Crowding prediction on mass rapid transit systems using a weighted bidirectional recurrent neural network

Crowding on Mass Rapid Transit (MRT) Systems has been the subject of intense scrutiny from a multitude of academic fields. Crowding prediction can assist system managers in finding effective ways to ease traffic pressure. Forecasting crowding levels is a challenging task due to a series of complex factors. Deep learning models are a common approach often applied to traffic prediction, but due to the imbalance within available data, these models that are based on historical data cannot forecast crowding in Rapid Transit Systems effectively. To solve this problem, this study proposes a model named the weighted resample bidirectional recurrent neural network (WRBRNN). First, the training data were split into different sub-datasets according to certain predetermined labels. During this training time, every mini-batch size sequence was weighed and resampled from different sub-datasets. In this study, the authors carefully arranged the traffic data attributes into several time series, allowing the bidirectional time series information and the model to make reliable predictions. This work performed a case study of the Bay Area Rapid Transit, US system with a year's worth of historical data from 2017. Their results reveal that the model WRBRNN performed well when predicting crowding in an MRT system.