Existing methods for transportation mode detection (TMD) using mobile sensing make it generally possible to distinguish between walking, cycling, and motorized transport. However, our means of transport evolve and we develop radically new ways of transporting ourselves, thus new TMD sub-classification methods are needed to distinguish these new transport forms. As we transition from fossil-fueled cars to electric vehicles, switch to bikes with electric motors, ride in hybrid buses, or do city sightseeing on Segways, new challenges arise in distinguishing these from a mobile sensing perspective. Distinguishing electric vehicles (EVs) from fossil-fueled vehicles (FFVs) is a challenge, where traditional methods based on features such as GPS speed, or statistics on raw accelerometer data, are insufficient. In this paper, we present methods for distinguishing EVs from FFVs using smartphones with built-in inertial sensors, by reliably identifying idle-engine motor vibrations through features built on frequency analysis. We provide an extensive analysis of the challenges involved in making the EV/FFV distinction, as well as practical tools based on the methods. This includes analyzing the measurable similarities and differences between EVs and FFVs, and developing methods of reliably separating them. The presented tools implement the methods as classifiers built using machine learning. The analysis of our experiments shows that we can achieve an accuracy of 89-95% distinguishing EVs from FFVs, even with on-body phones.

The vast availability of mobile phones with built-in movement and location sensors enable the collection of detailed information about human movement even indoors. As mobility is a key element of many processes and activities, an interesting class of information to extract is movement patterns that quantify how humans move, interact and group. In this paper we propose methods for detecting two common pedestrian movement patterns, namely individual following relations and group leadership. The proposed methods for identifying following patterns employ machine learning on features derived using similarity analysis on time lagged sequences of WiFi measurements containing either raw signal strength values or derived locations. To detect leadership we combine the individual following relations into directed graphs and detect leadership within groups by graph link analysis. Methods for detecting these movement patterns open up new possibilities in — amongst others — computational social science, reality mining, marketing research and location-based gaming. We provide evaluation results that show error rates down to 7%, improving over state of the art methods with up to eleven percentage points for following patterns and up to twenty percentage points for leadership patterns. Our method is, contrary to state of the art, also applicable in challenging indoor environments, e.g., multi-story buildings. This implies that even quite small samples allow us to detect information such as how events and campaigns in multistory shopping malls may trigger following in small groups, or which group members typically take the lead when triggered by e.g. commercials, or how rescue or police forces act during training exercises.