Prediction of Short Term Traffic Speeds Using Deep Learning Models

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2024

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Institute of Electrical and Electronics Engineers Inc.

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Abstract

In this study, real-time traffic forecasting was conducted using traffic speed data obtained from Bluetooth sensors located in the city of Vigo, Spain. For this purpose, average speed data recorded at fifteen-minute intervals from five different sensors in the city since 2014 were used. Using past average speed values in a time series format as inputs, various deep learning methods were applied to predict traffic speed data for the next fifteen minutes. The Long Short-Term Memory (LSTM) method achieved the highest performance in traffic forecasting. Incorporating additional factors such as time, weather conditions, and environmental factors into the models, along with time-series traffic volumes, could further enhance the performance of near-future traffic forecasting. © 2024 IEEE.

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IEEE SMC; IEEE Turkiye Section

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Deep Learning, Long Short Term Memory, Traffic Prediction

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562

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