UAV-Based Transport Management for Smart Cities Using Machine Learning
Abstract
Highlights: What are the main findings? A smart transport management system based on UAV data integrating advanced machine learning and deep learning techniques is proposed to enhance road anomaly detection and severity classification. The system employs a comprehensive multi-stage framework, integrating a high-precision obstacle detection model, six specialized severity classification models, and an aggregation model to deliver accurate anomaly assessment, enabling strategic, data-driven road maintenance and enhanced transportation safety. What is the implication of the main finding? A scalable and efficient solution is proposed to enhance road safety and optimize transportation management through intelligent anomaly detection and severity assessment. This framework sets a benchmark for future smart city initiatives by leveraging advanced machine learning techniques for proactive infrastructure maintenance and decision-making. Efficient transportation management is essential for the sustainability and safety of modern urban infrastructure. Traditional road inspection and transport management methods are often labor-intensive, time-consuming, and prone to inaccuracies, limiting their effectiveness. This study presents a UAV-based transport management system that leverages machine learning techniques to enhance road anomaly detection and severity assessment. The proposed approach employs a structured three-tier model architecture: A unified obstacle detection model identifies six critical road hazards—road cracks, potholes, animals, illegal dumping, construction sites, and accidents. In the second stage, six dedicated severity classification models assess the impact of each detected hazard by categorizing its severity as low, medium, or high. Finally, an aggregation model integrates the results to provide comprehensive insights for transportation authorities. The systematic approach seamlessly integrates real-time data into an interactive dashboard, facilitating data-driven decision-making for proactive maintenance, improved road safety, and optimized resource allocation. By combining accuracy, scalability, and computational efficiency, this approach offers a robust and scalable solution for smart city infrastructure management and transportation planning.
