Advancing Smart Transportation via AI for Sustainable Traffic Solutions in Saudi Arabia's

The Saudi Arabian government has committed more than $100 billion (USD) to improving the country's transportation infrastructure, in line with Vision 2030 and the Sustainable Development Goals (SDGs) of the United Nations. The National Center for Transportation Safety (NCTS), which focuses on road safety, and the “Rental Contracts” initiative are two examples of the infrastructure development projects for which the FY2022 budget allotted 42 billion SAR. On the other hand, as cities become more populated, traffic congestion has worsened, making living more difficult. In response to these issues, the government is putting in place intelligent transportation systems that use Artificial Intelligence (AI) methods to predict traffic patterns and provide drivers with other routes that cut down on travel time. These AI-driven forecasts are anticipated to lessen traffic-related problems like pollution and health hazards, supporting the country's larger objectives for sustainable infrastructure. AI models, such as Random Forest (RF), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM), have been shown to be useful in traffic prediction based on empirical results. With a Mean Square Error (MSE) of 10.5, a Root Mean Square Error (RMSE) of 3.24, a Mean Absolute Error (MAE) of 2.15, and a Mean Absolute Percentage Error (MAPE) of 3.9%, the LSTM model outperformed both the RF and GRU models. These findings demonstrate how AI-driven models may help Saudi Arabia create transportation systems that are reliable, effective, and sustainable.