IAME Conference 2022

D4-1-1

Yasuhiro AKAKURA
National Institute for Land and Infrastructure Management, MLIT, Japanakakura-y83ab@mlit.go.jp

Analysis of Deterioration of Port Function and Long
Offshore Waiting under Global Shipping Crisis
Abstract
Global shipping crisis has become a crucial issue, causing delays in deliveringvarious goods and leading to shortages of those goods. Transport demand surged in
the second half of 2020; however, the container transport capacity was insufficient
owing to the lack of container boxes, ships, and sudden labor shortages due to the
spread of COVID-19. Numerous container ships have been waiting at offshore fordays at major ports, especially in North America, China, and Europe, causing aconsiderable deterioration in the punctuality of the world’s container services. In thisstudy, the deterioration of port functions of the world’s major ports under thecurrent crisis is analyzed. Furthermore, offshore waiting times and volumes at theterminals of the Los Angeles/Long Beach and Singapore ports are estimated using
Automatic Identification System data. Additionally, an automated calculation systemestimating the waiting time–volume of each terminal is developed. The container
handling efficiency of the Los Angeles/Long Beach port was found to continuously
decrease, while that of Rotterdam and Shanghai ports remained rather stable; thewaiting time–volumes at the Singapore-port terminals were comparatively small.
Furthermore, the significance of data accuracy for analyzing port/terminal performanceis discussed.

Keywords: Port Performance, Terminal Congestion, Berth Occupancy, Container
Handling Efficiency, AIS.
1. Introduction
The global shipping crisis has become a crucial issue, causing shortages ofimported consumer goods, including Christmas gifts, especially in North
America and Europe. In the second half of 2020, after the COVID-19 firstwave had subsided, container transport demand surged substantially in thesecontinents supported by strong consumer expenditure. For example, theimported containerized cargo weight in the US increased drastically after- 905 -

IAME Conference 2022

D4-1-1

September 2020 compared with the same month of 2019 as shown in Figure
1, despite strong capacity constraints.

Weight Chage Rate(vs. same month of 2019)

+30%
+20%
+10%
0%
-10%
-20%

1

3

5 7 9 11 12020

3

5 7 9 11(month)2021(year)

Figure 1 – Weight change of US imported containerized Cargo
Data Source: USA Trade Online
Container transport capacity consists of container boxes, ships, cranes,
chassis, and storage spaces at terminals and warehouses, and it wasinsufficient to fulfill the surging demand. Furthermore, the spread of COVID19 infection has incurred sudden disruptions due to labor shortages at many
ports, as shown in Table 1. As a result, numerous container ships have been
waiting for days to be berthed offshore at major ports, especially in North
America, China, and Europe, causing a significant deterioration in the
punctuality of world container services. These delays of ships have been
passed on to other terminals, and the issue has been difficult to resolve in ashort period. Global schedule reliability, the rate of arrival of container ships
within the next days of schedules, dropped from 78% in 2019 to 36% in 2021
(Sea-Intelligence, 2022). Sophisticated and lean supply chains, such as justin-time systems, have been damaged fatally by these unreliable andexcessively expensive services. Comparatively high-value commodities wereforced to select air transport alternatively, as the weight of US auto parts
imported from Asia by air surged after the second half of 2020, as shown in
Figure 2, even though the world auto production has suffered a downswing
due to lack of semiconductors since the second half of 2021. Simultaneously,
many shippers, presumably, have given up exporting their low-value cargoes.
It was estimated that $15.7 billion of US export was lost by port congestion in
the US between May and November 2021 (Steinbach, 2022).

Table 1 – Disruption of port function due to COVID-19 (After 2H ’20)

- 906 -

IAME Conference 2022

D4-1-1

PeriodCountry

After Winter '20

US

Nov '20UK

Dec '20

China

May to Jun '21

China

Jul to Sep '21

Vietnam

Aug '21China

Oct '21UK

Mar to May '22

China

Contents
Handling capacity of West Coast ports, especially Los Angeles/Long Beach port, have been insufficient, and many ships havebeen waiting at offshore of these ports (worsened after Summer '21)
Efficiency of container handling has drastically deteriorated and
CMS-CGM and MSC introduced port congestion surcharge
Calling at Dalian port was suspended
Port calling at Yantian port closed for 1 month, and many ships
waited at offshore of Yantian and other neighboring ports or omitted
their calls at these ports
Congestion of major ports have deteriorated
Meishan Terminal of Ningbo port was closed for 2 weeks by 1
positive case
Maersk skipped Felixstowe because of terrible congestion
Congestion of Shanghai port has worsened after local authorities
escalated lockdown measures in the city

Weight Chage Rate
(Average of 2H 2019 = 1.0)

4.0
3.5
Air

3.0
2.5
2.0
1.5

Sea

1.0
0.5
0.0

1

3

5 7 9 11 12020

3

5 7 9 11(month)2021(year)

Figure 2 – Weight change of US imported auto parts by mode
Data Source: USA Trade Online
Based on the abovementioned background, deterioration of port functions ofworld major ports under the current shipping crisis, causing long offshore
waiting, was analyzed by calculating berth occupancy and container handlingefficiency of each port. Furthermore, offshore waiting times and volumes at
the terminals of the Los Angeles/Long Beach and Singapore ports were
estimated using Automatic Identification System (AIS) data. Additionally,
based on the concept that offshore waiting data are crucial as an index forevaluating the soundness of terminal operations, an automated calculation
system that estimates the waiting times and volumes of each terminal wasdeveloped.
2. Literature Review- 907 -

IAME Conference 2022

D4-1-1

Many previous studies have discussed port congestion because it is one of the
crucial issues facing not only each port but also the entire maritime economy.
Among them, factorial analysis and countermeasures for port congestion
were mostly discussed. For example, Gidado (2015) categorized port
congestions in Africa, such as ship berth congestion, vehicle gate congestion,and cargo stack congestion, and identified the consequences of port
congestion on Logistics and supply chain operations. Jiang et al (2016)
developed a theoretical model for analyzing the port congestion
internalization of the shipping lines, considering the knock-on effect, i.e., thecongestion delay passed on from one port-of-call to the next. Jiménez et al(2021) applied a multiagent platform in the Port of Cartagena to alleviate
potential congestion in multiclient liquid bulk terminals, promoting a
consensus where the overall vessel waiting time is reduced. Bolat et al (2020)
discussed the most important factors of port congestion using existing worksof literature. Pruyn et al (2020) used Markov chain analysis to predict port
waiting time in the bulk shipping industry, which is caused by port congestion.
Among the previous studies relating to port congestion, recently, the analysis
utilizing AIS data formed one category. AbuAlhaol et al (2018) proposed port
congestion indicators from AIS data to alert for congestion levels that can becorrelated with weather, high demand, or a sudden collapse in the capacity
due to strike, sabotage, or other disruptive events. Kim and Lee (2018)
proposed a new deep neural network model using AIS data to predict themedium- and long-term traffic of the caution area. Bai et al (2021)
investigated how port congestion affects maritime transportation freight rates
for liquefied petroleum gas seaborne trade using AIS data. Pen et al (2022)
proposed high-frequency container port congestion measures using AIS data
for monitoring changes in port performance.
There were some analyses related to port congestion amid the current global
shipping crisis. As mentioned earlier, Steinbach (2022) assessed the US
foreign trade effects of port congestion and container shortages using event
studies and concluded that the US exported 24.5% fewer containers between
May and November 2021, amounting to export losses of $15.7 billion. Gui etal (2022) developed a methodology for identifying and prioritizing port
congestion risk during the COVID-19 pandemic. However, as to the current
crisis in that numerous container ships have been forced to wait offshore, nostudy analyzed the relationship between offshore waiting and terminal
operation.
As to offshore waiting due to terminal congestion, Los Angeles and Long
Beach ports are probably the only port authorities that publish the number of- 908 -

IAME Conference 2022

D4-1-1

offshore waiting ships; however, the statistics eventually do not show the real
number after the change of the queuing system of both ports in midNovember 2021. Gao et al (2016) traced container ship navigation in Japan’s
Seto Inland Sea and its oceanic waters using AIS data and identified offshore
anchoring ships whose speeds over the ground were below 3 knots andpositions were not near any berths at ports. Akakura and Takahashi (2021)
proposed a method for estimating the offshore waiting time of each ship bycalculating the total time and hourly ship speed between entering the port
and the berthing terminal and detecting anchoring signals using AIS data. As
a result, the offshore waiting time–volume was related to the berth occupancy
ratio, total twenty-foot equivalent unit (TEU) capacity of berthing ships, andactual delays of arrivals and overtime stays. However, these studies did nottarget the many ships’ long waiting times after mid-2020.
In contrast, as the delay of container services worsened more and more,some companies have started information services relating to port congestionand offshore waiting. Marine Traffic provides the numbers and times ofanchoring ships at designated offshore waiting sea areas for the port
congestion index as a pathfinder. Clarkson started offering a port congestion
index: share of carrying capacities at ports, including offshore waiting, toprofile the impact of port congestion on various shipping markets. Lloyd’s List
Intelligence (LLI) often indicates the number and capacity of offshore
anchoring ships in its articles. Vessel Value provides average delay hours andnumber of waiting ships by ship type as port congestion data. As for container
shipping, eeSea and Kuehne+Nagel provide the number of waiting container
ships, and IHS Markit and World Bank Group started the service “TheContainer Port Performance Index” to offer various port performanceindicators relating to not only offshore waiting but also gantry crane moves,although the data coverage was limited to only major shipping companies.
These information services are useful for grasping the outline of port
congestion; however, the data provided is categorized basically by port not byterminal. Additionally, many of these companies do not show a detailed
method for generating data, and inconsistency was found with the official
offshore waiting ships’ data of Los Angeles/Long Beach port.
The contribution of this present study to the literature is two-fold: First, thedeterioration in port function indices, such as berth occupancy and container
handling efficiency, are analyzed, considering the accuracy of ship movement
data. Second, terminal-based offshore waiting times and volumes are
estimated, and the relationship offshore waiting and quayside efficiency ofterminals amid the global shipping crisis is revealed.

- 909 -

IAME Conference 2022

D4-1-1

The remainder of this paper is structured as follows. In Section 3, thedeterioration of port functions of the world’s major ports under the current
crisis is analyzed. In Section 4, offshore waiting times and volumes at theterminals of the Los Angeles/Long Beach and Singapore ports are estimatedfor a detailed analysis of terminal operation. In Section 5, a discussion of theimportance of data accuracy and offshore waiting data is held. In Section 6,
conclusions are given.
3. Port Performance under Crisis
3.1. Data and Method
Changes in the port functions of the world’s major ports were analyzed using
LLI ship movement data. Table 1 shows the structure of the LLI data,
illustrating the actual arrival and sail dates/times of ports and anchorages.
These dates/times were arranged using the AIS data received at each port.
The waiting offshore of a port was recorded as arrival and sail at anchorage,
and the arrival time at each port in the ship movement data indicates thetime when a ship arrived in front of the calling berth and slowed to almost astop, according to the comparison of the LLI data with AIS data (Akakura and
Takahashi, 2021). The analysis targets were Los Angeles/Long Beach,
Rotterdam, Singapore, and Shanghai ports as the world’s top-tier andseverely congested ports.
Table 2 – Structure of LLI ship movement data
IMO No. VESSEL NAME
981**************
981**************
981**************
981**************
981**************
981**************
981**************
981**************
981**************
981**************
981**************
981**************
981**************

TEU
14,30014,30014,30014,30014,30014,30014,30014,30014,30014,30014,30014,30014,300PLACE NAME CNTRY ARRIVAL DATE/YIME SAIL DATE/TIME
Singapore
SGP
2021/6/2 0:39
2021/6/3 4:15
Cai Mep
VNM
2021/6/4 23:51
2021/6/5 14:53
YantianCHN
2021/6/8 1:47
2021/6/9 3:52
NingboCHN
2021/6/11 12:09
2021/6/12 8:14
YangshanCHN
2021/6/13 4:51
2021/6/14 9:20
Long Beach Anch. USA
2021/6/27 16:05
2021/7/2 1:46
Long BeachUSA
2021/7/2 4:11
2021/7/5 6:01
OaklandUSA
2021/7/6 15:13
2021/7/7 13:29
Busan
KOR
2021/7/19 3:19
2021/7/20 1:34
Yangshan Anch.
CHN
2021/7/20 22:49
2021/7/23 12:18
YangshanCHN
2021/7/23 14:26
2021/7/24 6:10
Ningbo Anch.
CHN
2021/7/24 17:23
2021/7/26 14:37
NingboCHN
2021/7/26 16:34
2021/7/27 12:54

Source: LLI
In this study, the port performance of each port was analyzed by calculating
the following indices: berth occupancy ratio and average berthing time per
TEU as the representation of quayside efficiency. Berth occupancy ratio R is
the ratio of time and space occupied by berthing ships, as shown in Equation(1):- 910 -

IAME Conference 2022

D4-1-1

R=

 (L

Ship

Ship

⋅ TShip ),LAll ⋅ TAll(1)where LShip and TShip are the occupied length (Figure 3) and time of the ship
and LAll and TAll are the all berth length and time, respectively.
Occupied Length
3 2B

3 2B

L

B
●

30°

●

●

●

●

●

●

●

●

Figure 3 – Occupied length of berthing ship
Average berthing time per TEU E (min/TEU) indicates the efficiency ofcontainer handling and is calculated using Equation (2):

E =

T

Ship

V

Ship

,

(2)

where V (TEU) is the container throughput of ports.
The prerequisite of this time series analysis is the assumption that thecondition for the berthing of ship and container handling of each port has notchanged considerably. The frequency of extremely bad weather, such as
strong wind, high wave, and thick fog, which disturbs smooth port operation
has changed by season and year. The total number of gantry cranes atcontainer terminals in Singapore and Shanghai ports had increased 3% and
4%, respectively, when comparing 2021 with 2019. However, generally,
these slight changes have little impact on the analysis result.
3.2. Change in Port Performance
Figure 4 shows the calculation result of the berth occupancy ratio of each port
based on berthing time and occupied length of each ship. The increase in theberth occupancy ratio of the Los Angeles/Long Beach and Singapore ports
after the fourth quarter of 2020 was considerably larger than that ofRotterdam and Shanghai. The average berth occupancy ratio of the Singapore
port in 2021 reached 90%, which was an abnormal level and meant almost no
margin for berthing space and time on this calculation result, while themaximum berth occupancy ratio of Los Angeles/Long Beach port in 2021 was- 911 -

IAME Conference 2022

D4-1-1

Change of Berth Occupancy Ratio(vs. 2019 Average)

67%. The berth occupancy ratios of various terminals at world major ports
before the current global shipping crisis were approximately 60% or less
(Akakura and Takahashi, 2021). Here, as it confirmed that some ships called
not terminal but shipyard at the Singapore and Shanghai ports, the ships
staying over one week continuously were excluded from its data.
+40%ptLA/LBBerth Occupancy Ratio+30%ptSingapore+20%pt+10%ptRotterdam

0%pt

Shanghai-10%pt

1Q

2Q 3Q
2020

4Q

1Q

2Q 3Q
2021

Port

2019

Los AngelesLong Beach

34.6%

Rotterdam

51.1%

Singapore

63.6%

Shanghai

49.8%

4Q

Figure 4 – Change of berth occupancy ratio
Figure 5 indicates the change of berthing time per throughput TEU, as theindex representing the handling efficiency of a ship to shore. This index
depends on berthing ship size because, for larger ships, more cranes can beutilized for loading and unloading container boxes. Therefore, comparison
between disparate ports is meaningless; however, monitoring the changes inthe same port is significant. The change of berthing time per TEU of the Los
Angeles/Long Beach and Singapore port surged after the fourth quarter in
2020, i.e., the degradation of container handling efficiency substantially,
whereas that of Rotterdam port increased in the first quarter of 2021 but
returned to the same level of 2019 in the following quarter.
Next, the relation of the two calculation results was analyzed, as the bothindices connected through berthing time. From the results, changes in port
performance were divided into two categories, as shown in Table 3. The firstcategory is located at “1a” in Table 3, Rotterdam and Shanghai ports. Bothindices were stable, even though many ships were forced to wait offshore.
The other category is located at “2b” in Table 3, showing that the portfunctions at the quayside of the Los Angeles/Long Beach and Singapore ports
have deteriorated severely. There were no ports located at “1b,” where
berthing times were stable but times per TEU deteriorated, meaning slump of- 912 -

Change of Berthing Time per TEU(min/TEU, vs. 2019 Average)

IAME Conference 2022

D4-1-1+ 0.40LA/LB+ 0.30Singapore+ 0.20Rotterdam+ 0.100.00-0.10

Shanghai

1Q

2Q 3Q
2020

4Q

1Q

2Q 3Q
2021

Berthing Time per TEU
Port

2019

Los AngelesLong Beach

0.47

Rotterdam

1.16

Singapore

0.57

Shanghai

0.35

4Q

Figure 5 – Change of berthing time per TEU
Table 3 – Dimensions of change in port performance
Berting Time per TEU
a.Stableb.Surge
Rotterdam
Berth
1b
1.Stable 1a
ShanghaiOccupancy
LA/LB
Ratio
2.Surge 2a2bSingaporecontainer handling, or at “2a,” where berthing times were prolonged but
handling efficiencies were stable, meaning enabling an increase of handling
container volume drastically, in Table 3.
The deterioration of port functions of the Los Angeles/Long Beach andSingapore ports were due to the prolonged berthing time. Figure 6 shows theaverage berthing time of all ports. In the figure, the surge of the Los
Angeles/Long Beach and Singapore ports after the fourth quarter in 2020 was
prominent, increasing the maximum from 70% to 80% of the 2019 average,
although their average berthing time in 2019 differed greatly: that of the Los
Angeles/Long Beach port was more than three times longer than that of theSingapore port. Most of the container services calling at the Los Angeles/Long
Beach ports are East–West trunk lines, while those of the Singapore port
contain many feeder services in Southeast Asia.

- 913 -

IAME Conference 2022

D4-1-1

Chage Rate of Average Berthing Time(hours, vs. 2019 Average)

+100%
LA/LB

+80%

Average Berthing Hours

Singapore

+60%
+40%

Rotterdam

+20%
0%
-20%

Shanghai

1Q

2Q 3Q
2020

4Q

1Q

2Q 3Q
2021

Port

2019

Los AngelesLong Beach

67.4

Rotterdam

40.2

Singapore

21.7

Shanghai

16.6

4Q

Figure 6 – Change of average berthing time

Average No. of Anchoring Ships

3.3. Ambiguous Data
Based on the LLI ship movement data, the quayside performance ofSingapore deteriorated severely. Simultaneously, the berth occupancy ratio
reached an abnormal level. Additionally, the number of anchoring ships at
offshore areas of the Singapore port based on the LLI data was substantiallylow, as indicated in Figure 7. These numbers were the average of anchoring
ships at noon of each day. The number of anchoring ships at offshore areas of
the Singapore port was underestimated, compared with the other reports,such as Varley and Murray (2021), 54–102 container ships waiting offshore of
the port from July to November 2021.
40
LA/LB

35
Shanghai

30
25
20
15
10

Rotterdam

50Singapore

7

10
2020

1

4

7
2021

10

(month)(year)

Figure 7 – Number of anchoring ships at offshore area by LLI data
It was considered that the anchoring of ships around the Singapore port
probably was not recorded properly by LLI. From the comparison with AIS
data, anchoring periods of some ships offshore of the terminal were included
during their arrival and departure times at the port, as shown in Figure 8. In- 914 -

IAME Conference 2022

D4-1-1

this case, berthing time in LLI ship movement data was 47 hours, eventhough true berthing time was only 17 hours and anchoring time at the east
offshore of the terminal was approximately 30 hours. Additionally, some ships
without berthing at any terminal at the Singapore port were identified as
calling ships in LLI data. Figure 9 shows an example of a ship passing throughthe strait and temporarily anchoring offshore the terminals for bunkering orother purposes.
From this comparison, the analysis result relating to the Singapore port statedin the previous section is not reliable. Therefore, further detailed terminalbased analysis targeting the Los Angeles/Long Beach and Singapore ports
was done using AIS data, as mentioned in the next section.

Figure 8 – Comparison LLI data with AIS data (Anchoring Ship)

Figure 9 – Comparison LLI data with AIS data (Passing Ship)
4. Offshore Waiting under the Crisis
4.1. Estimation Method
Akakura and Takahashi (2021) constructed a method for identifying offshore
waiting ships and estimating the waiting times of ships. Here, the same
method was adopted. Offshore waiting ships were identified by the following
criteria:- 915 -

IAME Conference 2022

D4-1-1(1) Ships that had the record of “At Anchor” in the navigational status of the
AIS signal;(2) Ships that continued at a ship speed of fewer than 3.0 knots over the
ground in the AIS signal for more than 2 hours;(3) Ships whose total time from port entry to berthing was equal to or greaterthan the times of ships in (1) and (2).
Here, criterion (2) corresponds to ships that forgot to switch the “At Anchor”
signal on, while criterion (3) corresponds to waiting ships that loiter or slow
down. Before calculating the total times from port entry to the berthing ofships, the area for identifying port entry and berthing for each port/terminal
was first set, as indicated in Figure 10. The port entry areas were set broadly
enough to cover the anchoring, loitering, and slowing down locations. The
results of the total time calculation of all berthed ships were placed inascending order by terminals, as shown in Table 4. In this case, the offshore
waiting ships are identified as ships No. 7 to 12. Ships No. 7, 11, and 12
correspond to criterion (1), ship No. 9 corresponds to criterion (2), and ships
No. 8 and 10 correspond to criterion (3). The offshore waiting times were
calculated as total times minus the required maximum times of normal
navigation without waiting; total time minus 3 hours in Table 4. The waitingtimes of ships that moved between terminals were also estimated in the samemanner. Finally, waiting time–volume per berth length was calculated byassuming 60% of slot utilization rate to monitor how many and how longcontainers had to wait offshore of terminals.
The estimation targets were the terminals of the Los Angeles/Long Beach andSingapore ports from mid-November 2020 to mid-January 2021.
Area for Identifying
Port Entry

Ship’s Track

Port Entry Time: t1
Anchoring

Berthing Time: t2

Area for Identifying
Berthing
Figure 10 – Image of settings for identifying offshore waiting ship
Source: Akakura and Takahashi (2021)- 916 -

IAME Conference 2022

D4-1-1

Table 4 – Image of estimation of offshore waiting times
Total Timet 2 -t 1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

1

2

3

8

9

10 11 1213.8 13.7 17.4 12.6 11.0 15.5

9.0

10.4

9.8

8.2

10.6 10.3

12.7 12.9 11.7 10.6
1.5

4

5

Ship
6
7

2.0

9.9

6.5

0.6

4.2

2.4

8.7

0.8

8.4

2.7

3.1

3.1

3.8

0.7

3.9

0.7

0.0

10.7

5.0

1.3

4.1

0.7

0.0

3.3

2.1

4.8

3.5

0.7

0.0

1.1

5.4

0.9

0.2

Le ge nds
Normal Navigation
At Anchor Signal
Speeds < 3.0kt
Anchor Signal & Speeds < 3.0kt

0.6

0.9

0.1

※ Numbers in cells mean average ship speeds

0.8

0.0

5.8

0.0

9.7

0.0

7.3

0.0

4.2

0.0

Offshore Waiting Time

2.8
10.0
1.2

Source: Akakura and Takahashi (2021)
4.2. Estimation Result
Figure 11 shows the change in the estimated number of the waiting ships at
the Los Angeles/Long Beach and Singapore ports every hour of all days. The
number of waiting ships ranges from about 10 to over 30 in both ports. To
evaluate the accuracy of the estimation result, Figure 12 illustrates thecomparison of this study’s estimation with statistical data of waiting ships
published by the Los Angeles port. The estimation result reproduced thestatistical data with good accuracy. Here, the time of port stats for each day
cannot be found, so the mark was set at noon of each day.

Number of Waiting Ships

40
35

Singapore

LA/LB

30
25
20
15
10
5
0
15 22 29
Nov. '20

6

13 20
Dec. '20

27

3 10
Jan. '21

Figure 11 – Number of offshore waiting ships- 917 -

IAME Conference 2022

D4-1-125
Number of Waiting Ships

Estimation

20
15

Port Stats

10
5015 22 29
Nov. '20

6

13 20
Dec. '20

27

3 10
Jan. '21

Figure 12 – Comparison of LA port stats and this study’s estimation
The waiting times of ships differed considerably among both ports. Figure 13
shows the share of waiting times of ships at both ports, in addition to thetotal of five ports in October 2019 (Akakura and Takahashi, 2021). The shareof more than 36 hours of waiting times at the Los Angeles/Long Beach port
exceeded 80%, whereas the waiting times for approximately half of the ships
at the Singapore port were equal to or shorter than 6 hours. The distributionof the share of waiting times at the Singapore port was similar to that of fiveports in 2019, which means that the Singapore port might have succeeded insuppressing the waiting times toward the same level before the current
shipping crisis.

Share of No. of Waiting Ships

100%

36h<
≤ 36h
≤ 24h

80%

≤ 18h

60%
≤ 12h

40%
20%
0%

2019Port
Los AngelesLong Beach
Rotterdam
ShanghaiNingbo

≤ 6h

LA/LB

Singapore

2019

Figure 13 – Share of waiting times of ships
Figure 14 illustrates the estimated result of offshore waiting by a terminal.
The left axis/green line is the average share of the number of waiting ships
among all berthing ships. The shares of around half of the terminals at the- 918 -

IAME Conference 2022

D4-1-1

8,000

60%

6,000

40%

4,000

20%

2,000

0%

0

Los Angeles

Pier E
Pier G
Pier J
Pier T
Brani
KeppelPasir P-1
Pasir P-2
Pasir P-3
Pasir P-4
Pasir P-5
Pasir P-680%

TrapacWB China
WB Ever
Yusen
Pier A
Pier C

10,000APMTEver P
Ferix M

100%

Long Beach

Waiting Time-volume per
Berth Length (h*TEU/m/mo.)Share of No. of Waiting Ships

Los Angeles/Long Beach ports exceeded 80%, while that of Pier C was only
9%. The shares of all terminals at the Singapore port were equal to or lessthan 60%. The right axis/blue bar is the waiting time–volume per berthlength per month. The time–volume of terminals at the Los Angeles/Long
Beach port differed greatly from below 200 to above 8,000 h*TEU/m/month,compared with the curbed figures of those of the terminals at the Singapore
port. These results of the terminals in the Los Angeles/Long Beach ports
indicated the importance of terminal-based analysis.

Singapore

Figure 14 – Estimation Result of Offshore Waiting by Terminal
4.3. Consideration about Terminal Operation
Berth occupancy ratio was also calculated by the berthing and leaving of eachship traced using AIS data. Table 5 shows the comparison of the berthoccupancy results presented in Section 4 based on LLI ship movement datawith that of this subsection. As for the Los Angeles/Long Beach ports, both
calculation results were at the same level, although both terms were not
identical. However, there was a huge gap between both calculation results ofSingapore port: the LLI ship movement data arguably could not classify
offshore waiting and berthing at the terminals. From this result, it was
revealed that the average berth occupancy ratio of the Singapore port has
been stable amid the current global shipping crisis, as the average ratio in2019 was 63.6%, as shown in Figure 4. This means that, in Table 3, theSingapore port should be placed at “1a” instead of at “2b.”- 919 -

IAME Conference 2022

D4-1-1

Table 5 – Comparison of calculation result of berth occupancy ratioPort
Los AngelesLong Beach
Singapore

LLI Movement data
4Q-'20
1Q-'21
4Q-'20
1Q-'21

60.4%
61.6%
85.2%
87.8%

Analysis by AIS data
15, Nov. '20~
15, Jan. '21
15, Nov. '20~
15, Jan. '21

60.2%
62.3%

Since waiting time–volume is assumed to be linked to terminal congestion,
the relationship between the berth occupancy ratio and offshore waiting time–
volume per berth length was calculated, as shown in Figure 15. All data were
one-month long, with two points for each terminal: November 15, 2020, toDecember 15, 2020, and December 16, 2020, to January 15, 2021, as thefigure of some terminals changed substantially. Akakura and Takahashi (2021)
analyzed this relation by five ports’ data of October 2019, indicating the
exponentiation relation between berth occupancy ratio and waiting time–
volume, within the ratio below 70% and the waiting time–volume below 1,600
h*TEU/m/month, as indicated by the gray-hatched area in Figure 15. Also, it
was revealed that many ports are still in this range amid the current crisis.
The terminals outside the range could be classified into two categories: (1)terminals of extra-large waiting time–volume but under-70% of berthoccupancy ratio, and (2) terminals of above 70% of berth occupancy ratio.
Almost all the terminals classified in category (1) were at the Los
Angeles/Long Beach port, and the berthing time was probably restricted tomaintain the efficiency of container handling. For example, if there is notenough space for storage containers in yards, the terminal operators might
instruct the next berthing ship to wait until the terminal’s preparation iscomplete. The berth occupancy ratio of terminals classified in category (2)leached around 80%, which is virtually the upper limit, considering that the
ship’s occupied lengths in Figure 3 are almost smaller than the total berthlength and vacant times are needed to change ships. Notably, the different
waiting time–volume of terminals at the Singapore port were mostly below
the regression curve, while those of the terminals at the Los Angeles/Long
Beach port were much above the regression curve. From the hearing survey,
it is said that stringent control of berthing time is almost unique to theSingapore port. This may enable subsequent ships to plan their berthing timeand slow down before anchoring. As for the Los Angeles/Long Beach port inthis period, ships must reach within 40 miles offshore of the port to get in thewaiting queue, thereby making ships experience longer waiting time. This
waiting system of the port was changed in November 2021 to count ships in
the waiting queue when they sail from previous ports.

- 920 -

IAME Conference 2022

D4-1-1

Waiting Time-Volume per
Berth Length (h*TEU/m/mo.)

12,000LA/LB
Singapore

10,0008,000
6,000

Regression Curve
before crisis

4,000
2,000

Range before crisis

0
0%

20%
40%
60%
Berth Occupancy Ratio

80%

Figure 15 – Berth occupancy ratio vs. waiting time–volume
5. Discussion
5.1. Data Accuracy Issue
Thus far, port performance under the current global shipping crisis wasanalyzed using LLI ship movement data in Section 3, and terminal-based
detailed analysis was performed using AIS data in Section 4. The analysis
result relating to the Singapore port in Section 3 was inappropriate because
offshore waiting periods of some ships were included between arrival
dates/times and sail dates/times of the port. Now, this kind of analysis isalready available, such as The Container Port Performance Index by IHS
Markit and the World Bank Group; however, there is a possibility that the data
are ambiguous. One of the main data sources for this kind of service is fromshipping companies; however, a data accuracy issue is also found in the datafrom the shipping companies. For example, the website of Ocean Network
Express provides arrival, berthing, and departure date/time of every port andship; offshore waiting ships should take longer times between arrival andberthing, compared with normal navigation ships. However, it was confirmedthat offshore waiting times sometimes were not included between arrival andberthing depending on ports and ships.
The situation for data on offshore waiting is the same. As mentioned earlier,
many companies have started to provide the number of waiting ships;
however, the validation of data accuracy conducted by these companies has
not been found. As the Los Angeles/Long Beach port is probably the only port
providing offshore waiting ship data, the number of waiting ships from the LLI
ship movement data was added to Figure 12 to compare the accuracy in
Figure 16, indicating the number was smaller than official port statistics. The
data of Marine Traffic, eeSea, and Kuehne+Nagel also showed differencesfrom the data of the Los Angeles/Long Beach port, as far as it confirmed. The- 921 -

IAME Conference 2022

D4-1-1

promotion of the availability of various data is desirable because it facilitates
relevant decisions of shippers and shipping companies and can lead to
improving the services of ports and terminals by creating a competitiveenvironment. Simultaneously, these companies must provide the data
definition and accuracy to not mislead users.
25
Number of Waiting Ships

This Study

20
15

Port Stats

10
5015 22 29
Nov. '20

LLI

6

13 20
Dec. '20

27

3 10
Jan. '21

Figure 16 – Data Comparison of No. of waiting ships at Los Angeles
5.2. Automated Calculation System for Offshore Waiting Data
The offshore waiting conditions of each port are now vital information not only
for terminal operators but also for shipping companies and shippers. However,almost all the ports fail to provide the data on waiting ships. Additionally,
offshore waiting time–volume widely varied among terminals within the same
port, as shown in Figure 14. Shippers that reserve services for spot cargoes
can utilize this data to avoid services calling at terminals with large offshore
waiting time–volumes. Furthermore, waiting time–volume can be one key
performance indicator for judging the soundness of terminal operation, and it
can be considered when discussing the efficiency and necessity of investment
in a terminal.
Considering this condition, an automated calculation system that estimates
the waiting time–volume of each terminal was developed. The target was
container terminals at the Yokohama port and the duration of the trial
operation was one-month long in February 2022. Figure 17 shows a sample
output of the system. This system is designed to list all berthing ships at eachterminal, identify offshore waiting ships, and calculate the waiting time–
volume of the ships, every time date changes, automatically. Simultaneously,
the system can calculate the berth occupancy ratios of terminals of theprevious day based on the berthing times of the ships. None of the errors- 922 -

IAME Conference 2022

D4-1-1

were detected during the trial, indicating the effectiveness of the system, i.e.,
it can be introduced at every port easily if AIS data is available.

Figure 17 – Output sample of automated calculation system
6. Conclusions
The global shipping crisis triggered by demand surges and capacity
constraints has caused long offshore waiting at congested terminals
worldwide. This crisis has had a fatal impact on the global supply chain,causing shortages of various goods, especially in North America and Europe.

- 923 -

IAME Conference 2022

D4-1-1To alleviate the congestion of terminals, analyzing port/terminal performance,
including offshore waiting, is crucially required.
This study analyzed the port performance of the world’s major ports using LLI
ship movement data. It was revealed that changes in port performance under
the current crisis were classified into two categories: ports with a rather
stable berth occupancy ratio and berthing time per TEU, and ports with surgesof both indices. Concomitantly, an ambiguous definition of ship movement
data at the Singapore port was found, implying that the analysis result
relating to the port was not reliable.
Furthermore, offshore waiting times and volumes of terminals of the Los
Angeles/Long Beach and Singapore ports were estimated utilizing AIS data.
Based on this result, the performances of terminals were classified into three
categories: terminals operating under the same range before the current
crisis, terminals with extra-large waiting time–volume and normal berthoccupancy ratio, and terminals with an upper limit of berth occupancy ratio.
Among the terminals in the third category, the waiting time–volumes at theterminals of the Singapore port were relatively small, while those of theterminals in the Los Angeles/Long Beach port were very large. The share of
waiting ships and waiting time–volume widely varied among the terminals at
the Los Angeles/Long Beach port. This indicates the importance of terminalbased analysis.
The significance of data accuracy was emphasized when analyzing
port/terminal performance. Ambiguous definitions of data may lead toirrelevant results. Furthermore, considering that offshore waiting data is vital
under the current crisis, an automated calculation system that estimates thewaiting time–volume of each terminal was developed.
The contribution of this study to the literature is as follows: (1) This study
analyzed the deterioration in port functionality indices while considering theaccuracy of ship movement data. (2) It estimated terminal-based offshore
waiting times and volumes and analyzed the relationship of offshore waiting
with quayside efficiency of terminals amid the global shipping crisis.
The author would like to continue port and terminal performance analyses tocontribute to solve the clogging problem of the global supply chain. To
alleviate the current congestion, it is crucial to perform analysis using
accurate data.

- 924 -

IAME Conference 2022

D4-1-1

Acknowledgment
This study was supported by JSPS KAKENHI Grant Number 22K04647.

References
ABUALHAOL I., FALCON R., ABIELMONA R. and PETRIU E., 2018, Mining port congestion
indicators from big AIS data, 2018 International Joint Conference on Neural Networks.
AKAKURA Y. and TAKAHASHI H., 2021, Analysis about delay of container trunk lines andoffshore waiting for calling to congested container terminals, 29th Conference of theInternational Association of Maritime Economics.
BAI X., JIA H. and XU M, 2021, Port congestion and the economics of LPG seaborne
transportation,
Maritime
Policy&Management,
https://doi.org/10.1080/03088839.2021.1940334
BOLAT, P., KAYİŞOĞLU, G., GÜNEŞ, E., KIZILAY, F. E., and ÖZSÖĞÜT, S., 2020,
Weighting Key Factors for Port Congestion by AHP Method, Journal of ETA Maritime
Science, 8(4), 252-273.
GAO X., MAKINO H. and FURUSHO M., 2016, Ship behavior analysis for real operating ofcontainer ships using AIS data, the international Journal on Marine Navigation and
Safety of Sea Transportation, 10(2), 213-220.
GIDADO U., 2015, Consequences of Port Congestion on Logistics and Supply Chain in
African Ports, Developing Country Studies, 5(6), 160-167
GUI D., WANG H. and YU M., 2022, Risk Assessment of Port Congestion Risk during the
COVID-19 Pandemic, Journal of Marine Science and Engineering, 2022(10), 150.
JIANG C., WAN Y. and ZHANG A., 2016, Internalization of port congestion: strategic
effect behind shipping line delays and implications for terminal charges and investment,
Maritime Policy & Management, 17(4), 273-287.
JIMÉNEZ P., GÓMEZ-FUSTER J. and PAVÓN-MARIÑO P., 2021, HADES: A multi-agent
platform to Reduce Congestion Anchoring Based on Temporal Coordination of Vessel
Arrivals—Application to the Multi-Client Liquid Bulk Terminal in the Port of Cartagena(Spain), Applied Science 2021(11), 3109.

- 925 -

IAME Conference 2022

D4-1-1KIM K. and LEE M. K., 2018, Deep learning-based caution area traffic prediction with
automatic identification system sensor data, Sensors, 2018(18), 3172.
PENG W., BAI X., YANG D., YUEN F., K. and WU J., 2022, A deep learning approach forport congestion estimation and prediction, Maritime Policy & Management,
https://doi.org/10.1080/03088839.2022.2057608.
PRUYN J.F.J., KANA A.A. and GROENEVELD W.M., 2020, Analysis of port waiting time
due to congestion by applying Markov chain analysis. In: Maritime Supply Chains, Editedby Thierry Vanelslander, Christa Systems, Elsevier, pp. 69-94.
SEA-INTELLIGENCE Maritime Analysis, 2022, Global Liner Performance report – 2021-FY,
Press Release, February 7, 2022.
SEA-INTELLIGENCE Maritime Analysis, 2021, Significant increase in multi-vessel sailings,
Press Release, September 28, 2021.
STEINBACH S., 2022, Port congestion, container shortages, and U.S. foreign trade,
Economics Letters, 213, 110392
VARLEY K. and MURRAY B., 2021, Port congestion eases in Asia while U.S. battles importdeluge, Bloomberg, November 19, 2021.

- 926 -