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Found 10 papers, 5 papers with code
CrashFormer: A Multimodal Architecture to Predict the Risk of Crash
Reducing traffic accidents is a crucial global public safety concern.
Judge Me in Context: A Telematics-Based Driving Risk Prediction Framework in Presence of Weak Risk Labels
Driving risk prediction has been a topic of much research over the past few decades to minimize driving risk and increase safety.
Will there be a construction? Predicting road constructions based on heterogeneous spatiotemporal data
This approach is costly and difficult to scale.
Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification
Using CNN, we capture semantic patterns of driver behavior from trajectories (such as a turn or a braking event).
Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights
Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.
A Countrywide Traffic Accident Dataset
Reducing traffic accidents is an important public safety challenge.
Databases Computers and Society
Discovery of Driving Patterns by Trajectory Segmentation
We apply the segmentation approach on a real-word, rich dataset of personal car trajectories provided by a major insurance company based in Columbus, Ohio.
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user.
Predicting protein-protein interactions based on rotation of proteins in 3D-space
Therefore, a unique category of prediction approaches has been devised which is based on the protein sequence information.
Characterizing Driving Context from Driver Behavior
Because of the increasing availability of spatiotemporal data, a variety of data-analytic applications have become possible.
