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 paper, authors study an alternative approach of offering positive incentives to drivers to take different routes. More specifically, the paper proposes an algorithm to reduce traffic congestion and improve routing efficiency via offering (personalized) incentives to drivers. Researchers 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, authors 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. The performance of the algorithm is evaluated using traffic data from the Los Angeles area. The experiments show congestion reduction of up to 5% in arterial roads and highways.
