Traffic Signal Phase and Timing Estimation From Low-Frequency Transit Bus Data

The objective of this paper is to demonstrate the feasibility of estimating traffic signal phase and timing from statistical patterns in low-frequency vehicular probe data. We use a public feed of bus location and velocity data in the city of San Francisco, CA, USA, as an example data source. We show that it is possible to estimate, fairly accurately, cycle times and the duration of reds for fixed-time traffic lights traversed by buses using a few days' worth of aggregated bus data. Furthermore, we also estimate the start of greens in real time by monitoring the movement of buses across intersections. The results are encouraging, given that each bus sends an update only sporadically ( $\approx$ every 200 m) and that bus passages are infrequent (every 5-10 min) . When made available on an open server, such information about the traffic signals' phase and timing can be valuable in enabling new fuel efficiency and safety functionalities in connected vehicles. Velocity advisory systems can use the estimated timing plan to calculate velocity trajectories that reduce idling time at red signals and therefore improve fuel efficiency and lower emissions. Advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red. Intersection collision avoidance and active safety systems could also benefit from the prediction.