A Short-Term Demand Forecasting Model from Real-Time Traffic Databy Changkyun Kim, VPI&SU, Blacksburg, United States,
Antoine G. Hobeika, VPI&SU, Blacksburg, United States,
Abstract: Developing real-time traffic diversion strategies is a major issue of Advance Traffic Management Systems (ATMS), a component of Intelligent Vehicle Highway Systems(IVHS). Traffic diversions attempt to maximize the use of available capacities in the roadway systems during congestion-causing events. In selecting the diversion route, the decision is based that the route may not become congested by the time the diverted drivers reach that part of the network. Thus the forecasting of expected traffic flow on various parts of the network in a prompt and accurate fashion would help determine the efficient alternate routes. In this research, a prediction model has been developed to provide traffic forecasts. It has two components. One component is an Auto Regressive Integrated Moving Average (ARIMA) model based on the previous traffic data. The other component is the average traffic flow for that period as obtained from previous days. These two components are combined to represent the fluctuations in the traffic flow behaviour.
Subject Headings: Intelligent transportation systems | Traffic models | Traffic flow | Highway and road management | Systems management | Forecasting | Data processing
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