Mobile Networks and Applications

, Volume 23, Issue 1, pp 167–176 | Cite as

Integrating Personalized and Accessible Itineraries in MaaS Ecosystems Through Microservices

  • Andrea Melis
  • Silvia MirriEmail author
  • Catia Prandi
  • Marco Prandini
  • Paola Salomoni
  • Franco Callegati


Mobility is a crucial sector for the livability of urban spaces, both in terms of accessibility for people with disabilities, and in terms of enjoyability by people with different interests. The deep transformation mobility is undergoing, heading towards commoditization of the full spectrum of transportation services, can lead to efficient solutions based on the same principle for all these needs. This paper shows how the approach based on the flexible orchestration of microservices allows to build applications that are, at the same time, more easily suited to the specific needs of different user categories, and more seamlessly integrated in the Mobility as a Service approach to smart mobility.


Mobility-as-a-Service Microservices Users with special needs 


  1. 1.
    Mirri S, Prandi C, Salomoni P (2014) A context-aware system for personalized and accessible pedestrian paths, In: 2014 International Conference on High Performance Computing & Simulation (HPCS), pp 833–840, IEEEGoogle Scholar
  2. 2.
    Palazzi CE, Teodori L, Roccetti M (2010) Path 2.0: A participatory system for the generation of accessible routes, In: 2010 IEEE International Conference on Multimedia and Expo (ICME), pp 1707–1711, IEEEGoogle Scholar
  3. 3.
    Pippuri S, Hietanen S, Pyyhtiä K. Maas finland. Scholar
  4. 4.
    Banerjee P, Friedrich R, Bash C, Goldsack P, Huberman B, Manley J, Patel C, Ranganathan P, Veitch A (2011) Everything as a service: Powering the new information economy. Computer 3:36–43CrossRefGoogle Scholar
  5. 5.
    Melis A, Mirri S, Prandi C, Prandini M, Salomoni P, Callegati F (2016) Crowdsensing for smart mobility through a service-oriented architecture, In: 2016 IEEE International Conference on Smart Cities Conference (ISC2), IEEEGoogle Scholar
  6. 6.
    Talasila M, Curtmola R, Borcea C (2013) Improving location reliability in crowd sensed data with minimal efforts, In: Wireless and Mobile Networking Conference (WMNC), 2013 6th Joint IFIP, pp 1–8, IEEEGoogle Scholar
  7. 7.
    Prandi C, Roccetti M, Salomoni P, Nisi V, Nunes NJ (2016) Fighting exclusion: a multimedia mobile app with zombies and maps as a medium for civic engagement and design. Multimedia Tools and Applications, pp 1–29Google Scholar
  8. 8.
    Petkovics Á, Simon V, Gódor I., Böröcz B (2015) Crowdsensing solutions in smart cities: Introducing a horizontal architecture, In: 13th International Conference on Advances in Mobile Computing and Multimedia (MoMM 2015), vol. 13, pp 33–37, ACMGoogle Scholar
  9. 9.
    Cortellazzi J, Foschini L, De Rolt CR, Corradi A, Neto CAA, Alperstedt GD (2016) Crowdsensing and proximity services for impaired mobility, In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp 44–49, IEEEGoogle Scholar
  10. 10.
    Mirri S, Prandi C, Salomoni P, Callegati F, Melis A, Prandini M (2016) A service-oriented approach to crowdsensing for accessible smart mobility scenarios. Mob Inf Syst:Google Scholar
  11. 11.
    Sassi A, Zambonelli F (2014) Coordination infrastructures for future smart social mobility services. IEEE Intell Syst 5(29):78–82CrossRefGoogle Scholar
  12. 12.
    Prandi C, Nisi V, Salomoni P, Nunes NJ (2015) From gamification to pervasive game in mapping urban accessibility, In: Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter, pp 126–129, ACMGoogle Scholar
  13. 13.
    Goncalves J, Hosio S, Rogstadius J, Karapanos E, Kostakos V (2015) Motivating participation and improving quality of contribution in ubiquitous crowdsourcing. Comput Netw 90:34–48CrossRefGoogle Scholar
  14. 14.
    Zambonelli F (2011) Pervasive urban crowdsourcing: Visions and challenges, In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp 578–583, IEEEGoogle Scholar
  15. 15.
    Mirri S, Prandi C, Salomoni P, Callegati F, Campi A (2014) On combining crowdsourcing, sensing and open data for an accessible smart city, In: 2014 Eighth Interna- tional Conference on Next Generation Mobile Apps, Services and Technologies, pp 294–299, IEEEGoogle Scholar
  16. 16.
    Melis A, Mirri S, Prandi C, Prandini M, Salomoni P, Callegati F (2016) A microservice architecture use case for persons with disabilities, In: 2nd EAI International Conference on Smart Objects and Technologies for Social Good, EAIGoogle Scholar
  17. 17.
    Anjum A, Ilyas MU (2013) Activity recognition using smartphone sensors, In: Consumer Communications and Networking Conference (CCNC), 2013 IEEE, pp 914–919, IEEEGoogle Scholar
  18. 18.
    Bujari A, Licar B, Palazzi CE (2012) Movement pattern recognition through smartphone’s accelerometer, In: Consumer communications and networking conference (CCNC), 2012 IEEE, pp 502–506, IEEEGoogle Scholar
  19. 19.
    Kjærgaard MB, Wirz M, Roggen D, Tröster G (2012) Detecting pedestrian flocks by fusion of multi-modal sensors in mobile phones, In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp 240–249, ACMGoogle Scholar
  20. 20.
    Iwasawa Y, Nagamine K, Yairi IE, Matsuo Y (2015) Toward an automatic road accessibility information collecting and sharing based on human behavior sensing technologies of wheelchair users. Proced Comput Sci 63:74–81CrossRefGoogle Scholar
  21. 21.
    Gygi B (2001) Factors in the identification of environmental sounds. PhD thesis, faculty of the University Graduate School in partial fulfillment of the requirements for the degree Doctor of Philosophy in the Department of Psychology, Indiana UniversityGoogle Scholar
  22. 22.
    Malkin RG, Waibel A (2005) Classifying user environment for mobile applications using linear autoencoding of ambient audio, In: Proceedings.(ICASSP’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., vol 5, pp v–509, IEEEGoogle Scholar
  23. 23.
    Peltonen V, Tuomi J, Klapuri A, Huopaniemi J, Sorsa T (2002) Computational auditory scene recognition, In: Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, vol. 2, pp II–1941, IEEEGoogle Scholar
  24. 24.
    Chu S, Narayanan S, Kuo C-CJ. (2009) Environmental sound recognition with time–frequency audio features. IEEE Trans Audio, Speech, Lang Process 17(6):1142–1158CrossRefGoogle Scholar
  25. 25.
    Ellis DP (1996) Prediction-driven computational auditory scene analysis for dense sound mixtures, In: Proceedings of the 1996b ESCA workshop on the Auditory Basis of Speech PerceptionGoogle Scholar
  26. 26.
    Hong J-y, Suh E-H, Kim S-J (2009) Context-aware systems: A literature review and classification. Expert Syst Appl 36(4):8509–8522CrossRefGoogle Scholar
  27. 27.
    Callegati F, Campi A, Melis A, Prandini M, Zevenbergen B (2015) Privacy-preserving design of data processing systems in the public transport context. Pac Asia J Assoc Inf Syst 7(4):Google Scholar
  28. 28.
    Callegati F, Prandini M, Melis A, Sartori L (2016) Public transportation, iot, trust and urban habits, In: 3rd international conference on Internet ScienceGoogle Scholar
  29. 29.
    Sampo Hietanen CEO I-F (2014) Mobility as a Service the new transport model?. Technical report, MaaS FinlandGoogle Scholar
  30. 30.
    Fowler M, Microservices JL Microservices.
  31. 31.
    Dragoni N, Giallorenzo S, Lluch-Lafuente A, Mazzara M, Montesi F, Mustafin R, Safina L (2016) Microservices: yesterday, today, and tomorrow, CoRR, vol abs/1606.04036Google Scholar
  32. 32.
    Machado R, El-Khoury R (1995) Monolithic architecture. Prestel Publishing,Google Scholar
  33. 33.
    Merkel D (2014) Docker: lightweight linux containers for consistent development and deployment. Linux J 2014(239):2Google Scholar
  34. 34.
    Newman S (2015) Building Microservices. O’Reilly Media Inc.,Google Scholar
  35. 35.
    Erl T (2005) Service-Oriented Architecture: Concepts, Technology, and Design. Prentice Hall PTR, NJ, USAGoogle Scholar
  36. 36.
    Christensen E, Curbera F, Meredith G, Weerawarana S et al (2001) Web services description language (wsdl) 1.1Google Scholar
  37. 37.
    Greene W (2005) Providing secure data and policy exchange between domains in a multi-domain grid by use of a service ecosystem facilitating uses such as supply-chain integration with rifd tagged items and barcodes. US Patent App. 11/069, 479Google Scholar
  38. 38.
    Greene W (2004) System and method for use of mobile policy agents and local services, within a geographically distributed service grid, to provide greater security via local intelligence and life-cycle management for rfld tagged items. US Patent App. 10/913,887Google Scholar
  39. 39.
    Lea G (2015) Microservices security: All the questions you should be asking.
  40. 40.
    Callegati F, Giallorenzo S, Melis A, Prandini M (2017) Insider threats in emerging mobility-as-a-service scenarios, In: 2017 50th Hawaii International Conference on System Science (HICSS)Google Scholar
  41. 41.
    Prandi C, Ferretti S, Mirri S, Salomoni P (2015) Trustworthiness in crowd-sensed and sourced georeferenced data, In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp 402–407, IEEEGoogle Scholar
  42. 42.
    Callegati F, Giallorenzo S, Melis A, Prandini M (2016) Data security issues in maas-enabling platforms, In: International Forum on Research and Technologies for Society and IndustryGoogle Scholar
  43. 43.
    Rochwerger B, Breitgand D, Levy E, Galis A, Nagin K, Llorente IM, Montero R, Wolfsthal Y, Elmroth E, Caceres J et al (2009) The reservoir model and architecture for open federated cloud computing. IBM J Res Dev 53(4):4–1CrossRefGoogle Scholar
  44. 44.
    Buyya R, Ranjan R, Calheiros RN (2010) Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services, In: Algorithms and architectures for parallel processing, pp 13–31, SpringerGoogle Scholar
  45. 45.
    Howe J (2006) The rise of crowdsourcing. Wired Mag 14(6):1–4Google Scholar
  46. 46.
    Parker JM (2010) Applying a system of systems approach for improved transportation, SAPI EN. S. Surveys and Perspectives Integrating Environment and Society, no. 3.2Google Scholar
  47. 47.
    Montesi F, Guidi C, Zavattaro G (2007) Composing services with jolie, In: 2007. ECOWS’07. Fifth European Conference on Web Services, pp 13?-22, IEEEGoogle Scholar
  48. 48.
    Montesi F, Guidi C, Zavattaro G (2014) Service-oriented programming with jolie, In: Web Services Foundations, pp 81–107, SpringerGoogle Scholar
  49. 49.
  50. 50.
    OpentripplannerGoogle Scholar
  51. 51.
    Mirri S, Prandi C, Salomoni P (2016) Personalizing pedestrian accessible way-finding with mpass, In: 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp 1119–1124, IEEEGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Andrea Melis
    • 1
  • Silvia Mirri
    • 2
    Email author
  • Catia Prandi
    • 2
  • Marco Prandini
    • 2
  • Paola Salomoni
    • 2
  • Franco Callegati
    • 1
  1. 1.Department of Electrical and Information EngineeringUniversitá di BolognaBolognaItaly
  2. 2.Department of Computer Science and EngineeringUniversitá di BolognaBolognaItaly

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