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Developing countries suffer from traffic congestion, poorly planned road/railnetworks, and lack of access to public transportation facilities. This contextresults in an increase in fuel consumption, pollution level, monetary losses,massive delays, and less productivity. On the other hand, it has a negativeimpact on the commuters feelings and moods. Availability of real-time transitinformation - by providing public transportation vehicles locations using GPSdevices - helps in estimating a passenger's waiting time and addressing theabove issues. However, such solution is expensive for developing countries.This paper aims at designing and implementing a crowd-sourced mobilephones-based solution to estimate the expected waiting time of a passenger inpublic transit systems, the prediction of the remaining time to get on/off avehicle, and to construct a real time public transit schedule. Trans-Sense hasbeen evaluated using real data collected for over 800 hours, on a daily basis,by different Android phones, and using different light rail transit lines atdifferent time spans. The results show that Trans-Sense can achieve an averagerecall and precision of 95.35% and 90.1%, respectively, in discriminatinglightrail stations. Moreover, the empirical distributions governing thedifferent time delays affecting a passenger's total trip time enable predictingthe right time of arrival of a passenger to her destination with an accuracy of91.81%.In addition, the system estimates the stations dimensions with anaccuracy of 95.71%.

Trans-Sense: Real Time Transportation Schedule Estimation Using Smart Phones