Indoor and Outdoor Navigation in Smart Mobility Usage Scenarios

Chapter

Abstract

Business and privately motivated journeys today are often driven by ad hoc decisions and a mix of different means of transport. A very distinct property of any mobility scenario that claims to be Smart is the fact that it needs to incorporate contextual and personal preferences during the selection process of a given means of transport at a given time and place. This results in great challenges, especially where different means of transport intersect, like at train stations, park and ride spots, parking garages, where travelers switch from one mode to another. In order to support a smooth transition, Smart Mobility systems need to allow for a seamless transition supported by an indoor-outdoor navigation solution. This chapter will highlight the different components needed to build up a seamless indoor-outdoor navigation solution, including spatial data management, indoor positioning, visualization of indoor maps, and a user-friendly turn-by-turn navigation. Furthermore, it will also discuss possible business models to justify the investments needed for an indoor-outdoor navigation system.

Keywords

Europe Transportation Marketing 

References

  1. [30]
    Uber (2016) Website https://www.uber.com/de/. Accessed 1 8 Sept 2016
  2. [123]
    Drive Sweden (2016) Website http://www.drivesweden.net/en. Accessed 1 8 Sept 2016
  3. [124]
    Cradle-to-Cradle Products Innovation Institute (2016) About the institute. http://www.c2ccertified.org/about. Accessed 1 8 Sept 2016
  4. [125]
    Wikipedia (2016) Grenander, Alfred (1925). https://de.wikipedia.org/wiki/Alfred_Grenander. Accessed 1 8 Sept 2016
  5. [126]
    Wikipedia (2016) U-Bahnhof Samariterstraße 1930. https://de.wikipedia.org/wiki/U-Bahnhof_Samariterstraße. Accessed 1 8 Sept 2016
  6. [127]
  7. [128]
  8. [129]
    Maslow AH (1943) A theory of human motivation. Psychol Rev 50(4): 370–396CrossRefGoogle Scholar
  9. [130]
    Teleatlas (2016) Website www.teleatlas.com. Accessed 1 8 Sept 2016
  10. [131]
  11. [132]
    openstreetmap (2016) Website www.openstreetmap.org. Accessed 1 8 Sept 2016
  12. [133]
    Neis P, Zielstra D, Zipf A, Struck A (2010) Empirische Untersuchung zur Datenqualität von OpenStreetMap – Erfahrungen aus zwei Jahren Betrieb mehrerer OSM-Online-Dienste, AGIT 2010. Symposium für Angewandte Geoinformatik, SalzburgGoogle Scholar
  13. [134]
    Goetz M, Zipf A (2011) Extending OpenStreetMap to indoor environemnts, bringing volunteered geographic information to the next level. In: Rumor M, Zlatanova S, LeDoux H (eds) Urban and Regional Data Management: UDMS Annual 2011, Delft, pp 47–58Google Scholar
  14. [135]
    Google Maps (2016) Website www.google.de/maps. Accessed 2 0 Sept 2016
  15. [136]
    Bing Maps (2016) Website https://www.bing.com/mapspreview?cc=de. Accessed 2 0 Sept 2016
  16. [137]
    Dechter R, Pearl J (1985) Generalized best-first search strategies and the optimality of A*. J ACM 32(3): 505–536MathSciNetCrossRefMATHGoogle Scholar
  17. [138]
    Bauer M, Neis P, Weber C, Zipf A (2007) Kontextabhängige Landmarken für mobile 3D Navigationsanwendungen. In: 4. Fachgespräch: Ortsbezogene Anwendungen und Dienste. LMU, MünchenGoogle Scholar
  18. [139]
    Winter S, Tomko M, Elias B, Sester M (2008) Landmark hierarchies in context. Environ Plann B 35(3): 381–398CrossRefGoogle Scholar
  19. [140]
    Mytaxi (2016) Website https://de.mytaxi.com/index.html. Accessed 1 8 Sept 2016

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2017

Authors and Affiliations

  1. 1.Heidelberg mobil international GmbHHeidelbergGermany
  2. 2.Heidelberg mobil international GmbHHeidelbergGermany

Personalised recommendations