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A Mobile Application Survey in Bangkok Metropolitan Region
Jetpan WETWITOO a , Masaru KOMORI b, Naohiko HIBINO c, Hisao UCHIYAMA d,
Atsushi FUKUDA ea,bCreative Research and Planning Co.,Ltd., 1-20-22 Ebisu, Shibuya-ku, Tokyo 150-0013,
JapanaE-mail: j_wetwitoo@crp.co.jpbE-mail: komori@crp.co.jpcNational Graduate Institute for Policy Studies, 7-22-1 Roppongi, Minato-ku, Tokyo 106-8677,
Japan; E-mail: hibino@grips.ac.jpdTokyo University of Science, 2641 Yamazaki, Noda, Chiba, 278-8510 Japan; E-mail:uchiyama@rs.noda.tus.ac.jp
eNihon University, 7-24-1 Narashinodai, Funabashi, Chiba 274-8501, Japan; E-mail:fukuda.atsushi@nihon-u.ac.jp
Abstract: Travel survey in Bangkok Metropolitan Region (BMR) has been constantly
conducted since 1990 on an interview basis. Recent technology enabled a GPS-based travel
survey to be conducted via a smartphone application. With this method, travel information
can be obtained more precisely compared to interview-based survey as travel location can beinstantly obtained from GPS tracker and other travel information can be recorded immediately
after the completion of the travel. This study is the first large-scale pilot survey on the
GPS-based travel survey in BMR, covering 308 samples in 10-days consecutive study period.
Based on the unique survey data from mobile application survey, travel characteristics in
BMR such as modal shift and time/day travel fluctuation can be presented. Problems foundduring the survey period and directions for future implementation are also discussed.
Keywords: GPS, Mobile Application, Travel Survey, Bangkok
1. INTRODUCTION
In the Bangkok Metropolitan Region (BMR), the urban railway master plan has beenformulated and reviewed several times since the 1990s. While the development of major
railway routes in BMR has been reached to a certain prospect, the Thai government considers
that there are still some issues on the existing master plan. An official request was made to
The Japan International Cooperation Agency (JICA) for the assistance for the formulation of
the second master plan, so-called “M-MAP2”. During the course of this study, the issue
regarding the difficulties in conducting a household travel survey, so-called, Home Interview
Survey (HIS), has been raised. The HIS has been continuously conducted from 1995 until thelatest survey conducted in 2013. However, many limitations from HIS has been reported. Oneof the most distinguish limitations from HIS is that HIS has been conducted only on one day,and the interview has been conducted without considering the difference of travel pattern ineach day of week. In other words, each sample will be interviewed at different days of week.
With this aggregate data utilized, the result of analysis could be strongly affected by thefluctuation of trip characteristics. The lack of spatial data is also one of the issues in HIS.
Corresponding author.

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Typically, the location of origin and destination of each trip will be asked in HIS. However, it
is difficult to define the exact coordinate in practice. It is also possible to ask the routeinformation during the interview. However, the data such as path and time spend on eachsection is still difficult to obtain in practice.
With these limitations in HIS, the travel survey based on mobile application survey(hereafter, mobile application survey) has been proposed by JICA study team as an alternative
method to HIS. This pilot survey primarily aims to give an example of the mobile application
survey in Bangkok and provide discussion for further actual implementation. The mobile
application survey can be conducted with a smartphone at any time under a very simple
operation. The data can be easily collected in multiple days so limitation in survey period in
HIS could be solved by applying mobile application survey. So, in this paper, the first issue
which we want to highlight is the trip characteristics during weekdays and weekends, which isstill lagging in HIS data, can be analyzed. Any sample can start the trip recording with GPS
turned on, then he/she can change the mode of transportation from the application once he/she
made a transfer to the new mode. Then, the sample can finish the trip recording from theapplication, and then fill the additional information such as trip purpose, fare, and lateness.
With GPS turned on, coordinate and time of the sample can be easily collected through GPS
server. Thus, the limitation of spatial data in HIS can be supported by GPS data from mobile
application survey. Other trip data such as sample attribute, trip origin and destination, trip
purpose and mode of transportation can be obtained from mobile application survey as well.
However, the second issue which we want to highlight in this study is the limitations in
mobile application survey. The major disadvantage in mobile application survey is a sample
bias toward smartphone user as smartphone penetration is still limited in senior people andlow-income people. Furthermore, by considering the cost to collect the data per head, HIS
could be relatively cheaper as mobile application survey requires a training process, especiallywhen targeting the sample who is not familiar with smartphone operation. In terms of total
cost, it is still difficult to compare the total cost between HIS and mobile application survey atthis moment as the scale of mobile application survey conducted in this study is still relatively
smaller than the interview-based travel survey such as HIS.
In terms of past studies, Shen and Stopher (2014) provide an extensive review ofGPS-based travel survey and its methodology. In Japan, this survey method is widely known
as “probe-person survey”. Several implications such as in Japan (Hato et al., 2006), Australia(Stopher et al., 2007), The Netherlands (Bohte and Maat, 2009), USA (Chen et al., 2010), andSingapore (Cottrill et al., 2013). In Thailand, GPS technology was utilized in several travel
studies, including Tipagornwong (2003), Ishizaka and Fukuda (2004) and Tongsinoot (2017).
However, all three studies utilize the GPS data from probe taxis in Bangkok so the robustnessof trip characteristics is still limited when compared to the GPS data taken from the individual
travelers.
Furthermore, there has been a long discussion of the possibility to obtain travel survey
data based on the mobile phone data from the service provider. Instead of conducting a travel
survey, travel data is obtained by purchasing data from a service provider. However, thismethod is still difficult to be implemented in practice due to two issues. First, since only GPS
data can be obtained from this method, additional algorithm is needed to process the trip
characteristics. The current artificial intelligent used to estimate the trip characteristics such asmode of transportation, trip purpose, fare is still not 100% accurate. Second, issues regarding
personal information and privacy are strongly regulated in many countries, including Thailand
as well. Table 1 compares the advantages and disadvantages from HIS, mobile application
survey, probe-based survey, and data from mobile phone service operator from four aspects;
survey period, sample size, availability of spatial data, and availability of trip characteristics.
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Table 1. Comparison between HIS and mobile application survey

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SurveyAspectSurvey period

Sample size

Spatial dataTripcharacteristics(mode, trip
purpose, fare,
etc.)
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HIS
• Difficult toincrease survey
period due to thenature of aninterview• Only 1 day, day ofweek collected
depends on
survey conductor• Easier to increasesample size frominterview

Mobile Application
Survey• Easier to increase
survey period
thaninterview-based
survey because
data is collected
through mobile
application• In this study, 10
days
consecutively• Difficult toincrease sample
size as training tosample isrequired

Probe-Based
Survey• Easier to increase
survey period thaninterview-based
survey because
data is collected
through a GPS
deviceData from Service
Operator• Survey is not
required• Difficult toincrease sample
size as training tosample isrequired• Depends on thebudget• Can be obtained
indirectly through• Can be obtained directly from GPS
additional
analysis• Can be obtained• Can be obtained• Depends on the• The additional
directly fromdirectly from thesurvey design
algorithm isinterviewrecord in the• If the user is notneeded to processapplication (if thethe triprequired to reportapplication is
the tripcharacteristics.
designed so thatcharacteristics, thethe users must
additional
record their trip
algorithm isinformation)needed.

Comparing to the past studies in Thailand and abroad, the unique characteristics of ourstudy can be described into two points. First, unlike some studies which applied techniques
and algorithms to define travel detail such as modal choice and trip purpose, our study
requires samples to define trip information directly into the trip diary in the mobile
application. Second, as the main target is for the actual implementation at the metropolitan
level, so our sample size of 308 is considerably high compared to other studies. Larger sample
size enabled us to analyze the trip characteristics with higher robustness as shown in section
3.
2. METHODOLOGY
2.1 Sample Criteria and Recruitment
In order to achieve the preferred age distribution, the samples are recruited from various
method such as an introduction from the recruitment staff, recruitment from social media andfrom recruitment websites. There are three main criteria in the selection process. First, thesample must be working in Bangkok CBD. Second, the sample must be living inside BMR.
Residential locations of each sample are shown in Figure 1. Third, the sample must be asmartphone user, with Android OS version 5, 6 or 7. Once their participation is confirmed,
their basic information, including name, home address, workplace address, mobile phone3

119version, is collected.

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Figure 1. Distribution of the residential locations
2.2 Survey Activity
During the course of the survey period, each sample is required to collect their trip
information from the following procedure:
Each sample must record every trip information they made during the survey period of 10days
The trip information can be collected through mobile application by
1. Press “Start” at the beginner of the trip
2. The trip will always start with walking. Select other modes of transportation whentransferring to the new mode
3. Press “End” at the destination
4. Then select the trip purpose
5. Finally, indicate the fare, in case of using public transportation.
6. After completing the trip recording, indicate the availability of time-constraint andinformation of trip lateness through the trip diary.
If there are any technical issues; for instance, a device freezes or the trip is not recorded;
it should be informed to the administrative team as soon as possible.
The sample should leave the comments for short trips/abnormal activities

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Figure 2. Trip recording process
2.3 Survey Schedule
Table 2. Survey schedule

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No.

Date of Survey

Survey Period

Sample Numberst

1 Survey

May 17, 2018 (Thu) – May 26, 2018 (Sat)10 days111 Samplesnd

2 Survey

May 24, 2018 (Fri) – June 2, 2018 (Sat)10 days

87 Samples

3rd Survey

Jun 3, 2018 (Sun) – Jun 12, 2018 (Tue)

10 days110 Samples

2.4 Survey Item
Table 3. Survey items

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Items
Travel timeand location
Trip distanceSourceGPS log

Trip purpose

Main user input

Mode oftransportation

Main user input

Fare [baht]Timelimitation
Arriveearly/late

Main user input
Optional questionin Web Diary
Optional questionin Web Diary

GPS log

Reference
Day, Day of week, Time, Latitude, Longitude; every one
secondEstimated from trip location
Work, Private, Business, Pick up, Other, Go home
Walk, MRT/BTS/ARL, Air con Bus, Non-air con Bus, OfficeBus, Van Bus, Car, Taxi, Motorcycle, Motorcycle Taxi,
Songthaew, TukTuk, Railway, Boat, Bicycle, OtherIn the case of public transit
Yes, in case if the sample must arrive at the specific time, suchas meetings
In case if there is a time limitation, this question will show up

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3. SURVEY ANALYSIS
In this section, general analysis and the analysis of commuting pattern will be presented.
Unlike the usual travel survey which conducted only one day in weekday and weekend,
application survey can collect the data cover the whole week. It is expected that the analysis
can provide better accuracy on the difference between weekday and weekend. Furthermore,this data can be further analyzed on the specific day of week or time of day which enable us todiscuss the policy implication in more detail.
3.1 Data preparation
3.1.1 Data Screening
After the data collection period, several problems during the data collection were reported.
The case where the sample forgot the press “End” button or the case of unavailability of the
GPS signal is among the top problems found. This creates many unrealistic data so thefollowing criteria are applied to screen unrealistic data out from the analysis. The following
criteria a-e are applied with the main analysis where around 75% of samples are left after
screening. The criteria a-g are applied only with trip rate analysis and around 64% of samples
are left after screening.
a. Trip distance less than 50km will be consideredb. Trip distance more than 500m will be consideredc. Trip average speed less than 60km/h will be consideredd. Trip average speed more than 1km/h will be considerede. Trip travel time less than 3 hours will be consideredf. A trip within the trip chain which has no “go home” trip purpose trip will be screened out
g. A “go home” trip purpose trip without trip chain (only go home trip) will be screened out
3.1.2 Representative Mode
In a single trip, multiple transportation modes can be recorded. Thus, for the analysis, therepresentative mode of each trip has to be set. Generally, there are two methods to define therepresentative mode. In the first method, the representative mode will be assigned to the mode
which accumulates the longest travel distance in such trip. While in the second method, themode priority is set based on the capacity and characteristics of mode. In each trip, the modewith the highest priority will be assigned as a representative mode regardless of the distanceor travel time spend on such mode of transportation. Although the discussion of whether
which method is more rational is still ongoing, this study applies the second method as thissurvey is a part of urban railway master plan. In other words, a higher priority to the modewith the highest transport capacity such as railway will be given, as ordered from top tobottom in Table 4.
3.1.3 Mode Grouping
Since there are so many modes of transportation in Thailand, for simplicity, similar modes oftransportation will be grouped together as follows.

6Table 4. Transport mode grouping and priority

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Priority
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Surveyed mode
State Railway of Thailand (SRT) Trains
Urban Rail (BTS, MRT, ARL)
Air-Conditioned Bus and BRT
Non-Air-Conditioned Bus
Van
Personal Car
Taxi
Motorcycle
Motorcycle Taxi
Song Taew
Tuk Tuk
Office Bus
Boat (Express, River Crossing)
BicycleWalk
OtherNew groupingRail
Aircon Bus
Bus & Van
Car
Taxi
Motorcycle
Paratransit

Other

3.2 General Analysis
In this section, some examples of basic analysis on the trip characteristics are presented. With
mobile application utilized, several unique trip characteristics such as speed, time distribution,and day distribution can be analyzed as follows:(1) Trip Count by DistanceA higher share of the short distance trip is observed in this survey.

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Figure 3. Trip Count by Distance

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(2) Trip Count by Fare
There is a high proportion of the trip within the fare range of 0-29 baht, which is within therange of bus, or metro with 2-3 stops.

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Figure 4. Trip Count by Fare

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(3) Trip Rate by Day of Week
Trip rate does not vary across the day of week. From this, it can be implied that Bangkok
people tend to go out even on the weekend. The total average trip rate is 2.49 trip per day.

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Figure 5. Trip Rate by Day of Week

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(4) Trip Count by Speed
Based on the average speed measured by GPS, most of the trip lies within the range of 10-20km/h. Total average speed is 19.43 km/h.

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Figure 6. Trip Count by Speed

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(5) Trip Distance by Trip Purpose
Considering the trip distance by purpose, going to work and go home trips show the longer
trip distance than other trip purpose.

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Figure 7. Trip Distance by Trip Purpose

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(6) Trip Distance by Mode
Considering the trip distance by mode of transportation, trip distance characteristics for rail,bus, and car seems to be quite similar in weekday. While on weekend, car is usually used forshorter distance trip such as shopping and recreations.

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Figure 8. Trip Distance by Mode

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(7) Trip Purpose
There is a higher share of going to work and go home trips in weekday. While on holiday, ahigher share of private trip is found.

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Figure 9. Trip Purpose

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(8) Departure Time and Arrival TimeOn the weekday, the morning peak hour share is between 7-9 AM and is higher up to 12%.
The evening peak which is between 17-19 AM shares around 10%. On holiday, the peak houris less concentrated. Instead, the noon peak is observed as people tend to depart or arrive for a
private trip around 12-13 PM.

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Figure 10. Distribution of Departure Time and Arrival Time

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(9) Departure Time by Trip Purpose
When considering departure time by trip purpose, the peak hour share of going to work
purpose trip is around 25-30% which is considerably high value. This could reflect the current
traffic congestion during morning peak hour in Bangkok. Transportation Demand
Management (TDM) strategies might be needed to alleviate this. In this chart, business trip inholiday has been excluded because of the low sample size. The result of arrival time
distribution is quite similar to departure time with around one-hour lag distribution.

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Figure 11. Distribution of Departure Time by Trip Purpose
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(10)
Modal Share by Trip Purpose
When focusing on going to work trip purpose, the share of railway user is significantly higher
on the weekday. Furthermore, when compared to other trip purpose, the share of public
transport user (rail and bus) in go to work trip purpose is rather higher than others. A higher
share of car user is found in private and go home trip.

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Figure 12. Modal Share by Trip Purpose

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(11)
Late/Early Arrival
From the optional question in web diary, holiday trip tends to be more punctual than weekday
trip. Similarly, trip in weekday shows a higher tendency of being late.

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Figure 13. Late/Early Arrival Distribution
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3.3 Commuting Trip Analysis

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Apart from the general trip analysis shown in section 3.2, data taken from the mobile
application survey enable us to analyze the trip characteristics in further details. In this section,
the analysis specific for commuting trip, including travel time analysis and commuting modal
shift analysis, and will be presented.

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(1) Main Screening Criteria
Commuting trip (in this survey, recorded as “go to work trip purpose”) with unrealistic travel
pattern will be screened out before analysis. Multiple commuting records in one day will betargeted as the unrealistic commuting trip. If it is obvious that those multiple commuting
records are in the separate trip chain (i.e., one trip in the morning, another trip in the evening),
all records will be taken. However, if those multiple commuting records are in the same trip
chain, only the longest trip distance record will be selected as a representative and otherrecords will be discarded.

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(2) Commuting Travel Time by Mode
Commuting with highway-based modes such as car, motorcycle, and bus in weekday requires
more commuting time than on holiday. On the contrary, commuting with rail requires moretravel time than highway-based modes, yet, on average, there is less difference in travel time
between weekday and holiday.

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Figure 14. Average Commuting Travel Time by Day of Week by Mode

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(3) Screening Criteria for Modal Analysis
Next, the commuting modal shift analysis will be presented. In this analysis, the maincommuting mode and the alternative mode will be determined and analyzed. The main modeof commuting is assumed as the mode that sample uses 50% or more during the survey period.
The next frequently used mode is assumed as the first alternative mode. However, if there isno commuting mode that shares 50% or more, such sample will be discarded from thisanalysis. Due to the difference commuting trip characteristics found in the previous analysis,
only weekday commuting trip is considered. Screening criteria can be summarized as shown
below.

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(4) Commuting Modal Shift
In this analysis, the main commuting mode and their alternative modes will be presented. 73%
of sample commuting with car and motorcycle as the main mode only use their main mode to
travel to work. The main alternative for car user is motorcycle, and vice versa, car for
motorcycle user. However, considering sample travel with metro (MRT/BTS/ARL), metrouser up to 50% change their commuting mode during the study period. Car and bus are themain alternatives for metro users.

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Table 5. Share of commuting modal shift on the selected main modes

First Alternative Mode

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Metro
Aircon Bus
Non-Aircon Bus
Office Bus
Van
Car
Taxi
MC
MC taxi
SRT
No change
N=

Main Mode (unit: %)
Car
MC
Metro
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(5) Commuting Mode Shift by Day of Week
In this analysis, in which day people shift the commuting mode to their first alternative mode
will be analyzed. The result showed that, regardless of the mode of transportation, the sample
tends to change their commuting mode to these first alternative modes on Thursday andFriday. One of the possible explanation is that people are getting tired during the end of theweek but more evidence is needed to confirm our hypothesis.

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Figure 15. First Alternative Mode Usage Distribution by Day of Week

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(6) Commuting Travel Time
On average, people spend around one hour for their commuting to work with the main mode.
However, when considering the commuting time on every alternative mode, it is found outthat commuting with the alternative mode tends to be faster than the main mode. Fastercommuting time found in alternative mode could be one of the reasons to explain thecommuting mode shifting.

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Figure 16. Commuting Travel Time by Main Mode and Alternative Mode
4. SURVEY ISSUE
This section is dedicated to the issues discovered during the survey period including bothtechnical and survey problems. It is suggested that the following issues should be addressed
before the future implementation of the application survey.
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4.1 User-Related Issue
Although most of the samples use application perfectly – recording complete trip (pressing
both departure and arrival button) and adding comments, some samples tend to forget about
trip recording and require the reminding from the administrative team. There are several user
related problems, such as
•
•
•
•
•
•

sample forget to record a whole trip
forget to change mode, or end the trip
not confirm the trip correctness
not indicate whether the trip has time limitation or notturn off internet/GPS while recording
change mode, or end the trip by mistake

It also requires a lot of effort to collect your own travel data for 10 days consecutively sosome sample decides to quit before 10 days’ study period. We suggest that better monitoring
and better incentive to samples should be included to improve the quality of the datacollection. In the long run, the improvement of the interface design is suggested to reduce themistake from users.
Furthermore, the issue regarding privacy should be also highlighted. Every sample
requires to turn on GPS during the trip recording so trip data, including commuting patterns,
are all recorded. This means all of the personal travel data will be handled to survey
administrator so we suggest that countermeasure to supervise the survey administrator should
be conducted to ensure the privacy of samples and to prevent the exploitation of personaldata.
4.2 Technical Issue
SignalDuring the analysis, we found that several differences between actual departure time andarrival time record by the user, and departure time and arrival time record by GPS. This timedifference could be more than 30 minutes in several cases. We also found several records
where the location of the sample is missing. This usually occurred when recording in low/no
signal area as such inside the building or in the subway. In the long run, we suggest that anadditional algorithm to predict the route/mode should be implemented to fill the missing data.
Freezing
There are plenty of comments received from the samples on the connection when they start
recording the trip; it takes some time to complete the process or the application even freezes.
This causes the users to reinstall the application several times.
GPS
With GPS turned on, battery drains very fast. This cause a huge problem to the sample who
travels in a long period of time. Currently, application records trip data every one second. We
suggest that the frequency of 5-10 seconds should be enough for the analysis.
Sleep ModeIn this study, the device must not be in a sleep mode during the recording time, otherwise, the
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data is not collected. Sometimes the device runs out of battery which can cause the disruption
in recorded data / not-completed data.
Compatibility
Currently, the application is only compatible with Android OS 5-7. iPhone users cannot
participate in this survey. We suggest that for the actual implementation, compatibility issue
should be carefully considered. Web-based access (through mobile phone or personal
computer) could be one of the options to make changes in the web diary.
Walking (mode of transport)
With the current application, every time the respondents start recording the trip, walking isrecorded as the first selected mode of transport although it is not a part of their trip. So, theyneed to make an amendment in their web diary. We suggest that any starting mode should beavailable for selection.
4.3 Survey Design Issue
Time Constraint
Regarding the application usage, samples need to select whether there is time constraint or not
via web diary. This creates confusion in the trip recording. Also, sometimes the sample forgets
to record it. We suggest that it would be better if the time constraint and late/early arrive
information is asked right after the completion of the trip.
Fare
After completing the trip record, fare information will be asked. However, this input is rather
based on the sample’s intuition. We suggest that in the long run, this input should be checkedwith a fare database based on the travel time, transportation mode and travel distance.
Sample Distribution
Regarding the TOR, the samples are divided into different groups by age and living area;
however, there is a group of elderly people (50s and 60s) who tend not to work further fromtheir houses. As a consequence, the sample recruitment process struggles in this issue. Also,
this survey targeting smartphone users only. Some attribute, such as the sample’s income andeducation, could be biased from the whole sample size.
Daily Information
We also suggest that information which impact travel behavior such as weather and eventsshould be recorded in the database. This could be done automatically without user input.
4.4 Integration with HIS
For the actual implementation, how to reduce the survey cost should be further discussed. In
this survey, there are three cost component; application development cost, survey cost, andanalysis cost. With the consideration of these cost components, the cost comparison with theconventional survey approach (i.e., HIS) should be carefully considered. However, we want topoint out that the data collected from mobile application survey and HIS could be different.
HIS collects the data from several samples only in one-day trip thus the data from HIS will be
the sample-based data. In other words, the data from HIS will provide a better variation on
sample’s attribute. The analysis of trip structure based on age, gender, income will be more
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reliable with the data from HIS. Mobile application survey, in contrary, collects the data froma limited sample size but in a longer period (10-days consecutively, for this trial). Therefore,
the data from the mobile application survey will be the trip based data. In other words, thedata from the mobile application survey will provide a greater day and time variation because
more trip will be collected. Further discussion regarding the type of data should be also
included to verify the needs for application survey. We suggest that the data from both surveys
could be utilized together for better analysis performance. One of the examples of themethodology to integrate both survey data together is available in Hato and Kitamura (2008).
5. CONCLUSION
The main purpose of this pilot survey is to give a test of mobile application survey in
Bangkok. Based on the result, this study aims to provide a discussion for further actual
implementation. With the target of actual implementation, first, we need to highlight thestructure of the model. The structure of the mobile application survey is rather different fromthe typical household travel survey, as it is very easy to increase the size of travel data basedon the same sample by extending the study period. However, comparing to the household
travel survey, it is more difficult to increase the number of the sample as the recruitment
process requires several conditions to be fulfilled. Several technical issues have been reported,
yet, we believe that with a better design of application along with better monitoring, thesetechnical problems can be diminished.
The data from the application survey provides unique characteristics which enable us toanalyze travel pattern from several aspects. As shown in table 1, one of the advantages frommobile application survey is the robustness in travel characteristics by the day of week. With
this data, we can understand several unique travel characteristics in Bangkok. For example,
unlike other cities, the trip rate on weekend is considerably high, especially on Saturday. This
could be due to the fact that many workers in Bangkok have to work on Saturday. We also
found that the peak hour in weekday is in the morning 7-9 am. While in the weekend, thepeak hours are rather evenly distributed across morning, noon, and evening. Furthermore, thecommuting trip pattern can be analyzed as shown in section 3. The unique characteristic frommobile application survey enables us to investigate the modal shift pattern during each the dayof week. For example, the survey reveals that the commuter tends to change their commuting
mode on Thursday, Friday, and Saturday. Transport policy could be adjusted according to the
day variation. One of our hypotheses to the modal shift on Thursday, Friday and Saturday isthat people become tired or bored during the end of the week. However, further investigationis needed to explain the reason behind this change.
As discussed in Table 1, another important data from this survey is the GPS data. The
GPS data provides the data of the exact location of in each second; this gives an opportunity
for us to analyze the tempo-spatial data in further details. With application survey data,
analysis such as bottleneck location as well as the route shift pattern during each day of week
can be determined. For demand forecast model, LOS of each mode in the same route or same
OD can be referenced from GPS data. This will further enhance the accuracy of the modal
split model estimation in demand forecast model. Unfortunately, due to the time constraint,
we cannot analyze the GPS data for this study within time.
In terms of methodology, the method used in this study is merely one of the examples
from many methods. For example, we ask the sample to answer the total fare during thecourse of the trip. However, this answer could be different from the actual total fare as the
sample may use the commuting ticket or they may have forgotten the exact fare in each mode
which they have paid. To solve this, we suggested in section 4 that by utilizing the GIS data
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and the mode data from the user record, the total fare can be identified with a fare databaseand a simple algorithm. Similarly, we also required the sample to identify their trip purpose.
However, as applied in Chen et al. (2008), trip purpose can be identified from analgorithm-based on GIS data and mapping data. With the ability to collect the data and thebenefit of robust travel data, mobile phone-based travel survey could be one of the promising
choices for an actual implementation for transportation planning in the next upcoming years
from now.
REFERENCES
Bohte, W., & Maat, K. (2009). Deriving and validating trip purposes and travel modes
for multi-day GPS-based travel surveys: A large-scale application in the Netherlands.
Transportation Research Part C: Emerging Technologies, 17(3), 285-297.
Chen, C., Gong, H., Lawson, C., & Bialostozky, E. (2010). Evaluating the feasibility of apassive travel survey collection in a complex urban environment: Lessons learned
from the New York City case study. Transportation Research Part A: Policy andPractice, 44(10), 830-840.
Cottrill, C. D., Pereira, F. C., Zhao, F., Dias, I. F., Lim, H. B., Ben-Akiva, M. E., &Zegras, P. C. (2013). Future mobility survey: Experience in developing asmartphone-based travel survey in Singapore. Transportation Research
Record, 2354(1), 59-67.
Ishizaka, T., A. Fukuda. (2004). Utilizing probe car for travel survey in developing
country: A case study in Bangkok. Proceedings for 59th Annual Conference of Japan
Society of Civil Engineers, 505-506. (in Japanese)
Hato, E., S. Itsubo, and T. Mitani. (2006). Development of MoALs (Mobile Activity
Loggers Supported by GPS- Phones) for Travel Behavior Analysis. TRB 85th Annual
Meeting Compendium of Papers (CD- ROM), Washington, D.C.
Hato, E., & Kitamura, R. (2008). Data-oriented travel behavior analysis based on probe
person systems. TRB conference proceedings, 187-196
Shen, L., & Stopher, P. R. (2014). Review of GPS travel survey and GPS data-processing
methods. Transport Reviews, 34(3), 316-334.
Stopher, P., FitzGerald, C., & Xu, M. (2007). Assessing the accuracy of the Sydney
Household Travel Survey with GPS. Transportation, 34(6), 723-741.
Tipagornwong, C. (2003). The use of GPS equipped taxis as probe vehicles for travel
time record in Bangkok (Master Thesis). Retrieved from Chulalongkorn University
Intellectual Repository. (in Thai)
Tongsinoot, L. (2017). Analysis of road traffic patterns using CDR and GPS (Master
Thesis). Retrieved from Chulalongkorn University Intellectual Repository. (in Thai)

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