  Developing countries suffer from traffic congestion, poorly planned road/rail
networks, and lack of access to public transportation facilities. This context
results in an increase in fuel consumption, pollution level, monetary losses,
massive delays, and less productivity. On the other hand, it has a negative
impact on the commuters feelings and moods. Availability of real-time transit
information - by providing public transportation vehicles locations using GPS
devices - helps in estimating a passenger's waiting time and addressing the
above issues. However, such solution is expensive for developing countries.
This paper aims at designing and implementing a crowd-sourced mobile
phones-based solution to estimate the expected waiting time of a passenger in
public transit systems, the prediction of the remaining time to get on/off a
vehicle, and to construct a real time public transit schedule. Trans-Sense has
been evaluated using real data collected for over 800 hours, on a daily basis,
by different Android phones, and using different light rail transit lines at
different time spans. The results show that Trans-Sense can achieve an average
recall and precision of 95.35% and 90.1%, respectively, in discriminating
lightrail stations. Moreover, the empirical distributions governing the
different time delays affecting a passenger's total trip time enable predicting
the right time of arrival of a passenger to her destination with an accuracy of
91.81%.In addition, the system estimates the stations dimensions with an
accuracy of 95.71%.
