Search Results for author:
Found 6 papers, 2 papers with code
A novel framework for adaptive stress testing of autonomous vehicles in highways
In this paper, we propose a novel framework to systematically explore corner cases that can result in safety concerns in a highway traffic scenario.
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
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.
Traffic4cast -- Large-scale Traffic Prediction using 3DResNet and Sparse-UNet
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.
A novel activity pattern generation incorporating deep learning for transport demand models
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.
Boosted Genetic Algorithm using Machine Learning for traffic control optimization
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.
An effective spatial-temporal attention based neural network for traffic flow prediction
In this study, a deep learning based traffic flow predictor with spatial and temporal attentions (STANN) is proposed.
