How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS-enabled location information, we resort to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android-based mobile phones and comprehensively experiment with the NTU campus shuttle buses as well as Singapore public buses over a 7-week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus operator initiated and GPS supported solutions. We further adopt our system and conduct quick trial experiments with London bus system for 4 days, which suggests the easy deployment of our system and promising system performance across cities. At the same time, the proposed solution is more generally available and energy friendly.

[1]  Ramesh Govindan,et al.  Energy-efficient positioning for smartphones using Cell-ID sequence matching , 2011, MobiSys '11.

[2]  Yi Wang,et al.  A framework of energy efficient mobile sensing for automatic user state recognition , 2009, MobiSys '09.

[3]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[4]  Yunhao Liu,et al.  WILL: Wireless indoor localization without site survey , 2012, 2012 Proceedings IEEE INFOCOM.

[5]  Richard P. Martin,et al.  Detecting driver phone use leveraging car speakers , 2011, MobiCom.

[6]  Chandramohan A. Thekkath,et al.  StarTrack: a framework for enabling track-based applications , 2009, MobiSys '09.

[7]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[8]  Feng Li,et al.  Public bus arrival time prediction based on traffic information management system , 2011, Proceedings of 2011 IEEE International Conference on Service Operations, Logistics and Informatics.

[9]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[10]  James Biagioni,et al.  EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones , 2011, SenSys.

[11]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[12]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[13]  Li Zhang,et al.  StarTrack Next Generation: A Scalable Infrastructure for Track-Based Applications , 2010, OSDI.

[14]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.

[15]  Wei Pan,et al.  SoundSense: scalable sound sensing for people-centric applications on mobile phones , 2009, MobiSys '09.

[16]  M. Hansen,et al.  Participatory Sensing , 2019, Internet of Things.

[17]  Romit Roy Choudhury,et al.  MoVi: mobile phone based video highlights via collaborative sensing , 2010, MobiSys '10.

[18]  Margaret Martonosi,et al.  SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory , 2011, MobiSys '11.

[19]  Hari Balakrishnan,et al.  Accurate, Low-Energy Trajectory Mapping for Mobile Devices , 2011, NSDI.

[20]  Mo Li,et al.  How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing , 2014, IEEE Trans. Mob. Comput..

[21]  Leonidas J. Guibas,et al.  Mobiscopes for Human Spaces , 2007, IEEE Pervasive Computing.

[22]  Calvin C. Newport Improving Wireless Network Performance Using Sensor Hints , 2011, NSDI.

[23]  Joongheon Kim,et al.  Energy-efficient rate-adaptive GPS-based positioning for smartphones , 2010, MobiSys '10.

[24]  James Biagioni,et al.  Cooperative transit tracking using smart-phones , 2010, SenSys '10.

[25]  Romit Roy Choudhury,et al.  Micro-Blog: sharing and querying content through mobile phones and social participation , 2008, MobiSys '08.

[26]  Yunhao Liu,et al.  Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays , 2012, IEEE Transactions on Parallel and Distributed Systems.

[27]  Rajesh Krishna Balan,et al.  Real-time trip information service for a large taxi fleet , 2011, MobiSys '11.

[28]  Guoliang Xing,et al.  PBN: towards practical activity recognition using smartphone-based body sensor networks , 2011, SenSys.

[29]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[30]  Romit Roy Choudhury,et al.  Did you see Bob?: human localization using mobile phones , 2010, MobiCom.

[31]  Guobin Shen,et al.  BeepBeep: a high accuracy acoustic ranging system using COTS mobile devices , 2007, SenSys '07.