A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data
The primary objective of this study is to investigate how the deep learning approach
contributes to citywide short-term crash risk prediction by leveraging multi-source datasets …
contributes to citywide short-term crash risk prediction by leveraging multi-source datasets …
A review of incident prediction, resource allocation, and dispatch models for emergency management
In the last fifty years, researchers have developed statistical, data-driven, analytical, and
algorithmic approaches for designing and improving emergency response management …
algorithmic approaches for designing and improving emergency response management …
Highway crash detection and risk estimation using deep learning
Crash Detection is essential in providing timely information to traffic management centers
and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding …
and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding …
Convolutional neural networks with refined loss functions for the real-time crash risk analysis
The real-time crash risk analyses were proposed to establish the relationships between
crash occurrence probability and pre-crash traffic operational conditions. Given its great …
crash occurrence probability and pre-crash traffic operational conditions. Given its great …
Real-time traffic incident detection based on a hybrid deep learning model
Small sample sizes and imbalanced datasets have been two difficulties in previous traffic
incident detection-related studies. Moreover, real-time characteristics of incident detection …
incident detection-related studies. Moreover, real-time characteristics of incident detection …
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
In the last fifty years, researchers have developed statistical, data-driven, analytical, and
algorithmic approaches for designing and improving emergency response management …
algorithmic approaches for designing and improving emergency response management …
Short‐term FFBS demand prediction with multi‐source data in a hybrid deep learning framework
The primary objective of this study is to predict the short‐term demand of free‐floating bike
sharing (FFBS) using deep learning approach. The FFBS trip data in Shanghai city are …
sharing (FFBS) using deep learning approach. The FFBS trip data in Shanghai city are …
Traffic monitoring and anomaly detection based on simulation of luxembourg road network
Traffic incidents which commonly result from traffic accidents, anomalous construction
events and inclement weather can cause a wide range of negative impacts on urban road …
events and inclement weather can cause a wide range of negative impacts on urban road …
Detection of pavement maintenance treatments using deep-learning network
Pavement maintenance and rehabilitation (M&R) records are important as they provide
documentation that M&R treatment is being performed and completed appropriately …
documentation that M&R treatment is being performed and completed appropriately …
Designing decision support systems for emergency response: Challenges and opportunities
G Pettet, H Baxter, SM Vazirizade… - 2022 Workshop on …, 2022 - ieeexplore.ieee.org
Designing effective emergency response management (ERM) systems to respond to
incidents such as road accidents is a major problem faced by communities. In addition to …
incidents such as road accidents is a major problem faced by communities. In addition to …