An Agent-Based Model to simulate bottom-up urban mobility policies, applying datasets from the smart city and considering human cognitive behaviours

我的博士项目旨在开发、校准和验证基于主体的模型(ABM)，该模型使用从智能传感器收集的数据并应用人工智能技术进行分析，以模拟城市交通政策，以确定有效的措施来减少拥堵和污染，考虑到主体(公民)简化的人类认知行为。纳入简化的人类行为可以为城市流动政策的实施提供另一种观点(自下而上)，并了解这些政策对每个公民的影响

My PhD project aims to develop, calibrate and validate an Agent-Based Model (ABM) using data collected from smart sensors and analysed applying AI techniques, to simulate urban mobility policies to identify efficient measures to reduce congestion and pollution, considering a simplified human cognitivity behaviour of the agents (citizens). The inclusion of simplified human behaviours could provide an alternative point of view (bottom-up) of the implementation of urban mobility policies and knowledge about how they affect to each citizen individually