PIANC-World Congress Panama City, Panama 2018

AI PORT INITIATIVES- POSSIBLE MODERNIZATION OF PORT OPERATION AND MANAGEMENT THROUGH CUTTING EDGE ICTs -byKenji Ono1, Masayuki Tanemura2 and Yasuhiro Akakura3

ABSTRACT
Improving port productivity may be currently one of the most common and prioritized agendas forinternational container terminal operators. This paper highlights and discusses possible application ofmodern information and communication technology (ICT) such as the internet of things (IoT), big data
and artificial intelligence (AI) to the container port operation and management. An importance of
creating a port big data based on the daily operations for fostering AI, as an artificial port manager, isconcluded. Difficulties and issues of port communities to invite and include the AI into their daily
operations and business practice are also addressed.

1.

INTRODUCTIONPort productivity may be one of the key issues for the global container terminal operators, which arecompeting each other for surviving in the global and regional container trade market. This situation ismore and more accelerated by reflecting recent changes in business environment at ports, which arestrongly requested by clients to be a more efficient logistics service provider among the global supply
chain networks. There is no doubt that the modern world economy fully depends on the global
production network and sophisticated supply chain management. Nowadays, materials, parts andcomponents for automotive assembly lines, for example, are gathered across oceans in a regular basis,hence ports, as essential connecting nodes of waterborne and land surface transportation networks,
are one of key players of global production activities. In this context, commitment to the extremely
sophisticated but complicated and fragile supply chain management requires the port community moretime and cost consciousness, with resulting in introduction of recent cutting-edge information andcommunication technology (ICT) for improving port operation efficiency and productivity. The global
port operators are now facing the client’s strong requests to renovate their business practice forproviding more quality logistics services with less port charges.

2.

APPLICATION OF THE RECENT CUTTING-EDGE ICTS

2.1

Automatization of container port operations

In 2005, Tobishima Container Terminal of Nagoya Port, Japan, commenced its operation, which
involves automated guided vehicles (AGV) and remote controlled rubber mounted yard gantry cranes.
Toyota Motor Company’s car assembly line technology was applied to the terminal operation system.
This was the first challenge to a mechanization and automatization of container terminal in Japan. In
those days, there were only few terminals which adopted such a cutting-edge ICT for container
terminals, among which the port of Rotterdam was also another icon of automatized container port.
More than ten years later, an automatization of container terminal operation has become a main streamof global mage-container ports. Table 1 highlights global top ten ports and their introduction of

1

Executive vice president, Kobe-Osaka International Port Corporation, k-ono@hanshinport.co.jp
coordination officer for international affairs, Ports and Harbor Bureau, Ministry of Land,
Infrastructure, Transport and Tourism (MLIT), Japan
3 Head, Port Systems Division, National Institute of Land and Infrastructure Management, MLIT
2 Senior

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automation technologies into container terminal operation. Among the top twenty container hub ports,
fifteen have had or will soon have at least partially automatized one or more terminals.
Rank

Ports

Container Throughput（'000 TEU）

Automatized terminal
operation

1

Shanghai

36,540Yes

2Singapore

30,920Yes

3

Shenzhen

24,200No

4

Ningbo - Zhoushan

20,620No

5

HongKong

20,110

Yes

6

Busan

19,450

Yes

7

Qingdao

17,510Yes

8

Canton16,970

No

9

Dubai

15,590Yes

10

Tianjin

14,100Yes

11

Rotterdam

12,240Yes

12Port Klang

11,890No

13

Kaohsiung

10,260Yes

14

Antwerp

9,650

Yes

15

Dalian

9,450

No

16

Xiamen

9,180

Yes

17

Tanjung Pelepas

9,100

Yes

18

Hamberg

8,850

Yes

19

Los Angels

8,160

Yes

20

Long Beach

7,190

Yes

Notes: i) Container throughputs are statistics in 2015.
ii)"Yes" means automatized terminal operation has been introduced or to be introduced

soon in some of terminals of the port.
Data:

Ports and harbor Bureau, Ministry of Land,Infrastructure, Transport
and Tourism, Japan

Table 1: Introduction of automatized terminal operation in the global top 20 ports

2.2

Recent challenges for ICT application at ports

In 2011, the government of Germany launched a new ICT policy, Industry 4.0, which aims at
introducing automation and data exchange in manufacturing technologies. It focuses on digitizationand interconnection of industries, value chains and business models, which are all likely depend on
the recent development of ICTs including cyber-physical systems, Internet of things (IoT), cloudcomputing and cognitive computing. (EU, 2017) An artificial intelligence (AI) based on deep learning
programing, accurate sensor technology and big data is also rapidly attracting a big attention of theworld business community. In line with these recent trends, the global port community is gradually
going to step in the more ICT based and client oriented port operation and management.
An example of the ICT driven operation and management in the port sub-sector may be the smartPORT logistics (SPL) concept initiated by the port of Hamburg, Germany, which includes IT based
traffic management system and real time information transmission on the port traffic and infrastructure
situation along with the demand-oriented networking via a central public cloud. While the services have2

PIANC-World Congress Panama City, Panama 2018

been just started after several years pilot phase, the SPL is considered a pioneer to pave the way ofdramatically changing the future port operation scene.
It has become a common business environment for shipping lines, terminal operators, shipping
agencies, forwarders and other port service entities to be connect each other for providing more
efficient and speedy port logistics services and trade processing. Challenges were mainly undertakenby shipping lines, among which Nippon Yusen Co. Ltd., for example, has initiated to build up new ship
operation and management information system by connecting ship navigation and landsidearrangement information through IoT technique. These challenges strongly request port administration
offices, terminal operators and other port logistics service providers to prepare integrated andseamless data and information exchange and processing system for more smoothly sharing shipside
information among port business colony with less cost and time consuming manners.
Considering the above mentioned global trend of creating more smart and competitive port operationand management, the government of Japan have decided to launch, as a part of its recent port policy
reforms, a new ICT based port concept, namely “AI terminal initiatives”, for improving port operation
productivity and port traffic traceability through employing AI for port terminal operation system
configuration. The new terminal system is expected to enable AI independently to operate container
yard cranes for minimizing crane movements. AI controlled terminal operation system is also expected
to manage container dray traffic at port for realizing quick container check-in and release.

3.

POSSIBLE ICT-DRIVEN PORT MANAGEMENT AND OPERATION

3.1

Port traffic and big data generation

Daily operations of port terminals involve a variety of administration, management and businessoriented information in association with ship accommodation, trade processing, cargo handling,storage and delivery, taxation, quarantine clearance, port facility management, border control andcargo dray. The information is essential for daily port logistics activities, therefore, are generated day
and night at port, and transmitted from/to port. Figure 1 schematically illustrates, by showing an
example of container importation, the system of port logistics administration and business flow, which
generates a variety of related data and information.
When container ship arrives at port, the ship and maritime information are transmitted to the port
authority, CIQ offices, container terminal operator and marine service providers by ship itself or throughshipping agent for their processing ship arrival and departure, customs clearance, quarantine
inspection, and other commodity importation and delivery related procedures. In modern ports,shipping lines, port authority and terminal operators are generally equipped information and dataprocessing systems for implementing these procedures. These systems are, however, not necessary
well linked each other. Some of data and information are automatically duplicated and transmitted, butmany are still to be manually fed and transmitted among these systems for storing and processing.
Furthermore, the parties involved are not always cooperative to mutually being connected, because of
their own tradition, practices and business secret. As such, there are long continued many difficulties
to develop a common architecture to share business data and information among the port businesscolony.
On the other hand, modern container terminals are mostly under tough port competition, therefore, theterminal operators are normally keen on improving terminal competitiveness including berth window
efficiency, and terminal yard and dray productivities. Reducing terminal lead time and check-in gate
waiting time is also big concerns extended by consignees. Border control for preventing terrorism andintrusion of alien species is among basic requirements in the modern port terminals.
The above mentioned policy agenda for creating more effective, efficient and secured terminal may becommon issues in modern ports, thus needs to be addressed accordingly. In this context, building

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properly designed data and information exchange and processing platform is considered of a great
importance for all port terminals to survive the global container port competitions.

Figure 1: Typical port procedures, and involved data and information processing（An example of import containers）

3.2

Possible solutions for the port policy issues by adopting cutting-edge ICTs

Among key elements for building the data and information exchange and processing platform as
referred in Section 3.1, big data is rapidly attracting an attention of port and shipping sub-sector
community. Big data is defined as data set with a size beyond the ability of commonly used softwaretools to capture, curate, manage, and process within a tolerable elapsed time. (Snijders et al., 2012)
An appearance of AI supported by deep learning programing has dramatically increased a significanceof big data. Now big data is strongly believed to have an unlimited potential as a gold mine of AI driven
economic and social developments.
Introducing IoT techniques into container terminal operations enables port terminal operators and otherport related entities to collect, process and store a bulky digitalized data from daily terminal operations
on 24 hours and 365 days per year basis without no terminal staff intervention. Rapidly improving
sensor technologies, in combination with IoT, are another development for creating port big data in an
effective and efficient manner.
Artificial Intelligence (AI) is also expected to become a newly emerging player of port operation andmanagement. AI will learn port operation skills and practices by analyzing the big data. Deep learning
is a recently highlighted programing technique for realizing more efficient and effective AI controlled
operation and management capacity, which will assist the terminal staff in all over daily operations andmanagement works. For example, AI will provide terminal planners with an indicative optimum solution
and best practices including best stowage and yard plans for assisting prompt decision making based

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on the past experiences included in the big data. It may enable terminal operators to undertake quick
control and processing of container cargo traffic in the fully automatized container terminal yard.
Table 2 identifies possible innovations in the area of port operation by mobilizing cutting-edge ICT.
Targeted terminal operation innovations and results of ICT applications are discussed and summarized.
Challenges currently being and to be undertaken by the Japanese ports are also shown on the table.
As a sensor technology for generating port big data, image analysis techniques, global positioning
system (GPS) and automatic identification system of ships (AIS) are identified as well as traditional
electric and mechanical sensor systems. Progress in these sensor technology is considered an epochmaking event for facilitating recently observed rapid development of port business data digitalization.

Table 2: Possible innovations of port operation by mobilizing cutting-edge ICTs

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3.3

Examples of big data application at port: AIS data

As stated in this paper, generating big data is probably one of the most important elements of AI
controlled port operation and management. Then, what does the big data in ports look like? The
authors consider that AIS (Automated Identification System) may provide one of most up-dated
examples of viable big data application in the area of port and navigation cannel planning, design,operation and management, thus demonstrate it in this section.
AIS is an automatic tracking system used on ships. AIS was initially introduced to improve navigation
safety, and to increase search and rescue capacity for ships. AIS integrates a standardized VHF
transceiver with a GPS positioning system, with other electronic navigation sensors such as a
gyrocompass or rate of turn indicator. AIS equipped ships can be tracked by AIS base stations locatedalong coast lines or, when out of range of terrestrial networks, through satellites. The International
Maritime Organization's International Convention for the Safety of Life at Sea (IMO SOLAS
Convention) requires AIS to be equipped for international navigation ships with 300 or more gross
tonnage, and all passenger ships regardless of the size.
While AIS information including unique identification, position, course, and speed of ships originallyintends to assist the watch standing officers in avoiding ship collision, it is recently found useful for
maritime authorities and port operators to track and monitor ship movements, and to identify suspicious
ships.

Figure 2: Track chart created based on AIS data
A figure in the left-hand side shows a ship track which entered ports of Osaka and Kobe. At 4: 50 in
the early morning, the ship passed the mouth of Osaka bay and it arrived at offshore Osaka port at
7:00. In the afternoon of the day, the ship transferred to the port of Kobe and departed the port at 22:00
at night. By superimposing these track charts of ships, the right figure visualizes navigation situation inTokyo Bay, where intensive traffic control is essential for ship security and navigation safety, andmaintaining port logistics capability. (Takahashi et al., 2007)
Figure 3 demonstrates possible visualization of ship turnaround and berthing at Hirara ports, Okinawa,
Japan. The figure provides information of detailed ship movement in the port waters therefore, port
engineers, for instance, can easily identify an actually used water area by the ship.
In the quay side, AIS based data identifies an exact location of GPS antenna which shows location ofship berthing. As such, AIS-based big data is currently being expected to assist in more detailed and6

PIANC-World Congress Panama City, Panama 2018sophisticated planning and designing works. AIS data also enables us to visualize an actual berthwindow situation as a chart shown in Figure 4.

Figure 3: Visualization of ship turnaround and berthing
Figure 4 visualizes berth windows of Dream Island International Container Terminal (DICT), Osaka,
Japan and Kwai Chung CT 8 (CT8), Hong Kong, China. Terrestrial AIS data of one week period from3th to 9th October 2010 was used by Akakura et al. (2012) for preparing the berth window charts. DICT
has a quay length of 1,100 meters and annual throughput of about 2 million TEUs, and CT 8; 1380
meters and 4 million TEUs.
Quay line

2010/10/3（Sun.）

2010/10/4（Mon.）

2010/10/5（Tue.）

2010/10/6（Wed.）

2010/10/7（Thu.）

2010/10/8（Fri.）

2010/10/9（Sat.）

200m

400m

600m

800m

1,000mDream Island International Container Terminal, Osaka, Japan (Oct. 3-9, 2010)
Quay line

2010/10/3（Sun.）

2010/10/4（Mon.）

2010/10/5（Tue.）

2010/10/6（Wed.）

2010/10/7（Thu.）

2010/10/8（Fri.）

200m

400m

600m

800m

1,000m

1,200mKwai Chung CT 8, Hong Kong, China (Oct. 3-9, 2010)

Figure 4: Berth window charts drawn based on AIS data

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2010/10/9（Sat.）PIANC-World Congress Panama City, Panama 2018

Berth window chart looks like a table with time line in the horizontal axis and occupied quay line in thevertical axis. Occupied berth window is defined as product of occupied quay length and occupied time
period by ships, which is shown in the chart in figure 4 as light gray areas. It is noted that DICT has aspace for accommodating more ships on Wednesday, and CT8; from Thursday afternoon to Saturday
morning. Controlling berth windows may be one of the most important operations for the port terminals
to increase in berth utilization, therefore, to obtain more incomes. Visualizing berth window charts may
become a powerful tool for terminal operators to improve berth widow productivity.

4.

AI PORT CHALLENGES

4.1

Possible system configuration

As discussed in Section 3.2, the ICT based management and operation system at ports are considerednotably promising to provide with attractive solutions for the currently requested port policy agenda.
There are many possibilities to dramatically increase terminal operation efficiency and competitiveness,
contribute to the global logistics innovation, and improve business profitability and working environment
at ports. AI will play a key role for the overall port operation and management through accurate andtireless data processing and analysis, self-sustained judgments and recommendations, andappropriate communications. Figure 5 illustrates a schematic view of AI-led terminal operation andmanagement system.

Figure 5: Basic configuration of AI-led port system

For cultivating terminal operation and management AI by mobilizing deep learning programming
technique, big data creation is an essential and vital challenges.

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As discussed in Section 3.1, possible sources of port big data should be a daily port operations andmanagement procedures such as ship operation, port traffic control, terminal operation, cargostorage/delivery, and check-in/out gate control. From these procedures and operations, a bulky datais generated in terms of arrived ship specifications, arrival and departure details, berth window situation,quay and yard crane movements, cargo location and inventory, consignee/consigner information,delivery time and final destination, tax clearance status, quarantine inspection, safety, security andenvironment protection information, and any other notifications, authorizations and permissions. Someof these data and information are transmitted from outside port, and others are directly typed in ordigitalized through sensors embedded in the terminal facilities and equipment. Smart data warehouse
have a function of collecting and storing these data and information in a fully digitalized form. As such,properly employing IoT coupled with sensor technologies will be a key skill for automatically digitalize,
glean, process and store the balky port operation related information, which is generated from daily
port activities in 24 hours and 365 days basis.
Another important information sources for AI as a port manager may be port statistics andadministration and management related documentations which include rules and regulations, port dues,
tariff and charges, port master plan and investment program, and authorizations, notifications andpermissions of port traffic. Availability of these information directly affect to the quality of port businesspractice and environment, thus, profitability of the port. In this context, the information must be also
digitalized and stored in the data warehouse for contribution of AI to support port administration,
operations and businesses.
4.2

Challenges

The above mentioned AI led port operations and management are still on the way of technicaldevelopment and institutional arrangement.
Major challenges however may include: i) smooth introduction of the newest sensor technologies for
efficiently and effectively collecting all terminal operation related information as digitalized data, ii)
materializing an accurate big data transmission between on-site sensors and the terminal control hostcomputer through IoT channels, iii) developing man-machine interface for assisting operator’s prompt
decision making, iv) renovating current terminal operating system by employing AI based architecture,and v) introducing appropriate countermeasures against computer virus and hacking. A variety of deeplearning software including many open software is currently available. Mobilizing these existing
software resources may save the project time and cost, and enable to input more human and financial
resources for developing AI loaded terminal operation system. Most advanced sensor technologies
such as image processing techniques also may contribute, as eyes of AI, to the system development.
Legal and social aspects of current port systems will be also requested to make necessary adjustment
for adopting these ICT-led port operation and management reforms. Particular issues to be address
may include: i) increase in a vulnerability of port logistics in association with employing highlysophisticated ICT for port operation and management system, ii) ambiguity of legal responsibility forthe third party liability at the AI controlled port, and iii) infringement of information ownership in
association with creating big data. AI port manager may have a big operation and management
capability much beyond human capacity, but is not the Omnipotent. Once AI port manager lose its
function due to natural or human made disasters, it may not easy humanly to recover it. When AI port
manager make a misjudgment or has a serious malfunction resulting in causing damages or violatingrights of the third party, who supposedly will take a responsibility for it?
It must be carefully consider and discuss how is the best course to invite AI into our ports as a good
partner. The port community are not always welcome to build a robot port. What needed in a practical
viewpoint may be how to create a well-designed human-AI collaborative system for making our port
operation and management smart. AI controlled terminal may be a common business interest of theport community and also a common challenge, therefore to be discussed globally. Some of the cuttingedge technologies are just waiting for the application at ports.

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CONCLUSIONSThis paper was prepared for inviting an attention of conference attendees to the recently developed
cutting-edge ICT, in particular artificial intelligence (AI), Internet of things (IoT) and big data at ports, inthe context of possible application to the port operation and management.
Considering continuously intensifying international port competition and recent diversification of port
uses, global mainstream ports have started their consideration of further involvement of cutting-edge
ICT into the port operation and management. Remarkable development of sensor technologies,
coupled with IoT, has been enabling us to create a big data at port, which may lead to employment of
AI at port. Actually some of global ports have already been tackling to introduce AI, IoT and big data
into their ports. On the other hand, inviting cutting-edge ICT to the ports may raise many issues andnew challenges from technological, legal and social aspects. In this context, starting discussion about
this emerging agenda, and sharing data and information among PIANC community will surely benefit
not only the conference attendees but also the whole member port community as well. The authors
are pleased to have this opportunity of 34th PIANC world Conference to raise this hot topic for our
PIANC colleagues.
References
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EC (2017). Digital Transmission monitor, Germany: Industrie 4.0, Directorate-General Internal Market,
Industry, Entrepreneurship and SMEs. European Commission. January 2017.
Snijders, C.; Matzat, U.; Reips, U. (2012). 'Big Data': Big gaps of knowledge in the field of Internet,
International Journal of Internet Science, 2012, 7(1), 1–5.
Takahashi, H.; Goto, K. (2007). Study on inflection to the port and harbour development by AIS data.
Technical Note of National Institute for Land and Infrastructure Management. No. 420 (in Japanese)10