Traffic Condition Forecasting System by Floating Car Data with Data Mining Method
نویسنده
چکیده
A traffic condition forecasting system using floating car data in conjunction with a data mining method is proposed. A register network is used to describe the congestion model. The register network denotes the dual graph of the actual road link connections. In this forecasting system, estimation and learning agents alternately calculate the results to improve the forecasting accuracy. To forecast future traffic conditions, it is essential to interpolate present traffic conditions. The forecast results for an hour ahead are used as the interpolation results after an hour. The standard deviation of the forecast velocity error is 14.03 km/h.
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