Stochastic Modeling of Lane Distribution and Dynamic Control of Managed Lanes for Highways

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Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers ...

We prove a universality result that reduces the free energy of rank-one matrix estimation problems in the setting of mismatched prior and noise to the computation of the free energy for a modified Sherrington–Kirkpatrick spin glass. Our main result is an a ...

This work deals with tailored reduced order models for bifurcating nonlinear parametric partial differential equations, where multiple coexisting solutions arise for a given parametric instance. Approaches based on proper orthogonal decomposition have been ...

The use of discrete choice models (DCMs) is a regular approach to investigating migration aspirations concerning destination choices. However, given the complex substitution patterns between destinations, more advanced model specifications than the multino ...

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

The ongoing electrification of the transportation fleet will increase the load on the electric power grid. Since both the transportation network and the power grid already experience periods of significant stress, joint analyses of both infrastructures wil ...

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

Objective: To characterize ambulatory knee moments with respect to medial knee osteoarthritis (OA) severity comprehensively and to assess the possibility of developing a severity index combining knee moment parameters. ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

The new era of shared economy has raised our expectations to make mobility more sustainable through better utilization of existing resources and capacity. In this thesis, we focus on the design of transport systems that stimulate multi-purpose trips with t ...

Mobility scholars have long been interested in challenging the automobile's hegemony in the street, particularly by highlighting how to develop urban cycling. The article contributes to this task by explaining how one type of conflict between the bike and ...

pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece. A unique observatory of traffic cong ...

Background Universal access to basic sanitation remains a global challenge, particularly in low- and middle-income countries. Efforts are underway to improve access to sanitation in informal settlements, often through shared facilities. However, access to ...

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and ...

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

There is a paradox in the relationship between bedload transport rates and flow variables: laboratory and field studies have reported on how bedload transport rates depend on flow variables through a power law, but none of the empirical laws fitted to the ...

Stochastic Modeling of Lane Distribution and Dynamic Control of Managed Lanes for Highways

Chattez avec Graph Search

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Scalable kernel logistic regression with Nyström approximation: Theoretical analysis and application to discrete choice modelling

Estimating Rank-One Matrices with Mismatched Prior and Noise: Universality and Large Deviations

Deflation-based certified greedy algorithm and adaptivity for bifurcating nonlinear PDEs

Sampling of alternatives in migration aspiration models

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Distributional Regression and Autoregression via Optimal Transport

GreenEVT: Greensboro Electric Vehicle Testbed

Social-Transmotion: Promptable Human Trajectory Prediction

Diverse parameters of ambulatory knee moments differ with medial knee osteoarthritis severity and are combinable into a severity index

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Optimizing the utilization of existing vehicle flows in last-mile passenger transport and logistics systems

Cycling Interaction Rituals in the Conflict against the Car. From the Bike Subculture to the City Scale and Beyond

pNEUMA dataset

Environmental determinants of access to shared sanitation in informal settlements: a cross-sectional study in Abidjan and Nairobi

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Challenges of COVID-19 Case Forecasting in the US, 2020-2021

Robust discrete choice models with t-distributed kernel errors

Spectral Estimators for High-Dimensional Matrix Inference

Scaling behavior of bedload transport: what if Bagnold was right?