Crowd-Sourced Web Survey for Household Travel Diaries Harsh Vardhan, Ishan Rai, Nidhi Kathait, Amit Agarwal * Department of Civil Engineering Indian Institute of Technology (IIT) Roorkee, Roorkee-247667, India Abstract Collecting travel data in the field is always a challenging task. It’s equally burdensome to respondents if the data is collected using face-to-face personal interview or self com- pletion surveys. To reduce the burden on respondent and to collect the time stamps and locations precisely, a few fully automated survey approaches are proposed with limited success mainly if required sample rate is higher in a large-urban agglomeration. This study presents an open-source, web-based, self-completion and/or personal-interview sur- vey platform, namely Travel Survey as a Service (TSaaS) which currently hosts two dif- ferent survey types. This study proposes to use the TSaaS platform as the crowd-sourced data collection approach for household travel diaries. The TSaaS provides flexibility to conduct multiple surveys for different purposes/locations simultaneously using a web- survey format. For better control of the data collection process, multiple survey links for household travel diaries (or any other survey) in a region can be created and eventually, collected data can be processed jointly or separately as per the requirements. The data is recorded in an efficient data structure. The data is recorded mainly in three tables, which are family, member and trip information. Personal information and location are neither asked nor tracked using devices or otherwise. To assist in recalling the activity locations, a location-search field is provided and integrated with a map. The permanent address, trip origin and destination are recorded as nearest landmark on the map and the location is shown as a marker on the map. The marker on the map can be adjust to correct the location if required. A pilot study was conducted in Jaipur and three dif- ferent data collection approaches is attempted. The approaches are compared in terms of survey completion rate, survey completion time and time-cost of each approach. The crowd-sourced web-survey turn out to be the most efficient in terms of the time-cost per completed survey record and most suitable to collect the large number of survey records in an urban agglomeration. Keywords: travel survey, household survey, trip diaries, activity-trip chain, person trip survey, crowd-sourced web survey 1. Introduction 1 Reliable traffic information is a key factor for effective planning, operation and man- 2 agement of traffic. In general, such information is collected using various travel surveys. 3 * Corresponding author Email address: [email protected](Amit Agarwal) 1
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Crowd-Sourced Web Survey for Household Travel
Diaries
Harsh Vardhan, Ishan Rai, Nidhi Kathait, Amit Agarwal∗
Department of Civil EngineeringIndian Institute of Technology (IIT) Roorkee, Roorkee-247667, India
Abstract
Collecting travel data in the field is always a challenging task. It’s equally burdensometo respondents if the data is collected using face-to-face personal interview or self com-pletion surveys. To reduce the burden on respondent and to collect the time stamps andlocations precisely, a few fully automated survey approaches are proposed with limitedsuccess mainly if required sample rate is higher in a large-urban agglomeration. Thisstudy presents an open-source, web-based, self-completion and/or personal-interview sur-vey platform, namely Travel Survey as a Service (TSaaS) which currently hosts two dif-ferent survey types. This study proposes to use the TSaaS platform as the crowd-sourceddata collection approach for household travel diaries. The TSaaS provides flexibility toconduct multiple surveys for different purposes/locations simultaneously using a web-survey format. For better control of the data collection process, multiple survey links forhousehold travel diaries (or any other survey) in a region can be created and eventually,collected data can be processed jointly or separately as per the requirements. The datais recorded in an efficient data structure. The data is recorded mainly in three tables,which are family, member and trip information. Personal information and location areneither asked nor tracked using devices or otherwise. To assist in recalling the activitylocations, a location-search field is provided and integrated with a map. The permanentaddress, trip origin and destination are recorded as nearest landmark on the map andthe location is shown as a marker on the map. The marker on the map can be adjustto correct the location if required. A pilot study was conducted in Jaipur and three dif-ferent data collection approaches is attempted. The approaches are compared in termsof survey completion rate, survey completion time and time-cost of each approach. Thecrowd-sourced web-survey turn out to be the most efficient in terms of the time-cost percompleted survey record and most suitable to collect the large number of survey recordsin an urban agglomeration.
A travel survey is a detailed investigation of the transportation system in a specific area4
and data collection exercise in which captured data reflects the real-world traffic condi-5
tions. The objectives of a travel surveys are(i) to analyze the issues and characteristics6
of existing transportation system in the study area, (ii) to quantify the spatio-temporal7
variations, (ii) to assess the potential for future development/extensions, etc.8
A few examples of popular travel/traffic surveys are inventory of network, person trip9
survey, vehicle count survey, turning movement counts, origin-destination survey, travel10
speed survey, parking survey etc. In past, majority of the surveys were manual and11
involved high usages of pen-paper. With advancement in technology, pen-paper based12
surveys are replaced by video-graphic surveys, web/app based surveys, global position-13
ing systems (GPS) based surveys, mobile-phone based etc. However, many developing14
countries are still relying mainly on pen-paper based surveys. A few surveys need hu-15
man inputs to collect variety of traffic data; the data can be collected at home, on-site,16
during trip etc. Based on approach to collect the data, surveys are categorized as per-17
sonal interview based survey, postal survey, telephonic surveys, application based survey18
etc. These surveys are required to study the travel behavior, regional transport model,19
travel demand, origin-destination survey, etc. A few other surveys which don’t need in-20
puts by respondents are classified vehicle counts at an intersection, at mid-block section,21
license plate surveys, transport facility surveys etc. In recent years, image-processing,22
sensor/GPS based surveys are becoming more common for many of these surveys.23
In the last couple of decades, there has been a sharp increase in travel demand in par-24
allel with economic growth. Given the complex transportation systems and large urban25
transportation networks, various analytical and/or simulation models are developed for26
traffic modeling, planning, and analysis (e.g. activity-based models, agent-based models27
etc.). These simulation models are data intensive i.e. variety of data is collected to syn-28
thesize/generate the scenario, calibrate and validate the models Agarwal (2017); Agarwal29
et al. (2019). Typically, socio-economic and travel (origin, destination, trip purpose, travel30
mode, trip length, etc.) characteristics of households are required. Such data is included31
in person-trip diary or household survey.32
The state-of-the-art approach for trip diary survey is manual and error-prone. Typ-33
ically, the data is recorded using pen-paper, collected on the site and data-entry is pro-34
cessed afterward for suitable use. With time, based on the need, these approaches are35
extended/improved by telephonic survey, postal survey, smartphone-based survey etc.36
Online and smartphone-based surveys are becoming more common due to lower cost,37
convenience and ease of access to internet. The present study presents a comparison38
of past survey techniques to collect person-trip diaries and propose an crowd-sourced39
web-based survey to collect the activity trip-chain diaries. For this, first an open-source,40
web-survey travel survey platform is proposed which is suitable for self-completion and/or41
person-interview.42
To begin, this study provides a review of the existing literature related to the different43
traffic survey techniques and lists the limitations in Section 2. Section 3 discusses the two44
countermeasures in support of the proposed approach and the ideas to conduct crowd-45
source web-survey. Section 4 presents and demonstrates the travel survey platform in46
details. Section 5 presents the pilot study in Jaipur and the results of the study. Finally,47
the study is concluded in Section 6.48
2
2. Literature Review49
In past, use of Pen (or pencil) paper for various traffic surveys is a common technique50
to collect the traffic characteristics, trip patterns etc. Hurst (1969); McClintock (1927). In51
such methods, a surveyor stands on the road side/transit stop and collect the information52
by observing (e.g. counting vehicles, passengers etc.). For person-trip diary surveys, an53
interviewer has to go door-to-door to collect the travel information or to interview a54
respondent at the intercept points along major roadways, transit routes Griffiths et al.55
(2000). Even today, personal interview surveys are used at many places because an56
interviewer (i) can explain/reformulate the unclear part (ii) use maps, pictures to make57
them understand, (iii) can translate the questionnaire in regional/local language, (iv)58
can fill out the questionnaire for the users who are unable to complete on their own59
etc. Zalewski et al. (2019).60
In 1980’s and early 1990’s, use of telephones for collecting the data has started to61
become popular Hitlin et al. (1987). For this, an interviewer is trained so that he/she62
can explain the aim of research, design of questionnaire and importance of data collection63
over a phone call. The responses are recorded by the interviewers in the desired format64
Richardson et al. (1995). Compared to face-to-face personal interviews, telephonic surveys65
(i) can maintain anonymity of the respondent, (ii) higher geographical coverage, (iii) are66
efficient in terms of costs and benefits Ampt (1989); Richardson et al. (1995). However,67
telephonic surveys lack in visual support which decreases the trust between interviewer68
and respondent. Additionally, it is limited to a short duration surveys and likely to have69
survey bias. Thus, a drop in the response rate of telephonic surveys is reported.70
Given the cost involved with person interview survey, self completion questionnaire71
surveys became apparent. These are the surveys, a respondent completes without as-72
sistance of an interviewer. In these survey, the questionnaire is delivered to respondent73
by mail or by post and then after completing, respondent mail it back or it is collected74
from respondent Richardson et al. (1995). Such surveys are about 3.5 times cheaper per75
completed survey than telephonic survey Hitlin et al. (1987). However, the lower response76
rate of self completion survey leads to higher cost per returned questionnaire.77
In 1990’s and 2000’s, computer administered interviews started to gain momentum78
over face-to face interviews and telephonic surveys. The self completion surveys (e.g.79
mail-back) was replaced with computer-assisted telephone interviews (CATI), personal80
interviews (CAPI). Use of computer-assisted data collection approach for personal inter-81
views (i) reduces time required to complete the survey (ii) improves the data quality by82
validating the data for possible errors during entry and (iii) saves time in data-entry and83
thus reduces costs Gravlee (2002). It also facilitates more complex questionnaire designs84
than pen-paper survey.85
With the increasing use of internet, use of web-surveys (also known as internet-survey)86
become prevalent in which the questionnaire is sent primarily over the internet. Compared87
to other data collection approaches, the main advantages of web-survey are (a) the low cost88
(b) greater potential to engage and interact with the participants and (c) automated data89
collection, etc. (Greaves et al., 2015; Bourbonnais and Morency, 2013). Auld et al. (2009)90
presents a web-survey for household travel survey with lesser chances of under-reporting91
of activities and trips. In order to get the accurate location and times, GPS logger is92
used. Auld et al. (2012) proposes a web-survey to record the responses of the users in93
a hypothetical emergencies which vary in terms of size, hazard level, time of day, etc.94
with no-notice. Similarly, Greaves et al. (2015) presents development and deployment of95
a web-based travel diary and optional-smartphone app to collect the travel data in inner-96
3
city Sydney. Clearly, the continuous tracking increases the accuracy of trip reporting97
and efficiency in data-feeding, it also has serious privacy concerns, gaps in the GPS logs98
inside dense urban areas. Similarly, Kazemzadeh et al. (2020) uses web-survey and in-field99
personal interview survey to study the perception of cyclists. The users are more positive100
and optimistic when answering web-based questionnaire. Naturally, at operational point101
of view, the web-survey is more comfortable compared to the in-field personal interview102
surveys.103
During early introduction of smartphones, use of handheld devices (e.g. Tablet PCs,104
iPAD, smartphones etc.) became a common trend. In the beginning, it was personal105
interview type in which an electronic questionnaire was filled in front of a respondent on106
the site, these are called computer-aided personal interview (CAPI) Sowa et al. (2015).107
With the increasing use of the Internet, online questionnaires have become a popular108
way of collecting information. Computer-assisted-self-interview (CASI) or self-completion109
techniques are gaining popularity. In this approach, respondents directly input their110
responses in the devices. The associated softwares in the devices can play recorded audio111
voice-overs, can show graphics for better understanding of the surveys. The recorded112
data is directly available for further processing. Similarly, online questionnaires are a113
sub-set of a wider-range of online research methods. For instance, computer-assisted web114
interviewing (CAWI) is an internet surveying technique in which the interviewee follows115
a script provided in form of a website Sowa et al. (2015). In short, a questionnaire is116
created as a program for the web interviews. It consist of pictures, audio and video clips,117
links to other web sites etc. The flow of questions is designed based on the responses118
and existing information in the questionnaire. Major advantages of the computer-aided119
surveys (e.g. CAWI, CAPI, CASI) are (i) reduces costs and required human resources,120
(ii) reduces burden on respondents (iii) maintain anonymity, privacy provided location is121
not tracked and personal information is not collected, etc. Brown et al. (2008); Bayart122
and Bonnel (2015). Further, CASI are cheaper than CAPI because it does not require123
handheld devices (e.g. smartphones, tablets etc.). On the opposite side, such surveys are124
biased because these surveys are restricted to a particular segment of population which125
have access to such devices and internet Mol (2017).126
Improvements in remote sensing Technologies such as vehicle instrumentation, GPS127
and their integration with geographic information system (GIS) database, offer lot of128
opportunities to enhance the detail and accuracy of the data collected by travel surveys129
in 21st century Griffiths et al. (2000). In order to accurately identify the location of130
origin-destination, use of global positioning systems (GPS) is very helpful Wolf et al.131
(1999). In this case, the locations of the travelers are continuously tracked using GPS132
of the standalone device (Auld et al., 2009) or integrated with smartphone (Hood et al.,133
2011; Stipancic et al., 2017). Thus, it has ability to gather the data streams of individual134
traveler’s trajectories throughout the day. Together with the time stamp from the de-135
vices, not only locations, but trip times, duration can also be recorded accurately. Thus,136
more reliable data can be collected by reducing the response time and cost of the survey137
significantly Mol (2017); Prelipcean et al. (2018). In fully automated data collection ap-138
proach, some information (e.g. trip purpose, preferences etc.) is not explicitly available139
from the survey data. Similar to web surveys, chances of biased results are higher in these140
type of surveys. Additionally, it is a matter of concern (i) whether enough respondents141
would be comfortable to provide information about daily activities and precise locations142
and (ii) travelers may change their travel behavior under the impression of being tracked143
Griffiths et al. (2000). Use of GPS technology in the survey increases the burden on the144
4
respondent in terms of significant battery depletion and cost of internet for transferring145
the recorded data over an interval Safi et al. (2013). Lee et al. (2016); Rieser-Schussler146
(2012) present a literature review of the emerging data collection techniques such as the147
mobile-positioning system, GPS and Bluetooth re-identification, automatic number plate148
recognition, technologies for travel demand modeling. However, the practical applications149
of these technologies are very limited (Lee et al., 2016).150
Some past studies explore options to generate trip diaries using various data sources.151
For instance, smart card (automatic fare collection system) data is used to detect trip152
direction, boarding time, home locations etc. Bagchi and White (2005); Zou et al. (2016);153
Chen and Fan (2018). In the similar direction, call data records (CDR) can also be used154
to reproduce trips in an urban area Colak et al. (2015); Zilske and Nagel (2014). Vehicle155
occupancy can be evaluated by detecting the WiFi devices using wireless routers Gore156
et al. (2019) and, with the help of link counts and Bluetooth data, origin-destination157
(OD) matrix can be estimated Michau et al. (2019). Some other techniques may not be158
used directly to synthesize the trip diaries, however to validate the model. For instance,159
Prajapati et al. (2020) presents a computer-vision techniques which records the number160
of vehicles and their trajectories under mixed traffic conditions. However, due to various161
reasons (e.g. privacy, permissions, availability of the data), these advanced technologies162
cannot be used everywhere and typical trip diaries must be collected using one of the163
survey approaches.164
From the foregoing discussion, it is clear that different survey techniques are used165
with a good mix of technologies and objectives. The present study focus on (i) quick166
completion of the survey (iii) a common, open-source web survey to collect the data167
(iii) use of a database to manage the survey data (iv) privacy concerns (v) consumption168
of battery (vi) assistance in recalling the activity locations, etc. Therefore, an open-169
source web survey platform is proposed to collect the data from various travel surveys170
simultaneously. This study focuses only on the development and deployment of the web-171
survey related to activity-trip chains of households. The web survey is suitable for self-172
completion as well as personal interview type approaches. The proposed survey overcomes173
the aforementioned limitations.174
3. Countermeasures175
3.1. Coverage176
As discussed in the previous section, technology is advancing progressively and use177
of computers, mobile phones (specifically smart phones) in various travel surveys is be-178
coming common. In India too, smart phones have become affordable and in reach of179
almost everyone. From 2016 data, total mobile subscriptions are about 0.96 billion for180
a population of about 1.3 billion Kanungo (2017). Out of 1.3 billion persons, roughly181
27% are under 14 and unlikely to have their personal mobile phones. This means, on an182
average every person who is older than 14 years has a mobile phone. Further, the market183
penetration of smartphones is 0.468 billion in 2017 Assocham (accesssed, 2019) i.e. every184
other person who is older than 14 years is having a smart phone. Further, use of internet185
is continuously rising due to low-rate data plans Rajkumar et al. (2016).1 This highlights186
the feasibility of better coverage using computer-aided self-completion surveys.187
1An example of data plan: it costs less than 150 Indian rupees for 28 days to make unlimited incoming,outgoing calls, 100 SMS per day and 1GB 4G data per day (July 2019).
5
Figure 1: Jaipur road network (in gray), ward (zone) boundaries (in red) and locations of institutes andcolleges (in blue)
3.2. Collection of data using crowd-sourced web-based self-completion survey188
Given high market penetration of smartphones and low cost of internet, a web-based189
survey is proposed which is a combination of self-completion and person-interview surveys.190
In the former, a survey link is distributed to users and they are requested to complete the191
survey in a time-frame (typically 1-2 weeks). In order to reach out to maximum number of192
persons, the proposal is to reach out to students of various high-schools/institutes/colleges193
in a city and each student will be asked to complete the activity trip chain diary of all194
members of the family. For instance, Figure 1 shows locations of the institutes/colleges195
in Jaipur city. It is highly likely that every student in these institutes/colleges carry a196
smartphone, if not, students can carry the QR (Quick Response) code with them and197
complete the survey at home with the available devices. Alternatively, the same ap-198
proach is also applicable to secondary and senior-secondary schools however, in this case,199
students are unlikely to have a smart-phone. Therefore, students of the schools are ex-200
pected to come to the computer-laboratories and complete the survey for all members201
of the family. Clearly, in this approach, households in which no one is studying in these202
schools/institutes/colleges, are missed from the survey and can be captured using door-203
to-door personal interviews in each of the ward. For instance, in 2011 about 4500 families204
(≈0.3% of total population of Jaipur district) used to live on footpath in Jaipur Census205
(accessed, 2019) which can’t be covered using self-completion surveys.206
4. TSaaS: Travel Survey as a Service207
4.1. Overview208
The TSaaS (Travel Survey as a Survey) is an open-source platform which facilitates209
web/mobile-based self completion or personal interview type surveys. Currently, two210
type of surveys are linked with it i.e., household trip diary surveys and public transport211
survey to understand the behavior of metro users. The survey types are listed on the212
6
homepage. The selection of a survey type will lead to the landing page of the survey213
type (see Figure 5(a)) and only a demo survey can be taken from here. The focus of the214
present study is to create a trip diary survey to record the activity-trip chain diaries of215
all members of the family and thus this is explained here in detail.216
The source-code of the project is hosted at GitHub2 and a demo survey can be217
started using https://tsaas.iitr.ac.in/hhs. For the present study, version ‘v0 2’ is used.The218
recorded data is saved on a secure server in JSON (JavaScript Object Notation) format.219
The design of the database in back-end is demonstrated in Section 4.2 and the used220
terminology is explained in Table 1.221
Table 1: Terminology for the household trip diary under TSaaS
term description
admin a person who controls the back-end admin panel
respondent a person who enters responses in the survey
surveyor a person who is doing survey (e.g. door-to-door)
supervisor a person who is supervising the group surveys
survey type survey with different objectives (e.g. household travel, public transport)
survey format predefined survey questionnaire for each survey type
4.2. Design of the database222
Technical details of the back-end In the back-end, Django3 is used which is open-223
source, has a clean Pythonic structure, follows a Model-View-Template (MVT) architec-224
ture, has a built-in admin panel and is capable of handling heavy traffic seamlessly. The225
admin panel is customized for easy monitoring and overview of trip profiles and facilitated226
with custom filters for quick overview of the recorded data. To record the travel diaries for227
multiple persons simultaneously in an urban agglomeration, a database which can handle228
a range of workloads, from single machines to many concurrent users is required. For229
this, various relational databases such as SQLite, PostgreSQL, MySQL and Oracle whose230
application data can interact with the default object-relational mapping layer (ORM)231
are compared and eventually, PostgreSQL is used. PostgreSQL is chosen because it is a232
powerful, open-source, object-relational database system which is reliable, robust and has233
good performance4. To make the database bootstrapping easier for testing, SQLite is used234
which is inbuilt with Python. It is a C-language library that implements a small, fast,235
self-contained, high-reliability, full-featured, SQL database engine5. In short, SQLite for236
local development and PostgreSQL in production are used. Two setting files – ‘produc-237
tion settings.py’ and ‘local setting.py – are incorporated for production and, testing and238
development respectively. A REpresentational State Transfer (REST), software architec-239
tural style is followed and a RESTful API is created using the Django-rest framework.6240
The web APIs allow web systems to request information from the database or create a241
2See https://github.com/teg-iitr/tsaas-frontend for front-end and https://github.com/teg-iitr/tsaas-backend for back-end of the TSaaS project.
of back-end (see Section 4.2) and a custom message is displayed in bold on the landing282
page (see Figure 5(b)).283
As soon as the survey is started, a survey id is created at the back-end and survey284
start time is recorded. At first, the family information is asked (see Figure 6) in which285
number of members are also asked. The family data is posted to server on submitting the286
family data and a family id is assigned. On the next page member information is asked287
which is continued until current member index is same as the number of members entered288
on the family page. If a member stays at home, the member page displayed again with289
increased member counter. On submitting the member page, a member id is generated290
on back-end, member information is posted to database and trip information is displayed.291
On the trip information page, state and district of the trip are asked and checked with292
respect to the defined study area in the back-end (see ‘CollegeList’ in Section 4.2). If the293
trip is beyond the study area, next member page is displayed and counter is increased.294
This will reduce the response time of a survey. If trip is in the study area, further trip295
information is asked until a respondent clicks on ‘Proceed’ (see Figure 8(f)) and confirms296
that all trips are added for the member. This will also end the survey if all trips of the297
last member are added. With this, the survey end time will be posted to the database.298
Figure 4: Trip information to check if trip is made in the study area
4.4. Structure of TSaaS and data recording procedure299
The design of the front-end is demonstrated in Figure 3 and discussed in Section 4.3.300
The start page of the household travel survey is shown in Figure 5. The household301
travel survey is categorized mainly in three categories; they are: family, member and trip302
information. Data fields and process for each of the category is explained next.303
Family information: On the family page (see Figure 6), at first, a respondent enters304
information about number of members in the family, motorized and non-motorized vehicle305
ownership. Afterwards, he/she selects one of the categories of monthly income from the306
drop down menu items. The income categories are nil, less than 5000 |, 5000 - 10000 |,307
10000 - 50000 |, 50000 - 1 lakh |8, 1 lakh - 2 lakh |, 2 - 5 lakh | and more than 5 lakh308
| to demonstrate the distribution of income and choices they make.309
The current survey format is supported only for the locations in India however, it310
is transferred to any other country with a few changes in the source-code. Further, a311
810 lakh = 1 million
11
(a) Start page for demo survey (b) Start page for IIT Roorkee
Figure 5: Landing pages for demo survey and configured for the students of IIT Roorkee.
(a) Data about the vehicle owner-ship and monthly income
(b) Permanent address (c) Integration of map for locat-ing landmark
Figure 6: Information collected through family page of TSaaS.
12
respondent selects the state and district for the permanent address, enters initials for312
nearest landmark (see Figure 6(b)). This gives a list of options and one of them can be313
selected as shown in Figure 6(c). This is performed by integrating Places API by Here314
Maps9. For the selected landmark, a marker and corresponding latitude and longitude315
are shown on the map. As instructed, the respondent can adjust the marker to change316
the nearest landmark which will also change the coordinates. After clicking on ‘Submit’317
button, together with the entered information, latitude and longitude of the landmark318
are sent to the server and the member information page is displayed.319
(a) Basic information of the mem-ber
(b) Income and mobile phone re-lated information
(c) licensing and other informa-tion
Figure 7: Information collected through member page of TSaaS.
Family member information: Figure 7 shows the information collected for each mem-320
ber. On the first screen (see Figure 7(a)) socio-demographic characteristics and income321
information are required. The income categories are same as that of on family page.322
On the next screen (see Figure 7(b)), information about the number of sim cards, data323
(internet) or phone usages during driving/traveling, information about Bluetooth, WiFi324
activation are required. This information will help in identifying the market penetra-325
tion of mobile phones, internet usages and number of Bluetooth and Wifi devices on the326
road. Such information is required when (i) various sensors are used to detect the num-327
ber of devices and then generate/validates trips Gore et al. (2019) (ii) call data records328
(CDR) are used to generate/validate the trip information Colak et al. (2015). The last329
screen of the member page (see Figure 7(c)) contains only radio buttons; information330
9See https://developer.here.com/documentation/places/topics/what-is.html. As of Mar. 2020, it pro-vides about 250,000 transactions per month under ‘Freemium’ licensing.