Hossain, Muhammad IqbalShuvo, Mohammad Asifur RahmanZubair, MuhtadiHossain, SarowarPurnata, Afsara Tahsin2025-10-062025-10-0620202020-10ID 16301132ID 16301113ID 16301127ID 16301130http://hdl.handle.net/10361/26821Cataloged from PDF version of thesis.Includes bibliographical references (pages 28-29).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2020.Traffic jams are a common phenomenon all over the world, especially in a densely populated country like Bangladesh. Due to this, people try heart and soul to tackle this problem by any means necessary to save time to reach their desired destination. Hence the traffic related research is a hot topic now a days which will be quite beneficial for all people living in congested cities. We also tried to do some research on the traffic network to find the most suitable traffic forecasting model to forecast or predict the future traffic value using time-series forecasting models. The only topic which deals with both, traffic prediction and traffic control is traffic timeseries analysis for which it is essential. In this paper, we have obtained a suitable data set containing data of the number of various vehicles for each hour for seven days straight. We have used this data set to feed into a few time-series forecasting models of our choosing. The models or algorithms considered are ARIMA, ETS, SNAIVE, PROPHET and the last one is the combination of all models we named it "mix". The study shows us the signi cant difference between each of the models and which one produces a more reliable and accurate prediction.40 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Traffic forecastingTime-series forecasting modelsPROPHETSNAIVEARIMAETSTime series analysisTraffic predictionTraffic managementTraffic estimation--Mathematical models.Urban transportation--Data mining.Traffic congestion--Forecasting--Mathematical models.Time-series analysis.Thesis