[HTML][HTML] Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review
Accurately modelling crashes, and predicting crash occurrence and associated severities
are a prerequisite for devising countermeasures and developing effective road safety …
are a prerequisite for devising countermeasures and developing effective road safety …
Local-global temporal difference learning for satellite video super-resolution
Optical-flow-based and kernel-based approaches have been extensively explored for
temporal compensation in satellite Video Super-Resolution (VSR). However, these …
temporal compensation in satellite Video Super-Resolution (VSR). However, these …
Lane-level street map extraction from aerial imagery
S He, H Balakrishnan - … of the IEEE/CVF Winter Conference …, 2022 - openaccess.thecvf.com
Digital maps with lane-level details are the foundation of many applications. However,
creating and maintaining digital maps especially maps with lane-level details, are labor …
creating and maintaining digital maps especially maps with lane-level details, are labor …
MG-TAR: Multi-view graph convolutional networks for traffic accident risk prediction
Due to the continuing colossal socio-economic losses caused by traffic accidents, it is of
prime importance to precisely forecast the traffic accident risk to reduce future accidents. In …
prime importance to precisely forecast the traffic accident risk to reduce future accidents. In …
Graph neural networks for road safety modeling: datasets and evaluations for accident analysis
We consider the problem of traffic accident analysis on a road network based on road
network connections and traffic volume. Previous works have designed various deep …
network connections and traffic volume. Previous works have designed various deep …
Single UHD image dehazing via interpretable pyramid network
Currently, most dehazing-based deep approaches are developed as an end-to-end manner
to reconstruct a degraded image in an unintelligible fashion. For these dehazing models, the …
to reconstruct a degraded image in an unintelligible fashion. For these dehazing models, the …
Dynamic identification of short-term and longer-term hazardous locations using a conflict-based real-time extreme value safety model
T Ghoul, T Sayed, C Fu - Analytic methods in accident research, 2023 - Elsevier
A novel and effective approach to safety management requires evaluating the safety of
locations over short time periods (eg minutes). Unlike traditional methods that are based on …
locations over short time periods (eg minutes). Unlike traditional methods that are based on …
From prediction to prevention: Leveraging deep learning in traffic accident prediction systems
We propose a novel system leveraging deep learning-based methods to predict urban traffic
accidents and estimate their severity. The major challenge is the data imbalance problem in …
accidents and estimate their severity. The major challenge is the data imbalance problem in …
[PDF][PDF] Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging.
Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-
resolution satellite images, which are utilized for extracting a range of traffic-related and road …
resolution satellite images, which are utilized for extracting a range of traffic-related and road …
Unveiling roadway hazards: Enhancing fatal crash risk estimation through multiscale satellite imagery and self-supervised cross-matching
Traffic accidents threaten human lives and impose substantial financial burdens annually.
Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and …
Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and …