Prediction of short term traffic speed using LSTM algorithm deep learning

Article Type:
Research/Original Article (دارای رتبه معتبر)
Traffic flow prediction has long been considered as a fundamental problem in an intelligent transportation system. Accurate and timely prediction of the traffic flow situation can be useful for traffic management organizations and individual drivers. A good traffic forecast may help travelers make better travel decisions, thus reducing terrible traffic congestion in cities and carbon dioxide emissions, and improving the efficiency of traffic operations. Recently, various deep learning models have been introduced to the field of prediction. While many studies performed traffic speed prediction with deep learning models. Considering the important effects of spatio-temporal factors and the excellent performance of recurrent neural networks in the field of time series analysis, in the research carried out, the characteristics of long-short-term memory (FI-LSTM) neural networks that combine sequential time data are suggested to be one It is a deep learning network. This research has compared four deep learning models including LSTM, GRU, ConvLSTM, and BiLSTM in three time steps of 5 minutes, 10 minutes, and 15 minutes. The results of this research show that the proposed model compares four deep learning models, as the interval of time steps increases, the errors more and the accuracy of the model decreases, according to the obtained results, the accuracy of the FI-LSTM model is 0.41% higher than the lowest accuracy of the deep learning model (BiLSTM), and also the proposed model for predicting traffic speed in a time step of 5 minutes is 1.34% It has performed better than the time step of 10 minutes.
Traffic Management Studies, Volume:18 Issue: 68, 2023
31 to 60  
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یک‌ساله به مبلغ 1,390,000ريال می‌توانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
  • حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران می‌شود.
  • پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانه‌های چاپی و دیجیتال را به کاربر نمی‌دهد.
In order to view content subscription is required

Personal subscription
Subscribe for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!