Smartphone-Based Incentive Framework for Dynamic Network-Level Traffic Congestion Management

By leveraging advances in smartphone-based personalization, big data availability for traffic, and network-level integration through information-based connectivity, this study proposes to develop new types of tools to manage congestion in real-time in traffic networks, especially during peak period commutes and under debilitating incidents. The study seeks to develop a smartphone-based framework to develop real-time incentives (monetary, value-based, travel-related credits, etc.) to influence drivers’ en route routing decisions to manage network-level system performance in congested dynamic traffic networks. In doing so, a key objective is to ensure that the proposed incentives are behavior-consistent, in that they are tailored based on travelers’ smartphone responses. Another objective is to explore how public and private sector transportation entities can collaborate through the use of new forms of incentives that leverage the emerging transportation future, in addition to the currently-used ones. The methodology consists of a three-phase approach. Analytical models and algorithms will be developed in the first phase to identify and implement the specific incentives to be deployed in real-time, using techniques from game theory, optimization, and machine learning. Phase 2 will involve driving simulator-based experiments to analyze the responses of drivers/travelers to the real-time incentives. In the third phase, the insights from the second phase will be used to fine-tune the analytical models and develop the modules that would form the components of a smartphone-based app.