Smartphone based system for real-time aggressive driving detection and marking rash driving-prone areas

Integration of the physical world with the computerized world has led to the manifestation of Cyber-Physical Systems (CPSs) in an attempt to build a better and smarter world. In this paper, such a CPS named D&RSense has been proposed to promote smart transportation in order to make travelling more comfortable and safe. By studying driving patterns of drivers, D&RSense can get valuable insights to their braking and accelerating styles which can help to give them real-time warnings when they drive aggressively. Detection of rash driving prone areas across the city can help to recommend which areas of the city need stricter surveillance. D&RSense involves smartphones of commuters and utilizes their accelerometer and GPS sensors to detect driving events like braking and acceleration as well as poor road conditions like bumps and potholes by applying the ensemble learning method for classification, Random Forest (RF). The accuracy of the same has been compared to other supervised machine learning classifiers like Naive Bayes, k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). Rash-driving prone areas and poor road segments during the course of the experiment have been plotted using Density-based spatial clustering of applications with noise (DBSCAN) algorithm. Effectiveness of the proposed application has been evaluated through extensive testing.