Traffic collisions are synonymous with road accidents worldwide. Falling of vehicles from heights is a significant cause of injury, property damage and death every year. This work addresses the research gap in the study of occurrence, classification and reporting of the vehicle falls. It presents an IoT system, which can detect and classify the occurrence of a vehicle fall event with the help of inbuilt sensors of a contemporary smartphone. The proposed system uses vehicular speed, linear acceleration and altitude values of smartphone and connected sensors to build a k-Nearest Neighbor (k-NN) based fall occurrence and classification model to report the occurrence and severity of a vehicle fall-off. The performance of the introduced model is evaluated with the help of metrics such as precision, recall and F1 score. © 2020 IEEE.