The purpose of this research is to develop real-time algorithms to reduce traffic congestion and improve routing efficiency via offering personalized incentives to drivers. The incentives and alternative routes should be chosen smartly in order to maximize the probability of acceptance by the driver and to avoid the creation of new congestion in other areas of the network. To this end, the research team proposes to exploit the wide accessibility of smart communication devices and develop a real-time look-ahead incentive-offering mechanism using individuals’ routing and aggregate traffic information. The proposed approach relies on historical data and state-of-the-art traffic prediction methodologies to continually predict congestion and traffic flow of the network. Using this prediction and based on individual preferences, the central controller offers personalized incentives to drivers with the goal of reducing the probability of congestion. The decisions about incentives are made via solving a series of carefully designed large scale stochastic optimization problems. The performance of the proposed algorithms will be evaluated using data from the Los Angeles area.
