Abstract:
Nowadays mass passenger flow phenomena at different degrees appear in many big cities with large-scale transit network, and those phenomena tend to be regular and complic...Show MoreMetadata
Abstract:
Nowadays mass passenger flow phenomena at different degrees appear in many big cities with large-scale transit network, and those phenomena tend to be regular and complicated. As the most popular public transport in Taipei, Mass Rapid Transit (MRT) is a quite effective traffic tool to relieve the pressure of passenger congestion, especially in rush hours. In this paper, a MRT passenger flow prediction model with deep neural network (DNN) is proposed. The various combinations of influenced factors including historical passenger flow, temporal factors, directional factor and holiday factor have been trained as the inputs by using the dataset collected from Taipei Main stations. The experiments results revealed that the modeling approach and the input influenced factors are both important elements to affect the performances of the passenger flow prediction model.
Published in: 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA)
Date of Conference: 27-29 March 2017
Date Added to IEEE Xplore: 18 May 2017
ISBN Information:
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- IEEE Keywords
- Index Terms
- Prediction Model ,
- Different Combinations Of Factors ,
- Daily Flow ,
- Passenger Flow ,
- Mass Rapid Transit ,
- Passenger Flow Prediction ,
- Daily Passenger Flow ,
- Neural Network ,
- Deep Neural Network ,
- Big Cities ,
- Temporal Factors ,
- Rush Hour ,
- Root Mean Square Error ,
- Deep Learning ,
- Training Dataset ,
- Learning Rate ,
- Number Of Observations ,
- Test Dataset ,
- Nonlinear Model ,
- Hidden Layer ,
- Mean Absolute Percentage Error ,
- Layer-by-layer ,
- Neurons In Layer ,
- Autoregressive Integrated Moving Average Model ,
- Deep Neural Network Layers ,
- Types Of Datasets ,
- Backpropagation ,
- Model Architecture ,
- Flow Data ,
- Backpropagation Algorithm
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Prediction Model ,
- Different Combinations Of Factors ,
- Daily Flow ,
- Passenger Flow ,
- Mass Rapid Transit ,
- Passenger Flow Prediction ,
- Daily Passenger Flow ,
- Neural Network ,
- Deep Neural Network ,
- Big Cities ,
- Temporal Factors ,
- Rush Hour ,
- Root Mean Square Error ,
- Deep Learning ,
- Training Dataset ,
- Learning Rate ,
- Number Of Observations ,
- Test Dataset ,
- Nonlinear Model ,
- Hidden Layer ,
- Mean Absolute Percentage Error ,
- Layer-by-layer ,
- Neurons In Layer ,
- Autoregressive Integrated Moving Average Model ,
- Deep Neural Network Layers ,
- Types Of Datasets ,
- Backpropagation ,
- Model Architecture ,
- Flow Data ,
- Backpropagation Algorithm
- Author Keywords