• V Rama Krishna
• Akalankam Godha Devi
• Gangishetty Akhilesh
• .Bareddy Karthikeya Reddy

Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious threat to sustainable urban development. Recently,intelligent traffic system (ITS) has emerged as an effective tool to mitigate urban congestion. The key to the ITS lies in the accurate forecast of traffic flow. However, the existing forecast methods of traffic flow cannot adapt to the stochasticity and sheer length of traffic flow time series. To solve the problem, this paper relies on deep learning (DL) to forecast traffic flow through time series analysis. The authors developed a traffic flow forecast model based on the long shortterm memory (LSTM) network. The proposed model was compared with two classic forecast models, namely, the autoregressive integrated moving average (ARIMA) model and the backpropagation neural network (BPNN) model, through long-term traffic flow forecast experiments, using an actual traffic flow time series from OpenITS. The experimental results show that the proposed LSTM network outperformed the classic models in prediction accuracy. Our research discloses the dynamic evolution law of traffic flow, and facilitates the decision-making of traffic management.

Deep Learning, Long Short Term Memory Networks (LSTM), Training set, Test set, Tensorflow, Traffic Flow Prediction.

"TRAFFIC FLOW FORECAST USING TIME SERIES ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.f629-f632, January-2024, Available :http://www.jetir.org/papers/JETIR2401576.pdf

"TRAFFIC FLOW FORECAST USING TIME SERIES ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppf629-f632, January-2024, Available at : http://www.jetir.org/papers/JETIR2401576.pdf