Real-Time Transportation Mode Detection via Tracking Global Positioning System Mobile Devices

Abstract

This article presents a methodology for identifying travelers' transportation modes by tracking Global Positioning System (GPS)-equipped mobile devices in the traffic stream. Various mobile phone service providers have location-based services (LBS) that track the locations of their mobile phones. One major concern in using mobile phones for traffic monitoring is that the phones are not necessarily in passenger vehicles. The mobile device can be in a car, bus, or other modes that have distinct speed and acceleration profiles. In addition, querying the mobile device has monetary cost implications, and the higher the number of location queries from the server the higher the associated cost. This article focuses on the feasibility of using the characteristics of the trail of GPS data stream to identify the mode on which the mobile device is located. Currently available LBS in Toronto can only provide GPS data once every 5 min. Because of the sampling limitation, a GPS data logger is used to collect the trip data and the logged data is sampled at varying frequencies as if they are coming from the mobile phones. The analysis is conducted using neural networks (NNs) to determine the transportation mode. The analysis also examines the impact of varying sampling rates (number of pings per unit time) and monitoring duration (time length of data trail) on mode classification accuracy. In total, 60 h of GPS data were collected while traveling on various transportation modes throughout the Greater Toronto Area. Results confirm the potential of neural networks to successfully detect transportation modes from GPS data, both in peak and nonpeak periods. The results indicate that higher sampling frequency and longer monitoring duration result in higher mode detection rates. In addition, the route-specific neural networks perform better than the universal neural networks.