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Found 12 papers, 1 papers with code
A Real-time Evaluation Framework for Pedestrian's Potential Risk at Non-Signalized Intersections Based on Predicted Post-Encroachment Time
To address these research challenges, in this study, a framework with computer vision technologies and predictive models is developed to evaluate the potential risk of pedestrians in real time.
Better Batch for Deep Probabilistic Time Series Forecasting
Our method constructs a mini-batch as a collection of $D$ consecutive time series segments for model training.
Enhancing Deep Traffic Forecasting Models with Dynamic Regression
A common assumption in deep learning-based multivariate and multistep traffic time series forecasting models is that residuals are independent, isotropic, and uncorrelated in space and time.
Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss function in a sequence-to-sequence setting, simply assuming that the errors follow an independent and isotropic Gaussian or Laplacian distributions.
Optimal Parking Planning for Shared Autonomous Vehicles
Two specific planning scenarios are considered for the APPM: (i) Single-zone APPM (S-APPM), which considers the target area as a single homogeneous zone, and (ii) Two-zone APPM (T-APPM), which considers the target area as two different zones, such as city center and suburban area.
A Framework for Pedestrian Sub-classification and Arrival Time Prediction at Signalized Intersection Using Preprocessed Lidar Data
The mortality rate for pedestrians using wheelchairs was 36% higher than the overall population pedestrian mortality rate.
Deep Learning based Urban Vehicle Trajectory Analytics
In this dissertation, we focus on the `urban vehicle trajectory,' which refers to trajectories of vehicles in urban traffic networks, and we focus on `urban vehicle trajectory analytics.'
Development of Safety Monitoring System of Connected and Automated Vehicles considering the Trade-off between Communication Efficiency and Data Reliability
Thus, raw data must be sampled to reduce the size of the data over communication network and transmitted to the server for further processing.
Development of Simulation-based Lane Change Control System for Autonomous Vehicles
However, since many of the current lane-changing decision algorithms of autonomous vehicles are based on the human driver model, it is hard to know the potential traffic impact of such lane change.
Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach
In many spatial trajectory-based applications, it is necessary to map raw trajectory data points onto road networks in digital maps, which is commonly referred to as a map-matching process.
TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning
A generative model for urban vehicle trajectories can better generalize from training data by learning the underlying distribution of the training data and, thus, produce synthetic vehicle trajectories similar to real vehicle trajectories with limited observations.
Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction
With the increasing deployment of diverse positioning devices and location-based services, a huge amount of spatial and temporal information has been collected and accumulated as trajectory data.
