  Real-time optimization of traffic flow addresses important practical
problems: reducing a driver's wasted time, improving city-wide efficiency,
reducing gas emissions and improving air quality. Much of the current research
in traffic-light optimization relies on extending the capabilities of traffic
lights to either communicate with each other or communicate with vehicles.
However, before such capabilities become ubiquitous, opportunities exist to
improve traffic lights by being more responsive to current traffic situations
within the current, already deployed, infrastructure. In this paper, we
introduce a traffic light controller that employs bidding within micro-auctions
to efficiently incorporate traffic sensor information; no other outside sources
of information are assumed. We train and test traffic light controllers on
large-scale data collected from opted-in Android cell-phone users over a period
of several months in Mountain View, California and the River North neighborhood
of Chicago, Illinois. The learned auction-based controllers surpass (in both
the relevant metrics of road-capacity and mean travel time) the currently
deployed lights, optimized static-program lights, and longer-term planning
approaches, in both cities, measured using real user driving data.
