Overview of traffic incident duration analysis and prediction
Introduction Non-recurrent congestion caused by traffic incident is difficult to predict but
should be dealt with in a timely and effective manner to reduce its influence on road capacity …
should be dealt with in a timely and effective manner to reduce its influence on road capacity …
Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method
Identifying and quantifying the influential factors on incident clearance time can benefit
incident management for accident causal analysis and prediction, and consequently …
incident management for accident causal analysis and prediction, and consequently …
Real-time traffic accidents post-impact prediction: Based on crowdsourcing data
Traffic accident management is a critical issue for advanced intelligent traffic management.
The increasingly abundant crowdsourcing data and floating car data provide new support for …
The increasingly abundant crowdsourcing data and floating car data provide new support for …
A deep fusion model based on restricted Boltzmann machines for traffic accident duration prediction
Traffic accidents causing nonrecurrent congestion can decrease the capacity of highways
and increase car emissions. Some models in previous studies have been built based on …
and increase car emissions. Some models in previous studies have been built based on …
Modeling traffic incident duration using quantile regression
Traffic incidents occur frequently on urban roadways and cause incident-induced
congestion. Predicting incident duration is a key step in managing these events. Ordinary …
congestion. Predicting incident duration is a key step in managing these events. Ordinary …
Incident duration modeling using flexible parametric hazard‐based models
Assessing and prioritizing the duration time and effects of traffic incidents on major roads
present significant challenges for road network managers. This study examines the effect of …
present significant challenges for road network managers. This study examines the effect of …
Interpretation of Bayesian neural networks for predicting the duration of detected incidents
H Park, A Haghani, X Zhang - Journal of Intelligent Transportation …, 2016 - Taylor & Francis
This study introduces Bayesian learning to neural networks for accurate prediction of
incident duration. Network parameters are updated using a hybrid Monte Carlo algorithm …
incident duration. Network parameters are updated using a hybrid Monte Carlo algorithm …
Estimating freeway incident duration using accelerated failure time modeling
W Junhua, C Haozhe, Q Shi - Safety science, 2013 - Elsevier
Effective incident management requires a good understanding of various characteristics of
incidents in order to accurately estimate incident durations and help make more efficient …
incidents in order to accurately estimate incident durations and help make more efficient …
Prediction of traffic incident duration using clustering-based ensemble learning method
H Zhao, W Gunardi, Y Liu, C Kiew… - … engineering, Part A …, 2022 - ascelibrary.org
Traffic incidents are a primary cause of traffic delays, which can cause severe economic
losses. Effective traffic incident management requires integrating intelligent traffic systems …
losses. Effective traffic incident management requires integrating intelligent traffic systems …
Competing risks mixture model for traffic incident duration prediction
Traffic incident duration is known to result from a combination of multiple factors, including
covariates such as spatial and temporal characteristics, traffic conditions, and existence of …
covariates such as spatial and temporal characteristics, traffic conditions, and existence of …