A Path-Depended Passenger Flow Forecasting Model for Metro Rail Systems Using LSTM Neural Network

The primary goal of this work is to develop a framework for short term passenger flow prediction for metro rail transport systems. A reliable prediction of short-term passenger flow could greatly support metro authorities' decision process. Both inflow and outflow of the metro stations are strongly associated with the travel demand within metro networks. Sequestered station-wise analysis ignores the spatial correlations existing between the stations. This paper tries to merge the spatial with the temporal by employing an indirect method of computing flow through O-D estimates for the same. Path-depended station-pairs of O-D flow are considered for employing a customized LSTM network. Experimental results indicate that the proposed passenger flow prediction model is capable of better generalization on short-term passenger flow than standard models of learning compared. This work also establishes that O-D prediction provides an indirect estimation procedure for passenger flow. The specific use case for this work is Kochi Metro Rail Limited (KMRL). A highlight of the work is that the whole analytics and modelling procedures are written on a customized scalable big-data platform (Jaison Paul Data Analytics Platform) JP-DAP which was developed prior to this work.