Traffic Volume Prediction using Memory-Based Recurrent Neural Networks: A comparative analysis of LSTM and GRU

2023/03/22 by Lokesh Chandra Das, Das, Lokesh Chandra
Engineering · Social Sciences · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques #Traffic control and management #Transportation Planning and Optimization

paper · pdf · doi:10.48550/arxiv.2303.12643

openalex publication_date 2023/03/22 · openalex created_date 2023/03/25 · openalex updated_date 2026/07/28

Abstract

Predicting traffic volume in real-time can improve both traffic flow and road safety. A precise traffic volume forecast helps alert drivers to the flow of traffic along their preferred routes, preventing potential deadlock situations. Existing parametric models cannot reliably forecast traffic volume in dynamic and complex traffic conditions. Therefore, in order to evaluate and forecast the traffic volume for every given time step in a real-time manner, we develop non-linear memory-based deep neural network models. Our extensive experiments run on the Metro Interstate Traffic Volume dataset demonstrate the effectiveness of the proposed models in predicting traffic volume in highly dynamic and heterogeneous traffic environments.

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