ABS-SmartPriority: An Agent-Based Simulator of Strategies for Managing Self-Reported Priorities in Smart Cities

摘要

Smart cities still need the appropriate tools for allowing researchers to contribute in this growing field that will change the comfort and qualities of live of citizens. Smart cities can provide services such as informing of the less overcrowded tourist routes, path-finding for avoiding traffic jams, search services for parking, collecting tree branches from streets, and the finding of the nearest available public electric bicycles or cars. The levels of urgencies differ from some situations to others. Examples of urgent matters are the fast transportation critical patients and the removal of obstacles in main roads. Since smart cities are meant to manage huge amounts of requests, priorities should be automated and managed in a flexible way for new scenarios. Smart cities may need citizens to report the service priorities. However, citizens may have different criteria and could abuse the highest priority levels hindering the service performance for really urgent matters. A novel agent-based simulation open-source framework is proposed for testing different policies for normalizing and controlling self-reported priorities, with its simulator called ABS-SmartPriority. This approach is illustrated by simulating two different policies, in which the smart policy outperformed the one used as the control mechanism.