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Predicting bus arrival time based on participatory mobile phone sensing

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Sreeraj Puzhakkattiri
Sreeraj Puzhakkattiri

This document summarizes a seminar presentation about using mobile phone-based participatory sensing to predict bus arrival times. The proposed system involves sharing users on buses reporting their locations via cell tower signals to a backend server, which then matches the cell tower sequences to bus routes and estimates arrival times for querying users based on the bus's current location and historical data. An Android app was developed to collect accelerometer and audio data to detect when users board buses and classify the bus route. Experimental results found this approach could accurately predict bus arrival times using low power consumption methods compared to GPS.

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Predicting Bus Arrival Time with Mobile Phone based Participatory Sensing SEMINAR BY : SREERAJ P RIT, KOTTAYAM
CONTENTS INTRODUCTION EXISTING SYSTEM PROPOSED SYSTEM SYSTEM DESIGN IMPLEMENTATION AND EVALUATION CONCLUSION RELATED WORK REFERENCES
INTRODUCTION Bus arrival time is primary information to most city transport travelers. The bus transport services reduce fuel consumption and hence must be encouraged. Third party applications Bus arrival time prediction based on crowd-participatory sensing.
MOBILE PHONE BASED PARTICIPATORY SENSING Participatory sensing is the concept of communities (or other groups of people) contributing sensory information to form a body of knowledge. Mobile phones which has multiple sensors has made participatory sensing viable in large scale. Participatory sensing can be used to retrieve information about the environment, weather, congestion etc.
EXISTING SYSTEM Enquiry at Bus Depot. Bus companies provide bus time tables on the Internet. Installation of location tracking devices such as GPS in the bus.
DISADVANTAGES OF EXISTING SYSTEM It usually requires the cooperation of the bus operating companies. Companies dont update the time table on a regular basis. It requires installation of GPS which is very expensive. Also Power consumption of GPS is more.
PROPOSED SYSTEM Crowd-participated bus arrival time prediction using cellular signals. Independent of the bus companies Bridges the gap between querying users and the sharing users.
Querying Users Are the users who query about the arrival of the bus at a particular bus station. Sharing Users Are the users who are currently present in the bus and are sharing the data using their smartphones in order to predict the bus arrival time.
SYSTEM DESIGN
Implemented NOT in INDIA Sharing users Querying users Backend server: collecting the instantly reported information from the sharing users, and intellectually processing such information so as to monitor the bus routes and predict the bus arrival time.
Pre-processing Cell Tower Data Cell tower IDs are saved in a database using an initial experiment. Top 3 Cell towers are taken into consideration as mobile phones connect to the tower which provides maximum signal strength. Based on the Cell tower signals the bus route can be identified.
Bus Detection: Am I on the Bus? Audio Detection Beep sound from the card reader Sensors are active all the time
Accelerometer Readings In Singapore the trains also have a system which makes a beep sound. In order to avoid this accelerometer readings are taken with an interval of 20Hz.
Predicting bus arrival time based on participatory mobile phone sensing
Bus Classification Cell tower sequence matching Route in Database (1, 2, 4, 7, 8, 5, 9, 6) Route from sharing user (7, 8, 5)
Predicting bus arrival time based on participatory mobile phone sensing
Arrival time prediction When a user queries about the bus the backend server looks up the latest bus route status and calculates the arrival time at the particular bus stop. Historical data is also taken into consideration.
T=T2-t2+T3+TBS
Predicting bus arrival time based on participatory mobile phone sensing
EXPERIMENTAL METHODOLGY Android application Mobile phones: Samsung galaxy S2 i9100/HTC Desire 1GB/768MB RAM Dual core 1.2GHz Cortex A9 processor or 1GHz Scorpion processor
Backend server Java running on the DELL Precision T3500 workstation 4GB memory and Intel Xeon W3540 processor.
COMPLEXITY At Backend server O(lk) O(lkN) l - uploaded cell tower sequence length k cell tower set sequence length N total candidate sequences in DB.
CONCLUSION ADVANTAGES Power Consumption is less compared to GPS Real time availability of bus time table More accurate as the number of sharing users increase Independent of bus operating companies Cost effective
RELATED WORK Encourage more participants Encourage specific passengers like the driver to install the mobile client
REFERENCES G. Ananthanarayanan, M. Haridasan, I. Mohomed, D. Terry, and C. A. Thekkath, Startrack: A framework for enabling track-based applications, in Proc. ACM MobiSys, 2009, pp. 207220. P. Bahl and V. N. Padmanabhan, RADAR: An in-building RF-based user location and tracking system, in Proc. IEEE INFOCOM, 2000, pp. 775784. R. K. Balan, K. X. Nguyen, and L. Jiang, Real-time trip informa- tion service for a large taxi fleet, in Proc. ACM MobiSys, 2011, pp. 99112. Human localization using mobile phones, in Proc. ACM MobiCom
X. Bao and R. R. Choudhury, MoVi: Mobile phone based video highlights via collaborative sensing, in Proc. ACM MobiSys, San Francisco, CA, USA J. Biagioni, T. Gerlich, T. Merrifield, and J. Eriksson, Easytracker: Automatic transit tracking, mapping, and arrival time prediction using smartphones, in Proc. ACM SenSys, 2011
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Predicting bus arrival time based on participatory mobile phone sensing

  • 1. Predicting Bus Arrival Time with Mobile Phone based Participatory Sensing SEMINAR BY : SREERAJ P RIT, KOTTAYAM
  • 2. CONTENTS INTRODUCTION EXISTING SYSTEM PROPOSED SYSTEM SYSTEM DESIGN IMPLEMENTATION AND EVALUATION CONCLUSION RELATED WORK REFERENCES
  • 3. INTRODUCTION Bus arrival time is primary information to most city transport travelers. The bus transport services reduce fuel consumption and hence must be encouraged. Third party applications Bus arrival time prediction based on crowd-participatory sensing.
  • 4. MOBILE PHONE BASED PARTICIPATORY SENSING Participatory sensing is the concept of communities (or other groups of people) contributing sensory information to form a body of knowledge. Mobile phones which has multiple sensors has made participatory sensing viable in large scale. Participatory sensing can be used to retrieve information about the environment, weather, congestion etc.
  • 5. EXISTING SYSTEM Enquiry at Bus Depot. Bus companies provide bus time tables on the Internet. Installation of location tracking devices such as GPS in the bus.
  • 6. DISADVANTAGES OF EXISTING SYSTEM It usually requires the cooperation of the bus operating companies. Companies dont update the time table on a regular basis. It requires installation of GPS which is very expensive. Also Power consumption of GPS is more.
  • 7. PROPOSED SYSTEM Crowd-participated bus arrival time prediction using cellular signals. Independent of the bus companies Bridges the gap between querying users and the sharing users.
  • 8. Querying Users Are the users who query about the arrival of the bus at a particular bus station. Sharing Users Are the users who are currently present in the bus and are sharing the data using their smartphones in order to predict the bus arrival time.
  • 10. Implemented NOT in INDIA Sharing users Querying users Backend server: collecting the instantly reported information from the sharing users, and intellectually processing such information so as to monitor the bus routes and predict the bus arrival time.
  • 11. Pre-processing Cell Tower Data Cell tower IDs are saved in a database using an initial experiment. Top 3 Cell towers are taken into consideration as mobile phones connect to the tower which provides maximum signal strength. Based on the Cell tower signals the bus route can be identified.
  • 12. Bus Detection: Am I on the Bus? Audio Detection Beep sound from the card reader Sensors are active all the time
  • 13. Accelerometer Readings In Singapore the trains also have a system which makes a beep sound. In order to avoid this accelerometer readings are taken with an interval of 20Hz.
  • 15. Bus Classification Cell tower sequence matching Route in Database (1, 2, 4, 7, 8, 5, 9, 6) Route from sharing user (7, 8, 5)
  • 17. Arrival time prediction When a user queries about the bus the backend server looks up the latest bus route status and calculates the arrival time at the particular bus stop. Historical data is also taken into consideration.
  • 20. EXPERIMENTAL METHODOLGY Android application Mobile phones: Samsung galaxy S2 i9100/HTC Desire 1GB/768MB RAM Dual core 1.2GHz Cortex A9 processor or 1GHz Scorpion processor
  • 21. Backend server Java running on the DELL Precision T3500 workstation 4GB memory and Intel Xeon W3540 processor.
  • 22. COMPLEXITY At Backend server O(lk) O(lkN) l - uploaded cell tower sequence length k cell tower set sequence length N total candidate sequences in DB.
  • 23. CONCLUSION ADVANTAGES Power Consumption is less compared to GPS Real time availability of bus time table More accurate as the number of sharing users increase Independent of bus operating companies Cost effective
  • 24. RELATED WORK Encourage more participants Encourage specific passengers like the driver to install the mobile client
  • 25. REFERENCES G. Ananthanarayanan, M. Haridasan, I. Mohomed, D. Terry, and C. A. Thekkath, Startrack: A framework for enabling track-based applications, in Proc. ACM MobiSys, 2009, pp. 207220. P. Bahl and V. N. Padmanabhan, RADAR: An in-building RF-based user location and tracking system, in Proc. IEEE INFOCOM, 2000, pp. 775784. R. K. Balan, K. X. Nguyen, and L. Jiang, Real-time trip informa- tion service for a large taxi fleet, in Proc. ACM MobiSys, 2011, pp. 99112. Human localization using mobile phones, in Proc. ACM MobiCom
  • 26. X. Bao and R. R. Choudhury, MoVi: Mobile phone based video highlights via collaborative sensing, in Proc. ACM MobiSys, San Francisco, CA, USA J. Biagioni, T. Gerlich, T. Merrifield, and J. Eriksson, Easytracker: Automatic transit tracking, mapping, and arrival time prediction using smartphones, in Proc. ACM SenSys, 2011