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https://doi.org/10.2139/ssrn.4657166

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inferredcites →https://doi.org/10.1007/978-1-4471-3675-0openalex-extracted
An Introduction to Statistical Modeling of Extreme Values
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Estimating the severity of safety related behaviour
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The extreme value theory approach to safety estimation
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Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis
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Use of crash surrogates and exceedance statistics to estimate road safety
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Utilizing support vector machine in real-time crash risk evaluation
inferredcites →https://doi.org/10.1016/j.aap.2013.09.006openalex-extracted
Freeway safety estimation using extreme value theory approaches: A comparative study
inferredcites →https://doi.org/10.1016/j.aap.2018.01.024openalex-extracted
Modeling when and where a secondary accident occurs
inferredcites →https://doi.org/10.1016/j.aap.2018.08.004openalex-extracted
Bivariate extreme value modeling for road safety estimation
inferredcites →https://doi.org/10.1016/j.aap.2018.09.017openalex-extracted
Full Bayesian conflict-based models for real time safety evaluation of signalized intersections
inferredcites →https://doi.org/10.1016/j.aap.2018.09.023openalex-extracted
Before-after safety analysis using extreme value theory: A case of left-turn bay extension
inferredcites →https://doi.org/10.1016/j.aap.2018.12.007openalex-extracted
Validating the bivariate extreme value modeling approach for road safety estimation with different traffic conflict indicators
inferredcites →https://doi.org/10.1016/j.aap.2018.12.013openalex-extracted
A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data
inferredcites →https://doi.org/10.1016/j.aap.2019.105315openalex-extracted
Road safety of passing maneuvers: A bivariate extreme value theory approach under non-stationary conditions
inferredcites →https://doi.org/10.1016/j.aap.2019.105429openalex-extracted
Predicting real-time traffic conflicts using deep learning
inferredcites →https://doi.org/10.1016/j.aap.2020.105863openalex-extracted
Validity of failure-caused traffic conflicts as surrogates of rear-end collisions in naturalistic driving studies
inferredcites →https://doi.org/10.1016/j.aap.2020.105949openalex-extracted
Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods
inferredcites →https://doi.org/10.1016/j.aap.2021.106122openalex-extracted
An integrated methodology for real-time driving risk status prediction using naturalistic driving data
inferredcites →https://doi.org/10.1016/j.aap.2021.106159openalex-extracted
Random parameters Bayesian hierarchical modeling of traffic conflict extremes for crash estimation
inferredcites →https://doi.org/10.1016/j.aap.2021.106503openalex-extracted
A high-resolution trajectory data driven method for real-time evaluation of traffic safety
inferredcites →https://doi.org/10.1016/j.aap.2023.107268openalex-extracted
Cycle-level traffic conflict prediction at signalized intersections with LiDAR data and Bayesian deep learning
inferredcites →https://doi.org/10.1016/j.aap.2023.107378openalex-extracted
Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review
inferredcites →https://doi.org/10.1016/j.amar.2013.09.001openalex-extracted
Analytic methods in accident research: Methodological frontier and future directions
inferredcites →https://doi.org/10.1016/j.amar.2019.100100openalex-extracted
Bayesian hierarchical modeling of the non-stationary traffic conflict extremes for crash estimation
inferredcites →https://doi.org/10.1016/j.amar.2019.100106openalex-extracted
Bayesian hierarchical modeling of traffic conflict extremes for crash estimation: A non-stationary peak over threshold approach
inferredcites →https://doi.org/10.1016/j.amar.2020.100111openalex-extracted
A bivariate Bayesian hierarchical extreme value model for traffic conflict-based crash estimation
inferredcites →https://doi.org/10.1016/j.amar.2020.100113openalex-extracted
Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis
inferredcites →https://doi.org/10.1016/j.amar.2020.100135openalex-extracted
Multivariate Bayesian hierarchical modeling of the non-stationary traffic conflict extremes for crash estimation
inferredcites →https://doi.org/10.1016/j.amar.2020.100142openalex-extracted
Modeling traffic conflicts for use in road safety analysis: A review of analytic methods and future directions
inferredcites →https://doi.org/10.1016/j.amar.2021.100193openalex-extracted
An Extreme Value Theory approach to estimate crash risk during mandatory lane-changing in a connected environment
inferredcites →https://doi.org/10.1016/j.amar.2021.100204openalex-extracted
Bayesian dynamic extreme value modeling for conflict-based real-time safety analysis
inferredcites →https://doi.org/10.1016/j.amar.2022.100217openalex-extracted
Using traffic flow characteristics to predict real-time conflict risk: A novel method for trajectory data analysis
inferredcites →https://doi.org/10.1016/j.amar.2022.100248openalex-extracted
A hybrid modelling framework of machine learning and extreme value theory for crash risk estimation using traffic conflicts
inferredcites →https://doi.org/10.1016/j.amar.2023.100275openalex-extracted
Traffic conflict prediction using connected vehicle data
inferredcites →https://doi.org/10.1016/j.amar.2023.100276openalex-extracted
Assessing traffic conflict/crash relationships with extreme value theory: Recent developments and future directions for connected and autonomous vehicle and highway safety research
inferredcites →https://doi.org/10.1016/j.amar.2023.100302openalex-extracted
Real-time crash risk forecasting using Artificial-Intelligence based video analytics: A unified framework of generalised extreme value theory and autoregressive integrated moving average model
inferredcites →https://doi.org/10.1016/j.engappai.2023.106367openalex-extracted
Developing an evolutionary deep learning framework with random forest feature selection and improved flow direction algorithm for NOx concentration prediction
inferredcites →https://doi.org/10.1016/j.eswa.2011.02.066openalex-extracted
Traffic safety forecasting method by particle swarm optimization and support vector machine
inferredcites →https://doi.org/10.1016/j.matdes.2023.112392openalex-extracted
Machine learning-assisted extreme value statistics of anomalies in AlSi10Mg manufactured by L-PBF for robust fatigue strength predictions
inferredcites →https://doi.org/10.1016/j.ssci.2013.12.003openalex-extracted
Shifted Gamma-Generalized Pareto Distribution model to map the safety continuum and estimate crashes
inferredcites →https://doi.org/10.1016/j.trc.2019.04.015openalex-extracted
From univariate to bivariate extreme value models: Approaches to integrate traffic conflict indicators for crash estimation
inferredcites →https://doi.org/10.1016/j.trc.2020.102683openalex-extracted
A novel approach for real time crash prediction at signalized intersections
inferredcites →https://doi.org/10.1016/j.ymssp.2022.109049openalex-extracted
Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling
inferredcites →https://doi.org/10.1080/15389588.2023.2191286openalex-extracted
Taxi drivers’ traffic violations detection using random forest algorithm: A case study in China
inferredcites →https://doi.org/10.1080/15472450.2022.2106564openalex-extracted
Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data
inferredcites →https://doi.org/10.1080/19427867.2020.1861503openalex-extracted
Safety analysis of unsignalized intersections: a bivariate extreme value approach
inferredcites →https://doi.org/10.1139/cjce-2013-0558openalex-extracted
Traffic conflict techniques for road safety analysis: open questions and some insights
inferredcites →https://doi.org/10.1177/0361198119841555openalex-extracted
Application of Extreme Value Theory for Before-After Road Safety Analysis
inferredcites →https://doi.org/10.1177/0361198119841556openalex-extracted
Comparison of Traffic Conflict Indicators for Crash Estimation using Peak Over Threshold Approach
inferredcites →https://doi.org/10.1177/03611981211062891openalex-extracted
Real-Time Crash-Risk Optimization at Signalized Intersections
inferredcites →https://doi.org/10.1177/03611981221128812openalex-extracted
Traffic Conflict Prediction at Signal Cycle Level Using Bayesian Optimized Machine Learning Approaches
inferredcites →https://doi.org/10.1214/aos/1176343003openalex-extracted
Statistical Inference Using Extreme Order Statistics
inferredcites →https://doi.org/10.3141/2083-12openalex-extracted
Derivation and Validation of New Simulation-Based Surrogate Safety Measure
inferredcites →https://doi.org/10.48550/arxiv.1605.08695openalex-extracted
TensorFlow: A system for large-scale machine learning
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