A Machine Intelligent Cloud Based Framework to Monitor Driving Behavior of Vehicles Using Internet of Things
Keywords:
Vehicle Driving Behavior Monitoring, Intelligent Transportation Systems, Machine Learning, oT, Cloud, Classification AccuracyAbstract
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).
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In this work, a machine intelligent cloud-based framework is proposed to monitor the driving behavior of vehicles in the city using IoTs. The performance of the framework is tested using Python. From the results it is found that RF model performs better in identifying the accurate driving behavior of a vehicle with highest CA. In future, we will try to enhance the CA by using new supervised models and
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