Utilizing Real-Time Travel Information, Mobile Applications and Wearable Devices for Smart Public Transportation

We propose a cloud platform that utilizes real-time travel information, a mobile application and wearable devices for smart public transportation. This platform is capable of retrieving the required data automatically, reporting real-time public transportation information and providing users with personalized recommendations for using public transits. Novel features of this platform include the measure of the current walking speed of the user and the use of real-time estimated arrival times of public transits at different locations for travel recommendations. We also present our on-going work of developing the proposed platform for the public transportation system in Hong Kong. We aim to develop this platform for passengers for aiding their decisions and reducing their journey times, thereby improving their commuting experience and encouraging the use of public transportation.

[1]  Oded Cats,et al.  Real-time bus arrival information system-an empirical evaluation , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[2]  Ties Brands,et al.  Short-Term Prediction of Ridership on Public Transport with Smart Card Data , 2015 .

[3]  Jan-Dirk Schmöcker,et al.  Effects of Transit Real-Time Information Usage Strategies , 2014 .

[4]  Oded Cats,et al.  Improving Public Transport Decision Making, Planning and Operations by Using Big Data: Cases from Sweden and the Netherlands , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[5]  J. M Y Leung,et al.  Real-time integrated re-scheduling for public transit , 2016 .

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

[7]  William Chow,et al.  Impacts of Real-Time Passenger Information Signs in Rail Stations at the Massachusetts Bay Transportation Authority , 2014 .

[8]  Lei Tang,et al.  Ridership effects of real-time bus information system: A case study in the City of Chicago , 2012 .

[9]  Andry Rakotonirainy,et al.  Pervasive Technology and Public Transport: Opportunities Beyond Telematics , 2013, IEEE Pervasive Computing.

[10]  Mark R. McCord,et al.  Transit passenger origin–destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets , 2015 .

[11]  Bin Yu,et al.  Bus arrival time prediction at bus stop with multiple routes , 2011 .

[12]  Erik T. Verhoef,et al.  A revealed-preference study of behavioural impacts of real-time traffic information , 2013 .

[13]  Alan Borning,et al.  Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders , 2011 .

[14]  Klarissa Ting-Ting Chang,et al.  An Investigation of Usability of Push Notifications on Mobile Devices for Novice and Expert Users , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[15]  K. Watkins,et al.  Best Practices for Transportation Agency Use of Social Media , 2013 .

[16]  Satish V. Ukkusuri,et al.  A novel transit rider satisfaction metric: Rider sentiments measured from online social media data , 2013 .

[17]  Ana Carolina Salgado,et al.  Contextual information in user information systems in public transportation: A systematic review , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.