Journal Article

Machine learning based IoT system for secure traffic management and accident detection in smart cities

Abouhawwash, Mohamed, Balasubramanian, Saravana Balaji, Munshi, Asmaa, Almukadi, Wafa, Balaji, Prasanalakshmi, Prabhu, T. N., K, Venkatachalam

2023

Overview

In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.

• Publisher: PeerJ. Ltd,PeerJ, Inc,PeerJ Inc
• Subject: Accident detection / Agents and Multi-Agent Systems / Agricultural production / Artificial Intelligence / Automation / Autonomous cars / Data Mining and Machine Learning / Data transmission / Deep learning / Fatalities / Internet of Things / Machine learning / Methods / Roads & highways / Secure early traffic-related EveNt detection / Security and Privacy / Sensors / Smart cities / Traffic accidents / Traffic accidents & safety / Traffic congestion / Traffic control / Traffic engineering / Traffic flow / Traffic information / Traffic jams / Traffic management / Traffic management system / Traffic planning / Traffic signals / Transportation planning / Transportation systems / Travel time / Vehicles