Investigating RNNs for vehicle volume forecasting in service stations

Accurate forecasting of customer demand can be critical for increasing operational efficiency and augmenting customer satisfaction, particularly in scenarios that involve multiple service units. In this paper, we focus on the problem of predicting the volume of vehicles in a network of gas stations and conduct an exhaustive investigation of different classes of recurrent neural networks for this problem. Particularly, we investigate the trade-off between the accuracy and the overall complexity of sets of RNNs that employ varying number of models. We compare higher granularity models, where an RNN is learned from a particular dataset, to more general models sets, where a single neural network is learned from different but related datasets. Our results show that creating less specific models that integrate information from different related problems can decrease the computational cost of model learning with only a small decrease in terms of model accuracy.