• TANUJ NANGIA

In India, road travel is the most widespread transportation system. Traffic forecasting process is assessment and quantification of traffic volume based on defined/identified parameters which include number of vehicles and number of citizens/people who will leverage a specific type of transportation infrastructure in the future. There are multiple methods available to analyze and forecast the traffic volume considering multiple factors. The objective/purpose of this study is to suggest the model which can be leveraged to predict future traffic estimates considering multiple factors and to highly suggest the finest model for future traffic estimates. Four distinct methods have been analyzed for establishing traffic growth rates as part of this study- • Transport Demand Elasticity • Simple Exponential Smoothing • Past Vehicle Registration • Auto Regressive Integrated Moving Average Technique (ARIMA) Based on our comparative analysis of the results generated by methods in scope, it can be established that the traffic growth rate predicted by transport demand elasticity technique is significantly higher in comparison to other methods in scope. However, the traffic growth rate calculated using ARIMA is an average of other three methods in scope. Hence, the results (traffic volume growth rate) generated by ARIMA can be considered as satisfactory.

Time Series, ARIMA, Regression, Econometrics, Statistical Packages for Social Sciences, Single Exponential Smoothing, Registered Vehicles

"Modeling Growth Trend and Forecasting Techniques for Vehicular Population in Chandigarh- A Case Study.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.a456-a461, September-2022, Available :http://www.jetir.org/papers/JETIR2209046.pdf

"Modeling Growth Trend and Forecasting Techniques for Vehicular Population in Chandigarh- A Case Study.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppa456-a461, September-2022, Available at : http://www.jetir.org/papers/JETIR2209046.pdf