UrbanMobilitySense: A User-Centric Participatory Sensing System for Transportation Activity Surveys

Transportation activity surveys collect the travel behavior of people, including when, where, and how they travel for urban planning purposes. Traditionally, transportation activity surveys are carried out using conventional questionaires, which are labor intensive and error prone. In this paper, we have developed a smartphone-based mobility sensing system, called UrbanMobilitySense, which captures human mobility information automatically to conduct transportation activity surveys. The UrbanMobilitySense system was designed to address two critical issues: 1) energy conservation and 2) privacy preservation. To optimize the energy utilization of smartphone, we avoid using the GPS sensor when the user is at long-stay places and filter out redundant data before data uploading. To preserve personal privacy, each smartphone maintains the user's long-stay places by two separate profiles: 1) private place profile and 2) public place profile. The former maintains the privacy-preserved places (e.g., home), whereas the latter maintains the public places (e.g., parks). We implement the UrbanMobilitySense system to conduct real-world transportation activity surveys, study the performance of our system through extensive experiments, and analyze the computational complexity of the proposed algorithms. The outcome of our work has been deployed in Singapore to support the Land Transport Authority's transportation activity surveys.

[1]  Emiliano Miluzzo,et al.  The BikeNet mobile sensing system for cyclist experience mapping , 2007, SenSys '07.

[2]  Deborah Estrin,et al.  Employing user feedback for semantic location services , 2011, UbiComp '11.

[3]  Brian J. d'Auriol,et al.  The election algorithm for semantically meaningful location-awareness , 2007, MUM.

[4]  Deborah Estrin,et al.  SensLoc: sensing everyday places and paths using less energy , 2010, SenSys '10.

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

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

[7]  Qi Qi,et al.  Wireless sensor networks in intelligent transportation systems , 2009 .

[8]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[9]  Deborah Estrin,et al.  Discovering semantically meaningful places from pervasive RF-beacons , 2009, UbiComp.

[10]  Florian Michahelles,et al.  Accuracy of positioning data on smartphones , 2010, LocWeb '10.

[11]  Xing Xie,et al.  Understanding transportation modes based on GPS data for web applications , 2010, TWEB.

[12]  Mirco Musolesi,et al.  Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application , 2008, SenSys '08.

[13]  Hock-Beng Lim,et al.  Transportation activity analysis using smartphones , 2012, 2012 IEEE Consumer Communications and Networking Conference (CCNC).

[14]  Shashi Shekhar,et al.  Discovering personally meaningful places: An interactive clustering approach , 2007, TOIS.

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

[16]  Hojung Cha,et al.  Mobility prediction-based smartphone energy optimization for everyday location monitoring , 2011, SenSys.

[17]  Mo Li,et al.  How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing , 2012, IEEE Transactions on Mobile Computing.

[18]  Wen-Chih Peng,et al.  CarWeb: A Traffic Data Collection Platform , 2008, The Ninth International Conference on Mobile Data Management (mdm 2008).

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

[20]  Hock-Beng Lim,et al.  A user-centric mobility sensing system for transportation activity surveys , 2013, SenSys '13.

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

[22]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[23]  Philippe Canalda,et al.  WiFi GPS based combined positioning algorithm , 2010, 2010 IEEE International Conference on Wireless Communications, Networking and Information Security.

[24]  Yu-Chee Tseng,et al.  Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Review from Wireless Sensor Networks towards Cyber Physical Systems , 2022 .

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

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