Reinforcement Learning-designed LSTM for Trajectory and Traffic Flow Prediction

Karimzadeh Motallebiazar, Mostafa; Aebi, Ryan; Mariano de Souza, Allan; Zhao, Zhongliang; Braun, Torsten; Sargento, Susana; Villas, Leandro (2020). Reinforcement Learning-designed LSTM for Trajectory and Traffic Flow Prediction (Unpublished). In: CDS.

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Trajectory and traffic flow prediction will play an essential role in Intelligent Transportation Systems (ITS) to enable a whole new set of applications ranging from traffic management to infotainment applications. 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 spatiotemporal dependencies. However, these neural networks tend to be over-complex and hard to design due to the broad set of hyper-parameters. We propose an automated framework to predict future trajectories and traffic flows in urban areas without human interventions. We employ Reinforcement Learning (RL) and Transfer Learning (TL) to generate high-performance LSTM predictors, which is referred as RL-LSTM. In addition, we introduce HERITOR (High ordEr tRaffIc convoluTiOn Rl-lstm), a novel deep learning algorithm for traffic flow prediction. Specifically, HERITOR attempts to capture pure spatiotemporal features of urban traffic. The extracted features are fed into the RL-LSTM to realize a high performance LSTM for traffic flow prediction. We examine the proposed trajectory and traffic flow predictors on two realworld, large-scale datasets and observe consistent improvements of 15% - 25% over the state-of-the-art.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

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

UniBE Contributor:

Karimzadeh Motallebiazar, Mostafa; Mariano de Souza, Allan; Zhao, Zhongliang and Braun, Torsten

Subjects:

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

Language:

English

Submitter:

Dimitrios Xenakis

Date Deposited:

31 Jul 2019 07:31

Last Modified:

07 Sep 2020 12:03

Additional Information:

Virtual Conference

BORIS DOI:

10.7892/boris.132241

URI:

https://boris.unibe.ch/id/eprint/132241

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