Study develops a cost model for field canals improvement projects in Egypt.
problem Accurately predict the preliminary costs of field canals improvement projects.
method Developed a parametric cost model using machine learning methods.
result Identified key cost drivers and developed a model for FCIPs.
CPCMs integrate causal drivers for robust portfolio optimization.
problem Degradation of classical portfolio models under structural breaks and lack of arbitrage consistency in machine learning.
method Causal PDE-Control Models integrating structural causal drivers, nonlinear filtering, and forward-backward PDE control.
result CPCM solvers achieve higher Sharpe ratios and lower turnover than benchmarks.
Improved risk assessment for UBI using telematics data and AdaBoost.
problem Class imbalance in predicting claims frequency for UBI.
method Cost-sensitive multi-class AdaBoost (SAMME.C2) algorithm.
result SAMME.C2 outperforms other models in handling class imbalances.
AI helps Vancouver identify where off-street parking saves time and space.
problem On-street parking inefficiencies and associated costs in Vancouver.
method Developed AI models for on-street and off-street parking, comparing time costs.
result Many areas off-street parking saves time and space, aligning with city goals.
Study dynamic portfolio choice under rotating drivers, revealing a new geometric structure.
problem Investment under changing drivers with mutual independence.
method Analyzes geometric structure of portfolio choice, focusing on drivers and their rotation.
result Optimal policy separates into static and hedging components, reflecting the dynamic nature of drivers.
Modeling house prices in Australia reveals supply limitations as the primary driver of extreme trends.
problem Understanding the resilience of Australia's housing prices despite changes in mortgage rates.
method Developed a differential equation model and used modern extreme value techniques on real-world data.
result Without supply increases, a 11% mortgage rate hike is needed to moderate extreme housing costs.
Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction a…
Real-time drowsiness detection on mobile phones reduces road trauma.
problem Driver drowsiness increases crash risk and road trauma.
method Depthwise separable 3D convolutions combined with early fusion of spatial and temporal information.
result Real-time drowsiness detection on mobile phones reduces road trauma.
Optimal transport and neural networks improve trade modeling accuracy.
problem Capturing subtler drivers of trade beyond supply and demand.
method Employing optimal transport and deep neural networks to learn a time-dependent cost function from data.
result Consistently outperforms traditional gravity models in accuracy.
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.
Study predicts individual treatment effects in ride-sharing competitions.
problem Understanding how team competitions affect individual drivers' outcomes.
method Analyzed data from 500 competitions, built machine learning models.
result Reduced out-sample prediction error by over 24%.
New method identifies drivers from car logs without reverse-engineering CAN protocol.
problem Identifying drivers from in-vehicle network logs without access to exact signal semantics.
method Machine learning techniques applied to off-the-shelf data.
result Driver re-identification accuracy of 75-85% on a dataset of 33 drivers.
Optimizes e-hailing drivers' passenger seeking to reduce congestion and pollution.
problem Reduces congestion and pollution by optimizing e-hailing drivers' passenger seeking.
method Uses Markov Decision Process (MDP) and imitation learning to model and optimize drivers' decisions.
result Achieves a 17.5% improvement in passenger return rate over a heuristic strategy.
Extended Jarrow-Rudd model with skewness and kurtosis for option pricing.
problem Valuation of options with non-normal market dynamics.
method Introduced a generalized Jarrow-Rudd (GJR) model with skewness and kurtosis, incorporating transaction costs and market driver influences.
result Demonstrated the GJR pricing model's effectiveness in fitting market data.
Comma.ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper illustrates one of our research approaches for driving simulation. One where we learn to simulate. Here we investigate v…
The potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method …
Improved driver identification accuracy using steering wheel data.
problem Accurately identifying drivers based on naturalistic driving behavior.
method Novel approach for window length parameter design, leveraging GRUs neural network.
result Increased driver identification accuracy from under 15% to over 65%.
The paper proposes a machine learning method to detect drivers' affective states using physiological signals.
problem Detecting and assessing drivers' affective states to improve driving safety and well-being.
method Multiview multi-task machine learning approach using physiological signals.
result Accounting for drive-specific differences significantly improves model performance.
Optimizes portfolios using neural network approximations of asset sensitivities to common drivers.
problem Optimizing portfolios with complex asset dynamics and common drivers.
method Model asset dynamics with PDEs, approximate sensitivities with neural networks, and use hierarchical clustering on sensitivity matrix for optimization.
result Achieves over-performance in portfolio optimization across various markets and datasets.
TNDE quantifies dynamic gene drivers from single-cell snapshots.
problem Reconstructing time-resolved regulatory effects in biological processes.
method Time-varying Network Driver Estimation (TNDE) using shared graph attention encoder and partial optimal transport.
result TNDE identifies stage-specific driver genes in mouse erythropoiesis.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between four distinct classes of driver behavior. Such encodings are learned wit…
Paper proposes personalized climate control for driver comfort.
problem Limited research on in-vehicle climate control and driver preferences.
method IoT platform for data collection, machine learning for driver behavior recognition, and personalized preference recommendation.
result Prototype demonstrates effective and accurate climate control for driver comfort.
Deep RL tackles fleet management and dispatching for ride-sharing platforms.
problem Optimizing dispatching and repositioning of drivers in ride-sharing platforms.
method Deep reinforcement learning approach treating drivers as a central system agent.
result Centralized decision-making improves overall fleet efficiency.
This paper optimizes driver repositioning using MARL and reward design for better service and traffic management.
problem Unserved passenger requests due to drivers' cruising behavior during passenger seeking.
method Mean field multi-agent reinforcement learning (MARL) with a reward design scheme and Bayesian optimization (BO) to solve bilevel optimization problems.
result Optimal toll charges and service charges can improve platform and city planner objectives by significant margins, leading to better traffic conditions.
Deep neural network detects driver intentions from video.
problem Detecting driver intentions for safer self-driving.
method Uses deep learning to analyze turn signals and emergency flashers.
result High per-frame accuracy in challenging scenarios.
Study uses LCRN to detect driver distraction from EEG signals.
problem Improving road safety by detecting driver distraction.
method Used a Long-term Recurrent Convolutional Network (LCRN) for EEG-based driver distraction detection.
result LCRN model outperformed state-of-the-art TSC models in detecting driver distraction.
Paper tackles drowsy driving by learning from weakly labeled car acceleration data.
problem Lack of labeled data for estimating driver drowsiness.
method Weakly supervised learning, scalable stochastic optimization.
result Algorithm learns from weakly labeled data, outperforming baseline methods.
We consider the problem of numerical approximation for forward-backward stochastic differential equations with drivers of quadratic growth (qgFBSDE). To illustrate the significance of qgFBSDE, we discuss a problem of cross hedging of an insurance related financial derivative using correlated assets. For the convergence…
Distributed, controllable energy storage devices offer several benefits to electric power system operation. Three such benefits include reducing peak load, providing standby power, and enhancing power quality. These benefits, however, are only realized during peak load or during an outage, events that are infrequent. T…
As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In thi…
Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety…
Study finds color-coded DMSs improve driver behavior.
problem Improving driver behavior through dynamic message signs.
method Random forest algorithm for route diversion, choice, and compliance analyses.
result Color-coded DMSs are more effective than alphanumeric ones.
Develops geometric BSDEs for modeling dynamic return risk measures.
problem Modeling continuous-time dynamic return risk measures.
method Introduces and develops Geometric Backward Stochastic Differential Equations (GBSDEs) and two-driver BSDEs.
result Establishes existence, regularity, uniqueness, and stability of solutions to GBSDEs.
Bitcoin's price direction is better predicted without additional drivers during high volatility.
problem Predicting Bitcoin's price direction using various determinants.
method Continuous local transfer entropy for feature selection and deep learning classification model.
result Bitcoin's price direction can be better predicted without additional drivers during high volatility.
DA-RNN predicts driving maneuvers up to 3 seconds ahead.
problem Adapting driving model to new drivers and vehicles.
method Domain-Adversarial Recurrent Neural Network (DA-RNN) for robust predictions.
result DA-RNN improves performance by 30% in real drivers and 114% in simulations.
Modeling driver trajectories using inverse reinforcement learning and random utility.
problem Modeling rational driver behavior in road networks from sparse sensor data.
method Apply random utility theory to model unknown reward function, introduce extended state, and use Markov decision process.
result Maximum entropy inverse reinforcement learning is a special case of the proposed approach.
We decompose the squared price-of-risk premium into three components: intervention-stable premium, confounding wedge, and information loss.
problem Decomposing the squared price-of-risk premium into its components
method Identifying an order-three obstruction to aggregation across portfolios
result The decomposition is estimable and detectable with a permutation-calibrated screen
New model identifies anticyclonic patterns causing drought and heat.
problem Identifying atmospheric drivers of drought and heat.
method Smoothed convolutional neural network classifier for anticyclonic circulations.
result Helps identify important drivers of hot and dry extremes in climate simulations.
Proposes a new method for determining LGD discount rates based on cost of capital.
problem Determining an appropriate discount rate for LGD estimation.
method Market-consistent pricing of defaulted loan portfolios to infer discount rates.
result Discount rates reflect both undiversifiable risk and time value of money.
Paper proposes a capsule attention mechanism for EEG-EOG vigilance estimation.
problem Driver vigilance estimation for safer transportation.
method Capsule attention mechanism following LSTM network for multimodal EEG-EOG analysis.
result Capsule attention improves vigilance estimation robustness and accuracy.
New formula for portfolio risk management using conditional PDEs.
problem Optimal diversification and risk management of portfolios.
method Closed-form formula for conditional probability, Gaussian copulas, conditional risk-neutral PDE.
result Dynamic monitoring of portfolio volatilities and weights from PDEs.
Olympic Games consistently exceed budgets, leading to unpredictable costs.
problem High costs and unpredictability of the Olympic Games.
method Statistical analysis of historical data to explain cost risks.
result Olympic costs follow a power-law distribution with infinite mean and variance.
The present work studies and analyzes general defaultable OTC contract in presence of a contingent CSA, which is a theoretical counterparty risk mitigation mechanism of switching type that allows the counterparty of a general OTC contract to switch from zero to full/perfect collateralization and switch back whenever sh…
In this paper, we study a class of Anticipated Backward Stochastic Differential Equations (ABSDE) with jumps. The solution of the ABSDE is a triple (Y,Z,ψ) where Y is a semimartingale, and (Z,ψ) are the diffusion and jump coefficients. We allow the driver of the ABSDE to have linear growth on the uniform norm of …
The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a smal…
New machine learning model identifies key drivers of market troughs.
problem Misrepresentation of market trough drivers by simpler models.
method Flexible DML average partial effect causal machine learning framework.
result Volatility of options-implied risk appetite and market liquidity are key drivers.
A rapid pattern-recognition approach to characterize driver's curve-negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k-means clustering-based support vector machine ( kMC-SVM) method is developed and used for classifying drivers into two types: aggressiv…