Real-time passenger location
estimation using CDRs and train
objects generated from crowdsourced
timetables
Takuya Kanno

Yoshihide Sekimoto

Abstract

The University of Tokyo,

The University of Tokyo,

Komaba, Tokyo 153-8508,

Komaba, Tokyo 153-8508,

JAPANJAPANkanno@csis.u-tokyo.ac.jp

sekimoto@iis.u-tokyo.ac.jp

Hiroshi Kanasugi

Ryosuke Shibasaki

The University of Tokyo,

The University of Tokyo,

Komaba, Tokyo 153-8508

Komaba, Tokyo 153-8508,

JAPANJAPANyok@iis.u-tokyo.ac.jp

shiba@csis.u-tokyo.ac.jp

This paper shows that cell phone Call Detail Records(CDRs) and train objects in GIS, generated fromcrowdsourced timetable information, can be used toestimate the train on which a specific passenger isriding. Passenger train ride likelihood calculation is first
conducted using CDRs and targeted train objects. Then,the results obtained are compared with the trains
specified in GPS logs. Empirical results obtained containboth good cases such as trains being estimated from
CDRs and train objects corresponding to the train
specified in GPS logs and bad cases such as theestimated train not corresponding to the actual train on
which the passenger is riding. Therefore, analysis ofeach case is also carried out. The application of train
objects and cell phone CDRs can facilitate analysis of
how passengers ride trains, identification of origindestination rail routes, and congestion/delay reduction.

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Copyright is held by the owner/author(s).
Ubicomp/ISWC'15 Adjunct , September 07-11, 2015, Osaka, Japan
ACM 978-1-4503-3575-1/15/09.
http://dx.doi.org/10.1145/2800835.2800984

Author Keywords
Call Detail Records; train objects; crowdsourcing train
diagrams; likelihood estimation

ACM Classification Keywords
H.2.8. DATABASE MANAGEMENT: Database
Applications: Spatial databases and GIS

1. Introduction
A variety of social issues has been promoted andresolved by analyzing Call Detail Records (CDRs) data,which are now widely available as a result of thepervasiveness of mobile phones.
Because CDR data are obtained continuously, muchanalysis related with movement pattern has beenperformed. UN Global Pulse utilizes such data for social
issues, such as disaster management, spread ofinfectious disease, socio-economics, and transportation[10]. In addition, CDR data have been used for
behavioral pattern predictions [8] [9], extraction ofpeople’s stays [7], route estimation, and road traffic
volume estimation [4]. Recently, origin–destination
trips have also been detected using CDR [2].
In addition, CDR's temporal density has increased as aresult of the pervasiveness of smartphones in recenttimes.
Another type of cell phone data called location area
update (LAU) can also be utilized. LAU shows which cell
tower area a user is in so that calls can be transferredto that user’s cell phone. LAU has been utilized tomeasure congestion, delay, and passenger flows in theParis underground transit system using undergroundcell towers [1].
However, because of the low spatiotemporal resolutionof CDR the amount of in-depth research conducted into

behavioral patterns at detailed levels, such as studies
using GPS is low [3].
Ishizuka et al. [5] detected train commuters using
CDRs and GIS information. This paper builds on theirresearch to determine which train a passenger is ridingfrom train timetables. This makes it possible tomeasure the railway demand at the time of commuting
in large cities such as Tokyo, where quite a lot ofpeople commute by train. The results obtained may
help to reduce congestion.
In concrete terms, we estimate the maximum likelihoodof real-time passenger train rides from crowdsourcing
type train objects and smartphone CDRs to obtaincomplete information about moving data such as routes
and trains ridden.
The remainder of this paper is organized as follows.
Section 2 describes how train objects are generated
from timetable information created via crowdsourcing.
Section 3 outlines the proposed method for detecting
the movement of commuters from CDRs. Section 4
describes the likelihood method of calculating train
objects for the commuting CDRs. Section 5 outlines theexperiments conducted and analyzes the resultsobtained.

2. Train objects from crowdsourcing typetimetable
In this section, the method by which train objects aregenerated from timetables created via crowdsourcing isexplained.
A crowdsourcing type timetable database called “Eki.locky”
already exists in Japan (“Eki” is the Japanese word forCellphone
data:
CallDetail Records (CDRs)
Call Detail Records (CDRs)
are large-scale infrastructure
data associated with theoperations
ofmobile
networks.
CDRs
typically
compriseinformationon
whencall/data
telecommunication
wasconducted and which cell
tower was used. CDRs havebeen
attracting
increasedattention recently because noadditional burden is placed onusers to acquire information,in contrast to GPS logs.

“Station.”) Eki.Locky is an iOS/Android train timetableapplication that displays the time for the departure of thenext train. Timetable information is created via a registerand is shared with all users. If the required station
timetable has not yet been registered, anyone can create
and upload the timetable file. Further, all users are able toaccess the timetable provided in the application. This isthe method used to obtain the timetable data. Thetimetable information covers 91% of the stations in Japan
(9,988 stations). The rest is mainly expired timetables inlocal areas. Timetable data are stored in NextTrain format.
This
service’s
web
page
URL
is
http://eki.locky.jp/site/top.
Timetables
for
eachtrain
aregenerated
byreconstructing the departure time of the same train fromthe timetable information of each station. The station
location is geocoded because the timetable does notinclude the station location on GIS, only the station name.
The latitude and longitude of trains are interpolated every
minute between the stations using the shape data of theroutes, based on the assumption that the train is moving
at a constant speed.

Figure 1: Timetables for each train are generated byreconstructing the departure time of the same train from thetimetable information of each station.

In this way, the data [time every minute, latitude,longitude] for each train object are generated from thetimetable.

3. Call detail records
CDR is explained in the sidebar on the left. In addition,
the details of CDR surveys used in this study are givenin the sidebar on the left of the ensuing page. The
temporal density using CDRs is especially high becauseof smartphone telecommunication.
Detecting the trajectory of the commuting CDRs:

Figure 2: Train objects identified at a particular moment. They
are generated by interpolating the timetable of each train.
Each color represents a different railway. Approximately 20
train objects are running at the same time on each route.

CDRs survey used in this study
In this study, only the CDRs ofsurvey subjects who consented
were used. The information includedin the CDR used in this paper was[user ID, communication start dateand time, latitude and longitude of
the start of communication basestations, communication end date
and time of the communication end,
and base station latitude andlongitude]. Distinct information that
could be used to identify the calland communication was notincluded.
•Survey period: February 1, 2013 toMarch 1, 2013.
•Acquired data: Both CDS and GPSlogs.
•Number of subjects: 48 persons•Conditions of subjects: Persons
using 3G Android smartphones andcommuting by car or train.
•Average communication intervalfrom CDRs: 2.9 min•Number of cell towers in the CDRs:
19,528 (entire country).

Figure 3: The Tokyo metropolitan railway network (blue lines) and mobile phone base station (orange triangles). Bold blue railways arethe lines targeted in this study. Please note that these base stations are included in only this survey’s CDRs, not all base stations are inthis area.

The stay point of CDRs is detected by clustering thespace-time information, as proposed by Kanasugi et al.
[6]. To set the trip during the stay, the procedure is asfollows. Extract a trip that is moving at 8:00 as thecommuting trip. The point sequence of CDRs includedin this trip is then set to the commuting CDRs
trajectory.

4. Estimation method using the riding oftrain object from CDRs
In this section, the methods used to obtain the most
likely train object as an estimation result based on thelikelihood function is described. The likelihood function
L consists of the following parameters: individual
location histories {PC (t):t = t0,...,tn}, where PC (t)represents a two-dimensional spatial point at time t in
CDR data, length of the CDR trajectory NC, CDR
location PC, and train object location PT.
By assuming that the CDR is recorded when a user iswithin radius R from a cell tower, the likelihood function
L includes the distance between train objects and thecell tower of the CDR at the point when the CDR isobserved. L is defined asNcL=∑ log f (PC (t i ),PT (t i ))5. Experimental evaluation(1)

5-1. Estimation target

i=1" 1$f (PC (i), PT ( j)) = # π R 2$ 0%

dist(PC (i), PT ( j)) ≤ Rotherwise

Figure 4: At the point when a CDR is observed, calculate thedistance between the train objects and the cell tower of theCDR. The probability of cell phone existence is defined as a
uniform distribution within concentric circles from the celltower. Then, likelihood calculation of the passenger riding thetrain is conducted. For the sensitivity analysis, calculate thelikelihood by changing the radius R (2 km, 3 km, 4 km).

(2)

For sensitivity analysis, calculate the likelihood bychanging the radius R (2 km, 3 km, 4 km).

The target railway lines were those on which the mostcommuting CDRs were detected. These lines were theJoban line, Joban rapid line, Joban local train, Hokusoline, Keisei line, Chiyoda line, Tobu Isezaki line, andTsukuba express line. These routes are directed toward
the center of Tokyo from its north-eastern end. Further,a total of 685 train objects are on the selected railway

routes. Four persons met the criteria of using one of
the targeted eight railways and commuting CDR
trajectory. To compare with the actual train being
ridden, we imposed a criterion that they should beincluded in the GPS logs of the trip. As a result, thenumber of persons was reduced to three.
5-2. Estimation result
We succeeded in identifying the actual train on which
the persons were riding on some trips.
5-2-1. Corresponding results
In this section, successful estimation results are shown.
Fig. 5 shows the likelihood L of each radius. There were
85 CDR points.

Figure 5: Top 10 likelihood values for the train object. A large
difference in the likelihood values exists in the top five trainobjects. The difference between the remaining objects is small.

Figure 6: Orange triangles show commuting trajectory of CDR.
Light blue squares and deep blue lines show the location per
minute and trajectory of the train most likely being ridden. Thepink squares and deep red line show the second most likely
train’s locations and trajectory in experiment A. In thenorthern area, the highest likelihood train’s trajectory and thetrajectory of CDR exists, but second most likely train’s
trajectory does not exist. The presence of a sequence of points
in this area increases the likelihood value of the maximum
likelihood estimated train.

Table 1: Top three highest likelihood train objects within eachradius. The actual train being ridden, as specified by GPS logs,
is TI54. In this case, the maximum likelihood estimation iscorrect for each radius

Train object ID according to likelihood
rank to observed passenger’s CDRs
Radiusinformula(2)Highestlikelihood

Secondhighestlikelihood

Thirdhighestlikelihood

2 km

TI54

TI85

TI96

3 km

TI54

TI85

TI81

4 km

TI54

TI81

TI85

The object ID of the train being ridden as identified
from the GPS log is TI54. This is the same as theestimated train object at all radiuses. The distribution
point of the CDR is over a wide range of approximately
50 km and may uniformly increase the likelihood value.
The CDR trajectory appears to have sufficient points,
the distance for which the CDRs exist in the section
appears to be sufficiently long, and the distribution
appears to be sufficiently uniform.
5-2-2. Result with insufficient likelihood
In this section, an estimation result that is correct only
at R = 4 km is presented. Fig. 7 shows the likelihood Lof each radius. There are 24 CDRs points.
The object ID of the train being ridden is identified fromthe GPS log as JF27. This is the same as the estimatedtrain object at only a radius of 4 km. The distribution
point of the CDR is over a wide range of approximately
30 km. In the cases where R = 2 km and 3 km, it could
be considered that the accuracy decreased because of

the assumption that the train object is moving atconstant speed did not match the actual movement. In
addition, there is the possibility that the train wasdelayed and may not match the train object.

Figure 7: Top 10 likelihood values for the train object. Themaximum likelihood in the case of 4 km is significantly higher
than that of the case for two positions or less and radius is 2
km or 3 km.

Table 2: Top three highest likelihood train objects
within each radius. The actual train being ridden, asspecified by GPS logs, is JF27.
Train object ID according to likelihood
rank to observed passenger’s CDRs
Radiusinformula(2)Highestlikelihood

Secondhighest
likelihood

Thirdhighest
likelihood

2 km

JKs20

JKe40

JKe36

3 km

JKs20

JKe36

JF27

4 km

JF27

JKs20

JKe36

Figure 8: Estimation result is correct at only R = 4 km. The distribution point of the CDR is over a wide range ofapproximately 30 km. In the cases where R = 2 km and 3 km, we believe that the accuracy decreased because theassumption that the train object is at constant speed did not match the actual movement. In addition, there is thepossibility that train was delayed and may not match the train object.

purpose and time of day inferred from mobile
phone data. Transportation Research Part C:
Emerging
Technologies.
http://dx.doi.org/10.1016/j.trc.2015.02.018

6. Conclusion and future work
6-1. Conclusion
Real-time estimation of the train being ridden by apassenger using crowdsourcing type train object andgeneral CDRs was successful in cases where the CDRs
had a wide ranging distribution and uniform distribution
made accurate estimation possible.
Under the assumption of constant probability ofoccurrence of communication within the circle, theresult of the likelihood calculation changed according to
the radius distance.

3.

W. Apichon. 2013. A study on human activity
analysis with large scale GPS data of mobile phoneusing cloud computing platform. Ph.D. Dissertation.
The University of Tokyo, Tokyo, Japan.

4.

Y. Hasegawa, Y. Sekimoto, T. Kashiyama, H.
Kanasugi. 2014. Transportation Melting Pot Dhaka:
Road-link based traffic volume estimation fromsparse CDR data. The 1st International Conference
on IoT in Urban Space (Urb-IoT 2014), Rome.

5.

H. Ishizuka, N. Kobayashi, S. Muramatsu, C. Ono.
2015. Detecting train commuters using CDRs andGIS
information.
NetMob2015
bookofabstracts::posters, pp.39-41, 2015

6.

H. Kanasugi, Y. Sekimoto, M. Kurokawa, T.
Watanabe, S. Muramatsu, R. Shibasaki. 2013.
Spatiotemporal route estimation consistent withhuman mobility using cellular network data. In
Proceedings of PerMoby2013, 267-272.

7.

M. Kurokawa, T. Watanabe, S. Muramatsu, H.
Kanasugi, Y. Sekimoto, R.
Shibasaki. 2013.
Extracting people’s stays from cellular network data.
NetMob2013, 2013.05.

8.

M. Ozer, I. Keles, I. H. Toroslu, P. Karagoz. 2013.
Predicting the change of location of mobile phoneusers. MobiGIS2013, pp. 43-50, 2013.

9.

M. Ozer, I… Keles, I. H. Toroslu, P. Karagoz, S.
Ergut. 2014. Predicting the next location changeand time of change for mobile phone users.
MobiGIS2014, pp. 51-59, 2014.

6-2. Future work
The method used to detect the commuting CDRs
trajectory has to be improved because only a few trips
were detected. More extensive evaluation is also
required. In addition, improvement of estimation
accuracy is required to deal with train delays andpassenger transfers.

Acknowledgements
We are grateful for the use of joint research data withKDDI R&D LABS, Eki.locky’s train timetable, National
Land Numerical Information’s railway data (MLIT), andOpen Street Map.

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