An Intelligent Transport System for Prediction of Urban Traffic Congestion Level

Developing a resilient infrastructure is crucial for nation-building by supporting innovations and promoting sustainable growth. The Kingdom of Saudi Arabia is striving to achieve the Sustainable Development Goals (SDGs) set by the United Nations. Industry, Innovation, and Infrastructure (I3) are some of the strategic objectives of the Kingdom’s Vision 2030 par with the United Nations’ SDGs. The objective is focused to develop trade and transport networks for international, regional, and local connectivity with an investment of billions of dollars to establish a robust transport network and improve the existing one for enhancing road safety to reduce the costs of deaths and serious injuries. For this, a control center for automatic monitoring could be established for 24x7 monitoring of traffic violators; the key project has been named the National Center for Transportation Safety, apart from launching the “Rental Contracts” facility with the Naql portal. Moreover, the growing urban population is causing more vehicles on the roads leading to more traffic congestion which has become severe during peak hours in the major cities causing several other issues such as environmental pollution, high greenhouse gases (GHGs) including CO2 emissions, health risks to the citizen and residents, poor air quality, higher risks of road safety, more energy consumption, discomfort to the commuters, and wastage of time and other resources. Therefore, in this research, we propose an intelligent transport system (ITS) for predicting traffic congestion levels and assist commuters in taking alternative routes to avoid congestion. An intelligent model for predicting urban traffic congestion levels using XGBoost, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) algorithms is developed. The comparative performance analysis of the techniques concerning the performance metrics: Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Error cost, Outlier sensitivity, and Model Complexity, demonstrate that the LSTM algorithm excels the other two algorithms.

@article{Rahmani2024,
  title     = {An Intelligent Transport System for Prediction of Urban Traffic Congestion Level},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mohammad Khalid Imam Rahmani and Shahnawaz Khan and Md Ezaz Ahmed and Khurram Jawad},
  doi       = {10.14569/IJACSA.2024.0151131},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151131}
}