ForecastBoost: An Ensemble Learning Model for Road Traffic Forecasting

Accelerated urbanization is causing ever-increasing road traffic around the world. This rapid increase in road traffic is posing several challenges, such as road congestion, suboptimal emergency services due to inadequate road infrastructure and lack of economic sustainability. To overcome such challenges, intelligent transportation systems have recently become increasingly popular. Traffic prediction is an important part of such intelligent traffic management systems. Accurate traffic prediction leads to improved traffic flow, avoids congestion and optimizes the timing of traffic signals, resulting in higher vehicle fuel efficiency. Lower fuel consumption due to better fuel efficiency also limits the carbon footprints that help in combating global warming. To accurately predict road traffic, this paper proposes the ForecastBoost model, which leverages an ensemble learning approach to predict road traffic. ForecastBoost integrates two regression learning algorithms, namely Extreme Gradient Boosting and Categorical Boosting, to predict road traffic. The first component handles missing values and sparse data and the second handles categorical features without overfitting. We train the proposed ForecastBoost with a publicly available real-world traffic dataset. The obtained results are evaluated using similar state-of-the-art algorithms such as Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS), Series-cOre Fused Time Series (SOFTS) and TimesNET. We use a well-known performance metrics containing several performance parameters, including mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE), to evaluate the performance of the proposed ForecastBoost. The evaluation results show that the proposed ForecastBoost outperforms the other models.