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

Abstract

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.

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

“…SAE has powerful feature extraction capabilities and advantages in data reconstruction that allow it to perform well in a variety of research contexts. Nayak and Chaubey (2020) used a taboo search algorithm to enhance the initial clustering prime selection, and introduced a SAE based on the Softmax regression (SR) classifier for prediction. On the basis of fully considering external factors such as date, weather, and air index, Hou et al (2022) proposed a passenger flow prediction method that integrates machine learning, time convolutional networks, and LSTM.…”

“…The stack autoencoder is essentially a deep-learning model composed of multiple coding layers and decoding layers (Nayak & Chaubey, 2020). Feature data can be obtained continuously through the learning mechanism of feature extraction layer-by-layer, so as to obtain deeper and more comprehensive feature vectors.…”

Parivahan: Passenger Demand Triggered Bus (Vahan) Routing in Intelligent Public Transport System

“…al. [2] had done a very good work on passenger flow prediction that could help scheduler to schedule vehicles as per passenger needs.…”

PAARGAMAN: Passenger Demand Provoked (On-The-Fly) Routing Of Intelligent Public Transport Vehicle with Dynamic Route Updation, Generation, and Suggestion

“…Public vehicle routing based on dynamic routing has network and fleet size limitations. Also, the demand of the passenger needs to be known by the route generator before the starting of the vehicle [17,18]. Dynamic vehicle routing by considering the larger fleet size with the route updates is employed in this research based on the passenger request.…”