Automating Mode Detection Using Neural Networks and Assisted GPS Data Collected Using GPS-Enabled Mobile Phones

Abstract

Next-generation transportation surveys will utilize Global Positioning Systems (GPS) to collect trip data. Due to their ubiquity, GPS-enabled mobile devices are becoming promising for use as survey tools. TRAC-IT is a mobile phone application that records travel behavior by collecting real-time GPS data and requiring minimal input from the user for data such as trip purpose, mode of transportation, and vehicle occupancy. To ease survey burden on participants, new techniques must be explored to derive more information directly from GPS data. This paper demonstrates the feasibility of using neural networks and assisted GPS data collected from GPS-enabled mobile phones to automatically detect the mode of transportation. Furthermore, this paper demonstrates that this technique can be optimized using a critical point algorithm to reduce the size of required GPS datasets obtained from GPS-enabled mobile phones, thus reducing data collection costs while saving mobile phone resources such as battery life.