Traffic Updates: Saying a Lot While Revealing a Little - Krumm, John; Horvitz, Eric (Thirty-Third AAAI Conference on Artificial Intelligence, 2019)
Taking speed reports from vehicles is a proven, inexpensive way to infer traffic conditions. However, due to concerns about privacy and bandwidth, not every vehicle occupant may want to transmit data about their location and speed in real time. We show how to drastically reduce the number of transmissions in two ways, both based on a Markov random field for modeling traffic speed and flow. First, we show that a only a small number of vehicles need to report from each location. We give a simple, probabilistic method that lets a group of vehicles decide on which subset will transmit a report, preserving privacy by coordinating without any communication. The second approach computes the potential value of any location’s speed report, emphasizing those reports that will most affect the overall speed inferences, and omitting those that contribute little value. Both methods significantly reduce the amount of communication necessary for accurate speed inferences on a road network.