Machine learning framework for sustainable traffic management and safety in AlKharj city

Abstract

As urban areas expand, cities face increasing challenges related to traffic congestion, accident rates, and environmental impact, all of which hinder sustainable growth and public safety. In AlKharj, a vibrant governorate in Riyadh, Saudi Arabia, traditional traffic management systems struggle to address these issues effectively. To tackle these challenges, we propose an Artificial Intelligence (AI) and Machine Learning (ML) framework aimed at transforming transportation infrastructure towards greater sustainability and resilience. This study highlights AI-driven advancements in traffic management, accident prevention, and energy optimization for AlKharj’s growing urban environment. We develop predictive models for accident hotspots, adaptive traffic systems, and fuel-efficient routing. Using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs), we forecast accident trends and energy consumption, providing strategic insights for urban planning. Our findings demonstrate the potential of AI to enhance efficiency, safety, and environmental sustainability in transportation, setting a benchmark for future sustainable urban mobility initiatives worldwide.