LSTM Network Integrated with Particle Filter for Predicting the Bus Passenger Traffic

The paper reports a combination of the deep learning technique and bayesian filtering to effectively predict the passenger traffic. The architecture of the model integrates the particle filter with the LSTM network. The time series sequential prediction is best achieved using LSTM network while Markovian behaviour is well extracted using Bayesian (Particle Filter) filters. The temporal and spatial features of the traffic data are analyzed. Three relevant temporal variations viz., morning, noon and post noon patterns are identified after the histogram analysis. These patterns are statistically modelled and the integrated model is used to accurately predict the passenger flow for the next thirty days, facilitating, the bus scheduling for that period. The experimental results proved that the proposed integrated model with coefficient of determination ( $$ ) value of 0.88 is functional in predicting the passenger traffic even when the training data set size is small.