509.604 PEOPLE
| People | Locations | Statistics |
|---|---|---|
| Mouftah, Hussein T. |
| |
| Dugay, Fabrice |
| |
| Rettenmeier, Max |
| |
| Tomasch, Ernst | Graz |
|
| Cornaggia, Greta |
| |
| Palacios-Navarro, Guillermo |
| |
| Uspenskyi, Borys V. |
| |
| Khan, Baseem |
| |
| Fediai, Natalia |
| |
| Derakhshan, Shadi |
| |
| Somers, Bart | Eindhoven |
|
| Anvari, B. |
| |
| Kraushaar, Sabine | Vienna |
|
| Kehlbacher, Ariane |
| |
| Das, Raj |
| |
| Werbińska-Wojciechowska, Sylwia |
| |
| Brillinger, Markus |
| |
| Eskandari, Aref |
| |
| Gulliver, J. |
| |
| Loft, Shayne |
| |
| Kud, Bartosz |
| |
| Matijošius, Jonas | Vilnius |
|
| Piontek, Dennis |
| |
| Kene, Raymond O. |
| |
| Barbosa, Juliana |
|
Rodrigues, Filipe
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (36/36 displayed)
- 2025Combining choice and response time data to analyse the ride-acceptance behavior of ride-sourcing driverscitations
- 2025Context-aware Bayesian mixed multinomial logit modelcitations
- 2025Pareto front for two-stage distributionally robust optimization problems
- 2024Representation Learning of Rare Temporal Conditions for Travel Time Prediction
- 2024Analyzing the Reporting Error of Public Transport Trips in the Danish National Travel Survey Using Smart Card Data
- 2024Model building, inference and interpretation: developing discrete choice models in the age of machine learningcitations
- 2023Unboxing the graph:Towards interpretable graph neural networks for transport prediction through neural relational inferencecitations
- 2023Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handlingcitations
- 2023Incident congestion propagation prediction using incident reports
- 2023Incident congestion propagation prediction using incident reports
- 2023Forecasting Parking Search Times Using Big Datacitations
- 2023Bayesian Optimization of Road Pricing using Agent-based Mobility Simulation
- 2023Unboxing the graphcitations
- 2022Weighted iterated local branching for mathematical programming problems with binary variablescitations
- 2022The Dynamic RORO Stowage Planning Problem
- 2022Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
- 2022Reinforcement Learning for Autonomous Mobility-on-Demand Systems: Udvidet resumé
- 2022Recurrent flow networks:A recurrent latent variable model for density estimation of urban mobilitycitations
- 2022Recurrent flow networkscitations
- 2022Predictive and prescriptive performance of bike-sharing demand forecasts for inventory managementcitations
- 2021Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systemscitations
- 2021Weighted proximity searchcitations
- 2021Deep survival modelling for shared mobilitycitations
- 2021Latent class choice model with a flexible class membership component: A mixture model approachcitations
- 2021Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
- 2020Is Travel Demand Actually Deep? An Application in Event Areas Using Semantic Informationcitations
- 2020Deep Survival Modelling for Shared Mobility
- 2020Lagrangian Duality for Robust Problems with Decomposable Functions: The Case of a Robust Inventory Problemcitations
- 2019Multi-output bus travel time prediction with convolutional LSTM neural networkcitations
- 2019Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputationcitations
- 2019Multi-step ahead prediction of taxi demand using time-series and textual datacitations
- 2019Comparing techniques for modelling uncertainty in a maritime inventory routing problemcitations
- 2018Robust Optimization for a Maritime Inventory Routing Problemcitations
- 2018An adjustable sample average approximation algorithm for the stochastic production‐inventory‐routing problemcitations
- 2018Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic datacitations
- 2016A MIP Based Local Search Heuristic for a Stochastic Maritime Inventory Routing Problemcitations
Places of action
| Organizations | Location | People |
|---|