Traffic Forecasting with Deep Learning

Kundu, Shounak and Desarkar, Maunendra Sankar and Srijith, P. K. (2020) Traffic Forecasting with Deep Learning. In: 2020 IEEE Region 10 Symposium, TENSYMP 2020, 5 June 2020 - 7 June 2020.

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Abstract

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.

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IITH Creators:

IITH Creators	ORCiD
Desarkar, Maunendra Sankar	UNSPECIFIED
Srijith, P K	UNSPECIFIED

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Carbon dioxide; Deep learning; Forecasting; Global warming; Learning systems; Predictive analytics; Regression analysis; Time series;Carbon dioxide emissions; Government agencies; Predictive performance; Regression model; State of the art; Traditional models; Traffic flow prediction; Traffic Forecasting

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

10 Aug 2021 06:05

Last Modified:

09 Mar 2022 10:32

URI:

http://raiithold.iith.ac.in/id/eprint/8786