Abstract
Abstract. Short time prediction is one of the most important factors in intelligence transportation system (ITS). In this research, the use of feed forward neural network for traffic time-series prediction is presented. In this paper, the traffic in one direction of the road segment is predicted. The input of the neural network is the time delay data exported from the road traffic data of Monroe city. The time delay data is used for training the network. For generating the time delay data, the traffic data related to the first 300 days of 2008 is used. The performance of the feed forward neural network model is validated using the real observation data of the 301st day.
Highlights
One of the most important activities related to traffic control is the planning for short-term forecasting, an example of which can be the prediction of daily traffic for the few days
Prediction system uses the emerging computer, communication and control technologies for managing and monitoring the transportation. Many factors such as weather condition, exhibitions and holidays can affect the quality of the traffic forecasting
Monroe is the eighth-largest city of the U.S For recording the traffic of this city, the data of permanent and temporary stations are used
Summary
One of the most important activities related to traffic control is the planning for short-term forecasting, an example of which can be the prediction of daily traffic for the few days. Prediction of traffic can be used to improve the traffic condition and reduces travel time having the available capacity. Prediction system uses the emerging computer, communication and control technologies for managing and monitoring the transportation. Many factors such as weather condition, exhibitions and holidays can affect the quality of the traffic forecasting. One of the prediction methods is the time-series forecasting. In time-series forecasting, the historical data are collected and analyzed to make a model. This model is extrapolated for forecasting the future values (Zhang, 2012)
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