M2M-Enabled Real-Time Trip Planner

Abstract

Uncertainty is a key factor that prevents a commuter from using public transportation system. More and more transportation agencies are incorporating real-time Trip Planners to empower commuters with opportune information. However, such systems require continuous status updates from the vehicles and involves expensive communication cost. In this paper we propose an architecture that takes advantage of Machine-to-Machine Communication concepts and provides a degree of intelligence to the vehicles, to alleviate unnecessary communication between the vehicles and the Trip Planner.