Benefit of Online Real-Time Data in the Braess Paradox with Anticipatory Routing

The routing problem in traffic networks is a prominent problem of distributed adaptive systems. The online routing game model is used to investigate the routing problem, when each subsequent agent of the traffic flow may select different route based on real-time data. Recent investigations proved that if the agents of such system use shortest path search strategy, then in some situations sometimes the multi-agent system may be worse off with real-time data than without real-time data. This paper contributes to the state-of-the-art by proving the guaranteed worst case benefit of online real-time data in the Braess paradox where each subsequent agent of the traffic flow may select different route using anticipatory techniques.