Revisiting the Route Choice Problem: A Modeling Framework Based on Mental Representations

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Dedicated bus lane network design under demand diversion and dynamic traffic congestion: An aggregated network and continuous approximation model approach

Nikolaos Geroliminis, Mehmet Yildirimoglu

This paper proposes an integrated methodological framework to design a spatially heterogeneous bus route network and time-dependent service headways to serve travel demand that varies over time and space. Travelers choose to use either the transit mode or ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Modeling and optimization of dedicated bus lanes space allocation in large networks with dynamic congestion

Nikolaos Geroliminis, Dimitrios Tsitsokas, Anastasios Kouvelas

Dedicated bus lanes provide a low cost and easily implementable strategy to improve transit service by minimizing congestion-related delays. Identifying the best spatial distribution of bus-only lanes in order to maximize traffic performance of an urban ne ...

2021

Predicting exposure concentrations of chemicals with a wide range of volatility and hydrophobicity in different multi-well plate set-ups

Kristin Schirmer, Julita Maria Stadnicka-Michalak

Quantification of chemical toxicity in small-scale bioassays is challenging owing to small volumes used and extensive analytical resource needs. Yet, relying on nominal concentrations for effect determination maybe erroneous because loss processes can sign ...

NATURE RESEARCH2021

Vehicle sharing systems: A review and a holistic management framework

Michel Bierlaire, Nikola Obrenovic, Selin Ataç

Although different vehicle sharing systems (VSSs) use different vehicle types, the management challenges and optimization problems to be solved are similar or even the same. This observation led us to create a generalized and holistic VSS management framew ...

2021

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

Robert West, Alberto Garcia Duran

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state ...

IEEE2021

Modeling, estimation, and control in large-scale urban road networks with remaining travel distance dynamics

Nikolaos Geroliminis, Dimitrios Tsitsokas, Anastasios Kouvelas, Isik Ilber Sirmatel

City-scale control of urban road traffic poses a challenging problem. Dynamical models based on the macroscopic fundamental diagram (MFD) enable development of model predictive perimeter control methods for large-scale urban networks, representing an advan ...

2021

Learning the Exciton Properties of Azo-dyes

Anne-Clémence Corminboeuf, Ksenia Briling, Alberto Fabrizio, Sergio Vela Llausi

The ab initio determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine l ...

AMER CHEMICAL SOC2021

A Pareto Dominance Principle for Data-Driven Optimization

Daniel Kuhn, Tobias Sutter

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...

2024

A Simulation-Based Heuristic to Find Approximate Equilibria with Disaggregate Demand Models

Michel Bierlaire, Virginie Janine Camille Lurkin, Stefano Bortolomiol

Oligopolistic competition occurs in various transportation markets. In this paper, we introduce a framework to find approximate equilibrium solutions of oligopolistic markets in which demand is modeled at the disaggregate level using discrete choice models ...

INFORMS2021

Page 3 sur 3

Dedicated bus lane network design under demand diversion and dynamic traffic congestion: An aggregated network and continuous approximation model approach

Nikolaos Geroliminis, Mehmet Yildirimoglu

PERGAMON-ELSEVIER SCIENCE LTD2021

Predicting exposure concentrations of chemicals with a wide range of volatility and hydrophobicity in different multi-well plate set-ups

Kristin Schirmer, Julita Maria Stadnicka-Michalak

NATURE RESEARCH2021