29th Conference of the International Association of Maritime Economists

AKAKURA Yasuhiro
National Institute for Land and Infrastructure Management, MLIT
akakura-y83ab@mlit.go.jp
TAKAHASHI Hironao
Waterfront Vitalization and Environment Research Foundation
takahashi@wave.or.jp

Analysis about Delay of Container Trunk Linesand Offshore Waiting for Calling
to Congested Container Terminals
Abstract
Delays of container services have increased since the formation of three major shipping
alliances accompanying the upscaling of container ship size. These delays have had a serious
impact on sophisticated global supply chains such as just-in-time systems. This study quantifies
the delay on container trunk lines, analyzes the causes, and estimates the offshore waiting timeof calling ships at congested terminals.
Delays of container ships deployed on trunk lines were calculated by comparing the actual
arrival/departure times in ship movement data and the scheduled times at calling ports. The
offshore waiting time of ships for calling at container terminals was estimated by calculating
the total time and the hourly ship speed between entering port and the berthing terminal, anddetecting anchoring signals utilizing AIS data.
The results revealed that approximately 80 % of delays on trunk lines in 2018 occurred at ports
in China, Europe, and North America. In these ports, the offshore waiting time-volume index
proposed in this paper was related to the berth occupancy ratio, defined as the ratio of occupied
time and space by ship berthing, the total TEU capacity of berthing ships, and the actual delaysof arrivals and overtime stays.
Keywords: container service, delay, punctuality rate, offshore waiting, berth occupancy.

IAME 2021 Conference, Rotterdam, Netherlands

11. Introduction
The extent of delays in container services has increased considerably in recent years. Figure 1
shows the punctuality rate, defined as the rate of arriving within 24 hours of the scheduled timeat each port, of world container services. Punctuality rates decreased continuously from 2016
to 2018, recovered in 2019, and then decreased again in 2020. In November and December of
2020, the rate fell to below 50 %. Although various factors are assumed as causes of the low
punctuality rate, one of the largest is congestion at ports and terminals. On the East-West
container trunk line between Europe/North America and East Asia, the integration of alliances
and their services has progressed accompanying the upscaling of container ship size, and as aresult, services tend to concentrate on calls at specific ports and terminals. For example, among
18 services on the North Europe and East Asia route by three major alliances, 17 services calledat the Port of Rotterdam and 14 services called at the Port of Shanghai in 2019.

Punctuality Rate

85%

2016
2019

80%
75%

2017

70%

2018
2020

65%
1

2

3

4

5

6

7

8

9 10 11 12 (month)Figure 1 – Punctuality rate of world container services
Source: Sea-Intelligence (2020) & Alan Murphy (2017)
The deterioration in punctuality of maritime container services has a great impact on global
supply chains. Lean and sophisticated supply chains, exemplified by the just-in-time system,
also have the aspect of fragility against transport disruptions. For example, car production by
Japanese auto makers in the U.S. stagnated during the 2014-2015 U.S. West Coast port
disruption. Although auto makers attempted to divert transportation of auto parts to air cargotransport, it was difficult to maintain a sufficient supply of parts. Similarly, Japanese auto
factories in Europe were forced to interrupt production due to an insufficient transport capacityof container service in December of 2020. As an example, because one Japanese auto makerstocks parts only for around 5 to 7 days at factories in Europe and North America, alternative
air transportation from Japan is needed when maritime container services are delayed for morethan several days.
Considering above mentioned background, in this study, first, the delays of container ships
deployed in the services of three major shipping alliances on East-West trunk lines were
IAME 2021 Conference, Rotterdam, Netherlands2calculated by comparing the actual arrival/departure times in the ship movement data and thescheduled times at calling ports. Based on the results, the offshore waiting times of ships callingat container terminals were estimated by calculating the total time and hourly ship speed
between entering port and the berthing terminal and detecting anchoring signals utilizing AIS(Automatic Identification System) data.
2. Literature Review
At one time, shipping companies published the punctuality rates of their services on web pages
such as “On-Time Performance” of the Mitsui OSK Line, however, that kind of information
cannot be found recently. Sea-Intelligence collects the punctuality rates of world container
services, as shown in Figure 1, but does not release detailed data by ship and port or reports
analyzing delay factors. In the academic field, Notteboom (2006) discussed the managementof the time factor in liner services and introduced the results of a survey concerning the sourcesof schedule unreliability on the East Asia–Europe route. Pani et al (2013) constructed a model
for prediction of delayed arrival at container terminals by utilizing machine learning in order
to allocate resources more efficiently. Salleh et al (2017a, 2017b) developed a prediction model
for container ship arrival and departure punctuality under different environments and indicatedthat arrival punctuality depended on many factors, such as the punctuality of departure fromthe previous port, the vessel and current port condition, and the reliability of agents.
Hasheminia and Jiang (2017) analyzed the delay of container ships and scheduled operations
during a 9-month period at seven terminals of three North American ports and pointed out thepossibility that shipping lines strategically balanced the trade-off between delay costs andschedule recovery costs. Grida and Lee (2018) estimated the berthing and sailing times of megacontainer ships from a dataset of vessel sizes, distances between ports, port demand, and otherfactors. Yu et al (2018) attempted to predict the delay or advance of ships at Gangi Terminalin the Port of Ningbo-Zhoushan in China and discussed the value of the prediction method fordaily container terminal operation. These previous studies, however, did not have any direct
relationship with the large decrease in container service punctuality in recent years shown in
Figure 1.
As to offshore waiting due to terminal congestion, Gao et al (2016) traced container shipnavigation in Japan’s Seto Inland Sea and its oceanic waters by AIS data and identified offshore
anchoring ships whose speeds over ground (SOG) were below 3 knots and positions were notnear any berths at ports. Marine Traffic provides the numbers and times of anchoring ships at
designated offshore waiting sea areas for the index of port congestion. However, in both cases,
the research and data did not analyze the relationship between the berthing terminal and theoffshore waiting ship. From another point of view, many previous studies have examined the
optimization of berth allocation, including Lai and Shih (1992), Imai et al (2001), Dai et al(2008), Buhrkal et al (2011), and Bierwirth and Meisel (2015). Although vessel waiting time
IAME 2021 Conference, Rotterdam, Netherlands3

is one important criterion for optimization, the objective of those studies was to improve theterminal operating efficiency of a specific or virtual terminal by simulating the effect ofallocation policies and procedures, and estimation of the actual offshore waiting times forvarious terminals was outside the scope of research.
The contribution of the present study to the literature is two-fold: First, the delay of East-West
container services was quantified in broad terms, and the causes of delays were analyzed.
Second, an estimation method for the offshore waiting times of ships at each container terminal
was constructed, making it possible to discuss the relationship between waiting time and thesituation and characteristics of terminals such as the degree of congestion and delay of ship
arrival.
The remainder of this paper is structured as follows. In section 3, the delays of major services
on East-West trunk lines at each port are calculated and the causes of those delays are analyzed.
Section 4 describes estimation methods for offshore waiting times and analyzes the results
associated with various factors. Section 5 discusses a method for decreasing delays of container
services and waiting times of ships at terminals, and section 6 summarizes the conclusions.
3. Delay of Trunk Line Services
3.1 Calculation Method and Data
Delays of ships deployed in major services on East-West trunk lines were calculated bycomparing the actual arrival/departure times and scheduled times at calling ports. The actual
times were determined from the ship movement data supplied by Lloyd’s List Intelligence(LLI). Table 1 shows the data structure of the LLI data, including the actual arrival and sailingdates and times. These dates and times were arranged by utilizing AIS data received at each
port. Figure 2 shows the comparison between the AIS data and the ship movement data. From
this comparison, the arrival time in the ship movement data indicates the time when a ship
arrived in front of the calling berth and slowed to almost a stop.
Comparison with the actual ship schedule was sometimes difficult when using the LLI ship
movement data. First, ships sometimes changed their order of calling at ports for some reason,such as port congestion. In this case, the data were not used to calculate delays. Second, thearrival and departure times at ports where AIS data were not received were automatically input
as 11:00 and 13:00, respectively. Among major container ports, AIS data were not available
for the Ports of New York and Wilmington. Therefore, the data for those ports were excluded
from the analysis. Because both ports received AIS data in the 2017 movement data, the authors
asked LLI the reason for the missing data, but did not receive an answer.

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4

Table 1 – Data structure of LLI ship movement data
IMO No.
982****
982****
982****
982****
982****
982****
982****
982****
982****
982****
982****
982****VESSEL NAME TEU Capa. PLACE NAME CNTRY ARRIVAL DATE/YIME SAIL DATE/TIME**********
12,400 Singapore
SGP
2018/9/2
0:43
2018/9/3
2:52**********
12,400 Cai Mep
VNM
2018/9/4
23:45
2018/9/5
15:33**********
12,400 Yantian
CHN
2018/9/8
9:47
2018/9/9
12:58**********
12,400 Ningbo
CHN
2018/9/11
23:14
2018/9/12
18:14**********
12,400 Yangshan
CHN
2018/9/16
4:51
2018/9/17
6:12**********
12,400 Long BeachUSA
2018/9/28
4:11
2018/10/3
7:03**********
12,400 Oakland
USA
2018/10/4
16:13
2018/10/6
5:40**********
12,400 Busan
KOR
2018/10/19
3:19
2018/10/20
8:34**********
12,400 Yangshan
CHN
2018/10/21
14:26
2018/10/22
16:05**********
12,400 Ningbo
CHN
2018/10/24
16:34
2018/10/25
14:51**********
12,400 Hong Kong
CHN
2018/10/27
10:33
2018/10/28
2:45**********
12,400 Singapore
SGP
2018/10/31
12:51
2018/11/1
10:45

Source: LLI

IMO: 93******
Arrival in Movement Data: 11/11:53

Ship Track by AIS data
※11/11:36 means 11:36 of 11th day

Figure 2 – Comparison of AIS data and ship movement data at Port of Los Angeles
Source: AIS and Ship Movement Data of LLI
The target services of the analysis were 58 services on East-West trunk lines by three alliances,
2M, The Alliance and Ocean Alliance, as shown in Table 2, from April to December 2018. The
calling ports of the North Europe services included ports on the North Atlantic Ocean, theNorth Sea, etc., and some services also included ports on the Mediterranean Sea. Similarly, thecalling ports of the East Coast of North America services included ports on the Atlantic side of
the continent and some services through the Panama Canal also including ports on the Pacificside. The scheduled dates/times of the services were obtained from the websites of shipping
companies. Ships which were deployed for each service were identified by the MDS
Containership Databank and Ocean Commerce International Transportation Handbook 2019.
However, it was not possible to identify some ships because many ships changed their services
in the middle of the year.
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5Table 2 – Target services of analysis
Alliance(Shipping Company)

Europe - East Asia
North Europe
M editerranean

2M(M aersk/M SC)

AE1, AE2, AE5,
AE6, AE7, AE10

The Alliance(Hapag/ONE/Yang M ing)
Ocean Alliance(APL/COSCO/OOCL/Evergreen/CM A CGM )FE1, FE2, FE3,
FE4, FE5
AEU1, AEU2,
AEU3, AEU5,
AEU6, AEU7

North America - East Asia
West Coast
East Coast
TP10, TP11,
AE11, AE12,
TP2, TP6, TP8,
TP12, TP16,
AE15, AE20
TP9
TP17, TP18
EC1, EC2, EC4,
PN1, PN2, PN3,
M D1, M D2, M D3
EC5PS3, PS6
AWE1, AWE2,
CEN, CPNW,
AEM 1, AEM 2,
EPNW, M PNW, AWE3, AWE4,
AEM 3, AEM 6
AWE5, GM E2
OPNW※Service names are by underlined shipping company

Delays of container services were investigated from the following two viewpoints:
 Delays at ports of import: Delays at ports of import were calculated as the difference
between the actual and scheduled arrival date/time. Ports of import were identified by thecalling order of the ports in each service. The punctuality rates, defined as the rate ofarriving within 24 hours of the scheduled time, were also calculated.
 Occurred delays at each port: Delays which occurred at port i: ∆Di were calculated byequations (1) and (2).
=Di ATSi − STSi
(1)

∆Di = Di − Di −1(2)

where Di is the difference between the actual time of sailing ATSi and the scheduled timeof sailing STSi at port i. In case ∆Di was less than 0, it was juged that a delay did notoccur.
3.2 Calculation Results
The calculation results of the delays at the ports of import are shown in Table 3. The total
average arrival delays of the Europe-East Asia route were less than 1.0 day in both areas. The
average delays at East Asian ports were on the same level, while at European ports, the average
delay of 2M for the North Europe route and The Alliance for the Mediterranean route exceeded
1.0 day. Maximum delays, defined as the average of the maximum delays of the component
services, were in the range of about three to five days. Although the punctuality rates were in
the range of approximately 60 % to 75 %, the rates of 2M for the North Europe route and The
Alliance for the Mediterranean route were about 50 %. On the North America-East Asia route,the average delays at North American ports were longer than those at East Asian ports. Among
North American ports, the average delays of Ocean Alliance for the West Coast route and The
Alliance for the East Coast route reached nearly 2.0 days. The maximum delays for North
American ports were longer than those for East Asian ports for all alliances on both routes. The
punctuality rates for East Asian ports were larger than 60 % excluding The Alliance for the

IAME 2021 Conference, Rotterdam, Netherlands

6

West Coast route, while the total average for North American ports was about 50 %. The ratesof the Ocean Alliance for the West Coat route and The Alliance for the East Coast route wereless than 40 %.
Table 3 – Delays in ports of import
Route/Service

Europe/E Asia

Total Average
North Europe
2M
The Alliance
Ocean Alliance
M editerranean
2M
The Alliance
Ocean Alliance

Route/Service
Total AverageWest Coast
2M
The Alliance
N America
Ocean Alliance/E Asia
East Coast
2M
The Alliance
Ocean Alliance

Average Delay (day)East Asia
Europe
0.81
0.98
0.74
0.87
0.72
1.16
0.74
0.81
0.75
0.64
0.92
1.15
0.88
0.86
0.88
1.94
0.99
0.85

M ax Delay (day)East Asia
Europe
3.17
3.71
2.98
3.67
1.94
3.89
3.58
3.87
3.53
3.29
3.46
3.76
2.45
3.15
4.36
4.77
3.79
3.63

Punctuality Rate
East Asia
Europe
69.5%
66.4%
72.5%
67.4%
67.9%
52.1%
74.9%
72.9%
75.2%
78.0%
64.9%
64.9%
61.3%
66.9%
71.7%
49.3%
63.5%
74.7%

East Asia
0.91
1.01
0.78
1.20
1.01
0.81
0.53
1.03
0.94

East Asia
3.06
3.18
2.25
3.50
3.60
2.96
1.43
2.97
4.47

East Asia
67.4%
60.6%
70.0%
52.8%
60.9%
73.3%
85.6%
60.2%
69.9%

N America
1.43
1.54
1.00
1.59
1.92
1.34
0.95
1.81
1.41

N America
4.47
4.70
3.95
4.95
5.04
4.27
3.25
4.91
4.87

N America
53.5%
51.2%
66.7%
55.5%
34.5%
55.5%
65.4%
38.1%
57.1%

The calculation results of occurred delays at each port arranged by areas by routes are shownin Figure 3. The total values of the routes did not correspond to the figures in Table 3 because
shortening the delays at ports compared with their previous ports were regarded as 0 delay, as
noted in the explanation of equation (2). Delays in China, Europe, and North America occupied
large parts of the total delay, accounting for approximately 80 % of delays on all routes.
The occurred delays of major ports are listed in Table 4. On the Europe-East Asia route, on
average, delays of more than a half-day occurred at the Ports of Ningbo and Shanghai, wherearound 90 % of the services called, while the delay at the Port of Singapore was only around 4
hours. Among European ports, the longest delay was at the Port of Rotterdam, followed by thePort of Hamburg. Similarly, the delays that occurred at the Ports of Shanghai and Ningbo
exceeded 0.6 days for the North America-East Asia route. Among North American ports, thelongest delay was at the Port of Vancouver, followed by the Port of Savannah.

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7Occurred Delay (day)

5.0
4.0
3.0

China

2.0

Other E Asia

Canal

1.0
0.0

China
Canal

Other
E AsiaEuropeN America

N EU
MedEurope - E Asia

WC
EC
N America - E Asia

Figure 3 – Occurred delay at ports by routes
Table 4 – Occurred delay in each port
Europe - East Asia
Area

China
Korea
SE Asia
North
EuropeMediter-raneanPort

No. ofService

NingboShanghai
YantianQingdao
Busan
Singapore
Rotterdam
Hamburg
Antwerp
Piraeus
Valencia
Malta

25
24
17
10
13
23
15
11
9
6
5
5

North America - East Asia
Average
Delay
(day)
0.51
0.62
0.20
0.57
0.28
0.18
0.45
0.33
0.22
0.31
0.22
0.32

Area

China
Korea

SE Asia
West
Coast
East
Coast

Port

Shanghai
Ningo
YantianQingdao
Busan

Singapore
Vancouver
Los Angeles
Oakland
SavannahCharleston
Norfolk

No. ofService
21
16
16
9
17
12
8
6
6
13 (10)
9 (8)
9 (6)

Average
Delay
(day)
0.67
0.67
0.24
0.32
0.31
0.26
1.05
0.44
0.38
0.63
0.27
0.52

※As to the average delays of North America-East Asia route, the calling just after the Ports of New York andYork and Wilmington were excluded. The figures in parentheses are the number of assessed services.

3.3 Analysis of Causes
Delays occur due to differences between the actual time and scheduled time of navigation, port
entry including offshore waiting, container handling, and so on. Thus, the actual time can belengthened for various reasons. Figure 4 shows the causes of delays according to the results ofa survey by Notteboom (2006). Although this survey was conducted about 15 years ago andthe situation may have changed since then, the greater parts of delays were caused byport/terminal congestion or low productivity.

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8Waiting for Others
Pilot/Tug
3.9%
4.7%
Weather etc.
on Route
5.3%

Port/Terminal
Low Productivity
20.6%

Port/Terminal
Congestion
65.5%

Figure 4 – Causes of schedule unreliability on Europe-East Asia route (2004)
Source: Notteboom (2006)
Because one of the most significant changes in the maritime container market during two
decades has been the upscaling of ship size, the relationship between ship size and delays at
ports of import by service was investigated, as shown in Figure 5. If container handling time isinadequate for mega container ships, delays should increase as ships become larger. However,a positive correlation between container ship size and delays cannot be seen in Figure 5,
indicating that the delays seen in recent years must be caused by other factors.

Average Delay
Port of Import (day)

2.5
2.0
1.5

East Coast

MediterraneanWest Coast

1.0

North
Europe

0.5
0.0
5,000

10,00015,000
Average Ship Size (TEU)

20,000Figure 5 – Relationship between ship size and delays at ports of import
Accompanying the trend toward larger ships, the number of alliances and services has
decreased continuously, and as a result, service calls have tended to concentrate on specificports and terminals. From Table 4, ports with many service calls were inclined to have longer
delays, except for the Port of Singapore. The relationship between average delays of ports inTable 4 and average delays of terminals at those ports is shown in Figure 6, except for ports
with a single terminal operator. Overall, average terminal delays have some relationship withaverage port delays, but average terminal delay also differ widely, from about double to half
IAME 2021 Conference, Rotterdam, Netherlands

9

of the average port delay. In any case, however, it can be assumed that congested terminals
with many delayed services tend to induce longer delays.
Average Terminal Delay (day)1.5

2

R =0.506

1:2
1:1

1.0

2:1

0.5

0.0
0.0

0.5
1.0
Average Port Delay (day)Figure 6 – Relationship between port delay and terminal delay
4. Offshore Waiting at Terminals
4.1. Estimation Method and Data
The previous section revealed that 80 % of delays on East-West trunk lines occurred at ports
in China, Europe, and North America, and it is possible that congestion of terminals where
calls by many services are concentrated causes a large part of those delays. Based on this result,the offshore waiting times were estimated by terminal at major ports, and the relationshipbetween the waiting time-volume index and various factors of terminals are analyzed in thissection.
As to the offshore waiting, generally, each port designates an anchoring area in its port area,
but the number of ships that can anchor there is restricted. Therefore, many ships wait outside
the port area, and some wait by slowing down or drifting. Figure 7 shows examples of the
tracks of offshore waiting ships. The left side of the figure shows the track of an anchoring ship,
and the right shows that of a drifting ship. This situation is the reason why it is difficult to grasp
all offshore ships simply by observing the anchoring area of each port. Therefore, in this study,
offshore waiting ships were identified by the following criteria:(1) Ships that had the record of “At Anchor” in the navigational status of the AIS signal(2) Ships that continued a ship speed over ground (SOG) in the AIS signal of less than 3.0
knots over 2 hours(3) Ships whose total time from port entry to berthing was equal to or greater than the times ofships in (1) and (2)IAME 2021 Conference, Rotterdam, Netherlands

10Figure 7 – Examples of tracks of offshore waiting ships (Left: anchoring, right: drifting)Here, criterion (1) and (2) indicate anchoring ships, which have a track similar to the example
shown on the left in Figure 7. Since the navigational status of AIS is switched by the operator,
and it is sometimes forgotten, criterion (2) was set based on the reference Gao et al (2016).
Criterion (3) captures ships that drift and slow down. The total times from port entry to berthingof ships were calculated by setting the areas for identifying port entry and berthing for eachport as indicated in Figure 8. The port entry areas were set broadly enough to cover the waiting,anchoring, and drifting locations, and were shaped in a circle to ensure that all ship navigation
times are on the same level from all directions. The results of the total time calculation of all
berthed ships were placed in ascending order by terminal as shown in Table 5, and, in this case,the offshore waiting ships are identified as ships from No. 7 to 12. Ships No. 7, 11, and 12 had
the record of an “At Anchor” signal corresponding to criterion (1), ship No. 9 navigated at a
speed below 3.0 knots for 3 hours, corresponding to criterion (2), and ships No. 8 and 10 met
criterion (3). Berthing at each terminal was judged when the ship speed was kept below 1.0
knot over 10 minutes at the front sea area of the terminal. The waiting times of ships that moved
between terminals were also estimated in the same manner.

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11

Area for Identifying
Port Entry

Ship’s Track

Port Entry Time: t1
Anchoring

Berthing Time: t2

Area for Identifying
Berthing

Figure 8 – Image of calculation of total time from port entry to berthing
Table 5 – Image of estimation of offshore waiting times of ships
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

9.0

10.4

9.8

8.2

10.6 10.3

12.7 12.9 11.7 10.6

0.6

4.2

2.4

8.7

0.8

2.0

5

Ship
6
7

13.8 13.7 17.4 12.6 11.0 15.5
1.5

4

9.9

6.5

2.7

3.1

12

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

8.4

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

Offshore Waiting Time

4.2

0.0
2.8
10.0
1.2

The data used in estimating the offshore waiting times were the AIS data by CLS (Collecte
Localisation Satellites) and LLI. There are two types of AIS data, which are received by surface
antenna and by satellite antenna, respectively. Although the data received by a surface antenna
are stable and have a high density compared with the data received by a satellite antenna, therange of that data is limited to the coastwise area. Therefore, the data received by satellite
antennas were also utilized to monitor ships movements in offshore sea areas. In addition, theAIS data by CLS and LLI were also integrated to increase the data density. During AIS dataprocessing, irregular navigational tracks with speed of over 30 knots were eliminated.

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12The analysis targets were the main terminals at top-tier ports in North America, Europe, andChina shown below, and the analysis period was one month (October 2019).
Port of Los Angeles (LAX): APMT, China Shipping, Eagle Marine, Ever Green, TraPac, Yang
Ming, and Yusen
Port Long Beach (LGB): SSA Pier A, SSA Pier C, LBCT, ITS, PCT, and TTIPort of Rotterdam (RTM): APMT MV2, APMT, ECT Delta-North, ECT Delta-South,
Euromax, and RWG
Port of Shanghai (Yangshan) (SGH): Shengdong, Guandong, and Shangdong
Port of Ningbo Port (NGB): BSCT, Daxie, Ganji, Meishan, NBCT, and Yuandong

50%

200

40%

150

30%

100

20%

50

10%

0

0%

APMT
Chi S
Egl M
Evr G
TraP
Yng M
Yusen
SSA A
SSA C
LBCT
ITS
PCT
TTI
APM2
APMT
ED-N
ED-S
Ermax
RWG
Sheng
Guand
Shang
BSCT
Daxie
Ganji
Meshn
NBCT
Ydng

250

LAX

LGB

RTM

SGH

Rate of Waiting Ships

No of Berthing Ships

4.2. Estimation Results
The numbers of berthing ships and the rate of offshore waiting ships during the period byterminal are indicated in Figure 9. The numbers of calling ships at terminals in the Ports of Los
Angeles and Long Beach were small, while the numbers at the terminals in Rotterdam,
Shanghai, and Ningbo were large and the rates of waiting ships were around 20 to 40 %. The
berthing ships at the terminals in the Ports of Rotterdam and Ningbo included many feeder
vessels, and the berth lengths of the terminals in the Port of Shanghai were relatively long.

NGB

Figure 9 – Number of calling and waiting ships by terminal
The most important point for shippers is how long deliveries of their cargoes are behindschedule, since long delays induced by long offshore waiting can have a serious impact on the
shipper's plans for production, sales, etc. Figure 10 shows the histogram of waiting time, andindicates that approximately 60 % of offshore waiting was finished within half a day. However,long waiting times were not rare, as 14 % of waiting ships waited longer than 1.0 day, and,these waiting times accumulated in ships, causing long service delays. Here, a ship waiting
over one week at Pier A terminal of the Port of Long Beach was excluded because the berthing
day of this ship was on schedule, and it was found that this waiting was simply for adjustmentof the ship’s schedule.
IAME 2021 Conference, Rotterdam, Netherlands

13No. of Wating Ships

200
150
100
50
0

6

12 18 24 30
Waiting Time (h)36Figure 10 – Histogram of waiting times
The size of waiting ships is also an important factor because ship size is equivalent to thevolume of waiting containers. The impact of waiting by a 20,000 TEU mega ship is extremely
large compared with that of a 1,000 TEU feeder ship. That is, the important point here is how
many and how long containers wait offshore from terminals. Thus, it is valid to measure thevolume of offshore waiting by TEU multiplied by waiting time, namely “time-volume.” Thecarrying container volume of each ship can be calculated by assuming the slot utilization rate
is 60 %, considering the fact that the average global rate excluding interregional service was
62.9 % in 2019 according to Drewry (2020).

12,5001,600

10,000

1,200

7,500

800

5,000

400

2,500

0

APMT
Chi S
Egl M
Evr G
TraP
Yng M
Yusen
SSA A
SSA C
LBCT
ITS
PCT
TTI
APM2
APMT
ED-N
ED-S
Ermax
RWG
Sheng
Guand
Shang
BSCT
Daxie
Ganji
Meshn
NBCT
Ydng

2,000

LAX

LGB

RTM

SGH

Average Size of
Waiting Ships (TEU)

Waiting Time-volume('000h*TEU/mo.)The waiting time-volume and average size (TEU capacity) of waiting ships by terminal are
shown in Figure 11. Large differences were observed in both the waiting time-volume and shipsize. The waiting ship size for terminals in the Port of Shanghai was very large, atapproximately 10,000 TEU or over, while those of many terminals in the Ports of Rotterdam
and Ningbo were less than 5,000 TEU. It was considered that waiting time-volume depended
upon the characteristics of the terminals such as the berth length, degree of congestion, andshare of delayed arrival ships.

0

NGB

Figure 11 – Waiting time-volume and average size of waiting ships by terminal
IAME 2021 Conference, Rotterdam, Netherlands

14

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

4.4. Analysis of Terminal Characteristics
Since waiting time-volume is assumed to be linked to terminal congestion, the relationshipbetween the berth occupancy ratio and waiting time-volume per berth length was calculated,
as shown in Figure 12. The berth occupancy ratio is defined as the share of occupied space andtime by berthing ships, including mooring lines, against total time and length. Basically, an
increase in the berth occupancy ratio led to an increase in waiting time-volume, and inparticular, waiting time-volume increased dramatically if the ratio exceeded about 30 %.
However, it was also found that waiting time-volume was affected by the other factors because
the correlation coefficient was not particularly high. For example, at the TTI terminal of thePort of Long Beach, the berth occupancy ratio and the waiting time-volume were 55 % and 66
hour*TEU/m respectively, while that same values at the Guandong Terminal of the Port of
Shanghai were 33 % and 704 hour*TEU/m, both deviating greatly from the regression curve.
1,600
1,200

3.10

y=4814x2
R =0.561

800

SGH
400
0
0%

NGB
RTM

LAX LGB
10% 20% 30% 40% 50% 60% 70%
Berth Occupancy Ratio

Figure 12 – Berth occupancy ratio vs. waiting time-volume per berth length
The characteristics of schedules possibly differ even at the same level of berth occupancy ratio.
Table 6 shows examples of the number of berths and services for each terminal. Berths andservices were categorized by mother vessel or feeder vessel. At the Yusen Terminal of the Portof Los Angeles and LBCT of the Port of Long Beach, the numbers of services per berth (B/A)
were equal to or less than 1.0, which means that offshore waiting does not occur unless eachship catches up with the prior ship of the same service. The waiting time-volume at the
Shandong Terminal of the Port of Shanghai was longer than at LBCT, although the berthoccupancy ratio was smaller. At the Euromax Terminal of the Port of Rotterdam and NBCT of
the Port of Ningbo, both the number of services and the waiting time-volume were much larger.

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15

Table 6 – Number of berths and services of terminals
Port
Terminal
No. ofMother Vessel(A)
BerthsFeeder Vessel
No. ofMother Vessel(B)
Services
Feeder Vessel
B/A (Feeder=0.5)
Berth Occupancy Ratio
Wating Time-Volume (h*TEU/m)

LAX
Yusen
3
0
2
1
0.83
23.7%
0

LGB
LBCT
2
0
2
0
1.00
41.2%
0

RTM
SGH
Euromax Shangdong
2
6
2
0
6
15
25
0
6.17
2.50
53.6%
35.4%
715
565

※Number of berths were actual use and not catalogue data.
※※Number of services was counted based on M DS data and excluded unknown data.

NGB
NBCT
2
2
10
7
4.50
59.8%
1,532

Table 6 implies that the number of services and ship size are linked to waiting time-volume.
Figure 13 shows the relationship between the total TEU capacity of berthing ships and waiting
time-volume per berth length. In spite of some scattering, a linear relationship can be seen inthis figure. At terminals where many large ships berth, the intervals of services on theirschedules are sometimes very short, and since large ships deployed in long-range service tend
to be delayed, offshore waiting tended to occur easily.
Waiting Time-Volume per
Berth Lenght (h*TEU/m/mo.)

1,600
1,200
y=1.93x2
R =0.596

800

RTM

400

SGH

NGB

LAX
LGB

0
0
100
200
300
400
500
Berthing Ship Capacity per Berth Length (TEU/m/mo.)Figure 13 – Total TEU capacity of berthing ships vs. waiting time-volume
Offshore waiting does not occur if all ships arrive and depart on time, and conversely, offshore
waiting increases as more ships are delayed and stay overtime. Figure 14 shows the actual
arrival and staying time of ships in comparison with their schedules at each terminal. This
comparison was restricted to only long-range services for which schedules could be obtained.
From the left side of the figure, the shares of on-time arrivals were small at all terminals. At
NBCT of the Port of Ningbo, more than half of arrivals were delayed over 24 hours, and many
arrivals were delayed more than 48 hours. NBCT recorded the highest waiting time-volume in
Figures 12 and 13, and this arrival delay condition was considered to be one of the main causesof the long waiting time-volume. On the right side of Figure 14, the majority of ship stays were
under the scheduled times, and overtime stays of more than 24 hours were recorded only at
IAME 2021 Conference, Rotterdam, Netherlands

16

LBCT of the Port of Long Beach. Comparing the figures in Figure 14, the delays of arrival on
the left were apparently large, indicating that offshore waiting was caused mainly by arrival
delays, at least during the period observed here.
LAX
Yusen

LAX
Yusen

LGB
LBCT

LGB
LBCT

RTM
Emax

RTM
Emax

Delay Arrival

SGH
Shang On TimeNGB
NBCT ≤ 0d
0%

≤ 1d

≤ 2d

≤ 3d

20%
40%
60%
80%
Share of No. of Berthing

SGH
Shang
3d<
100%

≤ 0d

Undertime Stay

≤ 1d ≤ 2d

Overtime

NGB
NBCT
0%

20%
40%
60%
80%
Share of No. of Berthing

100%

Figure 14 – Actual time of arrival (left) and staying (right) at each terminal
5. Discussion
5.1. Countermeasure against Delay for Shipping Companies
Delays in container services have a serious impact on global supply chains. Djankov et al(2006) indicated that each additional day that a product is delayed prior to being shipped
reduces trade by at least 1 %. Akakura (2018) estimated that the decrease of 6.8 % in tradevolume between North Europe and Japan due to the delay of direct container services in 2017,
which averaged 2.1 days, resulted in GDP decreases of $180m to $260m in North Europe and$70m to $90m in Japan.
Shipping companies do not set schedules with a large time margin if shippers select services
based on transport time on schedule and freight. Given this situation, a third index for selecting
services namely, punctuality, is required in order to reduce service delays. At one time,
shipping company websites provided service punctuality rates, but this information cannot befound recently. On the other hand, the Port of Vancouver publishes the share of arrivals within
8 hours of schedule by service as Gateway Vessel's On-Time Performance every month under
the West Coast Supply Chain Visibility Program, and the Port of Rotterdam publishes thedegree of delays of barge service by destination area every month as the Barge Performance
Monitor. In addition, CMA-CGM provides the “SEAPRIORITY reach” service, which assures
the arrival time at destinations in the U.S. for an additional charge, and this service hasreportedly sold well. These trends suggest there must be many shippers that desire highpunctuality service. Moreover, it is also possible that disclosing information on container
service delays will be the driving force for reducing those delays.

IAME 2021 Conference, Rotterdam, Netherlands

175.2. Countermeasures against Waiting at Container Terminals
Container terminals can improve their operating profit by accepting more services at the samefacilities, and offshore waiting does not occur if all ships arrive and depart on schedule. Under
these circumstances, there is no large incentive for terminal operators to reduce offshore
waiting time-volume by setting schedules with larger time margins. However, offshore waiting
decreases the punctuality of services, and if this induces a decrease in the number of customers
using the service, the container throughput of the terminal will also decrease. From thisviewpoint, waiting time-volume can be one key performance indicator (KPI) for judging thesoundness of terminal operation, and can also be considered when discussing the efficiency
and necessity of investment in a terminal. Furthermore, for shippers that reserve services forspot cargoes, real-time data is very useful for avoiding services calling at terminals with longoffshore waiting times. Figure 15 shows the number of anchoring container ships in the waiting
sea areas of ports according to Marine Traffic. The numbers have fluctuated greatly over thelong term, and after December 2020, a surge in container handling volume and lack of workers
caused by COVID-19 lengthened the staying time of ships and increased the number of ships
waiting offshore at the Ports of Los Angeles and Long Beach.
LGB

30
25

NGB

20
15
10

RTM

2/1

12/1

2020

10/1

4/1

0

8/1

5

6/1

No. of Anchoring Ships

35

2021

Figure 15 – Number of anchoring container ships offshore of ports
Data: Counted from Marine Traffic
The analysis results in the previous chapter revealed that terminals which had high berthoccupancy ratios and berthed many large ships also tended to have long waiting time-volumes.
In addition, offshore waiting possibly has a domino effect on the berthing terminal itself, as
well as on other terminals, through ship delays. In responding to this condition, the offshore
waiting time-volume proposed here is a useful index of terminal operation for improving thepunctuality of container services. Figure 16 shows the plot of the degree of waiting timevolume on the coordinate surface of the berth occupancy ratio and total TEU capacities ofberthing ships. From this result, although the waiting time-volume largely depends on actual
arrival delay and overtime stay, medium waiting time-volumes (100 to 699 h*TEU/m) tended
to occur at terminals with berth occupancy ratios of more than 30 % and berthing TEU
IAME 2021 Conference, Rotterdam, Netherlands

18capacities of more than 200 TEU/m, and large waiting time-volumes (over 700 h*TEU/m)tended to occur at terminals with occupancy ratios exceeding 50 % and more than 300 TEU/m.
According to the argument here, for example, strengthening the handling capacity of a terminalby increasing the number of gantry cranes will lead to a shortening of staying time, namely, adecrease in the berth occupancy ratio, and extending the berth length will decrease bothberthing TEU capacities per berth length and the berth occupancy ratio. However, in order todiscuss measures for restraining offshore waiting time-volume, accumulation of a longer andwider range of data and deeper analysis will be needed, as this study was based only on thedata for a one-month period at five ports. As shown in Figure 4, the main cause of delay is
port/terminal congestion, but other factors such as low terminal/port productivity and badweather also have some impacts. As to the latest situation, because of the spread of the COVID19 infection, deterioration of the handling capacity of the Ports of Los Angeles and Long Beach(2020/21 winter) and prohibition of calling at the Ports of Dalian (December 2020) and Yantian(June 2021) led to numerous offshore waiting ships at those ports, which also had a domino
effect on other ports. Since various factors are intricately related to waiting time-volume, moredetailed modeling is required. From another point of view, optimization of berth allocation canalso reduce offshore waiting time-volume, and many efforts have been made already in thisfield, as mentioned in Chapter 2.
Total TEU Capacity ofBerthing Ships (TEU/m/mo.)

500
700h*TEU≤

400300<700h*TEU

200<100h*TEU

100
0
10%

20%

30%
40%
50%
Berth Occupancy Ratio

60%

Figure 16 – Waiting time-volume vs. berth occupancy ratio and berthing TEU capacity
5.3. Consideration of Relationship of Shipping Companies and Terminals
Accompanying the rapid reorganization of shipping alliances, the services of the same alliance
may now berth at different terminals in the same port. Table 7 shows the berthing terminals ofeach alliance at the five ports studied here. Although there is a one-to-one correspondence
between alliances and terminals at the Port of Shanghai, there are several terminals that are
owned by shipping companies in the same alliances at the Port of Los Angeles. While a
dedicated terminal has the merit of greater flexibility when a ship’s schedule is disrupted, it
sometimes does not have a large capacity as a margin against concentrated calls, and drayage

IAME 2021 Conference, Rotterdam, Netherlands19

is necessary if containers are transshipped between ships berthing at different terminals. In
Figure 6 and Figure 12, differences were seen in the delay times and offshore waiting timevolumes of terminals at the same ports. Based on this condition, a movement to merge container
terminals has been seen. In January 2019, four terminals at the Port of Hong Kong establishedthe Hong Kong Sea Port Alliance and integrated their operation. The Singapore Port Authority
is now constructing a new mega container terminal called Tuas, and with the completion ofthat terminal, five existing terminals will eventually be merged at Tuas to increase operational
efficiency by eliminating inter-terminal haulage. At the Port of Rotterdam, the Container
Exchange Route project is in progress and will connect five container terminals in the
Maasvlakte area by a dedicated road network utilizing autonomous vehicles. Decreasing bothoffshore waiting time-volume at terminals and delays in service are expected as a result of theenhanced operational efficiency provided by this kind of consolidation of terminals.
Table 7 – Berthing terminals of each alliance
Alliance
2M

LAX
APMT

TTI

LGB

China Ship. ITS
Tra Pac
The Alliance
Yang Ming
Yusen
China Ship. LBCT
Eagle Marine PCT
Ocean AllianceEver Green

RTM
SGH
APMT
Guandong
APMT MV2
ECT Delta-N
ECT Delta-S Shangdong
RWG

ECT Delta-S ShengdongEuromax
RWG

NGB
Ganji
Meishan
BSCT
Daxie
Meishan
NBCT
Daxie
Meishan
NBCT
Yuandong

6. Conclusion
One of the major causes of service delays and offshore waiting at terminals is considered to be
the concentration of calling at specific ports and terminals induced by consolidation of services
accompanying the reorganization of shipping alliances. Offshore waiting occurs when a ship
arrives late or overstays at a congested terminal, and this not only lengthens the delay of theship concerned, but also causes offshore waiting of the following ships at the terminal and the
spread of service delays to other terminals and ships. For cutting-edge global supply chains, it
is essential to break this vicious circle.
This study quantified the delays on container trunk lines, analyzed the causes, and estimatedthe offshore waiting time of ships calling at congested terminals. The delays of container ships
deployed on trunk lines were calculated by comparing the actual arrival/departure times in theship movement data and the scheduled times at calling ports. The offshore waiting times ofships for calling at container terminals were estimated by calculating the total times and the

IAME 2021 Conference, Rotterdam, Netherlands

20hourly ship speeds between entering port and the berthing terminal, as well as the detectedanchoring signals, utilizing AIS data.
The results of this study revealed that the punctuality rate of trunk lines in 2018 was below
70 %, and approximately 80 % of delays occurred at ports in China, Europe, and North America.
At these ports, the delays of services at each terminal differed significantly even in the same
port. The offshore waiting time-volume index, which was newly proposed in this study,
displayed a relationship to the berth occupancy ratio and total TEU capacity of berthing ships,
and also depended on actual delays of arrival and overtime stays. Methods for decreasing
delays of container services and waiting time-volume at terminals were also discussed fromthe viewpoints of improving terminal efficiency and container service punctuality.
The contribution of this study to the literature is as follows: (1) This study quantified the delayof East-West container services in broad terms and analyzed the causes of delays, and (2)proposed an estimation method for the offshore waiting time of each ship at container terminals,
making it possible to discuss the relationship between waiting time-volume and the situations
and characteristics of terminals such as the degree of congestion, the total TEU capacity ofships, delays of ship arrival, and over stays of berthing ships.
As mentioned in section 5, the scope of this study when calculating the offshore waiting timevolume was limited in both time and the number of ports. The authors plan to address this issue
in future work by not only increasing the analysis targets, but also by modeling the occurrenceof offshore waiting at terminals, with the aim of improving the punctuality of global container
services and operational efficiency of container terminals.
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