LAUSR

This paper presents a self-organizing model to design effective traffic signaling strategies in order to reduce traffic congestion in urban areas. The proposed traffic signaling system is based on a pattern model of self-organization, i.e., digital infochemicals (DIs), which are analogous to chemical substances that convey information between interactive elements mediated via the environment. In the context of traffic systems, the DIs refer to information generated by vehicles and dissipated by the urban transportation infrastructure. Based on the exploratory analysis with one single intersection, we demonstrate that the DI-based strategy performs significantly better than both the fixed and trigger-based scheduling strategies in terms of queue length and waiting time under both fixed and dynamic traffic demands.