Professional cyclic high mobility: modelling state transitions

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We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

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, ...

Ozonation of secondary-treated wastewater for the abatement of micropollutants requires a reliable control of ozone doses. Changes in the UV absorbance of dissolved organic matter (DOM) during ozonation allow to estimate micropollutant abatement on-line an ...

Multi-modal interactions at the network-level remain unexplored due to the lack of highresolution data for all transportation modes involved. The current work investigates the effect of multi-modal interactions at space-mean network speed for each mode usi ...

In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, b ...

Magnetic impurities generate a wealth of phenomena on surfaces. On metals, conducting electrons screen the magnetic moment giving rise to the Kondo effect. On superconductors, the Yu-Shiba-Rusinov (YSR) states emerge inside the superconducting gap due to t ...

The objective of this thesis is to develop a general methodology to incorporate a disaggregate demand representation in supply-oriented optimization problems that allows to capture the interplay between the behavior of individuals and the decisions to be o ...

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specif ...

Both numerical simulations and data-driven methods have been applied in dam's displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and ...

In most studies of dam's displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to im ...

Motivated by the widespread use of large gridded data sets in the atmospheric sciences, we propose a new model for extremes of areal data that is inspired by the simultaneous autoregressive (SAR) model in classical spatial statistics. Our extreme SAR model ...

The emission parameterization is a crucial part of numerical pollen dispersion models. This paper shows that Artificial Neural Networks (ANNs) can substantially improve the performance of the Ambrosia pollen emission in numerical pollen dispersion models s ...

Genetic association studies have become increasingly important in understanding the molecular bases of complex human traits. The specific analysis of intermediate molecular traits, via quantitative trait locus (QTL) studies, has recently received much atte ...

The functional linear model extends the notion of linear regression to the case where the response and covariates are iid elements of an infinite-dimensional Hilbert space. The unknown to be estimated is a Hilbert-Schmidt operator, whose inverse is by defi ...

This paper advances further the analysis of previous exploratory research conducted by the authors on how social innovation can foster resilience in a digital governance context. The process of innovation in social policy, as well as the building of resili ...

Research was conducted to measure the effects of the economic crisis on public transport quality, user satisfaction, and transit demand. The survey used in this research extended a research effort that began in 2008; the survey aimed to analyze the variabi ...

Spatial patterns are deeply linked to ecological processes and this relationship lies at the core of landscape ecology. In turn, landscape patterns are influenced by physical, biological and anthropogenic factors. The aim of this study was to explore how s ...

In this article, we will measure individuals’ and groups’ aptitude for movement and consider them relative to their living environment by developing a typology of motility. This will help highlight both the range and variety of skills for being mobile, and ...

Making sense of concept maps is an ongoing challenge for the concept mapping community. This paper introduces a multi-level analysis strategy by combining quantitative and qualitative methods to triangulate changes in students’ concept maps. Quantitative a ...

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in ...

Professional cyclic high mobility: modelling state transitions

Chat with Graph Search

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Robust discrete choice models with t-distributed kernel errors

Application of UV absorbance and electron-donating capacity as surrogates for micropollutant abatement during full-scale ozonation of secondary-treated wastewater

Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment

Integrating advanced discrete choice models in mixed integer linear optimization

Tunneling processes through magnetic impurities on superconducting surfaces: Yu-Shiba-Rusinov states and the Josephson effect

A general framework for the integration of complex choice models into mixed integer optimization

Enhancing discrete choice models with representation learning

Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

Improve the Model Stability of Dam's Displacement Prediction Using a Numerical-Statistical Combined Model

Simultaneous autoregressive models for spatial extremes

Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

Large-scale variational inference for Bayesian joint regression modelling of high-dimensional genetic data

Methodology And Convergence Rates For Functional Time Series Regression

Understanding the Social Implications of the Digital Transformation: Insights from Four Case Studies on the Role of Social Innovation to Foster Resilience of Society, Electronic Participation

Measuring the Effects of Economic Crisis on Users' Perceptions of Public Transport Quality

Integrating geo-biodiversity features in the analysis of landscape patterns

Measuring and typifying mobility using motility

Multi-level analysis strategy to make sense of concept maps

Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets