This study explores the concept of utilizing chaotic traffic flow as a computational resource to mitigate traffic congestion. Using a scaled miniature traffic model and computer simulations, we quantified traffic complexity through the Lyapunov exponent and analyzed the effects of various interventions on traffic dynamics. Our findings show that chaotic behavior in traffic can contribute to increased energy efficiency and improved average speeds. Specifically, we observed that as vehicle density decreased, the complexity of traffic flow can increase, leading to reduced energy consumption per vehicle. Furthermore, interventions such as route selection and signal control influenced traffic dynamics, with potential implications for traffic management strategies. This research highlights the possibility of using chaotic traffic behavior as a novel computational approach to optimize traffic flow and reduce congestion.