MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting

Karimzadeh, Mostafa; Schwegler, Samuel Martin; Zhao, Zhongliang; Braun, Torsten; Sargento, Susana (28 June 2021). MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting. In: 17th International Wireless Communications & Mobile Computing Conference - IWCMC 2021 (pp. 564-569). IEEE 10.1109/IWCMC51323.2021.9498905

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Predicting traffic flow in large cities is beneficial for a wide range of applications, including vehicle navigation services, vehicle routing, and traffic congestion management. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn long-term dependencies. However, these neural networks only learn the temporal traffic information for each trajectory (moving object), failing to take advantage of spatial information shared by neighboring trajectories. This paper introduces MTL-LSTM (Multi-Task Learning-based LSTM) traffic flow estimator, which attempts to explore both temporal and spatial dependencies among adjacent trajectories. Specifically, we employ LSTM predictors with the MTL approach to explore traffic flow patterns across urban trajectories. To examine the proposed model, we predict traffic flow in Porto’s city using a data set from buses and taxies. Our experiments show improvements of 10% to 15% over the state-of-the-art.

Item Type:

Conference or Workshop Item (Paper)


08 Faculty of Science > Institute of Computer Science (INF)
08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)

UniBE Contributor:

Karimzadeh Motallebiazar, Mostafa; Schwegler, Samuel Martin; Zhao, Zhongliang and Braun, Torsten


000 Computer science, knowledge & systems
500 Science > 510 Mathematics
500 Science
600 Technology






Dimitrios Xenakis

Date Deposited:

23 Apr 2021 12:12

Last Modified:

17 Sep 2021 12:47

Publisher DOI:


Uncontrolled Keywords:

Deep Learning; Multi-Task Learning; Traffic Flow Prediction; Intelligent Transportation system (ITS)




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