Enhancing Traffic Congestion Forecasting with Explainable Deep Learning: A Framework Using LIME for Transparent Intelligent Transportation Systems

Intelligent transportation systems aim to improve traffic management and road safety, manage traffic effectively, and reduce roadway system congestion. This optimally requires estimating future traffic congestion. Unfortunately, the most popular machine learning and deep learning techniques can be unsuitable for model development in this task due to interpretability challenges. This study attempts to provide a solution to this challenge by creating a tool that integrates Local Interpretable Model-agnostic Explanations (LIME) into any traffic congestion forecasting system. This tool is applied to the Metro Interstate Traffic Volume dataset, which contains samples of traffic and road system congestion along with temporal, weather, and contextual data. For global feature analysis, a Random Forest Regressor is used as a baseline model, while a neural network model is developed to predict the congestion of the traffic and road system. The neural network model achieved a congestion prediction with an R² score of 0.612, a mean squared error of 0.026, and a mean absolute error of 0.129. The LIME tool also provides temporal feature insights, which show that examples of weekday/holiday status reduce the sample congestion prediction for the example, while precipitation increases it. At a global level, hour of the day, day of the week, temperature, and month of the year are the dominant factors in congestion prediction. These findings illustrate the value of adding interpretability to predictive models of traffic congestion when using explainable artificial intelligence.