University of Tennessee at Chattanooga Department of Engineering and Computer Science, United States.
University of Tennessee at Chattanooga Department of Engineering and Computer Science, United States.
Accid Anal Prev. 2021 Jan;149:105860. doi: 10.1016/j.aap.2020.105860. Epub 2020 Nov 7.
Given the ever present threat of vehicular accident occurrence endangering the lives of most people, preventative measures need to be taken to combat vehicle accident occurrence. From dangerous weather to hazardous roadway conditions, there are a high number of factors to consider when studying accident occurrence. To combat this issue, we propose a method using a multilayer perceptron model to predict where accident hotspots are for any given day in the city of Chattanooga, TN. This model analyzes accidents and their associated weather and roadway geometrics to understand the causes of accident occurrence. The model is offered as a live service to local law enforcement and emergency response services to better allocate resources and reduce response times for accident occurrence. Multiple models were made, each having different variables present, and each yielding varying results.
鉴于车辆事故对大多数人的生命安全构成的持续威胁，需要采取预防措施来应对车辆事故的发生。从恶劣的天气到危险的道路状况，在研究事故发生时需要考虑许多因素。为了解决这个问题，我们提出了一种使用多层感知机模型的方法，以便在田纳西州查塔努加市的任何特定日期预测事故热点的位置。该模型分析事故及其相关的天气和道路几何形状，以了解事故发生的原因。该模型作为一项实时服务提供给当地执法和应急响应服务部门，以更好地分配资源并减少事故发生时的响应时间。我们制作了多个模型，每个模型都有不同的变量，并且每个模型的结果都有所不同。
