Evaluation of a Probabilistic Framework for Traffic Volume Forecasting Using Deep Learning and Traditional Models - Tripathi, Abhay Narayan; Sharma, Bharti (International Journal of Experimental Research and Review, 2024)
Forecasting is a very important issue in the present times to optimize urban traffic management systems and their proper planning. It is often found that traditional forecasting models cannot fully approximate the non-linear and dynamic nature of traffic patterns. The presented paper proposes a framework that integrates multiple traditional models such as Autoregression (AR), Support Vector Regression (SVR), Exponential Smoothing, Historical Average, Random Walk and Artificial Neural Network (ANN) with deep learning models, especially Stacked Autoencoders, and enhances their predictive efficiency phenomenally. The forecast accuracy is then further enhanced through a dynamic probabilistic model integration strategy that adapts to real-time traffic conditions by dynamically weighting the models based on their performance. It is found that the hybrid framework proposed in the present paper performs much better than the traditional models in predicting traffic volume. Deep learning models with RMSE of 26.9976, AR (RMSE: 22.6679) and SVR (RMSE: 28.2221) provided remarkable prediction accuracy. Based on the results, the authors are confident that the integrated models are useful for real-time traffic management applications. However, they still have great potential for further refinement and optimization.