AI

Physical and Virtual Cell Phone Sensors for Traffic Control: Algorithms and Deployment Impact

Abstract

Decades of research have been directed towards improving the timing of traffic lights. The ubiquity of cell phones among drivers has created the opportunity to design new sensors for traffic light controllers. These new sensors, which search for radio signals that are constantly emanating from cell phones, hold the hope of replacing the typical induction-loop sensors that are installed within road pavements. A replacement to induction sensors is desired as they require significant roadwork to install, frequent maintenance and checkups, are sensitive to proper repairs and installation work, and the construction techniques, materials, and even surrounding unrelated ground work can be sources of failure. However, before cell phone sensors can be widely deployed, users must become comfortable with the passive use of their cell phones by municipalities for this purpose. Despite complete anonymization, public privacy concerns may remain. This presents a chicken-and-egg problem: without showing the benefits of using cell phones for traffic monitoring, users may not be willing to allow this use. In this paper, we show that by carefully training the traffic light controllers, we can unlock the benefits of these sensors when only a small fraction of users allow their cell phones to be used. Surprisingly, even when there is only small percentage of opted-in users, the new traffic controllers provide large benefits to all drivers