کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
526653 | 869173 | 2009 | 16 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting](/preview/png/526653.png)
Short-term traffic volume forecasting represents a critical need for Intelligent Transportation Systems. This paper develops a novel forecasting approach inspired by human memory, called the spinning network (SPN). The approach is then used for short-term traffic volume forecasting, utilizing a data set compiled from real-world traffic volume data obtained from the Hampton Roads traffic operations center in Virginia. To assess the accuracy of the SPN approach, its performance is compared to two other approaches, namely a back propagation neural network and a nearest neighbor approach. The transferability of the SPN approach and its ability to forecast for longer time periods into the future is also assessed. The results of the performance testing conducted in this paper demonstrates the superior predictive accuracy and drastically lower computational requirements of the SPN compared to either the neural network or the nearest neighbor approach. The tests also confirm the ability of the SPN to predict traffic volumes for longer time periods into the future, as well as the transferability of the approach to other sites.
Journal: Transportation Research Part C: Emerging Technologies - Volume 17, Issue 5, October 2009, Pages 510–525