The varying phenomena that characterize a pedestrian flow make it one of the most challenging traffic flow processes to manage and control. In the past three decades, we have started to unravel the science behind the crowd. This has led to some important insights that are not only needed to reproduce, predict, and manage pedestrian flow, but will also provide potential avenues to managing other phenomena. In this talk, we will provide a historic perspective on pedestrian flow theory and crowd management. We show some of the key phenomena that have been observed (in controlled experiments, in the field), and how these phenomena can be explained, used or prevented. We will also highlight some of the recent contributions in the field, including the role of AI, novel monitoring technology, and digital twins. We round up the talk showing how the finding can be generalized. We show how the game-theoretical modeling proposed for pedestrian flow models can form a basis for controlling connected autonomous vehicles. Using various examples, we show how self-organization, omnipresent in pedestrian flow, can inspire decentralized control approaches of other flow processes (e.g., autonomous vessels, drones). We show how approaches to reduce flow breakdown for pedestrian flows can be generalized for other flow processes.