LEVERAGING INTERNET OF THINGS (IOT) TO ENHANCE TRAFFIC MANAGEMENT SYSTEMS IN MEKELLE CITY

Abstract

Rapid urbanization and motorization in Mekelle City have created significant traffic management challenges, including recurrent congestion, road safety concerns, and inefficient manual control systems. Conventional traffic management approaches, which rely on fixed signal timing and limited real-time monitoring, have proven inadequate in addressing these issues. This study investigates the potential of leveraging the Internet of Things (IoT) to enhance traffic management systems in Mekelle City. A mixed-methods research design was employed, combining surveys, interviews, and simulation experiments. Primary data were collected from 102 road users and key stakeholders such as traffic police and urban planners, while secondary data were obtained from government reports and existing literature. A synthetic simulation was conducted to compare the performance of traditional fixed-time traffic signals with IoT-enabled adaptive signal control. The results demonstrate that the IoT-adaptive control strategy significantly reduces average queue length and vehicle delay while increasing throughput compared to the baseline fixed-time model. Specifically, average delays decreased by over 13 seconds per vehicle, while overall throughput improved. Survey findings further revealed strong public support for IoT-based traffic monitoring and violation detection systems, though opinions were divided on broader integration with cloud-based platforms and navigation services. The study concludes that IoT-enabled adaptive traffic management systems offer a feasible and impactful solution for Mekelle City, capable of improving mobility, reducing environmental impacts, and enhancing commuter safety. However, challenges remain, including infrastructure readiness, cost implications, and the need for comprehensive policy support. The research recommends a pilot deployment at major intersections, followed by phased city-wide implementation, integration with public transport systems, and further microscopic simulation using locally calibrated data. This work contributes to the growing field of smart city research in low-resource settings by providing an evidence-based framework for IoT-driven traffic management in Ethiopia