1402/12/07
جمال قاسمی

جمال قاسمی

مرتبه علمی: دانشیار
ارکید:
تحصیلات: دکترای تخصصی
اسکاپوس:
دانشکده: دانشکده مهندسی و فناوری
نشانی:
تلفن: 01135302902

مشخصات پژوهش

عنوان
Traffic Signal Prediction using ELMAN Neural Network and Particle Swarm Optimization
نوع پژوهش
JournalPaper
کلیدواژه‌ها
Traffic Signal, Neural Networks, Particle Swarm Algorithm, Wavelet Transform
سال
2016
مجله international journal of engineering
شناسه DOI
پژوهشگران Jalil Rasekhi ، Jamal Ghasemi

چکیده

Prediction of traffic is very crucial for its management. Because of human involvement in the generation of this phenomenon, traffic signal is normally accompanied by noise and high levels of non-stationarity. Therefore, traffic signal prediction as one of the important subjects of study has attracted researchers’ interests. In this study, a combinatorial approach is proposed for traffic signal prediction, based on Neural Networks and Particle Swarm Optimization algorithm. Elman Neural Network is chosen from amongst many types of Neural Networks due to its feedbacked structure. To this purpose, Particle Swarm optimization algorithm is utilized for adequate training of the Neural Network, instead of common gradient descent based methods. In this work, wavelet transform is employed as a part of the preprocessing stage, for the elimination of transient phenomena as well as for more efficient training of the Neural Network. Simulations are carried out to verify performance of the proposed method, and the results demonstrate good performance in comparison to other methods.