Search Results for author:
Found 7 papers, 4 papers with code
Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation
To gauge the practicality and acceptability of the policy, we conduct evaluations with human participants using the simulator, focusing on a range of metrics like traffic smoothness and safety perception.
Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets
State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features.
Enhancing crowd flow prediction in various spatial and temporal granularities
We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks.
Trajectory Test-Train Overlap in Next-Location Prediction Datasets
Next-location prediction, consisting of forecasting a user's location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading.
Generating Synthetic Mobility Networks with Generative Adversarial Networks
We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks.
A Survey on Deep Learning for Human Mobility
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more.
Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment.
