. AIS provides detailed, high-frequency vessel positioningand operation-related information including longitude, latitude, speed, course, mobile service identity of the vessel, vessel type, vessel dimension, rate of turn, navigation status, heading, etc. The rich information is made available by transponders on vessels which constantly transmit data with satellites and land-based stations. The number of participating vessels in AIS is large, as international voyaging ships with 300 or more gross tonnage are mandated by the International Maritime Organization (IMO, 2019). AIS data can be requested from many government agencies, for example the US Department of Homeland Security (USDHS, 2019) and the European Maritime Safety Agency (EMSA, 2019). Besides AIS, data on cargo volume and vessel movements at ports can be obtained from many port authorities and federal/national governments. In the US, international trade records by export/import and tons/TEUs are made available on the Maritime Administration website (MARAD, 2019). Similar data sources covering gross weight of goods handled at main European ports by cargo type, direction, reporting country, and partner maritime geographical areas are also accessible in Europe (Eurostat, 2019a). At the global level, historic records of coastal shipping and rail/maritime

3.3.2. On-time performance prediction Predicting freight on-time performance using ML is relatively recent. Cannas, Fadda, Fancello, Frigau, and Mola (2013) employed kmeans clustering and Ward's method to cluster vessel arrivals at the Cagliari International Container Terminal in Sardinia, Italy. The clustering results were then used to develop naive Bayes, decision trees, and random forest models with vessel characteristics (length, draft, gross tonnage, capacity, and vector type), vessel service (shipping lines, ports rotation, sailing direction, and previous visited port), and vessel containers (the number and type of containers to be processed) as inputs to predict the level of daily alarm related to vessel late arrivals. Later, Pani, Vanelslander, Fancello, and Cannas (2015) adopted Logistic Regression, decision tree, and random forest to forecast late and early ship arrivals at the Cagliari International Container Terminal and the PSAAntwerp terminal. Random forest was found the best in performance. Fancello et al. (2011) developed an ANN model to estimate ship arrival time, and then applied the estimated arrival time to optimize human resources allocation. Parolas, Tavasszy, Kourounioti, van Duin, and Cities (2017) applied SVM and ANN to predict the estimated arrival time of container ships at the port of Rotterdam. Based on mean absolute error, SVM outperformed ANN. Yu et al. (2018) employed backpropagation ANN, Classification and Regression Tree (CART), and random forest to estimate delay or early arrival of ships at Ningbo Ganji Container Terminal for the port of Ningbo-Zhoushan in China. Salleh, Riahi, Yang, and Wang (2017) performed prediction of containership arrival punctuality at ports of call using a Fuzzy Rule-Based Bayesian Network, which is a hybrid decision-making technique.

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ML methods has been mainly for prediction purposes so far. Depending on what is predicted, we propose the following taxonomy to classify the existing studies into three categories. The first category of studies aims at predicting values, such as container throughput, vessel fuel consumption, and ship arrival delay. The second category attempts to predict state of the international freight transportation systems, such as vessel position and condition of freight transportation assets. The subjects being predicted in the third category are actions to take, such as making choice of a route and vessel-berth assignment. Fig. 2 shows the distribution of the three categories in each of the application areas. Overall, 31 studies belong to the category of value prediction, 24 to state prediction, and only 6 to action prediction. Compared to value and state predictions, there is clearly a scarcity of ML research in terms of action prediction. Among the studies that fall into the “action prediction” category, we found that ML in almost all the action prediction studies was embedded in a larger optimization procedure, in which ML plays the role of value or state prediction whose output is then used for optimization performed by non-ML techniques in the procedure. The only exceptions are Vukadinovic et al. (1997) and Jiang and Zhu (2011) which used historic human dispatcher decisions to train ANN models for barge-tug assignments, such that the models can be used to as a substitute to mimic dispatcher decisions. Using ML in this way assumes that human dispatchers are experienced enough that their actual dispatching decisions are not too far from optimum (Jiang & Zhu, 2011). An advantage of this substitution is to reduce human dispatcher workload, especially when schedule disturbances occur (Vukadinovic et al., 1997). The possibility of using ML for optimization is relevant to a vivid, on-going, and larger debate in the OR, transportation, and data analytics communities, with an overall open question being how practitioners and researchers in each community can benefit from a more integrated view with ML methods. On the one hand, as mentioned above most existing ML work for optimization in IFTM has been in conjunction with OR models, with ML and OR playing complementary roles: ML offers a capability for descriptive and predictive analytics to support the prescriptive capability of OR models. However, no research has specifically investigated the benefits of embedding ML in the optimization procedure compared to not having ML. More efforts are warranted in this area. On the other hand, ML has great potential as a stand-alone procedure for optimization in place of OR models. The fundamental idea behind ML-based optimization is that the search for optimal solutions is based on data-driven learning, rather than OR theories. Once trained and having the ability to learn, an ML model is expected to perform much faster than OR models in generating good-quality solutions. This is especially pertinent to IFTM where decision problems such as vehicle maintenance optimization and operations planning are often in large scale and complex, which means that formulating and solving OR models can be challenging. As a more specific example, classic OR models for ship routing problems are known to be NP-hard (Karlaftis, Kepaptsoglou, & Sambracos, 2009). In addition, dynamic decisionsupport for routing decisions may require periodic re-optimization which is computationally expensive to run. To tackle such problems, reinforcement learning in combination with deep learning is especially suitable (James, Yu, & Gu, 2019). Research can be further extended to leveraging the strengths of ML and OR to come up with some hybrid paradigms for formulating and solving optimization problems.

container transport by country can be collected from the International Transport Forum (2019). For air cargo, several data sources are potentially useful for ML modeling. In the US, the Bureau of Transportation Statistics (BTS) has a long history of collecting and maintaining airline financial and operational information related to air cargo business, including revenue and revenue-ton-miles by carrier, geographic region, aircraft configuration and service class; and total freight weight carried by carrier, service class, and origin-destination pair (BTS, 2019a). Data on the amount of domestic and international air cargo transported by country and airport and on main routes are also available in Europe (Eurostat, 2019b). Furthermore, information on air cargo volume is routinely publicized by airport authorities and cities. On the global scale, country-level air cargo data dating from 1970 can be accessed from the World Bank TCdata360 website (World Bank, 2019). While only commercially available, the Official Airline Guide provides schedules and actual operation information on cargo as well as passenger flights (OAG, 2019). For land-based international freight transportation, the North America Transborder Freight Database documents monthly freight flows for US exports to and imports from Canada and Mexico starting from 1993 by commodity type and transportation mode (BTS, 2019b). In addition, with proliferating use of GPS in the trucking industry, an emerging source of microscopic data is truck GPS traces. Another innovative way to collect international trucking freight data is through the use of smart sensors installed on containers which have become increasingly popular. The original intention of the sensors is to monitor container location, intensity of shocks experienced by the container, whether the container is opened, and temperature variation during transit (Lee, Choi, Kim, & Kim, 2015). The use can go beyond that. ML models can be developed using the sensor data to support truck dispatching and routing decisions, assess hazardous risks, and manage ontime delivery and delays. Truck GPS traces and container sensor data are just two examples of Big Data for ML model development in the context of IFTM. As Big Data become more common and ubiquitous in the freight sector and the human society at large, significant opportunities are expected to leverage the rich information from Big Data to build new ML models with greater robustness and accuracy. In turn, the increased power of ML as fueled by Big Data will stimulate further data collection and access through augmented use of sensing and Internet-of-Things technologies. 5. Discussions The volume of ML research for IFTM has increased significantly in the past years. The plethora of research shows that ML is a versatile and powerful tool which enables researchers and practitioners to further understand the functioning of international freight transportation systems, better predict its evolution and future status, and offer stronger support for system- and component (e.g., vessel)-level planning and operation decisions. The review shows that all the ML models developed in the literature were used for prediction purposes, either standalone or with prediction output used for subsequent decision support. Among the different ML methods, the use of ANN has garnered the most attention, which may be attributed to two reasons. First, ANN has been long in existence in the ML field. In fact, it is one of the oldest ML algorithms (Ali, Greifeneder, Stamenkovic, Neumann, & Notarnicola, 2015). Over the years of its adaptation and applications, ANN has seen many successes (Ahmad et al., 2014). Second, ANN is a very flexible computational tool (Tu, 1996) capable of handling different types of variables and interactions among inputs. It is therefore not surprising that ANN has the longest history and broadest applications in IFTM. As shown in Fig. 1, other ML methods have drawn attention of researchers in more recent years. We expect this trend of more diversified ML use in IFTM to continue in the years to come. Our review of the literature also shows that the use of the various

6. Future research Given the rapid development of ML methods and applications along with the increasing data availability, ML use in IFTM has become and will continue to be a fertile research field. In view of what has been achieved and the remaining gaps, we suggest four directions for further explorations. First, the IFTM literature has seen widespread use of ANN, with more diversified applications of other ML methods in the past 8

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Fig. 1. Chronicle of the first use of an ML method and its specific application area in IFTM.

Value prediction

State prediction

Action prediction 0

4

8

12

16

20

24

28

32

Number of studies Demand forecast

Operations

Asset maintenance

Trajectory prediction

On-time performance prediction

Others

Fig. 2. Distribution of ML studies by category and application area using the proposed taxonomy.

quality of data critically affect the suitability of an ML method in tackling a specific IFTM problem. Future research is warranted to investigate the dependence of an ML method on the characteristics of data, with respect to a specific problem type. The dependence will be reflected in the quality of the solution and time needed to solve the problem. For example, with the increasing use of sensing and other Big Data, it would be interesting to know how the prediction accuracy of freight on-time performance can be enhanced and what can be done to improve freight on-time performance. The investigation across different ML methods is expected to lead to much-needed new understanding of the applicability of different ML methods to diverse IFTM problems, and the sensitivity of these methods to data types, problem sizes, and problem types. Lastly, the application areas listed in Section 3 are just some under the broad subject of IFTM. As the understanding and availability of data, and ML applicability to various IFTM problems continues to evolve, future research should explore using ML to tackle IFTM

decade. Future efforts should sustain this trend, in particular with attention to ensemble learning methods such as gradient boosting machine. Because of the sequential nature in ensemble development, gradient boosting machine has demonstrated superior performance for prediction over ANN and SVM (e.g., Barua, Zou, Noruzoliaee, & Derrible, 2020), but has not been considered in IFTM. Second, more research is needed for action predictions using ML, especially how ML can substitute and complement OR methods. In our view, reinforcement learning, which specifically deals with computationally efficient optimization in a dynamic environment, should be given more attentions. Equally important is to conduct research on comparing the solution quality and computation time using ML vs. OR methods. With the further understanding of the advantages and disadvantages of ML and OR, it is worth leveraging the strengths of ML and OR to come up with new methodologies that hybridize the two approaches for optimization-based action prediction problems in IFTM. Third, for all ML applications, data is the key. The type, size, and 9

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problems that were not considered previously. To give an example, for carrier selection, clustering-based ML methods can be used as a substitute for traditional discrete choice models for selecting the cluster (e.g., carrier) to which an item (e.g., a shipment) belongs with the highest probability. Similar to the use of ML for optimization, it would be a worthwhile effort to develop hybrid approaches that bring the best from existing and ML methods.

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