Studies related to public transportation systems help the commuting public by increasing road safety and circulation. These result in optimized traffic flow, shorter origin-destination travel time and reduced incident rate. Vehicular Ad-Hoc Network uses a number of sensors to gather data on the road. Intelligent Transportation Systems (ITS) draw inference from the gathered data. In this paper we discuss our experience of using Auto Regressive Integrated Moving Average (ARIMA) based techniques emphasizing on the integration of short-range and long-range dependencies of the historical traffic volume. We also analyze traffic data for patterns across different types of roads and derive computational complexity of ARIMA. Finally, improvements are identified for better prediction. We empirically show that SARIMA and ARIMA-GARCH exhibit similar road traffic prediction. ARIMA-GARCH is better than ARIMA and SARIMA for prediction, with stable model order across different historical traffic volumes. We further analyzes model orders across different types of roads and historical traffic volume; and its implications for practical applicability in ITS.