POVA: Traffic light sensing with probe vehicles

Abstract

We develop a system called POVA for traffic light sensing in large-scale urban areas, where traffic light sensing aims to detect the status of traffic lights which is valuable for many applications such as traffic management, traffic light optimization and real-time vehicle navigation. The system employs pervasive probe vehicles that just report real-time states of position and speed from time to time. The important observation motivating the design of POVA is that a traffic light has a considerable impact on mobility of vehicles on the road attached to the traffic light. However, the system design faces three unique challenges, i.e., discrete probe reports, uneven distribution of reports over time and space, and variable interval of light states. To tackle the challenges, we develop a new technique that makes the best use of limited probe reports as well as statistical features of light states. It first estimates the state of a traffic light at the time instant of a report by applying maximum a posterior (MAP) estimation. Then, we formulate the state estimation of a light at any time into a joint optimization problem that is solved by an efficient heuristic algorithm. Trace-driven experimentation and field study show that the estimation error rate is as low as 21% even when the number of available reports is merely one per minute.