Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

中文翻译：

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安全物联网使交通系统中基于机器学习的事故预测

自新兴技术（如信息和通信技术（ICT），物联网（IoT），机器学习（ML），区块链和人工智能）的发展以来，智慧城市已经走了很长一段路。智能交通系统（ITS）是快速发展的智慧城市的重要应用。汽车事故严重程度的预测在智能交通系统中起着至关重要的作用。这项研究的主要动机是确定可能影响车辆事故严重性的特定特征。本文采用了一些分类模型，特别是Logistic回归，人工神经网络，决策树，K最近邻和随机森林来预测事故的严重程度。所有模型都经过验证，实验结果表明，这些分类模型具有较高的准确性。本文还解释了一种用于在与ITS相关的所有组件之间进行安全信息交换的安全通信体系结构模型。最后，论文实现了基于Web的消息警报系统，该系统将用于通过智能IoT设备向用户发出警报。