Dynamic Routing of Heterogeneous Users After Traffic Disruptions Under a Mixed Information Framework

Abstract

This research focuses on reducing traffic congestion using the competing strategies between informed and uninformed drivers. Under a mixed information framework, a navigation app provides within-day route suggestions to informed drivers using predicted information about the time-varying route habits of uninformed drivers. The informed users detour from initially proposed routes to minimize network congestion after traffic disruptions, pushing the system toward optimal equilibrium, while uninformed drivers make day-to-day decisions which push the system toward user equilibrium. Simulations considering varying fractions of informed drivers show that congestion is reduced during abrupt phase transition before reaching equilibrium by approximately 59.2% when 20% of drivers are informed, and is nearly eliminated when 80% of drivers are informed, which could be achieved through connected vehicle technologies. Shared memory multi-core parallelization improved the computational efficiency.