Fleet Management Using IoT and Machine Learning Techniques

This research study presents an unusual approach to vehicle operations by combining Arduino UNO with accelerometers, ultrasonic sensors, GPS sensors, and temperature sensors. The Arduino UNO functions with its powerful CPU, which combines sensor data for enhanced fleet management. The accelerometer detects aggressive driving habits, enabling prompt assistance for the implementation of safer practices. The implementation of the ultrasonic sensor ensures safe parking and obstacle avoidance, hence reducing the likelihood of collisions. The utilisation of the GPS sensor enables real-time automobile tracking and optimisation, hence enhancing operational performance. The temperature sensor display unit's cargo situations, making sure regulatory compliance and product exceptional. The integrated machine enhances security, efficiency, and reliability, enabling data-driven decision-making for enhanced fleet management. The examination emphasises the method's potential to revolutionise intelligent and efficient transportation systems. The objective is to utilise several machine learning models to forecast safety conditions using factors like as speed, distance, and temperature, with the aim of identifying the most suited model for the given application.