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ORGANISING 8000 VOLUNTEERS TO OBSERVE FOREIGN TRUCKING
OPERATIONS – A NOVEL, INNOVATIVE APPROACH USING SMARTPHONES
AND SOCIAL MEDIA
Henrik Sternberg
Department of Design Sciences, Lund University, SE-221 00 Lund, Sweden
1
ABSTRACT
The deregulation of the European transport market has been a controversial topic,
characterised by a lack of facts and figures on the actual impact of the regulations.
Given the crucial importance of road freight transportation to European society, and
the lack of figures and control of foreign vehicles operating domestically in other
countries, the purpose of this paper is to present a novel, innovative approach to
organise large-scale data collection using smartphones and social media (Facebook).
In total, 330 000 observations were collected by 8 000 volunteers, consisting
primarily of truck drivers, in Norway, Denmark and Sweden over a period of 10
weeks. To validate the observations, comparisons were made with the GPS logs
from four hauliers to measure the data quality of the approach. This paper provides
an initial contribution to theory building on novel methodological approach, organizing
volunteers using smartphone data collection. It outlines the findings as well as future
research on how this innovative methodological approach can be further developed.
2
INTRODUCTION
Freight transportation is the basis for trade enabling societal wealth. Ten million
Europeans are employed in the transport industry. In Europe, road-based
transportation accounts for 76% of the total freight transportation and is continuously
increasing its share due to cost efficiency and superior flexibility. Currently, the
European road freight transport market is undergoing a structural change due to
market deregulation (Kummer et al., 2014). The deregulation of road freight
transportation (trucking) in Europe has been a controversial topic (European
Commission, 2014b; Hilal, 2008), characterised by a lack of facts and figures on the
actual impact of the regulations. This is in contrast to the American road freight
deregulation through the Motor Carrier Act of 1980, that has received ample attention
in the academic literature (e.g., Allen, 1990; Belman and Monaco, 2001; Corsi, 2005).
Some of the few authors that have addressed the European freight deregulation
have investigated flags of convenience (Kummer et al., 2014), social working
conditions (Hilal, 2008) and effects on the international haulage sector (Lafontaine
and Malaguzzi Valeri, 2009). Logistics and in particular transportation have a large
environmental and societal impact (Wu and Dunn, 1994) and the societal costs of
freight transportation are rarely internalised, causing concern among policy makers
(Runhaar and Heijden, 2005; Stern, 2008).
Policy makers need adequate data to understand and analyse freight movements.
This is due to the overall social and environmental impacts of freight transportation, in
particular concerning the deregulation. Despite the importance of this, previous
research clearly shows a significant lack of available data (Lafontaine and Malaguzzi
Valeri, 2009; McKinnon and Leonardi, 2009) and most of the available statistical
sources themselves express their concerns on this matter (e.g. Eurostat, 2013).
Currently, most European countries do not have road charging schemes and lack the
1
ability to retrieve data on truck movements, in particular foreign trucks movements
inside the country. As a result, conventional data collection methods seem no longer
adequate to satisfy all data needs. With smartphones becoming increasingly
available (International Data Corporation, 2014), several researchers have pointed to
them as being the next great tool to enable large-scale data collection in
transportation (Bohte and Maat, 2009).
The purpose of this paper is to present a novel approach to data collection in
freight transportation by the use of volunteers, smartphones and social media. Due to
the novelty of the data collection approach, the author acknowledge that extensive
work on data collection tool calibration, volunteer interaction, data quality, reliability
and validity remains to be done. This research aims to contribute to theory building
by describing the methodological approach, illustrating the opportunities it offers and
sharing some insights on aspects such as incentivising volunteers and data quality.
For the sake of clarity, this paper does not address the direct policy implications of
the study, but focuses on the methodological aspects of the research. This paper is
organised as follows: First a brief outline of European trucking deregulation is given
(intended for readers not already familiar with the directives), followed by an outline
of previous research that relates to the study presented. Then a narrative on the
chronology of the study is presented, focused on describing the process and the
factors that created momentum in the volunteer participation. That is followed by
comparisons between the volunteer data and actual GPS data from four hauliers.
Finally, conclusions and recommendations for future research are offered. Due to the
large amount of data collected in Sweden, Denmark and Norway, it was decided to
use Denmark as the focal country when presenting the data.
3
ROAD TRANSPORT DEREGULATION IN EUROPE
The international traffic between European Union (EU) countries is completely
deregulated, whereas the domestic freight transport markets are still regulated,
currently through Regulation (EC) 1072/2009 (2009)1. It states: “Hauliers who are
holders of Community licences provided for in this Regulation and hauliers authorised
to operate certain categories of international haulage service should be permitted to
carry out national transport services within a Member State on a temporary basis in
conformity with this Regulation, without having a registered office or other
establishment therein”. The Regulation is from here on referred to as the Cabotage
Regulation.
The word “cabotage” originates from the sea domain and according to most
dictionaries, applies to transport between two locations within a country, carried out
by a foreign carrier. A foreign haulier carrying out national transports is generally
referred to as cabotage. “Temporary” in the Cabotage Regulation is defined as no
more than three cabotage transports in another country within one week, upon the
completion of an international trip. Notably, the Cabotage Regulation’s definition of
temporary cabotage does not exclude systematic cabotage, which means that in
practice a foreign haulier can spend 365 days in another EU country, as long as the
haulier ensures having an international trip every week. Schramm (2012) suggests
1
http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009R1072&rid=2
2
the conceptual definition of “big cabotage”, meaning that large-scale international
hauliers with a critical mass of international trips, can act as domestic hauliers by
continually rotating their trucks between two countries with three domestic trips in
each of the countries.
Some road transports carried out by a foreign haulier between two domestic
locations are not cabotage, but combined transports. Combined transport is
promoted within the EU through the Combined Transport Directive (Council Directive
92/106/EEC)2, hereafter referred to as the CT Directive. The CT Directive seeks to
promote combined transport operations through liberalisation of road cabotage, the
elimination of authorisation procedures for combined transport operations, as well as
financial support through fiscal incentives for certain combined transport operations.
In order to be eligible for the provisions in the CT Directive, the movement of goods
must meet a number of criteria, including:
1.
Goods must be moved in a load unit which is more than 20’ (6m) long;
and
2.
Goods must be moved by rail or inland waterway or maritime transport,
where this section exceeds 100 km as the crow flies; and
3.
Goods must be moved by road transport on the initial and/or final leg of
the journey either:
 between the point where the goods are loaded and/or unloaded and the
nearest suitable rail loading station; or
 within a radius of 150 km as the crow flies from the inland waterway port or
seaport of loading or unloading.
The CT Directive is supported by other EU policies, such as the Weights and
Dimensions Directive (Council Directive 96/53/EC)3 which currently provides for
Member States to permit movement of heavier intermodal load units by road when
used in combined transport operations (European Commission, 2014a).
In this paper European hauliers are placed in two categories: EU15 hauliers (from
Member States prior to 1 May 2004), and new Member State hauliers. The distinction
is made because of the major price difference between the two categories due to
significantly lower wage levels in the new Member States (European Commission,
2014b). The price difference has led to EU15 hauliers going out of business or
flagging out their trucks, as shown by the sample from Austria, where 50% of the
trucks for freight transport were flagged out over a period of 10 years (Kummer et al.,
2014).
4
RELATED LITERATURE
4.1 Smartphones in transport data collection
According to the International Data Corporation (2014), the world market for
smartphones grew by 28.6%, and only in the first quarter of 2014, 281.5 million
smartphones were shipped. With most smartphones containing GPS positioning,
various sensors, and information on the owner, transport researchers around the
2
http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:31992L0106&from=EN
3
http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:31996L0053&from=EN
3
world are currently experimenting with various types of smartphone-based
approaches to data collection (Bohte and Maat, 2009). Collecting smartphone data
for transportation research is still novel, but is developing fast due to the promise of
large datasets that can be collected in a cost efficient manner.
Generally, two types GPS data collection have been used in previous research.
One is a GPS device together with another input device, such as a personal data
assistant (PDA) or a logbook. This requires participants to actively input information
for each trip and quickly becomes a very expensive method (Bohte and Maat, 2009).
Hence, according to Bohte and Maat (2009), passive GPS, that requires no
intervention from participants and collects data automatically, is typically the
approach chosen. The drawback of this approach is that it creates a large need for
post-processing of the data.
As with other probabilistic methods, the analysis of geographic movement patterns
is by default very prone to errors (Bierlaire et al., 2013). The poor quality of GPS data
collected from smartphones precludes the use of state-of-the-art map matching
methods (ibid.), yet with complementary data on the actual characteristics of some
trips, the quality of the analysis can be greatly increased (Bohte and Maat, 2009; Du
and Aultman-Hall, 2007). Despite additional descriptions and templates for matching
GPS data to trip purpose, trips with many stops still represent a great challenge to
analyse (Du and Aultman-Hall, 2007). In practice this means that the greatest
challenge is distribution traffic with frequent stops.
4.2
Using volunteers to collect transport data
Early studies on collecting GPS data have shown that, with technology,
volunteers are typically positive to participating in research projects (Murakami and
Wagner, 1999). Sternberg et al. (2014) have shown that truck drivers to some extent
are motivated to participate in research programmes, in particular when given
incentives. Klaus et al. (2008) used 234 drivers to collect data on 1431 working days.
The drivers made manual logbook entries on their working activities in a study with
the purpose of estimating the effects of new driver time regulations and drivers’ time
use. In addition to the potential for collecting large amounts of empirical data that
might otherwise be hard to come by, early experiments on using smartphone apps
with drivers by Sternberg (2011) indicated that there were some quality problems in
the collected data that needed to be taken into account. He found that some drivers
were reporting delays, creating some skewness in the dataset. After scrutinising an
initial set of 820 self-reported driver work days and using the principle of immediate
scrapping of all data from any driver who did not enter a plausible record on one
occasion, Sternberg found that only 343 driver work days held high data quality.
5
DATA COLLECTION WITH VOLUNTEERS, SMARTPHONES AND SOCIAL
MEDIA
5.1 Pre-phase
In the autumn of 2012 in Sweden, the European trucking deregulation hit the
Swedish hauliers and an increasing number of national long-haul drivers were losing
their jobs4. Meanwhile in the debate, the only available data was Eurostat, stating
that cabotage in Sweden was limited to 5% of the for-hire-and-reward transport
4
The total number of employed truck drivers (all segments) in Sweden did not decrease.
4
market. The national transport union and the road hauliers’ association disagreed
with the figures and stated that with sometimes up to 60% of the trucks on the roads
being foreign, something in the statistics was not right.
Considering the heavy debate regarding the European freight deregulation, the
lack of reliable data sources and previous positive experience using volunteers to
collect data, the authors and two assistants spent two weeks at the Port of
Gothenburg in November 2012 observing trucks. Table 1 lists the haulier (truck)
nationalities.
Table 1: Two weeks of truck observations at Port of Gothenburg (November/December 2012).
Truck nationality
Albania
Belarus
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Italy
Latvia
Lithuania
Luxemburg
Macedonia
Norway
Poland
Rumania
Slovenia
Sweden
Switzerland
The Netherlands
Ukraine
Number of trucks
1
1
92
3
10
5
41
9
5
49
2
1
1
65
18
3
82
3
163
32
1
1723
3
5
4
A look into spot markets, such as Timocom, indicated that in Denmark and
Sweden, domestic trips on the spot market were dominated by cabotage operations
carried out by hauliers from low-cost countries (such as the new EU Member States).
Spot markets, the counting of traffic carried out by the Swedish Road Haulage
Association (Svensk Åkeritidning, 2013), discussions with people in the industry and
the authors’s own observations formed a picture of the market that was in contrast
with the Eurostat figures (Eurostat, 2013). According to Eurostat at that time, the
Netherlands, Germany and Denmark were supposed to be the biggest cabotage
operators in Sweden. Against this background, the author developed a simple
Android app to track foreign trucks. The user would enter the licence plate number,
click submit, and the licence plate number and GPS position data of the phone would
be sent to a central database. First, relatives of the authors, working as drivers,
tested the app. Then the Swedish Transport Workers’ Union was contacted and a
small test group of 15 drivers was established. The tests showed that the app
seemed to work fine and some of the volunteer drivers were very eager to use it. The
Swedish Transport Workers’ Union grew more interested in the project and offered to
5
finance a full-scale version, including the development of an iOS app and a website.
The project was given the name “The Cabotage Study” (“Cabotagestudien” in
Swedish), since drivers typically use the word “cabotage” in a much wider sense, but
the purpose was clearly to take a snapshot of the actual state on the roads. The
volunteers were instructed to report all foreign trucks they saw, regardless of
nationality, type of transport, etc. As a result, the volunteers captured trucks
regardless if they were carrying out international transport or transport within the CT
Directive or Cabotage Regulation.
The app contained warning texts, strongly advising against using it while driving. In
the information material as well as continuously in social forums, volunteers were
informed and reminded to respect the privacy of foreign drivers and individual
companies. In a deregulated market, shippers’ transport purchasing policies are
governed by the strategy of the company. The researcher’s advice to domestic
drivers concerning job security and safety was always to encourage informed
consumption, that is, using their power as end consumers rather than stigmatising
foreign truck drivers who in many cases already live under harsh social conditions
(Hilal, 2008).
5.2 The first data collection phase
By mid-April 2013 both an Android and an iOS app were developed (see Figure 1),
the study was launched in Sweden and the authors opened the first Facebook page:
https://www.facebook.com/Cabotagestudien (in Swedish).
Figure 1: The data collection apps. The left image shows the licence plate entry screen (Android). The
right image shows the map displayed to the user after an observation had been submitted (iOS).
To recruit volunteers, the authors contacted Facebook forums for truck drivers with
more than 2 000 members and convinced them to spread the word of the scientific
study aiming to calculate the actual extent of foreign trucks in Sweden. Initially,
graphic images of the study’s coverage were manually created and posted on the
Facebook page. After some time, the development team had finished the map
software that displayed all the reported movements of the foreign trucks (as shown in
Figure 2). Only the first and the last characters of the trucks’ licence plates were
revealed to protect driver privacy.
6
Figure 2: Screenshot from the website page displaying the movement pattern of a truck in daily shuttle
traffic between East Denmark and Oslo, Norway. On the left, the page visitor can see the user names of
the volunteers and what observations they have made
(http://www.cabotagestudien.com/sv/rapporteringar/#/plates/).
News about the study spread and several volunteers in Denmark and Norway
started using the app as well. Every day the authors posted messages on the
Facebook page. The messages were shared up to 350 times and viewed by up to
45 000 people. The first stage (mid-April to the end of May 2013) resulted in over
163 000 observations collected by 5 000 volunteers.
5.3 The second data collection phase
Before the study was carried out, it was often argued that many foreign trucks in
Sweden did not follow the cabotage rules. The first phase of the data collection
revealed that many foreign trucks might not be leaving the country. The volunteer
drivers had collected about 50 photo samples of licence plate cheating, which they
shared with the authors (e.g. manipulated plates, the same truck with different plates
within a short period). Crowdsourcing data for research purposes and the public
nature of the project drew massive media attention to the study, which was reported
in hundreds of newspapers, magazines, radio shows and on the national televised
news. Ten organisations in Denmark, Norway and Sweden contributed financially in
order to launch the second data collection phase in the three countries.
In the second phase, 172 261 observations were collected from 8 October to 9
November 2013. To promote the app, national Facebook pages were also opened for
Denmark and Norway. Native staff were hired to moderate the national pages, offer
technical support, interact with volunteers, publish news and translate blogs from
Swedish to Danish and Norwegian. Some changes were the use of local languages,
user authentication and the ability to send additional information such as nationality
and comments (80% of the observations in the second phase contained nationality).
The app also contained top lists of volunteers, where they could see who had
reported the most trucks in each country, both daily and over the whole period.
7
Several haulage companies and trucking magazines volunteered to offer prizes, such
as magazine subscriptions, to the most vigilant volunteers.
A total of 172 261 observations were collected by approximately 8 000 app
volunteer users (some users were anonymous); 38 0861 were made in Denmark. In
total, 56 947 different licence plates were reported (most of them only once). 12 596
licence plates were reported by at least two observers, corresponding to 110 076
observations. The remaining 60 697 valid sightings were not used. The bulk of the
observations were clearly made by truck drivers, with the data collection patterns
corresponding to their regular routes. 2 109 users (543 in Denmark) contributed 10 or
more observations.
The methodological approach generated large amounts of data and the next section
addresses the quality of the data.
6
DATA QUALITY
This section outlines the data quality issues of the tested methodology. These
issues were determined by checking the dataset and by comparing the submitted
observations to the actual locations of the observed vehicles at the specified times.
Due to the heated debate regarding European freight deregulation, antagonistic
actions against the study were expected.
The CEO of a Danish company operating in Denmark with trucks registered in one
of the new Member States, stated the following to the authors to show how the
system could be "tricked" into providing faulty data : “I reported one of my trucks here
in West Denmark. Then 5 minutes later my friend reports the same truck in
Copenhagen and 10 minutes later I report it again here and we could see the moving
pattern of the truck, without the system reporting any error”. The authors had chosen
not to include any logic for visual automatic rejection of erroneous observations in
designing the system. This was done so that none of the logic would be revealed to
the users. If the observations had been immediately sorted out, the author feared that
an “arms race” would take place where antagonists would try to discover ways to
demonstrate flaws in the system.
Because most of the detailed validation data available for several of the hauliers
was from Denmark, and because several “attacks” on the system were carried out
from there as well, it was decided to use Denmark as the case county to analyse data
quality.
6.1
Expected error sources
Based on Sternberg (2011), the authors were expecting a large number of
erroneous reports from the beginning. Several sources of error needed to be taken
into consideration. The following non-antagonistic errors were observed:
 Technical errors: These occur, for example, when the smartphone sends
erroneous GPS coordinates (or no coordinates at all, making it look like an
observation was made at 0’0, off the coast of Africa).
 Double typing: Some double submissions were made. These were filtered out.
 Erroneous typing: This is an apparent weakness in the data collection method,
since there is no guarantee that the user has not entered the licence plate of a
personal car, trailer or a fictive licence plate. A few users reported their errors.
Since licence plates with only one observation are unlikely to have spent more
than a maximum of one day in a Scandinavian country, they were not of
interest for the purpose of the study anyway.
8

Impossible observations (e.g. a truck being at two different locations within an
implausible time interval, moving at a speed over 100km/h on a straight line).
In total, 1 488 out of the 172 261 observations in Denmark were scrapped after
accounting for these expected errors. The final source of error, expected but found
impossible to adjust for, was fake licence plates. In particular, Swedish observers
(and the authors during field trips) took several pictures of truck drivers trying to hide
their licence plates, trucks driving without licence plates, etc., and sent them to the
team. Fewer such pictures were taken or sent from Denmark or Norway during the
time of the study (8 Oct. to 9 Nov. 2013).
6.2
Error sources revealed by comparisons of volunteer reports and GPS logs
from haulier trucks
Thirteen hauliers from Bulgaria, Romania, Germany, Sweden, Latvia and Spain
were requested to share information with the study. The hauliers were selected
based on either high observation frequency in the study or because they were
connected to the authors through other research projects. When a haulier did not
reply after being contacted, a reminder was sent. After months of persuading and
communication with various interest organisations, four hauliers (two Romanian, one
German and one Swedish) finally agreed to share their data. The data was mainly
GPS logs from their trucks’ onboard systems plus, in one instance, additional
consignment notes for one week for 20 of the Romanian trucks. The data matched
the time period of the data collection5. In total, the data set contained 99 trucks
having carried out both international transports and cabotage in Scandinavia during
the period of the study. 45 were in the TOP 100 list of most observed trucks.
The comparison between the volunteer reports and truck GPS logs clearly
indicated:
1. The volunteers have a bias towards observing trucks from the new Member
States.
2. Some drivers and owner operators created fake observations of trucks they
had previously reported.
Firstly, we looked at the volunteer observation bias. This bias resulted in the
distribution between the EU15 and new Member States vehicles being somewhat
false. Thanks to the validation case data, the frequency of observations from the
volunteers of EU15 and new Member States’ vehicles respectively could be
compared to the actual appearances. This showed that new Member States’ vehicles
were reported 1.31 times a day, whilst EU15 vehicles were only reported 0.79 times
per day.
Secondly, the comparison between the data collection and the validation data
revealed that 4% of the Danish observations had to be discarded due to quality
issues. A fit between the datasets was defined as being both a time match (max 10
minutes difference) and a position match (radius of 30 km), though nearly all of the
remaining 96% matched were within 1 minute and 1 km. The authors tried to contact
some of the antagonistic volunteers without success.
5
For the purpose of readability from here on, data collection, if not stated otherwise, refers to data collected in the
second data collection phase.
9
6.3 Field trip
The authors spent 10 days in a camper on the roads in Scandinavia during the data
collection. They observed 199 trucks on the route from Fredrikshavn to Padborg and
from Rödby to Copenhagen. In order to generate a small unbiased set, he would
report all trucks encountered on parking lots where the team took their breaks. Since
the authors’ data collection was carried out Friday to Saturday, it does indicate which
truck nationalities stay for longer durations in Denmark. A comparison with the
volunteer observations is illustrated in Figure 3.
Figure 3: Percentages of truck nationalities observed by volunteers (left column) and the authors (right
column). PL is for Polish, BG for Bulgarian and DE for German trucks.
7
DISCUSSION AND FUTURE RESEARCH
Due to the novelty of the methodological approach presented in this paper,
extensive work remains. This section discusses some of the challenges encountered
during the study and outlines opportunities for future research.
7.1 Volunteer interaction
Organising drivers in social media is a challenge. Protectionism was clearly the
strongest motivational factor for a majority of the volunteers participating. Without
such a strong motivational factor, the study would likely have had fewer volunteers,
unless stronger incentives were offered. Yet, drivers are typically happy to contribute
to research projects (Sternberg, 2011). Many drivers contacted the university and
expressed great appreciation for the work.
One sponsoring company employed foreign drivers (on a business to business
basis, i.e., the foreign drivers were hired as contractors through a third-party
contractor for temporary staff) in Swedish trucks (paid below the levels stipulated in
national trade union agreements), which created a raging debate on the Facebook
page. In order to keep a broad audience on the Facebook page, frequent moderation
was a must, particularly since the language used by some participants was very
hateful towards foreign drivers. The team members moderating the pages were glad
to observe that the most vigilant volunteers never were among the Facebook users
they had to ban.
7.2 Validation of data collection
Getting validation data was a very time consuming task. Thirteen hauliers were
requested to participate, but only four accepted. Of the nine who did not want to
participate in the study, only one haulier gave an actual explanation. The company
had a twofold motivation as to why they could not participate. Firstly, they believed it
was not in the interest of their customers; secondly, the company uses the driver
support system, TomTom Work (tracking and driver communication system), that
10
only stores GPS history 3 months back. The Spanish haulier that did not respond
was a contact provided by the shipper who was interested in knowing if the haulier
carried out cabotage. The remaining hauliers were all small and registered in new
Member States. That shared in common that they were frequently observed over
extensive periods in one country.
As outlined in the introduction, the deregulation and in particular the term
“cabotage” have fuelled a heated debate in many EU15 states. One of the Romanian
hauliers only operates international traffic and does not do any cabotage transports.
The CEO agreed to share data when promised to receive a certificate from the study,
stating that the haulier had shared their data and did not carry out cabotage. Shortly
after, the company used the certificate in their business development. The second
Romanian haulier was frequently accused of illegal cabotage and the CEO viewed
the data sharing as an opportunity to show that the company had nothing to hide.
7.3 Future work on the methodological approach
Drivers are biased in their reporting and tend to report new Member State trucks
more frequently than EU15 ones. Some larger mega-hauliers are known to apply
rigid internal control measures to prevent illegal cabotage, hypothetically creating a
company reporting bias (i.e. drivers are aware and focus on observing small- or
medium-sized haulier vehicles). The limitations of the current dataset does not
support or reject the existence of such bias.
The data collection shows where the foreign trucks are positioned in relation to the
roads the domestic drivers travel on themselves. An illustration of this is the
thousands of trucks that cross the border between Germany and Denmark every day.
A large number of those trucks stop right after the border in Padborg, which is a main
logistics hub where goods are consolidated for distribution in all of Scandinavia. The
majority of Danish drivers drive inside Denmark, though, and thus the most
observations they made were in the Kolding area, despite Padborg (border crossing)
being the most trafficked (see Figure 3). This needs to be further analysed in future
studies.
Sharing and combining the collected data with other datasets that apply a big data
approach is likely to offer new and interesting insights. The major challenges are the
identity of the trucks and the protection of driver privacy.
8
CONCLUSION: OUTLINE OF A VOLUNTEER-BASED APPROACH
Organising thousands of volunteer smartphone users through social media to
collect transport data is a novel methodological approach showing promise.
Confirming previous research (for example, Bohte and Maat, 2009), the smartphone
approach allows for extensive data collection in a very resource efficient manner. The
approach has the advantage of providing an instant snapshot of trucking positions,
without having to rely on historical reporting that might be difficult or even impossible
to validate. Considering the lack of data highlighted by previous researchers (for
example, McKinnon and Leonardi, 2009), the volunteer-based approach has the
potential to become a complement to existing data sources, in particular since many
of the new Member States have not yet established statistical routines for reporting
their operations.
11
Currently, the methodological approach itself generates public interest. This is
likely to change, if the approach becomes widespread. Until then, researchers and
policy makers using it can benefit from effective data collection and real-time
dissemination.
The study reported in this paper, confirms previous research by Klaus et al.
(2008), that drivers have a huge potential to contribute to data collection. Though the
volunteers display some bias in observing foreign trucks from new Member States
rather than EU15 trucks, over 96% of the observations were correct. This indicates
that using validation datasets to complement volunteer observation datasets is
important.
Finally, it should be noted again that this paper, to the authors’ best knowledge,
represents a first modest contribution to a novel methodological approach. As
outlined in the paper, much work remains to take full advantage of the large
opportunities offered by the proposed methodological approach.
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