Latest revision as of 23:46, 28 January 2021

Abstract

The growth of Intelligent Traffic System (ITS) have recently been quite fast and impressive. Analysis and prediction of network traffic has become a priority in day to day planning in social, economic and more widespread set of areas. With a vision to further contribute to this vast field of research, we propose an approach to forecast level of traffic congestion on the basis of a time series analysis of collected data using machine learning. Moreover, the proposed approach allows us to find a correlation between varying parameter of weather and level of traffic congestion. Traffic data collected from Uber Movement for the city of Mumbai, India was fed to multiple of pre assessed machine learning algorithm. Comparative analysis of the results of the different machine learning algorithms used have shown us that logistic regression works best with an accuracy of 85% on the collected Uber data. Thus our model can accurately predict the time to travel between different nodes (locations) in Mumbai city based on the data collected from Uber Movement.

Original document

The different versions of the original document can be found in:

• http://dspace.bracu.ac.bd/xmlui/bitstream/10361/10818/1/13310009%2c%2014101197%2c%2014101001%2c%2014201061_CSE.pdf

• http://dspace.bracu.ac.bd/xmlui/handle/10361/10818,

https://academic.microsoft.com/#/detail/2901039154

• http://xplorestaging.ieee.org/ielx7/9023468/9033528/09033922.pdf?arnumber=9033922,

http://dx.doi.org/10.1109/i2ct45611.2019.9033922