With rapid urbanization, population growth, and the natural aging of public infrastructure, cities in Jordan and across the region face mounting challenges in maintaining safe, efficient, and sustainable transportation systems. Among the most pressing concerns is the deterioration and poor visibility of traffic signs, which are vital for regulating movement, reducing accidents, and ensuring road safety. Damaged, missing, obscured, or faded signage compromises driver awareness, contributes to traffic violations, increases confusion, and endangers both pedestrians and motorists. According to the World Health Organization (WHO), poor road infrastructure, including signage deficiencies, is a major contributing factor to the severity and frequency of traffic accidents worldwide.
In this context, SMART-RAIL, a national research and infrastructure initiative led by Dr. Taqwa Alhadidi of Al-Ahliyya Amman University, introduces an innovative, AI-powered platform that revolutionizes how roadway assets—specifically traffic signs—are monitored, analyzed, and maintained. The project integrates high-resolution data collection technologies, including the ROMDAS Road Measurement System and laser profilometers, with advanced machine learning models to automatically detect, classify, and assess the physical and functional condition of road signs across more than 2,000 kilometers of Jordan’s road network. This transition from manual inspection to automated analysis represents a major leap in efficiency, precision, and cost-effectiveness for national infrastructure management.
The technical core of SMART-RAIL lies in its use of cutting-edge computer vision models such as YOLOv8, YOLOv9, and the transformer-based OWL-ViT architecture. These models are trained to handle a wide range of environmental variables, including low lighting, glare, occlusion, weather changes, and motion-related distortions. Captured images are geotagged, processed using AI-enhanced pipelines, and integrated into an interactive GIS-based digital map, enabling decision-makers to visualize the exact location, classification, and condition of each traffic sign. This real-time visualization allows for prioritized, data-driven maintenance planning, budget allocation, and infrastructure investment.
To date, the project has successfully processed more than 130,000 roadway images, uncovering that approximately 18% of road signs are either missing, severely damaged, or functionally compromised, necessitating immediate attention. In areas where corrective actions were implemented based on SMART-RAIL’s analysis, road asset condition ratings improved by 12% within six months. Preliminary municipal reports also indicate noticeable reductions in traffic violations, smoother vehicle flow, and improved safety outcomes in these zones, demonstrating the project’s measurable social, operational, and economic benefits.
Beyond its national scope, SMART-RAIL contributes meaningfully to global development goals. It advances multiple United Nations Sustainable Development Goals (SDGs), including SDG 3.6 (reduce road traffic deaths), SDG 9 (build resilient infrastructure), SDG 11 (make cities inclusive, safe, and sustainable), SDG 13 (support climate-resilient infrastructure), and SDG 17 (promote cross-sector partnerships between governments and academic institutions).
By combining artificial intelligence, geospatial analytics, and multi-stakeholder collaboration, SMART-RAIL offers a scalable, replicable model for infrastructure modernization and public safety. It positions Jordan—and potentially the wider Arab region—as a leader in smart, sustainable transport solutions for the future.
