こうえいフォーラム第 31 号 / 2023.6

EVALUATING TRIP CHARACTERISTICS FROM CALL DETAIL
RECORD USING MACHINE LEARNING
携帯電話記録（CDR）を活用した機械学習による人流特性の把握

ダス ルベル*・中村 茂**・ジャイン***Rubel DAS, Shigeru NAKAMURA and KYAING

急速な都市化は、都市交通インフラへの過剰な負担を招いている。携帯電話事業者が保持するCall Detail Record (CDR)（通話時刻、通話時間、通話に使用した基地局などが含まれる）は、このインフラの使用状況を把握するものとして期待されているビッグデータである。近年、そこから有用な情報を抽出する研究が進んでいるが、トリップチェーンを考察した研究は未だ少ない。トリップチェーンとは人の活動軌跡であり、トリップの開始地点とその後の経由地点及び目的地の時系列である。本研究では CDR を用いて、機械学習の手法として用いられるマルコフ連鎖モデルによりトリップチェーンを解析するモデルを開発した。対象地域であるヤンゴンを 17 の地域に区分し地域間の移動を示すトリップチェーンを生成したところ、開発したモデルは実態を良く再現出来ることが分かった。

Keywords ： CDR、隠れマルコフモデル、トリップ特性、ミャンマー、データマイニング

1.

INTRODUCTION:

Personal Travel Surveys (PTS). HTS/PTSs were generally

The fast development of cities today makes it difficult forundertaken by employing retrospective or diary surveys,

urban planners to handle all the emerging demands. Forininstance, the population of a town can be highly dynamic.

information in face-to-face/telephone interviews or record

The island of Manhattan, in New York City, is home totheir travel information on paper- or internet-based

about 1.6 million inhabitants. This number, though, may

questionnaires. For this reason, respondents need toroughly double during any workday. Such conditions haveremember the details of trips like start and end times,

been causing traffic congestion to grow increasingly at

origin and destination, and travel modes. Consequently,

temporal and spatial scales. Investing in transportation

there is a probability of underreporting trips that have ainfrastructure seems to be an obvious solution to easeshort distance or duration. To overcome this issue, in thecongestion. However, these investments did not evidently

late 1990s transportation planning researchers drew on

improve the performance of transportation networks, as

Global Positioning System (GPS) to carry out the surveys

expected, because new traffic facilities resulted in

and it rapidly became quite popular. However, a travelincreased traffic demand. Since the budget limitation forsurvey with GPS devices has the following limitations: (1)infrastructure

issue,

purchasing the devices can be expensive, (2) the collected

promoting low-cost technology like public transportation

data is usually incomplete because respondents often(e.g., buses) has become essential. Not to mention thatforget to take with them the devices and (3) devices must

public transportation is comparatively more environment-

be distributed and then recovered for as long as afriendly and contributes to the achievement of sustainable

respondent participates in the survey, and (4) the sample

development goals (SDG).

size is limited by the number of available GPS devices.

development

isaconcerningwhich

respondents

eitherreporttheir

travelBus routing and operational attributes like frequency

Since this survey method is expensive and challenging toare planned based on Household Travel Surveys (HTS) or

implement, the data that was already being collected byincreasingly more equipped mobile phones drew theattention of traffic researchers. During the last decades,*
コンサルティング事業統括本部 中央研究所 先端研究センター** コンサルティング事業統括本部 中央研究所*** Yangon Technological University, Department of civil eng.

there has been a surge in the amount of location data that1

こうえいフォーラム第 31 号 / 2023.6

has been generated by mobile big data (MBD). There are

anticipating the arrival. A variety of machine learning

three main reasons for the surge. First, recent hardware

approaches (Kapicioglu, 2013) are used in existingdevelopment made the positional devices smaller, require

literature, including decision trees, Bayesian networks,

less electric power, and consequently easier to embed innaïve Bayesian classifiers, fuzzy logic, hierarchicalmobile phones. Second, The US government’s “Enhanced

conditional random fields, discriminant function analysis,911” requirement has mandated US wireless carriers toand support vector machine. Machine learning creates aprovide accurate location estimates of emergency calls,

hypothesis based on a given algorithm with a training

accelerating the adoption of positioning technologies bydataset, and this hypothesis is set aside to be applied tomobile phone manufacturers. Third, widespread adoption

future data. For example, a person can be detected in aof positioning hardware has catalyzed the development ofplace that was not a real destination but rather a stop in alocation-aware software. For these reasons, it is notrip. Therefore, an algorithm for identifying significant

surprise how much nowadays transportation planninglocations (hereafter referred to as the point of interest,

depends on MBD.

“POI”) is essential. In this study, we propose a new hidden

The application of Call Detail Record (CDR) for mobility

Markov model (HMM) approach. As a classifier to copeplanning is not a new practice since mobile positional datawith a sequence of data, an HMM is an appropriate

contains spatiotemporal stamps and can be generated

approach for detecting trip chains. We utilized the same

without additional interventions. The challenge is how to

Myanmar CDR data as Lwin et al. (2018), who estimatedhandle such big data or how to understand people’s

the hourly link population, utilized. The raw CDR data

behavior from this data. Another ongoing challenge is how

was collected by the largest cell operator in Yangon. We

to modify the traditional traffic planning models to makereceived the raw data from Yangon Technologicalthem able to handle big data. Asakura and Hato (2004)

University which is our joint research partner related toutilized mobile positional data for movement tracking and

Big Data analysis.

stay extraction while Bar-Gera (2007) estimated traffic

Our proposed HMM model can predict the probability of

speed. Candia et al. (2008) utilized CDR data to identify

choosing the next POI given that the current location isthe detection of emergencies. Gonzalez et al. (2008)

known. Here, we like to emphasize that in a dimension of

explored individual travel patterns from mobile positionaltransportation

data. The results of these studies showed that people havemeaningless to estimate the exact trip purpose of a specifica high degree of temporal and spatial regularity; everyone

individual. Rather, it is sufficient to know the probabilityischaracterized by atraveldistanceplanning

andpolicymaking,itiswith time-that a traveler has a certain purpose under certain

independent characteristics and a significant probability

conditions. Moreover, people go from one place to anotherof return to a few highly frequented locations. Calabrese

before returning to their origin. The probability could thenet al. (2013) validated the applicability of mobile phone

be used to synthesize the sequence of activities for a trip

data in transportation planning. They compared mobile

chain. This model outcome could be a good input for thephone data with vehicle odometer readings. Toole et al.

activity-based demand-forecasting model (Bowman and(2015) estimated the travel demand from CDR data.

Ben-Akiva, 2001).

Alexander et al. (2015) utilized mobile positional data for

Here we propose an HMM with millions of geotagged

estimating origin-destination trips by purpose and time ofdata points as an input that performs many aspects of trip

day. The above-mentioned studies, however, did notcharacteristics. We begin by outlining the system

consider trip chains explicitly. The exploration of trip

architecture in Section 2. In section 3 we explain our

chain is essential for various transportation planning formethods of extracting, cleaning, and clustering method of

example bus route planning and bus frequency.

data. We discuss the results of our model in section 4. We

MBD enables the identification of the activity chain ofsummarize our study in section 5.

people. Most of these movements are carried out in known

2.

places such as the home, the workplace, a relative’s house,

HIDDEN MARKOV MODEL (HMM):

a favorite cinema, or a shopping mall. Hence, patterns of

Hidden Markov models (HMMs), named after thethese movements can be studied, and a prediction system

Russian mathematician Andrey Andreyevich Markov, who

can be designed by storing all the destination places anddeveloped
2

much

of

relevant

statisticaltheory,isこうえいフォーラム第 31 号 / 2023.6

introduced and studied in the early 1970s. Recently, HMM

from where trips originate depending on the time of thehas become popular as a tool in machine learning. Theseday.

models have been used successfully for different fields like• S

speech recognition, character recognition, and mobile

contains either a single index (i.e., 𝑖 ) or morecommunication techniques. HMMs are statistical models

than one index (i.e, ij etc). Here 𝑖, 𝑎𝑛𝑑 𝑗 are theto capture hidden information from observable sequential

members of the set of N distinct TAZ in thesymbols. The CDR datasets comprise a long sequence ofmodel. Here, TAZs are the potential locations of

directional measurements that are observed by fixeda user.

location towers when the user makes calls or accesses a• It is a logical supposition that a person may

mobile network. The trip-chain system being modeled isstay in the same location after making a call.

assumed to be a Markov process with unknown

Here, an individual’s origin is presented by 𝑠𝑖 .

parameters. Han and Sohn (2016) proposed a hidden

The person might decide one from two situations:Markov model for exploring trip-chain by using a smart(a) the person stay in same cluster and (b) thecard.

person moved to a different cluster. For the

Figure 1 shows the structure of HMM where𝑖, 𝑗, 𝑎𝑛𝑑 𝑘 are traffic analysis zones (TAZs).

represents the state of individuals. It

latter, the person may select a TAZ with a given
probability. If a TAZ is not accessible to theperson, the probability of that TAZ is zero.
• The initial state distribution is 𝜋 = {𝜋}𝑁
𝑖=0 . In

our model, 𝜋𝑖 denotes the probability that theindividual

at location 𝑠𝑖 ,when atrip isbeginning. The most probable state is usually thehome in the morning and the office in theevening.
• The transition model is the core part of themodel.

The

state

transition

probability

distribution is 𝐴 = {𝑎𝑖𝑗 }. The matrix contains theFig. 1 Architecture of HMM

transition probabilities between each pair ofstates. The transition probabilities are computedfrom CDR data. However, these probabilities canalso be presented through spatial properties of
the

TAZs

environment(i.e.,

numberofrestaurants, distance, accessibility) that create
attractions for people. For example, people will
prefer the nearest shopping mall. We adopt the
Gibbs distribution (Tripuraneni et al., 2015) as
the transition model because it provides a means
to incorporate spatial features without making
Fig. 2 Flowchart of trip pattern analysis

any independent assumptions and generalizes
the discrete version of simpler random walk

Here, 𝑎𝑖𝑗 represents the probability of moving from TAZ

models.

𝑖 to TAZ 𝑗. Note that 𝑎𝑗𝑖 does not need to be equal to 𝑎𝑖𝑗 .
Besides, 𝑎𝑖𝑖 represents the probability that the person will3.

not change the TAZ. The elements that formally define an

DATA MINING:
Telecommunicationcompany

collects

CDR

record

HMM are (𝑆, 𝜋, 𝐴) which must be adapted to our purpose.

mainly for billing purpose. To utilize such big data, weFigure 2 illustrates the linear formation of Figure 1. The

need to process it. Figure 3 shows the framework for data

model assigns each people to one of all possible locations

clustering and filtering of the CDR database.

3

こうえいフォーラム第 31 号 / 2023.6(1) Filtering: We have utilized one week of data of CDR.

of closely positioned base stations is that the

Each day of the database contains about 10,970,000

connection of a mobile phone may be switched fromcall records before applying any filter technique.

one base station to another depending on the signal

These large quantity records were produced bystrength even though the mobile phone user may not

1,517,300 users, known as “PID”. Many of them have

travelaccessed telecommunication towers only for a single

telecommunication connectivity feature makes ittime in a whole day. Since those records cannot bedifficult for computing the geographical location of theutilized to predict trips, we have removed thoseuser. Moreover, such base station switching createsrecords. After removing those single recorded PIDs,

fictitious trips. To overcome such trips, we havethe useful unique users become 1,216,696 users. With

clustered closely situated base stations. Here, we havethis step, 19.8% of users were removed from theutilized the k-means clustering algorithm. After doingoriginal database. Further filtering technique wasseveral trial-and-error methods, we have selected 18

implemented based on model types (i.e.: morning

cluster points as shown in Figure 4. The maximum

HMM and evening HMM). Office going people move tocluster diameter is 2.5 km. In our trip analysis, we

office from home in the morning while from office toutilize those cluster points as the traffic analysis zone

home in the evening. Therefore, people’s flows are in(TAZ). The maximum and minimum values of the

opposite directions during the morning and evening

cluster id are 17 and 0 respectively. Figure 4 shows

periods. Therefore, we have analyzed the HMM forthe original base station locations and the k-means

two filtered data: one for morning HMM (6 am to 11.59

center and label of each cluster point. The colors ofam) and another for evening HMM (2 pm to 7 pm). It

base stations represent the membership of cluster

allows us to observe the changes in the trip pattern ofzones.

asignificant

distance.

Now,

this

the day the time. Total unique users are 882,363 in
the morning filtered data, while total unique users are
988,144 in the evening filtered data. For each model
type, we have utilized 75% data for model building
and the remaining data for model validation.

Fig. 3 Filtering and clustering of data
(2) Clustering the access points: The CDR data has 32

Fig. 4 clustering of base stations

base stations. Some base stations serve very large
areas where telecommunication network barriers like(3) Converting access time: The CDR database has a basetall buildings are quite a few. Some base stations areclosely

positioned.

Those

placeshave

station access time format is “20151201143243”. Herehigherthe format structure is YYYYMMDDHHMMSS which

population density or business activities. One featureis “year: month: day: hour: minute: second”. We have4

こうえいフォーラム第 31 号 / 2023.6(4)

converted the “ hour : minute: second” portion into a

residential zones (TAZ 13,5) have a lower probability

24-h seconds format where 23:59:59 becomes 86399.

of behaving as a trip origin. The implication of theseThis time format conversion helps to do sorting or

findings is that TAZ 15,0 and 3 will create more trips

filtering of the whole database based on the accessin the evening. On the hand, TAZ 13 and 5 create atime. Thus, the identification of the sequence ofsmaller number of trips in the evening period. Somegeographic locations of a user becomes easier.

zones (i.e. TAZ 1, 10, and 7) do not show a significant

OD creator and Trip chain creator: After filtering anddifference in the evening and morning periods. Those

converting access time, CDR data were rearranged forzones can be considered mixed zone.

creating origin and destination (OD). In addition,intermediate points in between origin and destitation
were also computed. Thus, trip chains were created.
Finally, OD and trip chain were combined for making
HMM for two distincs periods.

4.

RESULTS:First, we validated our model by using testing data set.

Althoughtheproposedmethodology

adoptedanunsupervised machine learning tool that requires nocalibration process based on labeled data, the result should
be validated in a qualitative manner. We validated ourmodel by comparing the number of potential trips from

Fig. 6 Comparison of trip generation in the pm model

each zone. Though am model (Figure 5) shows variations,both am, and pm models (Figures 5 and 6) illustrate asimilar trend to the original data. we can say that HMM ofam and pm could predict trip origin with reasonableconfidence.

Fig. 7 Probability of making first call in certain TAZ(2) Probability of staying in the same location: Figure 8
shows the probability of a person staying in the same
TAZ within the time frame. The minimum value
among all zones in the am model is 0.48 while in theFig. 5 Comparison of trip generation in the am modelpm is 0.35. Here, we like to highlight the properties of(1) Probability of origin zone: The probability of the first

TAZ 0 which is in the central business district (CBD)

call in the morning and evening periods is presented

where people create many calls. The 𝑎00 is 0.57 in thein Figure 7. People may make the first call from home

am model and 0.4 in the pm model. People making thebefore going to work or from a place after reaching

first call in TAZ 0 have a higher probability to stay inthere. Those locations were considered the origin of

the same zone in am model than in the pm model.

the trip. As expected, commercial regions (TAZ 15,0,3)
in the pm period have a higher probability and5こうえいフォーラム第 31 号 / 2023.6

and standard deviation of the number of calls in themorning period. The calls in the evening period do nothave any obvious trend. For example, TAZ 16 has ahigher mean of calls and a relatively lower standard
deviation. It represents that people in TAZ 16 do nothave a large variety of characteristics and it can be aresidential zone. However, we can say confidently that
TAZ 15 is a CBD region. Another TAZs has no definite
features. Therefore, we can conclude that Yangon is acity of mixed zones.
Fig. 8 aii for am and pm HMM mode

Fig. 10 Standard deviation of calls in each zone
(4) Trip distribution: The distribution graphs show that

Fig. 9 Mean number of calls in each zone

people often prefer closer TAZ. Figures 11 and 12(3) Differentiating CBD zones and non-CBD zones: The

illustrate the trip distribution from three TAZ 14, 16,

central business district (CBD) is the commercial andand 17. These three zones were selected randomly.

business center of a city. The needs and features of a

The top two destinations from TAZ 14 are TAZ 1 and

CBD are quite different from other zones. For

9. The top two destinations for trip generator TAZ 16,

example, CBD areas need higher car parking capacity.

are TAZ 1 and 10. TAZ 17 shows a bit different result.

Therefore, identifying CBD and non-CBD zones in a

The top two destinations are TAZ 7 and 1 in thecity can be beneficial. The average number of calls andmorning while TAZ 2 and 7 in the evening. Somestandard deviation of the number of calls in each TAZ

people return to start TAZ after visiting different TAZ.

for each distinct period are presented in Figure 9 andFor example, 20.2% of trips return to TAZ 14 after

10. We can see that the mean call is larger in TAZ

visiting different TAZ within the same period.

0,4,12, and 15 in the morning period. These zones also

The trip distribution shows a different pattern in thehave a higher standard deviation meaning people

morning and evening (comparing Figures 11 and 12).

gather in those zones has higher variation considering

17% of trips originated from TAZ 14 go to TAZ 1 in am

the necessity of making calls. By considering thewhile 26% of trips go in pm. 6% of trips during thegeographic features of Yangon city, we can see thatmorning period go to TAZ 10 but no trips in the pm go

TAZ 0, 4, 12, and 15 are situated in CBD areas.

to TAZ 10. A significant variation can be observed in

Therefore, we can conclude that the CBD area has a

the destination TAZ 2 from TAZ 17. In the morning

higher mean and standard deviation of calls in the

TAZ 2 attracts only 10% of trips while 27% of trips gomorning period. In other words, we can distinguish

to TAZ 2 during the evening. TAZ 7 and 11 are

CBD areas and non-CBD areas by observing the mean
6

こうえいフォーラム第 31 号 / 2023.6

geographically closer to TAZ 17 and do not havesignificant variations in am and pm models.

Fig. 11 Trip distribution for each origin in am modelFig. 14 Trip distribution from TAZ 14 in pm

Fig. 12 Trip distribution from each origin in pm modelFig. 15 Trip distribution from TAZ 17 in am

Fig. 16 Trip distribution from TAZ 17 in pm

Fig. 13 Trip distribution from TAZ 14 in am

7こうえいフォーラム第 31 号 / 2023.6

TAZ 4 reach 16 through TAZ 10. Many trips do notcreate a trip chain in a particular period. As our study
focused on two distinct periods (i.e., am and pm), we
ignored the trip-chain in a day. One finding is that it
is important to consider a longer period for exploring
trip chain properties.
Table 1 Trip chain from TAZ 4 in different periods
Model amModel pm
Trip chain% triptrip chain
%trip
4_10
13.95
4_10
13.70
4_0
12.65
4_0
8.80
4_16
6.90
4_10_4
8.05
4_1
5.90
4_12
3.75
4_12
4.90
4_0_4
3.30
4_10_44.60
4_0_15
2.70
4_15
4.45
4_10_16
2.55
4_0_4
4.15
4_15
2.50
4_9
2.90
4_10_1
2.40

Fig. 17 all trips generated from TAZ14. Red circles
points attract more trips in evening (the unit ofaxes are % trip)*Trip percentage less than 2% are ignored

5.

CONCLUSIONS:Transportation planning needs a clear picture ofpeople’s movement within cities. Though traditional
survey methods can capture many attributes, they fail tocollect unbiased data. Moreover, the number of samples
Fig.18collected through the traditional survey is limited

all trips generated from TAZ17. Red circled
points have larger variation (the unit of axes
are % trip)

particularly due to budget limitations. The rise of mobile
positional data has generated a wealth of new data on
human mobility, but new tools must be developed to

Figures 13~16 show the trip distribution on the GIS

integrate these data and insights into traditional

map. Those figures show clearly that geographicaltransportation modeling approaches. The strength of thisdistance or accessibility has a clear influence on

big data is that data are automatically generated, and themaking trips.

trip information is real.

Figure 17 shows that 3 out of 18 TAZs attract more

Here, we have proposed a model for evaluating trip

trips from TAZ 14 in the evening period. Note that the

chain characteristics in cities like Yangon, Myanmar,

dotted line in the Figure represents the 450 line.

where updated survey data is not available. The model was

Therefore, the point above the dotted line attracted

validated by comparing with trip origins from our testing

more trips in the evening. The red circles recognize thedata. The ratio of trips from each zone was predicted

points which attract more trips in the evening. A large

correctly by our proposed model. Zonal variations werevariation of trip distribution can be found for TAZ 17

observed in whether people will go to different zones or(Figure 18). The red circles in the Figures show thestay in the same zones. Some zones show variation for the

points where large variations could be observed. The

time of the day. Depending on the telecommunication

red

tower access properties, we have classified the zones incircles

recognizethepointshavinglarge

variations.

Yangon. It shows that Yangon consists of mixed zones. In(5) Trip chain: we explored the trip chain from CDR data.

other words, residential zones are not only for residing but

For brevity, only trip chains generated from TAZ 4also have commercial activities. While thinking of smart

were provided in table 1. In the morning period, 2.4%

city planning, the outcomes of zonal characteristics shouldof trips from TAZ 4 reach TAZ 1 through TAZ 10.

be considered.

Similarly, in the evening period, 2.55% of trips from8

こうえいフォーラム第 31 号 / 2023.6

The massive CDR data allow us to explore the trip

CDR. ISPRS-International Journal of Geo-information, 7,

distributions within the city. We can see the clear

449.

influence of distance or accessibility. Therefore, trips10) Gonzalez, M., Hidalgo, C., Barabasi, A. (2008) Understanding

generated from zone 17 terminate mostly in nearby zones.

individual human mobility patterns. Nature, vol. 453, no.

Besides, we have found that many zones have a higher

7196, pp. 779– 782.

probability to produce trips in the morning than in the11) Tripuraneni,N., Gu,S., Ge, H., Ghahramani, Z. (2015)

evening.

Particle Gibbs for Infinite Hidden Markov Models, In NIPS,

We have shown that the ratio of trips for eachpages 2386–2394. Curran Associates, Inc., 2015.

destination varies in the different time frames. Besides,

12) Toole, J.L., Colak S., Sturt, B., Alexander, L.P., Evsukoff, A.,accessibility in terms of distance is a significant feature in

González, M.C.

trip distribution.

demand estimation using big data resources, Transportation
Research Part C

REFERENCE:
1)

Asakura, Y. Hato, E. (2004) Tracking survey for individual
travel behaviour using mobile communication instruments,
Transportation Research Part C: Emerging Technologies Vol
12, Issues 3-4: 273-291

2)Alexander, L., Jiang, S., Murga, M., and González, M. C.
(2015) Origin–destination trips by purpose and time of day
inferred from mobile phone data. Transportation ResearchPart C: Emerging Technologies, 58, Part B:240–250.

3)

Bar-Gera, H. (2007) Evaluation of a cellular phone-based
system for measurements of traffic speeds and travel times:
A case study from Israel, Transportation Research Part C:
Emerging Technologies Vol 15, Issues 6: 380-391.

4)Bowman, J. L. and Ben-Akiva, M. E. (2001) Activity-based
disaggregate travel demand model system with activity
schedules, Transportation Research Part A: Policy andPractice, vol. 35(1), pages 1-28.

5)

Calabrese F, Diao M, Di Lorenzo G, Ferreira, J. and Ratti, C.
(2013) Understanding individual mobility patterns fromurban sensing data: A mobile phone trace example.
Transportation Research Part C: Emerging Technologies 26:
301–313.

6)

Candia, J., González, M.C., Wang, P., Schoenhar, T.,Madey,G., and Barabási, A.L. (2008) Uncovering individual andcollective human dynamics from mobile phone records,
Journal of Physics A: Mathematical and Theoretical 41(22),224015.

7)

Han, G., and Sohn, K. (2016) Activity imputation for tripchains elicited from smart-card data using a continuous
hidden Markov model, Transportation Research Part B:
Methodological , vol 83, pp 121-135

8)Kapicioglu, B. (2013) Applications of machine learning tolocation data, PhD dissertation, Princeton University

9)

Lwin, K.K., Sekimoto, Y., Takeuchi, W. (2018). Estimation of
Hourly Link Population and Flow Directions from Mobile

9(2015) The path most traveled: Travel