no code implementations • 19 Feb 2024 • Linh Trinh, Quang-Hung Luu, Thai M. Nguyen, Hai L. Vu
In this paper, we propose a novel framework to systematically explore corner cases that can result in safety concerns in a highway traffic scenario.
1 code implementation • 31 Mar 2022 • Christian Eichenberger, Moritz Neun, Henry Martin, Pedro Herruzo, Markus Spanring, Yichao Lu, Sungbin Choi, Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman, Nina Wiedemann, Martin Raubal, Bo wang, Hai L. Vu, Reza Mohajerpoor, Chen Cai, Inhi Kim, Luca Hermes, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling, Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis, Jay Santokhi, Dylan Hillier, Yiming Yang, Joned Sarwar, Anna Jordan, Emil Hewage, David Jonietz, Fei Tang, Aleksandra Gruca, Michael Kopp, David Kreil, Sepp Hochreiter
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
1 code implementation • 10 Nov 2021 • Bo wang, Reza Mohajerpoor, Chen Cai, Inhi Kim, Hai L. Vu
The aim is to build a machine learning model for predicting the normalized average traffic speed and flow of the subregions of multiple large-scale cities using historical data points.
no code implementations • 6 Apr 2021 • Danh T. Phan, Hai L. Vu
We then develop different deep neural networks with entity embedding and random forest models to classify activity type, as well as to predict activity times.
no code implementations • 11 Mar 2021 • Tuo Mao, Adriana-Simona Mihaita, Fang Chen, Hai L. Vu
Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree, which is the best performing regressor, together in a single optimization framework.
no code implementations • Transportation Research Part C: Emerging Technologies 2019 • Loan N.N. Do, Hai L. Vu, Bao Q. Vo, Zhiyuan Liu, Dinh Phung
In this study, a deep learning based traffic flow predictor with spatial and temporal attentions (STANN) is proposed.