Leveraging Cellphones for Wayfinding and Journey Planning in Semi-formal Bus Systems: Lessons from Digital Matatus in Nairobi

  • Jacqueline Klopp
  • Sarah Williams
  • Peter Waiganjo
  • Daniel Orwa
  • Adam White
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


For many cities in the developing world, public transit consists mainly of semi-formal mini-buses (paratransit). However, little to no digital information is typically available on routes, bus stops, passenger boarding, service frequency or scheduled trip times. Cities that rely on these bus systems can benefit from the generation of digital data on these systems for planning and passenger information purposes. Perhaps more importantly, this data can provide the ability to generate citizen-based information tools, such as transit routing applications for mobile devices widely discussed in the smart cities dialog (Townsend in Re-Programming mobility: the digital transformation of transportation in the United States, 2014). Through our work in Nairobi, this paper shows that cell-phone technology that is ubiquitous in most countries can be used to generate a dataset in an open standard, GTFS. Citizens can then leverage open source tools made for that standard, enhancing access to information about the transit system. We argue that one of the most important components of our work in Nairobi was the engagement process that created trust in the data and knowledge of its existence for the development of civic technologies. The lessons learned in Nairobi can be translated to other areas with the potential to use mobile applications to develop data on essential urban infrastructure and to extend the use of that data by sharing it with a larger community.


Cell Phone Data Collection Process Smart City Public Transit Transit System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work would not have been possible with the critical thinking and hard work of the University of Nairobi, School of Computing & Informatics students: Ikamar Ekessa, Peter Kamiri, Samuel Kariu, Maureen Mbinya, Jackson Mutua, Mureri Ntwiga. Researchers at MIT’s Civic Data Design Lab also contributed significantly to the work including; Jonathan Andrew Campbell, Emily Eros, Alexis Howland, Lindiwe Rennert, Alicia Rouault, Christopher Van Alstyne, Catherine Vanderwaart. Special thanks to Wenfei Xu from the Civic Data Design lab who was instrumental in the development of the Matatu Map. Thanks also to Professor Lawrence Esho and his students at the Technical University of Kenya for their valuable feedback on the Digital Matatu map. We also gratefully acknowledge the support of the Rockefeller Foundation which provided a grant for this work. Special thanks to Benjamin de la Pena at the Rockefeller Foundation for his support of and critical insights on this work. We also benefitted greatly from conversations that compared projects with Prof. Chris Zegras, Albert Ching, Stephen Kennedy, Neil Taylor, Kevin Webb and Emily Jean Eros. Last but not least, we thank James Gachanja and Dr. Zachary Gariy at KIPPRA for hosting the workshops and the Kenya Alliance of Resident Associations for their work on the launch of the data and map for this work helped our thinking, made connections and showed us a way forward in sustaining data collection work.


  1. Caceres, N., Romero, L. M., Benitez, F. G., & Del Castillo, J. M. (2012). Traffic flow estimation models using cellular phone data. Intelligent Transportation Systems, IEEE Transactions on, 13(3), 1430–1441.CrossRefGoogle Scholar
  2. Ching, A., Zegras, C., Kennedy, S., Mamun, M. (2013). A user-flocksourced bus experiment in Dhaka: New data collection technique with smartphones. Transportation Research Record: Journal of the Transportation Research Board.Google Scholar
  3. Craig, W. (2005). White knights of spatial data infrastructure: The role and motivation of key individuals. URISA Journal, 16(2), 5–13.Google Scholar
  4. Elwood, S. (2006). Negotiating knowledge production: The everyday inclusions, exclusions, and contradictions of participatory GIS research. The Professional Geographer, 58(2), 197–208.CrossRefGoogle Scholar
  5. Elwood, S., Goodchild, M. F., & Sui, D. Z. (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the Association of American Geographers, 102(3), 571–590.CrossRefGoogle Scholar
  6. Eros, E., Mehndiratta, S., Zegras, C., Webb, K., Ochoa, M. C. (2014). Applying the general transit feed specification (GTFS) to the global south: Experiences in Mexico City and beyond. Transportation Research Record.Google Scholar
  7. Goodspeed, R. (2015). Smart cities: Moving beyond urban cybernetics to tackle wicked problems. Cambridge Journal of Regions, Economy and Society, 8(1), 79–92.Google Scholar
  8. Gosselin, K. (2011). Study finds access to real-time mobile information could raise the status of public transit. Next American City. Accessed March 6, 2015.
  9. Harvey, F., & Chrisman, N. (1998). Boundary objects and the social construction of GIS technology. Environment and Planning A, 30(9), 1683–1694.CrossRefGoogle Scholar
  10. Hein, J. R., Evans, J., & Jones, P. (2008). Mobile methodologies: Theory, technology and practice. Geography Compass, 2(5), 1266–1285.CrossRefGoogle Scholar
  11. Herrera, J. C., Work, D. B., Herring, R., Ban, X. J., Jacobson, Q., & Bayen, A. M. (2010). Evaluation of traffic data obtained via GPS-enabled mobile phones: The mobile century field experiment. Transportation Research Part C: Emerging Technologies, 18(4), 568–583.CrossRefGoogle Scholar
  12. Klopp, J. M., Marcello, E. M., Kirui, G., Wing, N., & Mwangi, H. (2013). Can the internet improve local governance? The case of Ruiru, Kenya. Information Polity, 18(1), 21–42.Google Scholar
  13. Klopp, J., Mutua, J., Orwa, D., Waiganjo, P., White, A., Williams, S. (2014). Towards a standard for paratransit data: Lessons from developing GTFS data for Nairobi’s Matatu system. In Transportation Research Board 93rd Annual Meeting (No. 14-5280).Google Scholar
  14. Klopp J., & Mitullah, W. (2015). Politics, policy and paratransit: A view from Nairobi. In R. Behrens, D. McCormick & D. Mfinanga (Ed.), Paratransit for African Cities. Routledge.Google Scholar
  15. Kyem, P. A. K. (2004). Of intractable conflicts and participatory GIS applications: The search for consensus amidst competing claims and institutional demands. Annals of the Association of American Geographers, 94(1), 37–57.Google Scholar
  16. McHugh, B. (2013). Pioneering open data standards: The GTFS story. Edited by Brett Goldstein with Lauren Dyson, 125.Google Scholar
  17. Ratti, C., Williams, S., Frenchman, D., & Pulselli, R. M. (2006). Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 33(5), 727.CrossRefGoogle Scholar
  18. Roth (2010). How google and Portland’s TriMet set the standard for open transit data. Streetsblog. Accessed January 5, 2014.
  19. Sieber, R. (2006). Public participation geographic information systems: A literature review and framework. Annals of the Association of American Geographers, 96(3), 491–507.CrossRefGoogle Scholar
  20. Talbot, D. (2013). African bus routes redrawn using cell-phone data. MIT Technology Review. Accessed October 6, 2014.
  21. Townsend, A. (2013). Smart cities: Big data, civic hackers, and the quest for a New Utopia. New York: W.W Norton.Google Scholar
  22. Townsend, A. (2014). Re-programming mobility: The digital transformation of transportation in the United States. . Accessed March 4, 2015.
  23. Wakefield, J. (2013). Mobile phone data redraws bus routes in Africa. BBC, Accessed September 30, 2014.
  24. Wang, H., Calabrese, F., Di Lorenzo, G., Ratti, C. (2010). Transportation mode inference from anonymized and aggregated mobile phone call detail records.In Intelligent Transportation Systems Conference (ITSC 2010), pp. 318–323.Google Scholar
  25. Williams, S., Marcello, E., & Klopp, J. M. (2014). Toward open source Kenya: Creating and sharing a GIS database of Nairobi. Annals of the Association of American Geographers, 104(1), 114–130.CrossRefGoogle Scholar
  26. Williams, S., White, A., Waiganjo, P., Orwa, D., Klopp, J. (Forthcoming). The digital Matatu project: Using cell phones to create an open source data for Nairobi’s semi-formal bus system. Journal of Transportation Geography (Working Paper Submitted October 2014).Google Scholar
  27. Wong, J. (2013). Leveraging the general transit feed specification for efficient transit analysis. Transportation Research Record: Journal of the Transportation Research Board, 2338(1), 11–19.CrossRefGoogle Scholar
  28. Woyciechowicz, A., & Shliselberg, R. (2005). Wayfinding in public transportation. Transportation Research Record: Journal of the Transportation Research Board, 1903(1), 35–42.CrossRefGoogle Scholar
  29. Zegras, P. C., Eros, E., Butts, K., Resor, E., Kennedy, S., Ching, A., Mamun, M. (2014). Tracing a path to knowledge? Indicative user impacts of introducing a public transport map in Dhaka, Bangladesh. Cambridge Journal of Regions, Economy and Society, rsu028.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jacqueline Klopp
    • 1
  • Sarah Williams
    • 2
  • Peter Waiganjo
    • 3
  • Daniel Orwa
    • 3
  • Adam White
    • 4
  1. 1.Center for Sustainable DevelopmentColumbia UniversityNew YorkUSA
  2. 2.Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.School of Computing and InformaticsUniversity of NairobiNairobi GPOKenya
  4. 4.GroupshotCambridgeUSA

Personalised recommendations