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MobilitApp

Improvement of algorithms to identify transportation modes for MobilitApp

gmarrugat
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MobilitApp

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  1. Improvement of algorithms to identify transportation modes for MobilitApp, an Android Application to anonymously track citizens in Barcelona Author: Gerard Marrugat Director: Mónica Aguilar Co-Director: Silvia Puglisi 26/05/2016

  2. 2/43 Index 1. 2. 3. Introduction MobilitApp Transport Mode Detection 3.1 APIs 3.2 Accelerometer Sensor Listener Analyzing Mobility Data 4.1 Collecting Data 4.2 Analyzing Data 4.3 Mobility Patterns Extra Features Conclusions and Future Work 4. 5. 6.

  3. 3/43 Index 1. Introduction 2. MobilitApp 3. Transport Mode Detection 1. APIs 2. Accelerometer Sensor Listener 4. Analyzing Mobility Data 1. Collecting Data 2. Analyzing Data 3. Mobility Patterns 5. Extra Features 6. Conclusions and Future Work

  4. 4/43 Introduction Smart City Urban and technological development focused on sustainability and able to satisfy citizens’ needs Smart City Areas Public Services • Socio-Cultural Environment • Medicine & Health • Sustainability • Image source: http://www.kikusui.co.jp/en/company-info

  5. 5/43 Introduction Smart Mobility Image source: http://www.arup.com/smart_mobility E-Mobility Non Fossil Fuels Smart Parking Car Sharing

  6. 6/43 Index 1. 2. MobilitApp 3. Transport Mode Detection 1. APIs 2. Accelerometer Sensor Listener 4. Analyzing Mobility Data 1. Collecting Data 2. Analyzing Data 3. Mobility Patterns 5. Extra Features 6. Conclusions and Future Work Introduction

  7. 7/43 MobilitApp Image source: http://www.arup.com/smart_mobility In collaboration with (Autoritat del Transport Metropolità)

  8. 8/43 MobilitApp • Anonymously Citizens Tracking Google APIs •Transportation Mode Detection • Real-Time Traffic Information Open Data Image source: MobilitApp Project

  9. 9/43 MobilitApp Requirements o Minimum OS version: - Android 3.0 (Honeycomb) o Location Mode: - High Accuracy (GPS+WPS) - Battery Saving (WPS) o Wi-Fi enabled GPS (Global Positioning System) WPS (Wi-Fi Positioning System) Image source: www.elcomercio.es

  10. 10/43 Index 1. 2. 3. Transport Mode Detection 1. APIs 2. Accelerometer Sensor Listener 4. Analyzing Mobility Data 1. Collecting Data 2. Analyzing Data 3. Mobility Patterns 5. Extra Features 6. Conclusions and Future Work Introduction MobilitApp

  11. 11/43 Transport Mode Detection Activity Recognition Places Directions Image source: http://apievangelist.com/2011/05/21/google-apis-console/ Problems • Not distinguish among motorized transports • External Service • Network connection • Providing Information

  12. 12/43 Transport Mode Detection Image Source: shutterstock

  13. 13/43 Transport Mode Detection Mobile Accelerometer Local Data Low Power Consumption Acceleration value along axis Sensor/Feature Consumption Accelerometer Magnetic Field Gyroscope WiFi GPS 0.23 mA 6.8 mA 6.1 mA 330 mA 145 mA Image Source:https://developer.android.com/ Gyroscope is also listened -> Rate of rotation around axis

  14. 14/43 Transport Mode Detection The State of Art •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) ref: https://goo.gl/dpl59g • 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 ACM (2014) ref: http://goo.gl/Lp7Nru Independent Application Tool to Collect Data Image Source: http://flightsafety.org/aerosafety-world- magazine/october-2013/data-delirium

  15. 15/43 Transport Mode Detection ASL Application Interface Data Data Collected Collected Timestamp X-Acceleration Y-Acceleration Z-Acceleration Image source: MobilitApp Project

  16. 16/43 Transport Mode Detection Once process collection is finished... Saved in a File Sent to the Server Deleted from Mobile

  17. 17/43 Transport Mode Detection Including ASL in MobilitApp Users Collaboration Image source: MobilitApp Project

  18. 18/43 Transport Mode Detection • ASL -> Background Service • 20 seconds every 2 minutes • Storage and Upload

  19. 19/43 Index 1. 2. 3. Introduction MobilitApp Transport Mode Detection 1. APIs 2. Accelerometer Sensor Listener 4. Analyzing Mobility Data 1. Collecting Data 2. Analyzing Data 3. Mobility Patterns 5. Extra Features 6. Conclusions and Future Work

  20. 20/43 Analyzing Mobility Data Total Downloads: 25 Observed Users: 7 Transportation Modes: 5 Spent Time: 30 h 15 min Note • Execution Times • Mobile Phone Location

  21. 21/43 Analyzing Mobility Data Different Transports -> Different Behaviours Horizontal Acceleration Statistical Analysis Peak Analysis

  22. 22/43 Analyzing Mobility Data Bus Horizontal Acceleration

  23. 23/43 Analyzing Mobility Data Car Horizontal Acceleration

  24. 24/43 Analyzing Mobility Data Motorbike Horizontal Acceleration

  25. 25/43 Analyzing Mobility Data Metro Horizontal Acceleration

  26. 26/43 Analyzing Mobility Data Train Horizontal Acceleration

  27. 27/43 Analyzing Mobility Data Statistical Analysis Peak Analysis Parameters Parameters Peak Area Mean Standard Deviation Peak Interval Variance Root Mean Square Error Maximum Value Minimum Value

  28. 28/43 Analyzing Mobility Data Statistical Analysis Standard Deviation (m/s2) Maximum (m/s2) Minimum (m/s2) Mean (m/s2) 5,17 0,72 8,2 0,015 Bus 5,26 0,63 6,14 2,45 Car 3,03 0,7 5,55 1,26 Motorbike 3,32 2,52 13,7 0,004 Metro 4,13 2,41 12,36 0,097 Train

  29. 29/43 Analyzing Mobility Data Road Vehicles Standard Deviation (m/s2) Maximum (m/s2) Minimum (m/s2) Mean (m/s2) 5,17 0,72 8,2 0,015 Bus 5,26 0,63 6,14 2,45 Car 3,03 0,7 5,55 1,26 Motorbike Rail Vehicles 3,32 2,52 13,7 0,004 Metro 4,13 2,41 12,36 0,097 Train

  30. 30/43 Analyzing Mobility Data Peak Analysis Road Vehicles Interval Length (s) Peak Area (m/s) 0,5 1,1 Bus 1,37 0,78 Car 0,52 0,66 Motorbike Rail Vehicles 0,6 1,57 Metro 0,67 1,65 Train

  31. 31/43 Analyzing Mobility Data Road Vehicles Interval Length (s) Peak Area (m/s) 0,5 1,1 Bus 1,37 0,78 Car 0,52 0,66 Motorbike Rail Vehicles Metro 0,6 1,57 0,67 1,65 Train  No differences among Rail Vehicles  Future Work

  32. 32/43 Analyzing Mobility Data

  33. 33/43 Index 1. 2. 3. Introduction MobilitApp Transport Mode Detection 1. APIs 2. Accelerometer Sensor Listener Analyzing Mobility Data 1. Collecting Data 2. Analyzing Data 3. Mobility Patterns 5. Extra Features 6. Conclusions and Future Work 4.

  34. 34/43 Extra Features Raspberry Pi 2 Model B Services - Web Page - Data Storage Features - Broadcom BCM2835 system on a chip (SoC) - 900MHz quad-core ARM Cortex-A7 CPU - 1GB de RAM - Debian Linux ARM

  35. 35/43 Extra Features - Low Power Consumption: 3,5 W/h -Processor Capacity Limited - Reduced Price (45$) - RAM memory - Size (6cm x 9cm)

  36. 36/43 Extra Features Image source: MobilitApp Project mobilitapp.noip.me

  37. 37/43 Extra Features MobilitApp Promotional Video Image source: MobilitApp Project

  38. 38/43 Extra Features •Silvia Puglisi, Ángel Torres Moreira, Gerard Marrugat Torregrosa, Mónica Aguilar Igartua and Jordi Forné. MobilitApp: Analysing mobility data of citizens in the metropolitan area of Barcelona. EAI International Conference on Smart Objects and Technologies for Social Good, October 2015 Ref: https://arxiv.org/abs/1605.06536 •Silvia Puglisi, Gerard Marrugat, Mónica Aguilar and Jordi Forné. How do you get there? Identifying means of transportation from mobile sensors patterns. The Communications&Networking Conference,January 2017(in process) 14th Annual IEEE Consumer

  39. 39/43 Index 1. 2. 3. Introduction MobilitApp Transport Mode Detection 1. APIs 2. Accelerometer Sensor Listener Analyzing Mobility Data 1. Collecting Data 2. Analyzing Data 3. Mobility Patterns Extra Features 6. Conclusions and Future Work 4. 5.

  40. 40/43 Conclusions and Future Work • Accelerometer Sensor Listener -> Scalable Solution • Patterns -> Transport Detection Task • Mobility Data Image source: https://www.traffic-masters.net

  41. 41/43 Conclusions and Future Work o E-Call o Transport Mode Detection Algorithm o Attractive to users o WiFi Metro Station o Improve Infrastructure

  42. 42/43 mobilitapp.noip.me MobilitApp Promotional Video

  43. 43/43 Improvement of algorithms to identify transportation modes for MobilitApp, an Android Application to anonymously track citizens in Barcelona Thank you Author: Gerard Marrugat Director: Mónica Aguilar Co-Director: Silvia Puglisi

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