Evolutionary City: Towards a Flexible, Agile and Symbiotic System
This addresses urban management challenges for city planners and residents by making cities more adaptable, though it appears incremental in applying existing technologies to urban systems.
This paper tackles the problem of rigid urban infrastructure struggling to adapt to changing demand by proposing an evolutionary city framework that uses sensing, simulation, and decentralized decision-making. In a case study, it optimizes traffic flow by adjusting lane allocations, enhancing efficiency and reducing emissions.
Urban growth sometimes leads to rigid infrastructure that struggles to adapt to changing demand. This paper introduces a novel approach, aiming to enable cities to evolve and respond more effectively to such dynamic demand. It identifies the limitations arising from the complexity and inflexibility of existing urban systems. A framework is presented for enhancing the city's adaptability perception through advanced sensing technologies, conducting parallel simulation via graph-based techniques, and facilitating autonomous decision-making across domains through decentralized and autonomous organization and operation. Notably, a symbiotic mechanism is employed to implement these technologies practically, thereby making urban management more agile and responsive. In the case study, we explore how this approach can optimize traffic flow by adjusting lane allocations. This case not only enhances traffic efficiency but also reduces emissions. The proposed evolutionary city offers a new perspective on sustainable urban development, highliting the importance of integrated intelligence within urban systems.