Predicting times of waiting on red signals using BERT
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Abstract (arXiv)
We present a method for approximating outcomes of road traffic simulations using BERT-based models, which may find applications in, e.g., optimizing traffic signal settings, especially with the presence of autonomous and connected vehicles. The experiments were conducted on a dataset generated using the Traffic Simulation Framework software runs on a realistic road network. The BERT-based models were compared with 4 other types of machine learning models (LightGBM, fully connected neural networks and 2 types of graph neural networks) and gave the best results in terms of all the considered metrics.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 20 Feb 2021.
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9 of 22 references.
References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2018Image Transformercited by 772
- 2020Graph-Bert: Only Attention is Needed for Learning Graph Representationscited by 177
- 2020ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Datacited by 137
- 2020Molecule Attention Transformercited by 92
- 2018Investigating performance of neural networks and gradient boosting models approximating microscopic traffic simulations in traffic optimization taskscited by 1
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