Traffic Forecasting with Deep Learning

Timely forecast of traffic is very much needed for smart cities, which allows travelers and government agencies to make various decisions based on traffic flow. This will result in reduced traffic congestion and carbon dioxide emission. However, traffic forecasting is a challenging task due to the h...

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Bibliographic Details
Main Authors: Kundu, Shounak, Desarkar, Maunendra Sankar, Srijith, P. K.
Format: Conference Proceeding
Language:English
Subjects:
CNN
Data models
Deep learning
Junctions
LSTM
Predictive models
Roads
smart city
Task analysis
Time series analysis
Traffic prediction
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Summary:Timely forecast of traffic is very much needed for smart cities, which allows travelers and government agencies to make various decisions based on traffic flow. This will result in reduced traffic congestion and carbon dioxide emission. However, traffic forecasting is a challenging task due to the highly complex traffic pattern. Standard time series techniques may not be able to capture the nonlinear and noisy nature of the traffic flow. In this paper, we investigate how the deep learning models capture these characteristics and provide better predictive performance over standard time series and regression models. We compare the performances of state-of-the-art deep learning models on two traffic flow data sets and show their effectiveness in traffic flow prediction over traditional models.
ISSN:2642-6102
DOI:10.1109/TENSYMP50017.2020.9230762