SeaCONEX: Simultaneously Visualizing Ocean Carrier Supply ChainService Networks and Continuous Vessel Movement Data

Mason Leon * Rithika Lakshminarayanan David Saffo Sara Di Bartolomeo

Cody DunneNortheastern University

Figure 1: The SeaCONEX Dashboard.

ABSTRACT

Supply chain disruption and the complexity of maritime andhinterland freight networks remain key issues around the world.SeaCONEX is not just a portmanteau of the humble container’sorigin, but a visualization tool that enables users to see the marineterminal connections for ocean carrier service rotations, investigatethe historical trajectories of vessels assigned to ocean carrier services,and assess deviations from a shortest-path optimal route. In thispaper, we present an approach to integrating ocean carrier servicenetwork information with continuous movement data for containerand roll-on/roll-off cargo vessels with reproducibility in mind andutilizing new open-source industry data standards. We developeda multiple-view visualization that presents commercial maritimefreight carrier service connections between marine terminals andlinks the spatial temporal movements of vessels assigned to eachservice over a 90 day period which can be filtered by carrier. A copyof this paper along with all supplemental material is available athttps://osf.io/k9nvc/

*Corresponding author. E-mails: [ leon.m — lakshminarayanan.r —saffo.d — dibartolomeo.s — c.dunne ]@northeastern.edu

Keywords: Human centered computing, Geographic visualization,Geospatial data, Supply chain management, Geographic informationsystemst

1 INTRODUCTION AND PROBLEM DOMAIN

The Container Express also known as the CONEX, was deployedby the U.S. military during the Korean War as a modular platform toimprove global logistics operations [10]. This containerization con-cept was soon reinforced by commercial intermodal1 freight service.On April 20, 1956, Malcolm McLean, “the father of containeriza-tion“, ushered in the modern supply chain era when his SS IdealX sailed from New York to Houston laden with 58 containers [10].As containerization took hold, other innovators soon followed suitwith their own proprietary equipment. Even among competitors,however, the seamless interchange of cargo across modes requireduniform design and securement mechanisms. McLean shared hispatent designs with the International Standards Organization (ISO)and common specifications eventually emerged [24].

The modern supply chain is global in scope, incredibly complex,and essential to the world’s economy. Ocean freight accounts formore than 80% of the total world merchandise trade [46]. The sheerscale of the maritime industry has led to many opportunities andchallenges as it undergoes digital transformation. This sector rep-resents a core component of the global supply chain, yet containerliner carrier operations often appear hidden from those outside theindustry. Despite advancements in freight handling and the rise

1Intermodal: The movement of freight, in a container or on a trailer, bymore than one mode of transportation. The movement can be made from railto truck to ship in any order [41]

in logistics information technology, supply chains remain vulnera-ble to disruption and are plagued by poor visibility and difficultyexchanging cargo and freight network information.

While standards exist for physical intermodal container equip-ment design, standards regarding the exchange of service informa-tion documenting every aspect of a container’s journey throughoutthe global supply chain are still under development. An increasinglydifficult question has been in regards to not only standardisationand sharing of carrier service information but also the level of datagranularity necessary for stakeholders. For example, the 2020-2021supply chain disasters and maritime service disruptions have demon-strated the need for new approaches to combining different datasets to analyze supply chain risk. Choke-points in the maritimesupply chain detrimentally impacted the ability of government andthe private sector response to COVID-19 such as the sourcing ofpersonal protective equipment and vaccine manufacturing. Addition-ally, COVID-19 changes in work, merchandise trade, consumption,and manufacturing patterns have demonstrated long simmering andevolving maritime supply chain challenges. The grounding of theEvergreen Ever Given in the Suez Canal tested an already strainedglobal supply chain and highlighted a worst case maritime riskscenario that linked key industry concerns for over-reliance uponthe just-in-time-manufacturing strategy, the impact of ultra-large-container-vessel (ULCV) deployment, and ocean carrier allianceconsolidation [12] [11]. Stakeholders may have had a better un-derstanding of maritime supply chain impacts using visualizationtools that combine both vessel movement and standardized servicenetwork data to provide insights. We present SeaCONEX- a toolto help investigate maritime connections between marine terminals,ocean carrier service rotations, and historical vessel movements.SeaCONEX combines vessel movement data along with networkconnectivity data to present users with a dashboard for investigat-ing carrier routes and possible anomalies or discrepancies betweenservices and vessel operations. We first discuss the specifics of acase study developed to demonstrate this idea. Then we describethe views designed to help users examine maritime supply chainnetworks.

The main contributions of this paper are:1. Adapting the Exploratory Data Analysis (EDA) Protocol for

Identifying Problems in Continuous Movement Data to processand visualize container vessel trajectories,

2. Extending open-source industry knowledge and standards tocombine inconsistent ocean carrier service information in auniform manner to support a container trade network node-linkvisualization,

3. Demonstrating a reproducible approach to combining vesselmovement data with ocean carrier service network data in asingle visualization tool.

2 RELATED WORK

An entire field of study, Maritime Informatics2 has recentlyemerged to encompass a variety of existing research areas in or-der to provide a more holistic understanding of the business domainand various approaches to industry data applications.

A significant focus relates to the study of AIS (Automatic Identi-fication System) [50] data for supply chain operations, port perfor-mance, marine science, maritime economics, and the environmentalimpacts of vessel movements. The processing and storage of AISoften involves GIS (Geographic Information System) techniques inaddition to large-scale “big-data“ computing concepts. For example,

2Maritime Informatics: the application of information systems to in-creasing the efficiency, safety, and ecological sustainability of the world’sshipping industry. It is an applied science, developed by data scientists tomeet the needs of practice and applied by practitioners and data scientistscooperatively. [25]

Fiorini, Michele et al. [9] present a pipeline using open source GISrelational database tools to process raw AIS data and visualize vesselroutes for maritime spatial planning. As indicated by Wang, Haoet al. [48] and Wang, Kai et al. [49], the quantity and poor qual-ity of AIS data is a challenge when performing exploratory vesselmovement analysis. Rajabi, Aboozar et al. [45] demonstrate howvessel analysis with AIS data can be of great value for “smart port“operations and marine terminal scheduling.

Through the development of open-source geospatial tools andlibraries, Anita Graser discusses the technical complexity requiredto manipulate spatial temporal data for exploratory analysis is notrestricted to the maritime domain [14–19]. In Exploratory DataAnalysis (EDA) protocol for identifying problems in continuousmovement data, she demonstrates how to address missing data, pre-cision, consistency, and accuracy problems in an AIS data [13].

Basole et al. [1] present a visualization tool in their paper thatenables exploratory analysis of innovations in the global supplychain network of electronics. The approach they use is meant to helpmake managerial decisions based on actionable insights that revealclusters, trends and outliers from the analysis. In their paper ontransit system maps, Wu et al. [51] present design considerations forcomputer generated transit maps that are not as easy to understandas human-drawn maps. They found that iterative user testing withhumans can help improve computer designed maps to a usable state.Park et al. [44] evaluate the design of a visual analytical system forsupply chain managers to make decisions. Their application sup-ported a number of functions important to decision makers includingnetwork information, industrial activities associated with each portand cost sensitivity.

3 A HYPOTHETICAL UNITED KINGDOM-BERMUDA MAR-ITIME NETWORK

In this section, we first talk about our client and then describethe case study we designed in line with the needs of our client todemonstrate our approach. We decided to construct this case studybecause some of the data we were provided was proprietary and theamount of data too large to visualize at once.

3.1 The Customer

The Volpe National Transportation Systems Center (Volpe) isa United States Department of Transportation (USDOT) ResearchCenter for advanced logistics and transportation technical expertisebased in Cambridge, MA. Volpe is a fee-for-service organization thatsupports a variety of national strategic transportation projects acrossthe entire transportation spectrum. For this project we partneredwith Volpe’s Situational Awareness and Logistics Division under theInfrastructure Systems and Technology Technical Center.

A key client of the division is the United Kingdom Ministry ofDefense (UKMOD). The Volpe team researches, designs, maintains,and develops key multimodal and multi-leg military supply chain lo-gistics information assets for UKMOD, allowing the organization tomanage mission critical cargo and equipment shipments worldwidein support of military operations, commitments to allied nations,peacekeeping efforts, and humanitarian aid. Volpe is interestedin innovative visualization tools for their customer to improve thepresentation of logistics data and tools to examine choke-points,performance, and resiliency in the UKMOD supply chain includ-ing tracking and understanding vessel movements, and viewing thenetwork of port connections.

To provide the end user of our visualization (Volpe) and by exten-sion, their customer (UKMOD), a useful and relevant example forhow our tool could support the mission, a hypothetical supply chainscenario (the case study) was developed using accurate geographicbackground and real commercial maritime carriers and services. Al-though the mix of ships and services available in the case study are

commercially operated, they are of similar capabilities and militarilyuseful for UKMOD requirements.

3.2 The Case Study

Bermuda is British Overseas Territory reliant on the United King-dom for protection and assistance. Should the UKMOD need to sendsupplies or materials to the island by sea, options could include:1. deploying a military or merchant vessel from the UK fleet;2. chartering or contracting of an available vessel from the interna-

tional market; or3. using a commercial cargo carrier with existing service.

There are no regularly scheduled direct UK-Bermuda ocean shippingservices. All inbound and outbound oceangoing container cargobetween the UK and Bermuda requires transshipment3 in the US. Itis important for the UKMOD to be aware of all possible options andconnectivity between the Bermuda Territory and the UK.

For example, during the COVID-19 crisis, global disruptions ofall modes of parcel and cargo shipments impacted Bermuda. As aresult, all mail between the UK and Bermuda since May 18, 2020had been sent via ocean container [4]. Bermudians did not startreceiving their 2020 Christmas cards until mid-March 2021 [8].

In light of this, the UKMOD needs to evaluate commercial ship-ping options between the UK and Bermuda. 6 ocean carriers, 9services, 18 deployed vessels, and their network of marine terminalswere identified for the case study, the details of which are summa-rized in Table 1. For the purpose of the case study, we assume aspecial partner 3PL coordinates freight forwarding, transshipment,and drayage between US or European terminals.

4 DATA

The data for the case study fell in to two main categories: servicerelated information and vessel movement information. For a com-plete list of the ocean carrier service data sources, pleas from oceancarrier and some industry websites [3, 5–7, 20–23, 26–40, 42, 43].

The vessel movement data over a 90 day period (December 16,2020- March 16,2021) was provided by Volpe. All data originatedin a variety of formats, requiring cleaning and transformation intoJSON and GeoJSON files representing an entity that might exist ina database. This format allowed us to statically host the case studymaterials and interactive visualization on a GitHub Pages site. Theentities include vessels, terminals, carrier service networks connec-tions, searoutes representing the shortest route over the sea betweentwo terminals, and vessel timestamped trajectories representing thespatial temporal movements of vessels. A look-up table entity of id’sand join relationships was also created for filtering, aggregation, andlinking in our visualization without requiring a back-end server andAPI. When applicable, our data schema design, relationships, enti-ties, fields, or data types were adapted from industry best practices asdocumented by the Digital Container Shipping Association (DCSA),Ship Message Design Group e.V (SMDG), Bureau International desContainers et du Transport Intermodal (BIC), and The IntermodalAssociation of North America (IANA). For a more detailed descrip-tion about each entity and the fields they contain, please refer to oursupplementary materials at https://osf.io/k9nvc/.

5 TASK ANALYSIS

We interviewed our partners at the Volpe Center to identifydomain-specific tasks the visualization should support. During thedesign process, we shared with the partner, example visualizationtools from industry software leaders as well as domain specific con-cepts from academia. This helped us better understand the client’s

3Transshipment: The unloading of cargo at a port or point where it isthen reloaded, sometimes into another mode of transportation, for transfer toa final destination [41]

needs and ensure SeaCONEX supported these tasks given the avail-able data-set. Table 2 contains a task breakdown of high-end usergoals using Brehmer and Munzner’s multi-level task typology [2].We identified the primary domain tasks to include: (a) examiningservice connections within the terminal networks for a carrier(s); (b)examining vessel information and trajectories for a vessel(s) and; (c)examining marine terminal information.

Similar to how one might use an interactive tool to explore air-ports and flight connections prior to purchasing a ticket for a trip,SeaCONEX users want to examine marine terminal and ocean car-rier connections prior to booking carriage of cargo. A user of Sea-CONEX should also be able to view the past movements of vesselsassigned to a service. This is important because should the vesselson a service appear to have historically made unscheduled stops ordeviations from an optimal navigation, the user may be interested inanother carrier/service offering.

6 VISUALIZATION DESIGN

Figure 1 shows the final design of SeaCONEX as a dashboardfollowing usability testing and feedback. Our visualization consistsof four linked views: a carrier filter pane, a vessel table, a node-linkdiagram (network map), and a geographic map. The dashboard im-plements the principles of presenting an overview, filtering, brushingand linking and details on demand. We will discuss the choices foreach view.

The carrier filters pane in Figure 1 encodes the logos for oceancarriers companies. On hover, a user is presented with additionalinformation about the carrier company and its service. Brushinglinks to the other views to filter the corresponding marine terminalsand vessels by the selected carrier(s).

The vessel table pane in Figure 1 displays vessels in a tabular for-mat with each column corresponding to a ship’s attribute. Brushingallows users to view data in the geographic map for the vessel(s) cor-responding historic geographic movements over the 90 day period.

The node-link diagram pane in Figure 2 represents a directednetwork graph with marine terminals (nodes) as circles and carrierservice connections (edges) as lines with arrows. By default, a usermay see terminals represented by a white color and all networkconnections, albeit in a faded out manner. Hovering over a marineterminal displays details such as the terminal’s identifying code, fa-cility name, address, and the port it belongs to. The hover interactionalso brings attention to the terminal of interest by changing the ter-minal’s color to deep red and taking advantage of the pop-out effect.The terminal encoding returns to default when the user interactionfocus changes. A selection in the carrier filters pane changes colorof the associated terminal nodes to a red outline with a pink fill andbrings the connections into focus using color highlighting. When inview, the connections are encoded with a blue or orange color rep-resenting a respective East or West trade lane. This is important tohighlight because a carrier may not visit a facility in both directions.The force-directed node-link diagram is advantageous for showingnetwork data because it removes geographic distance and allows theuser to focus clearly on specific nodes and edges connections.

The geographic map pane in (Figure 3) also displays marine ter-minals in the same encoding as the node-link diagram. The terminalelements here also feature the same interactivity and linking to thecarrier filter. Instead of displaying graph network edges, this viewshows connections between terminals as the optimal maritime routecalculated using the SeaRoute model from the European Union’sstatistical office. These elements are not visible by default and ap-pear red for a carrier selection. Although these connections may notbe accurate for navigation, they show the shortest distance betweenterminal pairings and provide a base comparison against actual ves-sel movements (trajectories). A vessel table selection links to thismap view and displays the corresponding vessel(s) trajectory for thedataset time period. The trajectory for each vessel is encoded with a

Table 1: An overview of the six ocean carriers and services chosen for the case study

Carrier Trade Service Vessel Deployment Num. Ports Num. Marine TerminalsBermuda Container Lines (BCL) Bermuda Oleander (O) 1 ConRo vessel 2 2Bermuda International Shipping Ltd. (BISL) Bermuda Bermuda Islander (BI) 1 LoLo vessel 2 2Somers Isles Shipping Ltd. (SISL) Bermuda Somers Isles (SI) 1 LoLo vessel 2 2Independent Container Line (ICL) Trans-Atlantic Trans-Atlantic (TAC1) 4 LoLo vessels 5 5Atlantic Container Line (ACL) Trans-Atlantic Atlantic (A) 5 ConRo vessels 8 10Hapag-Lloyd USA, LLC (HLUS) Trans-Atlantic US Flag Priority 1/Atlantic 3 (P1/AL3) 6 LoLo vessels 8 8

Table 2: Domain task and abstract tasks identified using Brehmer and Munzner’s multi-level task typology [2]

Task Domain Task Analytic Task(low-level)

Search Task(mid-level)

Analyze Task(high-level)

1 Examine carrier, service, and cargo network, connectivity for the case study. analyze produce derive2 Examine vessel trajectories (continuous movement data) for vessels belonging to a carrier or service rotation. query consume summarize3 Examine vessel information for vessels belonging to a carrier or service rotation. query consume summarize4 Examine marine terminal information for facilities within the cargo network of the case study. analyze consume discover

Figure 2: Node-link visualization of ports and connections

Figure 3: Map showing shortest and actual routes for ICL vesselsdeployed on the TAC1 service over the 90 day period

different color allowing users can visually compare the actual pathsfor vessels assigned to the same service as well as the optimal route.

Lastly, we provide a reset button to clear all user selections onthe page.

7 EVALUATION

To validate our design, we conducted a small-scale usabilitytesting in a class of 25 students in line with the design study “lite“methodology [47]. This helped us identify bugs, refine our design,and arrive at a more polished product for our partner.

Users relayed to the team that they could easily find ports androutes. We also understood that the purpose of the tool is hard to un-derstand for non-domain experts, thus we added more explanations

for those unfamiliar with maritime supply chain operations. Usersfound the feature of comparing actual vessel trajectories against idealshortest routes useful to understand route deviations and slowdownsin cargo transportation.

On introducing SeaCONEX to domain experts, the feedbackwas favorable. They found the interface easy to use and said thatcombining vessel movement data with carrier network informationwould help them in future analytical work to support the needs ofthe UKMOD.

The Volpe team found our data processing techniques and opensource data standard adoption of great interest. They are interestedlearning more about the open source tools we utilized and the in-dustry standards to help them address information interchange withcargo carriers. Our partner mentioned that our tool provided valu-able insight as to how they could enhance the visual appeal of theirlogistics tracking software.

8 DISCUSSION

The biggest challenge throughout the project was acquiring carrierdata and formatting it into a standard. Additionally, complicatedAIS data quality issues added difficulty to calculating and generatingvessel trajectories for the visualization.

In the future we would like to integrate more details in the vi-sualization, such as the details for the cargo carrying capacities ofdifferent vessels for a carrier, vessel and port performance metrics,and highlighting unexpected vessel port calls.

9 CONCLUSION

We designed SeaCONEX as a tool to allow logisticians to exam-ine nodes and edges in commercial maritime networks and evaluatemovement patterns for vessels deployed to the networks. Using theservice, carrier and vessel information, we presented users with avisualization that combines two major types of data into a unifiedview. Further work in this area could be used to address choke-pointsin maritime networks and optimize scheduling and routing of cargoand freight movement. We hope that the ideas presented in this paperwill be useful to future logistics researchers trying to make decisionsabout how to best present information in this data-rich domain.

10 ACKNOWLEDGEMENTS

We would like to thank the team working on the UKMOD projectfrom the Volpe National Transportation Systems Center, U.S. De-partment of Transportation for the opportunity to collaborate thisproject. We would also like to thank the students in the course forgiving us valuable feedback that helped refine our design.

REFERENCES

[1] R. Basole, M. Bellamy, and H. Park. Visualization of Innovation inGlobal Supply Chain Networks. Decision Sciences, 48, Feb. 2016. doi:10.1111/deci.12213

[2] M. Brehmer and T. Munzner. A multi-level typology of abstract vi-sualization tasks. IEEE Transactions on Visualization and ComputerGraphics, 19(12):2376–2385, 2013. doi: 10.1109/TVCG.2013.124

[3] D. C. S. A. (DCSA). Dcsa information model 3.0.https://dcsa.org/wp-content/uploads/2020/12/20201208-DCSA-P1-DCSA-Information-Model-v3.0-FINAL.pdf, dec 2020.

[4] G. o. B. Department of Post Office, Ministry of the Cabinet Office. Bpo- overseas mail advisory. https://www.gov.bm/articles/bpo-overseas-mail-advisory, may 2020. [Online; accessed20-April-2021].

[5] B. I. des Containers et du Transport Intermodal (BIC). Bicfacility code (locode). https://www.bic-code.org/bic-facility-codes/, 2021.

[6] S. M. D. G. S. e.V.). Smdg liner codes list. https://smdg.org/documents/smdg-code-lists/smdg-liner-codes/,february 2020.

[7] S. M. D. G. S. e.V.). Smdg terminal code list (tcl).https://smdg.org/documents/smdg-code-lists/smdg-terminal-code-list/, april 2021.

[8] G. Finighan. Covid-19 hit christmas cards arrive at last. The RoyalGazette.

[9] M. Fiorini, A. Capata, and D. D. Bloisi. AIS Data Visualizationfor Maritime Spatial Planning (MSP). International Journal of e-Navigation and Maritime Economy, 5:45–60, Dec. 2016. doi: 10.1016/j.enavi.2016.12.004

[10] N. R. C. U. C. for a Study of the Effects of Regulatory Reform onTechnological Innovation in Marine Container Shipping. IntermodalMarine Container Transportation: Impediments and Opportunities, vol.236 of Special report (National Research Council (U.S.). Transporta-tion Research Board)), pp. 17–20. Transportation Research Board,National Research Council, 1992.

[11] I. T. Forum. The impact of mega-ships. (10), 2015. doi: https://doi.org/10.1787/5jlwvzcm3j9v-en

[12] I. T. Forum. The impact of alliances in container shipping. (62), 2018.doi: https://doi.org/10.1787/61e65d38-en

[13] A. Graser. An exploratory data analysis protocol for identifyingproblems in continuous movement data. Journal of Location BasedServices, 15(2):89–117, Apr. 2021. Publisher: Taylor & Franciseprint: https://doi.org/10.1080/17489725.2021.1900612. doi: 10.

1080/17489725.2021.1900612[14] A. Graser and M. Dragaschnig. Exploring movement data in notebook

environments. In IEEE VIS 2020 Workshop on Information Visualiza-tion of Geospatial Networks, Flows and Movement (MoVis), 2020.

[15] A. Graser and M. Dragaschnig. Open Geospatial Tools for MovementData Exploration. KN J. Cartogr. Geogr. Inf., 70(1):3–10, Apr. 2020.doi: 10.1007/s42489-020-00039-y

[16] A. Graser, M. Dragaschnig, P. Widhalm, H. Koller, and N. Brandle.Exploratory Trajectory Analysis for Massive Historical AIS Datasets.In 2020 21st IEEE International Conference on Mobile Data Manage-ment (MDM), pp. 252–257. IEEE, Versailles, France, June 2020. doi:10.1109/MDM48529.2020.00059

[17] A. Graser, J. Schmidt, F. Roth, and N. Brandle. Untangling origin-destination flows in geographic information systems. InformationVisualization, 18(1):153–172, Jan. 2019. Publisher: SAGE Publications.doi: 10.1177/1473871617738122

[18] A. Graser, P. Widhalm, and M. Dragaschnig. Extracting Patterns fromLarge Movement Datasets. giforum, 1:153–163, 2020. doi: 10.1553/giscience2020 01 s153

[19] A. Graser, P. Widhalm, and M. Dragaschnig. The M³ massive move-ment model: a distributed incrementally updatable solution for bigmovement data exploration. International Journal of GeographicalInformation Science, 34(12):2517–2540, Dec. 2020. doi: 10.1080/13658816.2020.1776293

[20] Hapag-Lloyd. Interactive schedule. https://www.

hapag-lloyd.com/en/online-business/schedules/interactive-schedule.html, 2021. [Online; accessed4-March-2021].

[21] L. Hapag-Lloyd USA. Hapag-lloyd - your trusted us flagpartner. https://www.hapag-lloyd.com/content/dam/website/downloads/press_and_media/publications/Serviceflyer_US_Flag.pdf, 2021.[Online; accessed 4-March-2021].

[22] L. Hapag-Lloyd USA. Us flag services - north america to northeurope. https://www.hapag-lloyd.com/en/products/us-flag/bydeparture.html/north_america/north_europe.html, 2021. [Online; accessed 4-March-2021].

[23] L. Hapag-Lloyd USA. Us flag services - north europe to northamerica. https://www.hapag-lloyd.com/en/products/us-flag/bydeparture.html/north_europe/north_america.html, 2021. [Online; accessed 4-March-2021].

[24] M. Levinson. The Box: How the Shipping Container Made the WorldSmaller and the World Economy Bigger - Second Edition with a newchapter by the author. Princeton University Press, 2016.

[25] M. Lind, M. Michaelides, R. Ward, and R. T. Watson, eds. MaritimeInformatics, p. 7. Progress in IS. Springer International Publishing,Cham, 2021. doi: 10.1007/978-3-030-50892-0

[26] A. C. Line. Transit times. https://www.aclcargo.com/transit-times/transit-times/, 2021. [Online; accessed4-March-2021].

[27] A. C. Line. Vessel schedules. https://www.aclcargo.com/vessel-schedules/, 2021. [Online; accessed 4-March-2021].

[28] A. C. Line. Vessel specifications. https://www.aclcargo.com/vessel-specifications/, 2021. [Online; accessed 4-March-2021].

[29] B. I. S. Ltd. Bisl 2021 sailing schedule. https://www.bermudaislander.bm/BISL_Schedule_2021.pdf, 2021.[Online; accessed 4-March-2021].

[30] B. I. S. Ltd. Company profile. https://www.bermudaislander.bm/company_profile.html, 2021.[Online; accessed 4-March-2021].

[31] I. C. L. Ltd. Fixed weekly schedule. https://www.icl-ltd.com/fixed-weekly-schedule/, 2021. [Online; accessed 4-March-2021].

[32] I. C. L. Ltd. Sailing schedule. https://www.icl-ltd.com/media/2303/sailing-schedule-updated-3-4-2021-web.pdf,2021. [Online; accessed 4-March-2021].

[33] I. C. L. Ltd. Vessels, ports of call. https://www.icl-ltd.com/vessels-ports/, 2021. [Online; accessed 4-March-2021].

[34] N. G. M. Ltd. 2021 shipping schedule. https://neptunebermuda.com/wp-content/uploads/2021/01/2021-Shipping-Schedule-Final.pdf, 2021. [Online;accessed 4-March-2021].

[35] N. G. M. Ltd. Bermuda container line – who we are. https://neptunebermuda.com/who-we-are/, 2021. [Online; ac-cessed 4-March-2021].

[36] N. G. M. Ltd. Our ship. https://neptunebermuda.com/our-ship/, 2021. [Online; accessed 4-March-2021].

[37] S. I. S. Ltd. About us. https://www.somers-isles-shipping.com/about/, 2021. [Online;accessed 4-March-2021].

[38] S. I. S. Ltd. Mv somers isles - main particulars. https://www.somers-isles-shipping.com/ship/, 2021. [Online; ac-cessed 4-March-2021].

[39] S. I. S. Ltd. Shipping schedule 2021. https://www.somers-isles-shipping.com/schedule/, 2021. [Online;accessed 4-March-2021].

[40] S. I. S. Ltd. Somers isles shipping. https://www.somers-isles-shipping.com/, 2021. [Online; accessed 4-March-2021].

[41] I. A. of North America (IANA). Intermodal Glossary. IntermodalAssociation of North America (IANA), 1 ed., 2017.

[42] I. A. of North America (IANA). Iana north american inter-modal facilities directory. https://www.intermodal.org/

resource-center/intermodalsystem, 2021.[43] I. A. of North America (IANA). Uniform intermodal interchange

and facilities access agreement (uiia), equipment return locationdirectory (erld)). https://www.ianaequipreturn.com/ReturnLocation/publicPage.do/, 2021.

[44] H. Park, M. A. Bellamy, and R. C. Basole. Visual analytics for supplynetwork management: System design and evaluation. Decision SupportSystems, 91:89–102, Nov. 2016. doi: 10.1016/j.dss.2016.08.003

[45] A. Rajabi, A. Khodadad Saryazdi, A. Belfkih, and C. Duvallet. To-wards Smart Port: An Application of AIS Data. In 2018 IEEE 20thInternational Conference on High Performance Computing and Com-munications; IEEE 16th International Conference on Smart City;IEEE 4th International Conference on Data Science and Systems(HPCC/SmartCity/DSS), pp. 1414–1421. IEEE, Exeter, United King-dom, June 2018. doi: 10.1109/HPCC/SmartCity/DSS.2018.00234

[46] S. N. Sirimanne, J. Hoffman, W. Juan, R. Asariotis, M. Assaf, G. Ayala,H. Benamara, D. Chantrel, J. Hoffmann, A. Premti, et al. Review ofmaritime transport, 2019. Technical report, tech. rep, 2019.

[47] U. H. Syeda, P. Murali, L. Roe, B. Berkey, and M. A. Borkin. Designstudy ”lite” methodology: Expediting design studies and enabling thesynergy of visualization pedagogy and social good. In Proceedings ofthe 2020 CHI Conference on Human Factors in Computing Systems,CHI ’20, p. 1–13. Association for Computing Machinery, New York,NY, USA, 2020. doi: 10.1145/3313831.3376829

[48] H. Wang, X. Zhuge, G. Strazdins, Z. Wei, G. Li, and H. Zhang. DataIntegration and Visualisation for Demanding Marine Operations. Apr.2016. doi: 10.1109/OCEANSAP.2016.7485617

[49] K. Wang, M. Liang, Y. Li, J. Liu, and R. W. Liu. Maritime TrafficData Visualization: A Brief Review. In 2019 IEEE 4th InternationalConference on Big Data Analytics (ICBDA), pp. 67–72. IEEE, Suzhou,China, Mar. 2019. doi: 10.1109/ICBDA.2019.8713227

[50] Wikipedia. Automatic identification system — Wikipedia, thefree encyclopedia. http://en.wikipedia.org/w/index.php?title=Automatic%20identification%20system&oldid=1025591994, 2021. [Online; accessed 17-June-2021].

[51] H.-Y. Wu, B. Niedermann, S. Takahashi, M. J. Roberts,and M. Nollenburg. A Survey on Transit Map Layout –from Design, Machine, and Human Perspectives. Com-puter Graphics Forum, 39(3):619–646, 2020. eprint:https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14030. doi:10.1111/cgf.14030