Research graph
References from Predicting freeway incident duration using machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Hazard based models for freeway traffic incident duration
10.1016/j.aap.2012.12.037 · 2013 · External reference
Incorporating real-time weather conditions into analyzing clearance time of freeway accidents: A grouped random parameters hazard-based duration model with time-varying covariates
10.1016/j.amar.2023.100267 · 2023 · External reference
Predicting the duration of motorway incidents using machine learning
10.1186/s12544-024-00632-6 · 2024 · External reference
Explainable artificial intelligence visions on incident duration using eXtreme Gradient Boosting and SHapley Additive exPlanations
10.1016/j.multra.2025.100209 · 2025 · External reference
Prediction of the traffic incident duration using statistical and machine-learning methods: A systematic literature review
10.1016/j.techfore.2024.123621 · 2024 · External reference
Statistical and machine-learning methods for clearance time prediction of road incidents: A methodology review
10.1016/j.amar.2020.100123 · 2020 · External reference
Identification of critical factors for non-recurrent congestion induced by urban freeway crashes and its mitigating strategies
10.3390/su9122331 · 2017 · External reference
Role of road network features in the evaluation of incident impacts on urban traffic mobility
10.1016/j.trb.2018.08.013 · 2018 · External reference
Study on the Impact of Road Traffic Accident Duration Based on Statistical Analysis and Spatial Distribution Characteristics: An Empirical Analysis of Houston
10.3390/su142214982 · 2022 · External reference
An exploratory hazard-based analysis of highway incident duration
2000 · External reference
Traffic incident duration analysis and prediction models based on the survival analysis approach
10.1049/iet-its.2014.0036 · 2015 · External reference
Competing risk mixture model and text analysis for sequential incident duration prediction
10.1016/j.trc.2015.03.009 · 2015 · External reference
Jointly analyzing freeway traffic incident clearance and response time using a copula-based approach
10.1016/j.trc.2017.11.004 · 2018 · External reference
Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning
10.1016/j.aap.2024.107806 · 2024 · External reference
Prediction of Lane Clearance Time of Freeway Incidents Using the M5P Tree Algorithm
10.1109/tits.2011.2161634 · 2011 · External reference
Predicting incident duration using random forests
10.1080/23249935.2020.1733132 · 2020 · External reference
Traffic incident clearance time prediction and influencing factor analysis using extreme gradient boosting model
10.1155/2020/6401082 · 2020 · External reference
Dynamic Prediction of the Incident Duration Using Adaptive Feature Set
10.1109/tits.2018.2878637 · 2019 · External reference
Sequential prediction for large-scale traffic incident duration: application and comparison of survival models
10.1177/0361198119899041 · 2020 · External reference
Incident duration prediction using a bi-level machine learning framework with outlier removal and intra–extra joint optimisation
10.1016/j.trc.2022.103721 · 2022 · External reference
Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data
10.3390/ijerph191710903 · 2022 · External reference
Traffic accident duration prediction using text mining and ensemble learning on expressways
10.1038/s41598-022-25988-4 · 2022 · External reference
Traffic accident duration prediction using multi-mode data and ensemble deep learning
10.1016/j.heliyon.2024.e25957 · 2024 · External reference
A Novel Accident Duration Prediction Method Based on a Conditional Table Generative Adversarial Network and Transformer
10.3390/su16166821 · 2024 · External reference
Prediction in traffic accident duration based on heterogeneous ensemble learning
10.1080/08839514.2021.2018643 · 2022 · External reference
Enhancing vision-based traffic crash detection performance consistency across day-night scenes: A depth-aware and domain-adaptive network
10.1016/j.aap.2026.108405 · 2026 · External reference
A new framework for modelling traffic conflict interval time based on correlated random parameter duration model with heterogeneity in means and variances
10.1016/j.aap.2025.108282 · 2026 · External reference
A systematic review of resilience assessment and enhancement of urban integrated transportation networks
10.1016/j.jtrangeo.2025.104420 · 2025 · External reference
Exploring factors of e-bike rider’s hit-and-run behavior of e-bike crashes on urban and rural roads using Light Gradient Boosting machine and SHapley additive explanations
2026 · External reference
A real-time early warning framework for multi-dimensional driving risk of heavy-duty trucks using trajectory data
10.3390/systems14020204 · 2026 · External reference
Unveiling risk reconfiguration in freeway merging areas: A spatiotemporal framework for conflict prediction and hotspot migration in CAV mixed traffic
10.3390/sym18050831 · 2026 · External reference
Unresolved reference
2024 · External reference
