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Connected and autonomous vehicle technology has advanced rapidly in recent years. These technologies create possibilities for advanced AI-based traffic management techniques. Developing such techniques is an important challenge and opportunity for the AI community as it requires synergy between experts in game theory, multiagent systems, behavioral science, and flow optimization. This paper takes a step in this direction by considering traffic flow optimization through setting and broadcasting of dynamic and adaptive tolls. Previous tolling schemes either were not adaptive in realtime, not scalable to large networks, or did not optimize traffic flow over an entire network. Moreover, previous schemes made strong assumptions on observable demands, road capacities and users homogeneity. This paper introduces∆-tolling, a novel tolling scheme that is adaptive in real-time and able to scale to large networks. We provide theoretical evidence showing that under certain assumptions∆-tolling is equal to Marginal-Cost Tolling, which provably leads to system-optimal, and empirical evidence showing that∆-tolling increases social welfare (by up to 33%) in two traffic simulators with markedly different modeling assumptions.