Train Delay Prediction using Machine Learning
Lokaiah Pullagura1, Jeevaa Katiravan2
1Lokaiah Pullagura, Research Scholar, Dept. of CSE, Sri Satya Sai University of Technology & Medical Sciences. Sehore, (Madhya Pradesh), India.
2Dr. Jeevaa Katiravan, Associate Professor, IT Department, Velammal Engineering College, Chennai, (Tamil Nadu), India.
Manuscript received on November 20, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1312-1315 | Volume-9 Issue-2, December, 2019. | Retrieval Number: A2088109119/2020©BEIESP | DOI: 10.35940/ijeat.A2088.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Indian Railways operates both long distance and suburban passenger trains and freight services daily in the country. Trains get delayed frequently due to several reasons such as, severe weather conditions such as fog, traffic, signal failure, derailing of trains, accidents, etc, and this delay is propagated from station to station. If we can predict this in advance – it would be of great help for the commuters to plan their journey either for an earlier departure or postpone, and also lets railways to take measures to avoid delays further. In this paper, we used decision tree, a machine learning method used for predicting train delays, and Recurrent Neural Networks distinguished with various fixtures. For predicting train delays, Recurrent Neural networks with 2 layers and 22 neurons per each layer gave best results with an average error of 122 seconds.
Keywords: Train Delay(TD), Machine Learning, prediction, Decision tree, RNN.