کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10359417 | 869208 | 2005 | 24 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach](/preview/png/10359417.png)
چکیده انگلیسی
Short-term forecasting of traffic parameters such as flow and occupancy is an essential element of modern Intelligent Transportation Systems research and practice. Although many different methodologies have been used for short-term predictions, literature suggests neural networks as one of the best alternatives for modeling and predicting traffic parameters. However, because of limited knowledge regarding a network's optimal structure given a specific dataset, researchers have to rely on time consuming and questionably efficient rules-of-thumb when developing them. This paper extends past research by providing an advanced, genetic algorithm based, multilayered structural optimization strategy that can assist both in the proper representation of traffic flow data with temporal and spatial characteristics as well as in the selection of the appropriate neural network structure. Further, it evaluates the performance of the developed network by applying it to both univariate and multivariate traffic flow data from an urban signalized arterial. The results show that the capabilities of a simple static neural network, with genetically optimized step size, momentum and number of hidden units, are very satisfactory when modeling both univariate and multivariate traffic data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Transportation Research Part C: Emerging Technologies - Volume 13, Issue 3, June 2005, Pages 211-234
Journal: Transportation Research Part C: Emerging Technologies - Volume 13, Issue 3, June 2005, Pages 211-234
نویسندگان
Eleni I. Vlahogianni, Matthew G. Karlaftis, John C. Golias,