We are thrilled to inform you that our paper entitled “The short-term prediction of daily traffic volume for rural roads using shallow and deep learning networks: ANN and LSTM” has been published in the Journal of Supercomputing. Predicting daily traffic volume in the short term is of great importance for rural roads since it assists in relieving congestion, trip planning, and improving the level of service (LOS). Benchmark parametric methods like seasonal autoregressive integrated moving average (SARIMA) is not sophisticated enough to properly employ big data. Shallow learning techniques like the artificial neural network (ANN) cannot capture short-term and long-term time dependencies of daily traffic volume. Therefore, long short-term memory (LSTM) has been suggested to estimate the daily traffic volume of rural roads. The daily traffic volume for three types of roads, i.e., high-volume roads, international roads for transit of goods, and recreational roads leading to the city of Mashhad, Iran, was estimated using LSTM. Interested readers can read the preprint of the paper via https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4168660 or access it via https://link.springer.com/article/10.1007/s11227-023-05333-w.
