Transport-, Bygnings- og Boligudvalget 2018-19 (1. samling)
TRU Alm.del Bilag 287
Offentligt
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Study in addition to the traffic forecast
for the FBFL about possible traffic
diversion from Great Belt to Fehmarn
- Interim Report
Report
March 2019
 TRU, Alm.del - 2018-19 (1. samling) - Bilag 287: Opdateret analyse af international trafik på Storebælt, fra transport,- bygnings- og boligministeren
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Dingolfinger Straße 2
81673 München
Contact:
Dr. Markus Schubert
T +49 (89) 45 91 1127
[email protected]
Study in addition to the traffic forecast
for the FBFL about possible traffic
diversion from Great Belt to Fehmarn
- Interim Report
on behalf of
Femern A/S
Copenhagen
 TRU, Alm.del - 2018-19 (1. samling) - Bilag 287: Opdateret analyse af international trafik på Storebælt, fra transport,- bygnings- og boligministeren
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CONTENTS
1
 
2
 
2.1
 
2.2
 
2.3
 
2.4
 
3
 
3.1
 
3.2
 
3.3
 
3.4
 
4
 
4.1
 
4.2
 
4.3
 
Context
Basic Approach – Use of Mobile Phone Data (MND)
Technical Background
Data Privacy
Extrapolation
Expertise of Intraplan Consult GmbH
Study Design
Analysis of MND
Basic Extrapolation
Expansion to yearly Figures
Quality Assurance
Results
Total Great Belt Traffic
International traffic
International Traffic with the German Land-border
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2
 
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3
 
3
 
4
 
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6
 
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I
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1
CONTEXT
The Danish Ministry of Transport, Building and Housing had evaluated with help of COWI the so
called FTC forecast for the Femern Belt Fixed Link (FBFL)
1
. This forecast prepared by the FTC-
consortium led by Intraplan Consult, Munich, had been providing inputs for the financial analysis
of the FBFL-project.
In this quality assurance of COWI
2
the underlying method and the results of the FTC-study were
approved. Two elements were identified for which
additional research could strengthen the
results:
the expected transfer of car traffic from the Great Belt and the newly generated traffic.
The focus of this paper is on the expected transfer of car traffic from the Great Belt.
Differently from the ferry lines, for which detailed statistics about the international traffic between
Germany and Denmark/Scandinavia are available, the international traffic crossing the Great
Belt Bridge cannot be derived from regular statistics. There is a good knowledge about the total
traffic on the Great Belt Bridge from toll statistics, but this traffic is dominated by intra-Danish
traffic between Eastern and Western Denmark and no statistics are available on international
traffic over this route. This traffic had to be estimated in the FTC-study by route choice model
calculations using the parameters of costs, time and availability
3
, which in the case of Great Belt
were based on licence plate counts on international traffic, which is a standard approach for
traffic models.The model calculations came to 854.000 cars in 2022. From this figure, according
to the FTC-study, 718.000 cars would shift to the Fehmarn route per year, or 1.967/day, when
the FBFL is open
4
. This latter route then would provide a faster connection whereas today the
travel time between the Great Belt route and the Rødby – Puttgarden route is quite similar, when
considering waiting time and time for boarding and disembarking.
In 2017 it was decided to do a toll reduction on Great Belt. Therefore new calculations were
made showing how the number of vehicles shifting to the Fehmarn route is affected by this.
These calculations showed that 545 cars/day less would choose Femern Belt to Great Belt after
a toll-reduction of 25 % on Great Belt. That means that 1.422 cars would shift to the Fehmarn
route per day
5
. This number of transferred cars is the number used in the Financial Analyses of
the project
6
.
1
2
3
4
5
6
Intraplan Consult GmbH and BVU Beratergruppe Verkehr+Umwelt GmbH: Fehmarnbelt Forecast 2014
– Update of the FTC-Study of 2002, on behalf of Femern A/S, 2014
COWI: External quality assurance of the updated traffic forecast on the Fehmarnbelt project, November
2015
See FTC-study, chapter 4.2/4.3
See FTC-study, chapter 6.1.3, table 6-12.
Fehmarnbelt Forecast 2014 – Update of the FTC-Study of 2002, effects of Great Belt toll reduction on
the Fehmarn Fixed Link, on behalf of Femern A/S, 2017
Fakta om effekter for Storebæltsforbindelsen og Femern Bælt-forbindelsen, Transport-, og bygnings- og
Boligministeriet, december 2017.
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In an attempt to strengthen these results of transfer of car traffic from the Great Belt, further
studies have been made on this topic. First, a study was conducted mainly based on distribution
of postcards and licence plate counts. The study confirmed that there is a substantial share of
international traffic on Great Belt but the results of the study was still based on smaller samples
and did not fully close the data gap pointed out by COWI.
Therefore it was decided to use technological advances in a new approach and carry out a
completely new study based on extended study on data collected via cellular phones at large
scale; collection of data on origins, destinations, location/route and time.
There is some experience in UK, in USA and in Germany with the exploitation of such data for
similar cases. In the case on hand such analyses are obvious and sensible and this would close
the data gap critized by COWI. This study will be examined in this paper.
2
2.1
BASIC APPROACH – USE OF MOBILE PHONE DATA (MND)
7
Technical Background
Mobile phones generate among others "events", i.e.
when calling or receiving a telephone call
when submitting or receiving a SMS
when sending or receiving data (via Internet)
when switching on or off the device
periodically when on, but inactive
These "events" are saved at the mobile phone provider among other with three relevant
information:
ID number (IMEI)
location area code
time
From that it is possible to analyse a movement pattern for each device, which, in compliance
with data protection regulations, can be used for transport planning issues. Indeed, some mobile
7
MND = mobile network data
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phone companies use this big data source for side business and provide among others analyses
for transport related questions.
By using intermediate locations on the trip and by analysing the pattern of change from one area
to the next (rail: larger groups changing at the same time from one location to the next, whereas
road traffic is more constantly "flowing") the modal-split between road and rail can be analysed
as well.
2.2
Data Privacy
The use of these data, however, is strictly regulated and restricted by the EU directive on data
protection and the activities of the mobile phone companies are under observation and review of
the data protection authorities. Even in the case on hand the use of the data for this project had
to be approved legally.
Generally there are organisational and technical measurements to safeguard data protection.
Organisational:
Data leaving (the inner core of) the telephone company's databanks have to be
anonymized and aggregated. No one of the analysis team, including the transport planner resp.
the author of the study on hand, as well as the data analyst as subcontractor had access to any
original data.
There are three
technical measures
to exclude any data abuse:
(1)
(2)
(3)
The device-ID (IMEI) is anonymized (by automatic, certified random generator)
The anonymization is renewed every 24 hours
The anonymized data have to be aggregated: cases less than five have to be suppressed
By the approved measures it is absolutely impossible to assign any detail to individual persons.
2.3
Extrapolation
The single companies have no monopoly, neither in Denmark nor anywhere else. Because, for
the reasons described in chapter 2.2, they cannot exchange their data even if they would be
willing to cooperate any analyses based on mobile phone data are sample surveys. So, the
surveys have to be extrapolated to 100 % of the basic population.
However, generally the mobile phone companies know their clients structure with regard to age,
sex and kind of contract (business, private, etc.). From general market data (size of overall
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mobile phone market per segment, number of multiple users, persons not using mobile phones)
and
by regional demographic statistics
they can figure and relatively accurate their market
share per region and segment: By that they can extrapolate the data to 100 % of the mobile
phone holders.
2.4
Expertise of Intraplan Consult GmbH
Intraplan was among the first in Germany and Europe to use mobile phone data for transport
analyses and planning. We provided studies based on mobile phone data among others for
public transport associations (Hamburg, Rhein-Main), Municipalities (Nuremberg, Munich region)
and airports (Munich, Stuttgart). In these projects we co-operated among others with the
Swiss
company Teralytics,
Zurich, which is specialized on data analyses of MDN (Mobile Network
Data). For the project on hand Teralytics is serving as sub-contractor, exploiting, extrapolating
(see chapter 2.3) and providing the base analyses. They used data of the mobile phone
company 3DK.
3
3.1
STUDY DESIGN
Analysis of MND
The study was carried out with the following specifications:
Relevant are (mobile phone) users, who cross the Great Belt Bridge: There are tracked with
origin (location of start before crossing the bridge) and destination (location of destination after
crossing). Apart from the immediate trips of the users crossing the Great Belt Bridge the follow
up trips were analysed: origin and destination of the precedent trip and the same for the next trip
after the trip with the bridge crossing. This is necessary due to possible breaks on longer trips
which could be misinterpreted as trip end and the location of the break as destination. By
analysing the preceding and follow-up trips traffic chains could be analysed and the final origin
and destination within 24 hours could be found. As a "side product" of that it could be found if
there are round trips crossing the bridge within 24 hours. With regard to the international traffic
they could help to find out whether there was only a short stay abroad. This is important in the
German case: There may be trips from Eastern Denmark to Northern Schleswig-Holstein, which
are not relevant for a shift from Great Belt to FBFL. Because only the border crossing could be
tracked, but not the final destination or first origin in Germany, it may be an indication if there is a
retour trip within 24 hours that this traffic may be ending or originating close to the border. Apart
from that a split-up into road and rail traffic has been made.
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Origins and destinations have been aggregated into provinces (see Figure. 1). Apart from that it
had to be found out, whether a border has been crossed before reaching Great Belt resp. after
crossing the bridge. The locations of the relevant border crossings are shown in Figure 1. There
may be trips between two border crossings (i.e. between Germany and Sweden).
Apart from the users of 3DK mobile phones inbound roamers crossing the Great Belt Bridge had
to be tracked with origin and destination. To be able to expand the inbound roamers separately
the nation (network) was analysed.
With regard to the survey periods a good representation of the year is important. The following
weeks had been chosen for the analyses of MND:
Week 23/18
Week 28/18
Week 31/18
Week 38/18
Week 42/18
Week 47/18
and (not yet included in the results presented here)
Week 51/18
Week 05/19
Week 09/19
Week 16/19
Apart from that the results were edited per weekday.
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Figure 1:
Zones and border crossings, to which the results were aggregated
3.2
Basic Extrapolation
Basic extrapolation was made by Teralytics using the regional and sectoral market shares of
3DK in Denmark.
With regard to inbound roamers a first extrapolation was made on the basis of inbound tourism
data (Statsbank DK).
3.3
Expansion to yearly Figures
Toll statistics of Great Belt were available for each day, the day of the survey periods as well as
the other days. By that it was possible
(1)
(2)
to verify the extrapolation of Teralytics (see chapter 3.2).
to expand the results of the survey periods/survey days to yearly figures.
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With regard to (1) there was a strong correlation between the extrapolated results of Teralytics
with the toll counts. With regard to (2) each day of year was assigned to a "typical" survey day
(this is still preliminary due to the fact that four survey periods are still missing).
3.4
Quality Assurance
The preliminary results (bridge crossing) of the first two survey weeks we compared with the toll
statistics plus the railway traffic measured in train seats offered. Altogether in this period (weeks
23 and 28) 559.000 vehicles crossed Great Belt, of which 487.000 were passenger cars (see
Table 1). There were 525.000 train-seats available (two-way totals). Compared to these figures
the MND analysis gave 1,540 million trips of which 1,265 million were road trips and 275.000
railway trips. Given a (generally high, but in summer reasonable) occupancy rate for car with 2,2
and bus with 35 and a seat occupancy rate of 50 % for train, the figures of the MND analysis fit
quite quell to the Great Belt statistics without any additional necessity to change the
extrapolation process for the MND data.
toll counts
vehicles
motorcycles
pass. cars
buses
lorries
total
rail (seats)
total (pass)
Table 1:
8.212
487.331
2.083
60.887
558.513
525.000
-
estimated
passengers
8.212
1.072.128
72.905
60.887
1.214.132
262.500
1.476.632
1.265.428
275.297
1.540.725
MND bridge
crossing
Comparison between toll statistics and extrapolated MND counts on Great Belt for
the first survey weeks
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4
4.1
RESULTS
Total Great Belt Traffic
Traffic totals of the MND for the six weeks considered in this interim report were at 4,938 million,
of which 3,998 million were on the road and 940.000 in trains (see Table 2). Extrapolated to the
whole year 2018, using the different survey periods and assigning it to comparable periods (see
chapter 3.3), there were 33,5 million persons, crossing the bridge of which 25,9 million were on
the road and 7,5 million in the trains.
MND
analysis
(six weeks)
road passengers
rail passengers
total GB passengers
Table 2:
3.998.132
940.341
4.938.473
extrapolated
to whole year
2018
25.940.673
7.509.798
33.450.472
MND cases and (preliminary) extrapolation for 2018
Considering the countries of the providers of the mobile phone holders which should correlate
strongly with nationality, there are the following shares (see Table 3).
MND analysis
(six weeks)
Denmark
Germany
Sweden
Netherlands
Norway
Poland
UK
Other
total
Table 3:
4.694.886
84.329
53.641
16.339
2.162
20.853
7.601
58.662
4.938.473
network shares extrapolated to network shares
(in %)
whole year
(in %)
2018
95,07
1,71
1,09
0,33
0,04
0,42
0,15
1,19
100,00
31.993.004
509.610
305.869
105.760
7.446
128.399
44.366
356.018
33.450.472
95,64
1,52
0,91
0,32
0,02
0,38
0,13
1,06
100,00
Share of mobile phone networks (countries of networks)
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For the whole year around 96 % of the bridge crossers use mobile phones registered at a
Danish mobile phone provider. 4 % are roamers with the biggest share of Germany (mobile
phones registered at a German provider) and Sweden.
4.2
International traffic
The share of international traffic crossing the Great Belt according to the MND survey is,
extrapolated to 2018, at 10,4 %. This is quite a substantial share of the total traffic. Even for rail
the share of international traffic is at 8,7 %.
MND analysis (six
weeks)
road passengers
domestic DK
international
total
share of international
rail passengers
domestic DK
international
total
share of international
total GB passengers
domestic DK
international
total
share of international
Table 4:
4.380.003
558.470
4.938.473
11,3
29.961.942
3.488.529
33.450.472
10,4
839.488
100.854
940.341
10,7
6.857.017
652.781
7.509.798
8,7
3.540.515
457.616
3.998.132
11,4
extrapolated to
whole year 2018
23.104.925
2.835.748
25.940.673
10,9
Share of international traffic at Great Belt (passengers)
4.3
International Traffic with the German Land-border
International traffic on Great Belt is not only related to the German land-border, i.e. traffic
between Eastern Denmark and Germany and between Sweden and Germany, but there is a
considerable traffic between Denmark west of Great Belt and the Scandinavian Peninsula. Even
some trips between UK (via Esbjerg ferry) and between Norway and Eastern Denmark (via
Hirthals or Frederikshavn) can be observed on Great Belt. The relevant traffic in the sense of the
FTC study, that is between Germany/the Continent and Eastern Denmark and the Scandinavian
Peninsula, is only a part of the international traffic on Great Belt. This is shown in Table 5.
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MND analysis
(six weeks)
German land-border – Sweden
German land-border – DK East
other international ODs
21.568
201.455
234.594
extrapolated to
whole year 2018
125.273
1.250.931
1.459.545
total international traffic
thereof German land-border
Table 5:
457.616
223.023
2.835.748
1.376.203
International road traffic on Great Belt (passengers)
Of the total 2,836 million persons crossing Great Belt in 2018 for international road trips 1,376
million are related to the German land-border. This is a share of around 49 %. We assume the
fact that this share for passenger traffic is higher, because there are few lorries taking the detour
via Great Belt.
8
This is because lorries have a lower cruising speed than passenger cars and so
the detour via Great Belt is much longer measured in time. Apart from that the ferry crossing can
be used for rest time. This is valid also for buses. Therefore it can be concluded that much of the
nearly 1,4 million persons crossing the Great Belt Bridge on the way to or from Germany are car
passengers. Given an occupancy rate of 2,2
9
this would mean a yearly number of
around
626.000 passenger cars
using the Great Belt Bridge to/from Germany.
Around 19 % of the travellers between Germany and Eastern Denmark/Sweden via Great Belt
return within 24 hours (see Table 6).
MND analysis
(six weeks)
return within 24 hours
share of intern. Germany based traffic
Table 6:
40.383
18,1
extrapolated to
whole year 2018
259.364
18,8
International traffic on Great Belt with Germany for which the return is within 24
hours
These short trips should be related mostly to the German regions of Schleswig-Holstein and
Hamburg. Some of those trips would be bound for regions in the north of Schleswig-Holstein
which would be traffic not being a potential for route shift to the FBFL when this connections is
8
9
See Intraplan Consult GmbH and BVU Beratergruppe Verkehr+Umwelt GmbH: Fehmarnbelt Forecast
2014 – Update of the FTC-Study of 2002, on behalf of Femern A/S, 2014: see also page 2 of this report
At the average of the ferries it is 2,5 to 2,6, but we expect a higher share of business travellers with
lower occupancy rates
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on place. There may be shifts of trip destinations though, for example shopping or leisure trips
from Eastern Denmark to Flensburg which will be shifted to Ostholstein in consequence to the
FBFL. Considering that:
on the one hand interaction between Northern Schleswig-Holstein and Denmark is more
intensive than between Southern Schleswig-Holstein/Hamburg and Denmark due to the
shorter distance and the Danish minority in Germany living mostly in the north of Schleswig-
Holstein
on the other hand only 30 % of the Schleswig-Holstein population and 20 % of the joint
Schleswig-Holstein and Hamburg inhabitants are living in the areas not influenced by the
future FBFL. For areas north/north east of Neumünster the shortest route with Eastern
Denmark will be the Great Belt route even when the FBFL is in place
we would estimate that 5 to 10 percent of the traffic measured by mobile phone tracking
between the German landborder and Eastern Denmark/Sweden is bound to Northern Schleswig-
Holstein and thus we estimate that, related to 2018, between 90 % (550.000) and 95 %
(600.000) car trips are a potential for a route shift to FBFL.
5
SUMMARY AND COMPARISON WITH THE FTC 2014 STUDY
In the following table 7 the analyses shown above are compared with the FTC 2014 study.
In the FTC 2014 study 718.000 cars per year (related to the year 2022) have been expected to
shift from the Great Belt to the FBFL route (line 1, in table 7). By the toll reduction on Great Belt
which could not be considered in the FTC 2014 study, the transfer from the Great Belt to the
FBFL route will be reduced by 198.900 (line 2), giving a total of 519.100 cars per year or 1.422
per day (see line 3), which would be transferred from Great Belt to the FBFL.
In the MND analyses international traffic has been found to have a share of 10,9 %,
corresponding to nearly 1,289 million cars in 2018 (see line 4). From these international car trips
around 626.000 cars are related to the German landborder (see line 5). 18,8 % of these cars are
returning the same day (around 118.000, see line 6). A share of these day trips are related to the
relation Northern Schleswig-Holstein – Eastern Denmark/Sweden which will be only partly
subject of transfer to the FBFL, because for these regions – differently from Southern Schleswig-
Holstein and all regions south of it - the Great Belt route will remain the shortest connection
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Line
Summary of results of transfer of cars
from Great Belt to the Femern tunnel
1
2
3
FTC 2014 - number of cars to be transferred
(related to 2022)
Toll reduction in 2017 on Great Belt
Transfer of cars after reduction of Great Belt
tolls
Summary of results of the mobile phone
data analysis in 2018
1)
4
5
6
7
8
International traffic on Great Belt
Crossing Great Belt and the DK/DE
landborder same trip
2)
Hereof returning same day
High estimate for trips to/from close to the
border
Low estimate for trips to/from close to the
border
Cars per
year
Cars per
day
718.000
-198.900
519.100
1.967
-545
1.422
share
10,9 %
49 %
18,8 %
90 %
95 %
1.288.636
626.364
117.756
550.000
600.000
3.531
1.716
323
1.507
1.644
1) Based on 2,2 persons per car
2) Very few lorries and busses use the Great Belt DK/DE landborder route
Table 7:
Comparison of the results of the MND analyses with the FTC study 2014 including
the effects of the Great Belt toll reduction
Taking that into consideration the traffic potential for a shift from Great Belt to FBFL is between
(90 %) 550.000 (line 7 of line 5) to (95 %) 600.000 (line 8 of line 5) cars per year or 1.500 to
1.650 cars per day related to the year 2018 when the MND analyses have been made.
12