Abstract
MobilitApp is a platform designed to provide smart mobility services in urban areas. It is designed to help citizens and transport authorities alike. Citizens will be able to access the MobilitApp mobile application and decide their optimal transportation strategy by visualising their usual routes, their carbon footprint, receiving tips, analytics and general mobility information, such as traffic and incident alerts. Transport authorities and service providers will be able to access information about the mobility pattern of citizens to offer their best services, improve costs and planning. The MobilitApp client runs on Android devices and records synchronously, while running in the background, periodic location updates from its users. The information obtained is processed and analysed to understand the mobility patterns of our users in the city of Barcelona, Spain.
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References
DetectedActivity Google APIs for Android. https://developers.google.com/android/reference/com/google/android/gms/location/DetectedActivity. Accessed 18 Aug 2015
Funf open sensing framework. http://www.funf.org. Accessed 18 July 2015
MobilitApp Android App on Google Play. https://play.google.com/store/apps/details?id=com.mobi.mobilitapp. Accessed 18 July 2015
MobilitApp web App. http://mobilitapp.noip.me/. Accessed 18 July 2015
Hemminki, S., Nurmi, P., Tarkoma, S.: Accelerometer-based transportation mode detection on smartphones. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, p. 13. ACM (2013)
Michalevsky, Y., Nakibly, G., Schulman, A., Boneh, D.: PowerSpy: location tracking using mobile device power analysis. arXiv preprint arXiv:1502.03182 (2015)
Palazzi, C.E., Teodori, L., Roccetti, M.: 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. IEEE (2010)
Phan, T.: Improving activity recognition via automatic decision tree pruning. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 827–832. ACM (2014)
Sivakumar, R., Sathyanarayanan, R., Harikrishnan, T.: Battery optimization of Android phones by sensing the phone using hidden Markov model. J. Current Comput. Sci. Technol. 5(05) (2015)
Solove, D.J.: A taxonomy of privacy. University of Pennsylvania law review, pp. 477–564 (2006)
Townsend, A.M.: Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. WW Norton & Company, New York (2013)
Weber, A.M., Ladstätter, S., Luley, P., Pammer, V.: My places diary: automatic place and transportation-mode detection. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems, pp. 384–386. ICST (2014)
Yang, H.C., Li, Y.C., Liu, Z.Y., Qiu, J.: HARLib: a human activity recognition library on android. In: ICCWAMTIP 2014, pp. 313–315. IEEE (2014)
Acknowledgments
This work is supported by the Spanish Government through project INRISCO (INcident monitoRing In Smart COmmunities. QoS and Privacy, TEC2014-54335-C4-1-R). We are also grateful to Xavier Rosselló and Francesc Calvet from the Autoritat del Transport Metropolità de Barcelona for their valuable feedback during different stages of the project.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Puglisi, S., Moreira, Á.T., Torregrosa, G.M., Igartua, M.A., Forné, J. (2016). MobilitApp: Analysing Mobility Data of Citizens in the Metropolitan Area of Barcelona. In: Mandler, B., et al. Internet of Things. IoT Infrastructures. IoT360 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 169. Springer, Cham. https://doi.org/10.1007/978-3-319-47063-4_23
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DOI: https://doi.org/10.1007/978-3-319-47063-4_23
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