This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Sourav Kumar Bhoi , Krishna Prasad K
Monitoring driving behavior of vehicles is a very important area of research in the field of Intelligent Transportation Systems (ITSs). As vehicles are rising tremendously on the roads of cities, it is very much essential to track the driving behavior of a vehicle to reduce the accidents in the cities. The drivers who are not driving properly, for example, if a driver is continuously performing sudden acceleration, sudden deceleration, sudden left turn, sudden right turn, etc. then these types of vehicles need to be monitored and detected. In this work, a machine-intelligent cloud-based framework is proposed to monitor the driving behavior of vehicles in the city using internet of things (IoTs). The vehicles are installed with an on-board unit which is responsible for collecting all readings from the sensors and sending these readings to cloud using IoT for detection of driving patterns. Here, the cloud is deployed with a supervised machine intelligent model that is responsible for identifying the driving pattern after receiving the readings from a vehicle. The model is selected by conducting training and testing over a standard dataset. The performance of the framework is tested using Python tool. From the results, it is found that Random Forest (RF) model performs better in identifying the accurate vehicle behavior with highest classification accuracy (CA).
Post Doctoral Fellow, Research Center Department: Computer Science and Information Science, Institute of Computer Science and Information Science, Srinivas University, Mangaluru, Karnataka, India
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