  With rapid population growth and urban development, traffic congestion has
become an inescapable issue, especially in large cities. Many congestion
reduction strategies have been proposed in the past, ranging from roadway
extension to transportation demand management. In particular, congestion
pricing schemes have been used as negative reinforcements for traffic control.
In this project, we study an alternative approach of offering positive
incentives to drivers to take different routes. More specifically, we propose
an algorithm to reduce traffic congestion and improve routing efficiency via
offering personalized incentives to drivers. We exploit the wide-accessibility
of smart devices to communicate with drivers and develop an incentive offering
mechanism using individuals' preferences and aggregate traffic information. The
incentives are offered after solving a large-scale optimization problem in
order to minimize the total travel time (or minimize any cost function of the
network such as total Carbon emission). Since this massive size optimization
problem needs to be solved continually in the network, we developed a
distributed computational approach. The proposed distributed algorithm is
guaranteed to converge under a mild set of assumptions that are verified with
real data. We evaluated the performance of our algorithm using traffic data
from the Los Angeles area. Our experiments show congestion reduction of up to
11% in arterial roads and highways.
