Department of Computer Science, Old Dominion University, 3300 Engineering & Computational Sciences Bldg., Norfolk, VA 23529, USA.
Department of Electrical and Computer Engineering, Old Dominion University, 231 Kaufman Hall, Norfolk, VA 23529, USA.
Sensors (Basel). 2021 Nov 18;21(22):7652. doi: 10.3390/s21227652.
It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed decisions about their travel plans, thereby preventing congestion altogether or mitigating its nefarious effects.
人们已经广泛认识到，智慧城市和智慧社区提供的关键服务之一是智能交通。本文为 SEE-TREND 系统奠定了理论基础，该系统用于在智慧城市和智慧社区中进行安全的早期交通相关事件检测。SEE-TREND 通过对过往车辆进行匿名、概率性的交通相关数据收集来促进智能交通。然后，将收集到的数据进行聚合，并由其推理引擎用于构建对交通状态的信念，以检测交通趋势，并沿着道路传播相关的交通信息，以帮助驾车公众对其出行计划做出明智的决策，从而完全避免拥堵或减轻其不良影响。
