Deep learning classifies railroad accident causes from narrative reports.
problem Classifying accident causes from narrative reports is challenging.
method Applied deep learning with word embeddings to classify accident causes.
result Deep learning accurately classifies accident causes from narratives and identifies inconsistencies.
Transformer model predicts train axle vibrations for safer maintenance.
problem Prevent mechanical failures in railway axles.
method Integrates Deep Autoregressive solution with spectral methods and observation models.
result Transformer model (ShaftFormer) improves predictive maintenance for railway axles.
Conformal prediction improves signal detection accuracy in railway images.
problem Improving the reliability of machine learning models for railway signal detection.
method Applying conformal prediction to a novel dataset of train operator perspective images.
result The approach enhances the reliability of machine learning models for detecting railway signals.
Adaptive multiple kernel learning predicts railway points failures.
problem Predicting failures of railway points to minimize negative effects.
method Formulated as multiple kernel learning problem, robust algorithm considers missing data and point variance.
result Superior performance compared to state-of-the-art methods.
The paper uses conformal prediction to detect railway signals with confidence.
problem Deploying deep learning models in certified systems requires accurate uncertainty estimates.
method The paper uses conformal prediction and risk control to detect railway signals.
result The conformal prediction framework provides reliable and trustworthy uncertainty estimates for model performance.
Study uses Markov models to predict train delays in India's largest rail network.
problem Systemic delays in train arrivals causing passenger inconvenience.
method Two regression-based models applied to two years of train running-status data.
result Efficient algorithm for estimating delays with near accurate results.
New dataset and model improve real-time traffic accident prediction.
problem Reducing traffic accidents through better analysis and prediction.
method Deep neural network (DAP) with recurrent, fully connected, and embedding components.
result Significant improvements in predicting rare accident events.
Deep learning detects traffic accidents in real time using spatiotemporal data.
problem Detecting traffic accidents to improve safety and reduce delays.
method Used LSTM and GRUs on spatiotemporal sequential data with SMOTE oversampling.
result GRU model performs slightly better than LSTM in detecting traffic accidents.
Model predicts road vehicle accidents with high resolution.
problem Accidents on roads in Montreal.
method Big data analytics, machine learning (Balanced Random Forest algorithm).
result 85% of road vehicle collisions detected with 13% false positives.
Real-time system detects accidents to save lives and reduce congestion.
problem Traffic congestion and accidents causing casualties and wasted time.
method Big data processing and computational intelligence techniques.
result The system can predict the possibility of accidents, even with false alarms.
Study finds telemetric data not effective for predicting truck accident risk.
problem Estimating the risk of truck accidents using telemetric data.
method Used machine learning approaches: Random Forests and Convolutional Neural Networks.
result Neither approach successfully predicted truck accident risk.
H-ReIL learns to drive safely in near-accident scenarios.
problem Driving safely in high-risk near-accident situations.
method Hierarchical RL and IL approach.
result High-level policy switches between low-level policies for safe driving.
Machine learning detects drilling anomalies, reducing accidents and costs.
problem Detecting and preventing accidents during directional drilling.
method Time-series comparison using machine learning and Gradient Boosting classification.
result The model detects half of the anomalies with about 0.53 false alarms per day.
Zero-shot understanding of accidents from surveillance videos using vision-language models
problem Accident understanding from surveillance videos
method Three-stage pipeline with vision-language similarity, metadata-driven multi-prompt reasoning, and entropy-gated pairwise adjudicator
result Substantial improvement in harmonic-mean score over baseline
Model predicts severity of traffic accidents using spatial and temporal features.
problem Estimating severity of traffic accidents in aggregated and disaggregated data.
method Gradient Boosting models and Gaussian Processes for inference and feature importance.
result Complexity of road networks and other situational features significantly impact accident severity.
New method improves safety analytics by addressing imbalanced data issues.
problem Imbalanced safety datasets lead to inaccurate predictions and management problems.
method Extended accident triangle theory and three oversampling methods.
result Robust improvements in machine learning algorithms for safety analytics.
Deep neural networks predict CVCM track circuit failures early.
problem Subtle anomalies in CVCM track circuits lead to failures, causing disruptions.
method Deep neural networks classify anomalies before they escalate.
result Deep neural networks achieve 99.31% overall accuracy in detecting CVCM failures.
Bayesian model predicts crack evolution on rails with uncertainties.
problem Predicting crack evolution on railways due to complex interactions and uncertainties.
method Robust Bayesian multi-horizon approach with constraints.
result Trade-off between prediction accuracy and constraint compliance.
Study improves prediction of UK road accidents' severity using AI.
problem Improving prediction of UK road traffic accident severity.
method Combination of machine learning, econometric, and statistical methods on historical data.
result XGBoost model with RMSE of 0.176 and MAE of 0.087 outperforms naive forecasting.
Study on AI-driven modeling for high burnup accident-tolerant fuels in SMRs.
problem Design and optimization of high burnup accident-tolerant fuels for SMRs.
method Artificial intelligence and multi-scale modeling (neutronics, thermal hydraulics, fuel performance).
result Demonstrated the effectiveness of AI in modeling and optimizing SMR fuels.
Methodology to analyze traffic accidents using microscopic models.
problem Understanding and predicting traffic accidents and their impact.
method Developed a statistical approach using microscopic traffic models and SUMO.
result Approximate distribution of total losses as a mean-variance mixture.
Paper proposes a dataset quality process for ML systems.
problem Inadequate standards for ML datasets in safety-critical systems.
method Proposes a dataset specification and verification process.
result Demonstrates the process on a railway signal recognition system.
This study maps cycling risks and discomfort in Zurich, offering personalized route recommendations.
problem High cycling accidents and discomfort in Smart Cities.
method Geolocated bike accidents data, kernel density contours, weather, time, accident type and severity analysis.
result Empirical continuous spatial risk estimations and personalized route recommendations.
In this paper are presented methods of impact analysis on informatics system security accidents, qualitative and quantitative methods, starting with risk and informational system security definitions. It is presented the relationship between the risks of exploiting vulnerabilities of security system, security level of …
New method estimates traffic congestion delays using statistical causality.
problem Accurate estimation of traffic congestion delays during accidents.
method Proposes a novel time delay estimation method using lag-specific transfer entropy (TE) and Markov bootstrap techniques.
result Validated the method's efficacy using simulated and real data.
The paper introduces a new insurance pricing model based on driving mileage.
problem Weak link between insurance premiums and mileage, leading to overdriving and accidents.
method Developed a Pay-As-You-Drive insurance pricing model using a counting process and non-homogeneous Poisson distribution.
result The model provides theoretical results for better insurance pricing based on driving behavior.
Bayesian network used to analyze young drivers' risky behaviors in Tuscany.
problem Analyzing risky driving behaviors among young drivers in Tuscany.
method Bayesian probabilistic network applied to machine learning analysis of data.
result Bayesian network reveals relationships between young drivers' characteristics and risky behaviors.
This is a mainly expository article honoring my recently deceased friend and collaborator Krzysztof Galicki who died after a tragic hiking accident. I give a review of our recent work in Sasakian geometry. A few new results are also presented.
Synthesizes machine learning applications in reliability and safety.
problem Navigating the fragmented literature on ML for reliability and safety.
method Overview of ML categories, review of applications, discussion of Deep Learning.
result Machine learning can provide novel insights and improve accident prevention.
A new approach for functional data description is proposed in this paper. It consists of a regression model with a discrete hidden logistic process which is adapted for modeling curves with abrupt or smooth regime changes. The model parameters are estimated in a maximum likelihood framework through a dedicated Expectat…
Simulation speeds AV testing by 2-20 times over real-world methods.
problem Lack of scalable and rigorous testing for autonomous vehicles.
method Adaptive importance-sampling methods for rare-event probability evaluation.
result Accelerates accident probability estimation by 2-20 times over naive Monte Carlo methods.
The paper models delayed event occurrences in insurance data.
problem Systematic underestimation of event occurrence due to observation delays.
method Modeling the time between event occurrence and observation, considering event day and calendar effects.
result A granular model for event observation delay heterogeneity.
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The…
A method detects vehicles far from tunnel CCTV using AI.
problem Tunnel CCTV's height limits detection of far-away vehicles.
method Object detection algorithm with inverse perspective transform.
result Deep learning model trained on warped images detects vehicles more accurately.
A deep learning system detects more types of driver distractions with high accuracy.
problem Detecting various types of driver distractions to reduce road accidents.
method Genetically-weighted ensemble of convolutional neural networks.
result Achieves 90% accuracy in detecting more types of distractions than existing methods.
Recently, a marked Poisson process (MPP) model for life catastrophe risk was proposed in [6]. We provide a justification and further support for the model by considering more general Poisson point processes in the context of extreme value theory (EVT), and basing the choice of model on statistical tests and model compa…
Predicts arterial road incident duration with extreme gradient boosting.
problem Predicting incident duration on arterial roads, especially with limited data.
method Bi-level framework combining classification and regression models.
result Extreme gradient boosting outperformed other models by 53%.
It is demonstrated that the US economy has on the long-term in reality been governed by the Keynesian approach to economics independent of the current official economical policy. This is done by calculating the two-point correlation function between the fluctuations of the DJIA and the US public debt. We find that the …
IVMs help identify dangerous traffic conditions in real-time.
problem Real-time crash risk analysis in urban traffic systems.
method Import Vector Machines (IVMs) applied to historical crash and traffic data.
result IVMs successfully identify dangerous traffic conditions with computational advantage.
After September 2008, the advanced economies severe decline caused demand for emerging economies' exports to drop and the crisis became truly global, much deeper and broader than expected. In these times of global depression, most countries and companies are affected, some more than others. The financial crisis has tur…
A geometric analysis of the time series of returns has been performed in the past and it implied that the most of the systematic information of the market is contained in a space of small dimension. Here we have explored subspaces of this space to find out the relative performance of portfolios formed from the companie…
A new framework combines multiple loss reserving models for better predictive performance.
problem Combining multiple loss reserving models to improve predictive performance.
method Systematic framework that considers full distributional properties and features of reserving data.
result Optimized ensemble outperforms traditional methods and captures relevant quantiles.
Oddnet detects anomalies in dynamic networks using time series methods.
problem Detecting anomalies in temporal networks (e.g., transport, social networks).
method Feature-based network anomaly detection using time series methods.
result Demonstrated effectiveness on synthetic and real-world datasets.
AI predicts employee attrition to prevent turnover.
problem Predicting and preventing employee attrition.
method Ensemble classification and Linear Regression models.
result Predicts employee attrition with over 91% accuracy.
Proposes a convex model for mixed logit to handle individual heterogeneity.
problem Non-convex optimization in mixed logit models for individual heterogeneity.
method Sparse and low-rank decomposition for convex formulation.
result Convex formulation avoids simulation-based approximation and unstable model interpretation.
Automated suggestions help train technicians diagnose incidents faster.
problem Manual and time-consuming incident diagnosis by train maintenance technicians.
method Developed and deployed a learning machine to suggest diagnostics to technicians.
result The model refines its accuracy through feedback from experts and uses feature engineering.
Research explores unsupervised methods for detecting vessel behavior changes in real-time data streams.
problem Detecting shifts in vessel behavior for maritime traffic monitoring.
method Investigates unsupervised and semi-supervised change detection methods.
result Identifies shifts in vessel behavior for unusual events detection.
Proposes a deep learning model for predicting traffic speeds in real-time.
problem Difficulty in capturing traffic data's random, seasonal, non-linear, and spatio-temporal correlated nature.
method Hierarchical D-CLSTM-t model combining CNN and LSTM for short-term traffic speed prediction.
result D-CLSTM-t model outperforms other models in predicting traffic speeds.