A probabilistic hough transform for opportunistic crowd-sensing of moving traffic obstacles
Traffic congestion in developing cities like Nairobi, Kenya can be significantly impacted by the presence of Moving Traffic Obstacles (MTOs). These MTOs are events that temporarily exist on the road, moving with or against the direction of Traffic at slower speeds. They include two-wheelers, pushcarts, animals, and pedestri-ans, which have quite different inuence on Traffic com-pared with static obstacles, such as potholes and speed bumps. As smartphones and supporting 3G infrastruc-tures are wide spread even in developing countries, re-cent studies enabled frugal Traffic obstacle data collec-tion from smartphones in probe cars. Assuming the opportunistic, unevenly-distributed, sparse and errorful observation of Traffic obstacles, we propose an MTO de-tection algorithm extending an image analysis technique called Probabilistic Hough Transform for collective ob-servations as input. Based on our experiences with a small set of real-world data collected in a smartphone-based probe car project with Nairobi City County, we conducted experiments with simulated observation data to see the effectiveness of the algorithm.