Predicting Passenger Flow in BTS and MTS Using Hybrid Stacked Auto-encoder and Softmax Regression

Nayak, Archana M.; Chaubey, Nirbhay

In recent era, the deep learning techniques are effectively applied and achieved an amazing result in numerous fields. Meanwhile, for the past few years the transportation industry also gets modernized due to the influence of big data. With these two trending topics, the traditional issue found in transportation industry while predicting the passenger flow is again taken into consideration in this method for solving the issues in passenger flow forecasting. In this method, the passenger flow prediction for both Bus Transit System (BTS) and Metro Transit System (MTS) mode of transportation is carried out. The gathered passenger details is clustered by dynamic clustering as summer, winter, weekend, weekdays and public holidays. Initial cluster centroid selection is enhanced by Tabu search algorithm, which furthermore improves the performance of dynamic clustering algorithm. Following this clustering, the stacked auto-encoder (SAE) with softmax regression (SR) classifier is introduced for prediction purpose. Finally, the Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) of the Cluster-SAE-DNN (Proposed) method is compared with SAE-DNN based prediction approach. The implementation for this prediction process is carried out in Matlab. Final results illustrate that this proposed method provide high prediction result with less error rate than SAE-DNN.