Advanced Information Feedback Coupled with an Evolutionary Game in Intelligent Transportation Systems

It has been explored for decades how to alleviate traffic congestions and improve traffic fluxes by optimizing routing strategies in intelligent transportation systems (ITSs). It, however, has still remained as an unresolved issue and an active research topic due to the complexity of real traffic systems. In this study, we propose two concise and efficient feedback strategies, namely mean velocity difference feedback strategy and congestion coefficient difference feedback strategy. Both newly proposed strategies are based upon the time-varying trend in feedback information, which can achieve higher route flux with better stability compared to previous strategies proposed in the literature. In addition to improving feedback strategies, we also investigate information feedback coupled with an evolutionary game in a 1-2-1-lane ITS with dynamic periodic boundary conditions to better mimic the driver behavior at the 2-to-1 lane junction, where the evolutionary snowdrift game is adopted. We propose an improved self-questioning Fermi (SQF) updating mechanism by taking into account the self-play payoff, which shows several advantages compared to the classical Fermi mechanism. Interestingly, our model calculations show that the SQF mechanism can prevent the system from being enmeshed in a globally defective trap, in good agreement with the analytic solutions derived from the mean-field approximation.