A Naive Bayes Classifier Approach to Incorporate Weather to Predict Congestion at Intersections

Saurav Barua¹

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.2 , pp.72-76, Jun-2020

Online published on Jun 30, 2020

Copyright © Saurav Barua . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The study endeavored to model the influence of weather phenomena on traffic congestion considering various type and time of days at different intersections in Dhaka city. A Na?ve Bayes Classifier method was adopted to model this causation relation from the field survey data using Scikit-learn software. The data obtained was divided into training and testing dataset. The proposed Na?ve Bayes Classifier model showed 72.25% and 85.03% accuracy in training and testing respectively. The root mean square error (RMSE) and mean absolute error (MAE) of the model were 0.46 and 0.28 respectively. The Na?ve Bayes Classifier model showed good prediction potential to assess the influence of weather condition on traffic congestion. The outcomes of the study can be utilized to develop Traffic Management System (ATMS) and Advanced Traveler Information System (ATIS) for Dhaka city. Hence, the motorists can decide which intersections to travel while route choice in advance and congestion will be reduced consequently

Key-Words / Index Term :
Traffic congestion, weather condition, Na?ve Bayes Classifier, Scikit-learn, road intersections

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