Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Unconscious Learning Effect on Driver Attention Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Strategies to Increase the Response Rate of Smartphone-Based Travel Surveys in Afghanistan: Exploring the Effects of Incentives and Female Survey Conductors

Authors : Qudratullah, Takuya Maruyama

Published in: Intelligent Transport Systems for Everyone’s Mobility

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Smartphone-based travel surveys have been used widely in developed countries as promising alternatives to traditional travel data collection methods. In addition, researchers in developed countries have employed several strategies to increase response rate of the surveys. However, few studies have investigated these issues in developing countries. Therefore, to consider the case of a developing country, we conducted three smartphone-based travel surveys in two Afghanistan cities. The first survey was conducted in 2015 in Kabul. In 2017, we have conducted two additional surveys in Kabul and Khost cities. Two improvements were made to the survey method: offering rewards to increase response rate and appointing female survey conductors to target female participants. The main objectives of this study are: (1) to investigate the effect of rewards on the response rate. (2) to demonstrate female survey conductor’s effects on female response rate. (3) to compare response rates of Khost and Kabul surveys. The results reveal that the overall survey response rate in Kabul increases significantly after the improvements are made to the survey method. Rewards have shown to increase male response rate, and female response rate is improved by female survey conductors.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Exploring System Characteristics of Autonomous Mobility On-Demand Systems Under Varying Travel Demand Patterns
next chapter Sensing Information Dissemination Strategy for Collective Perception in VANET Based on the Relative Position of Vehicles and the Road Structure
Literature
1.
Alio, X.D., Marquet, O., Guasch, C.M.: Keeping track of time: A smartphone-based analysis of travel time perception in suburban environment. Travel Behav. Soc. 9, 1–9 (2017)CrossRef
2.
Alsger, A., Tavassoli, A., Mesbah, M., Ferreira, L., Hickman, M.: Public transport trip purpose inference using smart card fare data. Trans. Res. Part C 87, 123–137 (2018)CrossRef
3.
Asakura, Y., Hato, E., Maruyama, T.: Behavioral data collection using mobile phones. In: Rasouli, S., Timmermans, H., (ed.) Mobile technologies for activity-travel data collection and analysis. Heshey PA, USA, 17–35 (2014)
4.
Assemi, B., Jafarzadeh, H., Mesbah, M., Hickman, M.: Participants’ perceptions of smartphone travel surveys. Trans. Res. Part F 54, 338–348 (2018)CrossRef
5.
Berger, M., Platzer, M.: Field evaluation of the smartphone-based travel behavior data collection app “SmartMo”. Trans. Res. Proc. 11, 263–279 (2015)
6.
Bhat, C.R.: Conducting travel survey using portable devices-challenges and research needs. Trans. Res. Proc. 11, 199–205 (2015)
7.
Boyd, D., Crawford, K.: Critical questions for big data. Inf. Commun. Soc. 15(5), 662–679 (2012)CrossRef
8.
Briand, A.S., Côme, E., Trépanier, M., Oukhellou, L.: Analyzing year-to-year changes in public transport passenger behavior using smart card data. Trans. Res. Part C 79, 274–289 (2017)CrossRef
9.
Calabrese, F., Diao, M., Lorenzo, G.D., Jr, J.F., Ratti, C.: Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Trans. Res. Part C 26, 301–313 (2013)CrossRef
10.
Chen, C., Bian, L., Ma, J.: From traces to trajectories: How well can we guess activity locations from mobile phone traces? Transp. Res. Part C 46, 326–337 (2014)CrossRef
11.
Chen, C., Ma, J., Susilo, Y., Liu, Y., Wang, M.: The promises of big data and small data for travel behavior (aka human mobility) analysis. Transp. Res. Part C 68, 285–299 (2016)CrossRef
12.
Comendador, J., López-Lambas, M.E.: Pain and joy of a panel survey on transport studies. Trans. Res. Proc. 18, 248–255 (2016)
13.
Cottrill, C., Pereira, F., Zhao, F., Dias, I., Lim, H., Ben-Akiva, M., Zegras, P.: Future Mobility Survey: Experience in developing a smartphone-based travel survey in Singapore. Transp. Res. Rec. 2354, 59–67 (2013)CrossRef
14.
Danalet, A., Mathys, N.A.: The potential of smartphone data for national travel surveys. Paper presented at the 17th Swiss transport research conference, Monte Verità/Ascona, 17–19 May 2017 (2017)
15.
Demissie, M., Phithakkitnukoon, S., Sukhvibul, T., Lisboa Bento, B.C.: Inferring passenger travel demand to improve urban mobility in developing countries using cell phone data: a case study of Senegal. IEEE Trans. Intell. Transp. Syst. 17, 2466–2478 (2016)CrossRef
16.
17.
Gadzinski, J.: Perspective of the use of smartphones in travel behavior studies: findings from a literature review and a pilot study. Transp. Res. Part C 88, 74–86 (2018)CrossRef
18.
Geurs, K.T., Thomas, T., Bijlsma, M., Douhou, S.: Automatic trip and mode detection with MoveSmarter: First results from the Dutch mobile mobility panel. Trans. Res. Proc. 11, 247–262 (2015)
19.
Gohar, M., Muzammal, M., Rahman, A.: SMART TSS: Defining transportation system behavior using big data analytics in smart cities. Sustain. Cities Soc. 41, 114–119 (2018)CrossRef
20.
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(5), 779–782 (2008)CrossRef
21.
Gould, J.: Cell phone enabled travel survey: the medium moves the message In: Zmud, J., Lee-Gosselin, M., Munizaga, M., Carrasco, J.A. (eds.) Transportation survey methods, 1st edn. Bangley, UK, pp. 51–70 (2013)CrossRef
22.
Greaves, S., Ellison, R., Rance, D., Standen, C., Rissel, C., Crane, M.: A web-based diary and companion smartphone app for travel/activity surveys. Trans. Res. Proc. 11, 279–310 (2015)
23.
Hoogendoorn-Lanser, S., Schaap, N.T.W., OldeKalte, M.: The Netherlands mobility panel: an innovative design approach for web-based longitudinal travel data collection. Trans. Res. Proc. 11, 311–329 (2015)
24.
Ingvardson, J.B., Nielsen, O.A., Raveau, S., Nielsen, B.F.: Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis. Trans. Res. Part C 90, 292–306 (2018)CrossRef
25.
26.
Iqbal, S., Choudhury, C.F., Wang, P., González, M.C.: Development of origin–destination matrices using mobile phone call data. Transp. Res. Part C 40, 63–74 (2014)CrossRef
27.
Ketelaar, P.E., Van, B.M.: The Smartphone as your follower: The role of smartphone literacy in the relation between privacy concerns, attitude and behavior toward phone-embedded tracking. Comput. Hum. Behav. 78, 174–182 (2018)CrossRef
28.
Kitchin, R.: The real-time city? Big data and smart urbanism. Geo J. 79, 1–14 (2014)
29.
Liu, Y., Kang, C., Gao, S., Xiao, Y., Tian, Y.: Understanding intra-urban trip patterns from taxi trajectory data. Geo. Syst. 14, 463–483 (2012)CrossRef
30.
Long, Y., Thill, J.C.: Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing. Comput. Environ. Urban Syst. 53, 19–35 (2015)CrossRef
31.
Maruyama, T., Sato, Y., Nohara, K., Imura, S.: Increasing smartphone-based travel survey participants. Trans. Res. Proc. 11, 280–288 (2015)
32.
Mohammadian, K., Bricka, S.: Conducting travel surveys using portable devices-role of technology in travel surveys. Trans. Res. Proc. 11, 242–246 (2015)
33.
Montini, L., Prost, S., Schrammel, J., Schussler, N.R., Axhausen, K.W.: Comparison of travel diaries generated from smartphones data and dedicated GPS devices. Trans. Res. Proc. 11, 227–241 (2015)
34.
Nitsche, P., Peter, W., Simon, B., Brandle, N., Maurer, P.: A strategy on how to utilize smartphones for automatically reconstructing trips in travel surveys. Proc. Soc. Behav. Sci. 48, 1033–1046 (2012)CrossRef
35.
Nitsche, P., Peter, W., Simon, B., Brandle, N., Maurer, P.: Supporting large-scale travel surveys with smartphones–a practical approach. Transp. Res. Part C 43, 212–221 (2014)CrossRef
36.
Pedersen, M.J., Nielsen, C.V.: Improving survey response rates in online panels: effects of low-cost incentives and cost-free text appeal interventions. Soc. Sci. Comput. Rev. 34(2), 229–243 (2016)CrossRef
37.
Pew Research Center: App vs. web for surveys of smartphone users (2015)
38.
Pew Research Center: Smartphone ownership and internet usage continues to climb in emerging economies (2016)
39.
Phithakkitnukoon, S., Horanont, T., Lorenzo, G.D., Shibasaki, R., Ratti, C.: Activity-aware map: identifying human daily activity pattern using mobile phone data. Human Behav. Underst. 6219, 14–25 (2010)CrossRef
40.
Qudratullah, Maruyama, T.: Reasons for non-response to smartphone-based travel survey in two Afghanistan cities. Asian Trans. Stud. 5(3), 523–542 (2019)
41.
Qudratullah, Khalid, A., Maruyama, T., Sato, Y.: Investigating response, data sending, and completion rates of a smartphone-based travel survey conducted in Kabul, Afghanistan, presented at 15th World Conference on Transport Research - WCTR 2019 Mumbai 26–31 May (2019)
42.
Safi, H., Assemi, B., Mesbah, M., Ferreira, L.: An empirical comparison of four technology-mediated travel survey methods. J. Traffic Trans. Eng. 4(1), 80–87 (2017)
43.
Sato, Y., Maruyama, T.: Examining difference between paper-based and web-Based self-Reported departure/arrival time using smartphone-based survey, to be presented at 15th World Conference on Transport Research-WCTR 2019 Mumbai 26–31 May (2019)
44.
Shin, D., Aliaga, D., Tuncer, B., Arisona, S.M., Kim, S., Zund, D., Schmitt, G.: Using smartphones for transportation mode classification. Comput. Environ. Urban Syst. 53, 76–86 (2015)CrossRef
45.
Stopher, P.: Collecting, Managing, and Assessing Data Using Sample Survey, 1st edn. Cambridge University Press, New York, USA (2012)
46.
Susilo, Y., Woodcock, A., Liotopoulos, F.: Deploying traditional and smartphone app survey methods in measuring door-to-door travel satisfaction in eight European cities. Trans. Res. Proc. 25, 257–2275 (2017)
47.
Thomas, T., Puello, L.L.P., Geurs, K.: Intrapersonal mode choice variation: evidence from a four-week smartphone-based travel survey in the Netherlands. J. Trans. Geogr. (2018) (in press)
48.
TRB Travel survey methods committee (ABJ40): The online travel survey manual: a dynamic documents for transportation professionals, chapter 26.0 using smartphones for travel behavior studies (2014). Accessed 20 Aug 2018
49.
Vich, G., Marquet, O., Guasch, C.M.: Suburban commuting and activity spaces: using smartphones tracking data to understand the spatial extent of travel behavior. Geogr. J. 183, 426–439 (2017)CrossRef
50.
Wang, Z., He, S.Y., Leung, Y.: Applying mobile phone data to travel behavior research: a literature. Travel Behav. Soc. 11, 141–155 (2018)CrossRef
51.
Zegras, P.C., Li, M., Kilic, T., Lozano-Gracia, N., Ghorpade, A., Tiberti, M., Aguilera, A.I., Zhao, F.: Assessing the representativeness of a smartphone-based household travel survey in Dar es Salaam. Tanzan. Trans. 45(2), 335–363 (2018)CrossRef
52.
Zhao, F., Pereira, F.C., Ball, R., Kim, Y., Han, Y., Zegras, C., Ben-Akiva, M.: Exploratory analysis of a smartphone-based travel survey in Singapore. Transp. Res. Rec. 2494, 45–56 (2015)CrossRef
53.
Zhao, Z., Koutsopoulos, H.N., Zhao, J.: Individual mobility prediction using transit smart card data. Trans. Res. Part C 89, 19–34 (2018)CrossRef
54.
Zhong, G., Yin, T., Zhang, J., He, S., Ran, B.: Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data. Transportation (2018) (in press)
55.
Zhu, J., Ye, X.: Development of destination choice model with pairwise district-level constants using taxi GPS data. Transp. Res. Part C 93, 410–424 (2018)CrossRef
Metadata
Title
Strategies to Increase the Response Rate of Smartphone-Based Travel Surveys in Afghanistan: Exploring the Effects of Incentives and Female Survey Conductors
Authors
Qudratullah
Takuya Maruyama
Copyright Year
2019
Publisher
Springer Singapore
Book
Intelligent Transport Systems for Everyone’s Mobility

Print ISBN: 978-981-13-7433-3

Electronic ISBN: 978-981-13-7434-0

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-7434-0_18

Premium Partners

    Image Credits