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

## TL;DR

Trans-Sense is a crowd-sourced mobile phone system that accurately estimates public transit schedules and passenger wait times in real-time, providing a cost-effective solution for developing countries to improve transit efficiency and passenger experience.

## Contribution

It introduces a novel crowd-sourced approach using smartphones to estimate transit schedules and passenger wait times without GPS infrastructure, suitable for developing countries.

## Key findings

- Achieves 95.35% recall and 90.1% precision in station detection.
- Predicts passenger arrival times with 91.81% accuracy.
- Estimates station dimensions with 95.71% accuracy.

## Abstract

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%.