RoadAware: Learning Personalized Road Information on Daily Routes with Smartphones

In this paper, we introduce RoadAware, an infrastructure-less system that leverages a smartphone to collect road information on people's daily routes to and from work. Unlike previous research that utilizes moving vehicles equipped with sensors to collect road information, RoadAware does not need the support from infrastructure, road-side facilities or other users and focuses on serving individuals during his/her daily commute to and back from work. RoadAware can provide road information including 1) travel distance and time between traffic lights; 2) duration of red and green signals of each traffic light; and 3) traffic volume on the road between traffic lights. RoadAware builds a model that can deduce the road information based on the wait time and the length of the waiting queue in front of the car when it stops at a traffic light. The road information not only benefits individuals in their daily commutes (e.g., predict waiting at red lights and remind whether there is a delay on daily route) but can also be collected for traffic optimization that otherwise requires costly sensor deployment. We developed RoadAware on Windows smartphones, and our extensive real-world test shows that RoadAware can provide road information with acceptable accuracy.