Railway Infrastructure and Traveller usage Prediction and Rendering Solutions
Krishna Mohan Ankala1, Jyothirmai Kanigolla2

1Dr. Krishna Mohan Ankala*, CSE, JNTU Kakinada, Kakinada, India.
2Jyothirmai Kanigolla, CSE, JNTU Kakinada, Eluru, India.

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 915-917 | Volume-8 Issue-12, October 2019. | Retrieval Number: J92960881019/2019©BEIESP | DOI: 10.35940/ijitee.J9296.0981119
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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: This project introduces the primary establishments of Big Data connected to Smart Cities. An IOT based mechanism is proposed to be connected to various areas. In this project, we are trying to predict and provide the solution to improvise the railway / bus infrastructure and their services. Indian local & state railways or buses are a mode of transport service where thousands of people process every minute. Thus our proposed system involves data collection of the users based on id, username, gender, age, the timing of travel, station source and destination to monitor the user travel behavior. Thus the collected data can be used for analytics and prediction. Predicting the consumer’s count and behavior who uses the railway services are solved through the R Programming. The data analytics are performed using R studio. For this work, In R programming, we use K-means algorithm for clustering and use Naive Bayes algorithm for machine learning and solution defining. Finally, the predictive output is sent for public access using shinyapps.io. These results are useful to the travelling systems for giving better services to passengers.
Keywords: Clustering, Classification, IOT, Smart Card
Scope of the Article: Clustering