2011 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2011)
Seattle, WA USA
Mar. 21, 2011 to Mar. 25, 2011
Gayathri Chandrasekaran , WINLAB, Rutgers University, North Brunswick, NJ 08902, USA
Tam Vu , WINLAB, Rutgers University, North Brunswick, NJ 08902, USA
Alexander Varshavsky , AT&T Labs, Florham Park, NJ 07932, USA
Marco Gruteser , WINLAB, Rutgers University, North Brunswick, NJ 08902, USA
Richard P. Martin , WINLAB, Rutgers University, North Brunswick, NJ 08902, USA
Jie Yang , Stevens Institute of Technology, Hoboken, NJ 07030, USA
Yingying Chen , Stevens Institute of Technology, Hoboken, NJ 07030, USA
In this paper, we consider the problem of tracking fine-grained speeds variations of vehicles using signal strength traces from GSM enabled phones. Existing speed estimation techniques using mobile phone signals can provide longer-term speed averages but cannot track short-term speed variations. Understanding short-term speed variations, however, is important in a variety of traffic engineering applications -- for example, it may help distinguish slow speeds due to traffic lights from traffic congestion when collecting real time traffic information. Using mobile phones in such applications is particularly attractive because it can be readily obtained from a large number of vehicles. Our approach is founded on the observation that the large-scale path loss and shadow fading components of signal strength readings (signal profile) obtained from the mobile phone on any given road segment appear similar over multiple trips along the same road segment except for distortions along the time axis due to speed variations. We therefore propose a speed tracking technique that uses a Derivative Dynamic Time Warping (DDTW) algorithm to realign a given signal profile with a known training profile from the same road. The speed tracking technique then translates the warping path (i.e., the degree of stretching and compressing needed for alignment) into an estimated speed trace. Using 6.4 hours of GSM signal strength traces collected from a vehicle, we show that our algorithm can estimate vehicular speed with a median error of ± 5mph compared to using a GPS and can capture significant speed variations on road segments with a precision of 68% and a recall of 84%.
T. Vu et al., "Tracking vehicular speed variations by warping mobile phone signal strengths," 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Seattle, WA USA, 2011, pp. 213-221.