Filipe Rodrigues

CoRR, 2024

Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities.

[BibT_eX]

[DOI]

Carolin Schmidt

Mathias Niemann Tygesen

CoRR, 2024

Railway network delay evolution: A heterogeneous graph neural network approach.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2024

Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning.

[BibT_eX]

[DOI]

Carolin Schmidt

Daniele Gammelli

Niklas Christoffer Petersen

Proceedings of the European Control Conference, 2024

2023

Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling.

[BibT_eX]

[DOI]

Anders Parslov

Expert Syst. Appl., December, 2023

Prediction of departure delays at original stations using deep learning approaches: A combination of route conflicts and rolling stock connections.

[BibT_eX]

[DOI]

Expert Syst. Appl., November, 2023

Deep Evidential Learning for Bayesian Quantile Regression.

[BibT_eX]

[DOI]

Frederik Boe Hüttel

CoRR, 2023

Mind the Gap - Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand.

[BibT_eX]

[DOI]

Frederik Boe Hüttel

CoRR, 2023

Incident congestion propagation prediction using incident reports.

[BibT_eX]

[DOI]

Mathias Niemann Tygesen

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility, 2023

On the Importance of Stationarity, Strong Baselines and Benchmarks in Transport Prediction Problems.

[BibT_eX]

[DOI]

Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

Representation Learning of Rare Temporal Conditions for Travel Time Prediction.

[BibT_eX]

[DOI]

Niklas Petersen

Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

Graph Reinforcement Learning for Network Control via Bi-Level Optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

The Dynamic RORO Stowage Planning Problem.

[BibT_eX]

[DOI]

Alastair Ronald Main

Dario Pacino

Proceedings of the Computational Logistics - 14th International Conference, 2023

2022

Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models.

[BibT_eX]

[DOI]

Nicola Ortelli

Michel Bierlaire

IEEE Trans. Intell. Transp. Syst., 2022

Modeling Censored Mobility Demand Through Censored Quantile Regression Neural Networks.

[BibT_eX]

[DOI]

IEEE Trans. Intell. Transp. Syst., 2022

Generalized multi-output Gaussian process censored regression.

[BibT_eX]

[DOI]

Pattern Recognit., 2022

Recurrent flow networks: A recurrent latent variable model for density estimation of urban mobility.

[BibT_eX]

[DOI]

Daniele Gammelli

Pattern Recognit., 2022

Unboxing the graph: Neural Relational Inference for Mobility Prediction.

[BibT_eX]

[DOI]

Mathias Niemann Tygesen

CoRR, 2022

Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand.

[BibT_eX]

[DOI]

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management.

[BibT_eX]

[DOI]

CoRR, 2021

Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand.

[BibT_eX]

[DOI]

CoRR, 2021

Modeling Censored Mobility Demand through Quantile Regression Neural Networks.

[BibT_eX]

[DOI]

Inon Peled

CoRR, 2021

Gaussian Process Latent Class Choice Models.

[BibT_eX]

[DOI]

Georges Sfeir

Maya Abou-Zeid

CoRR, 2021

Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems.

[BibT_eX]

[DOI]

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Beyond Expectation: Deep Joint Mean and Quantile Regression for Spatiotemporal Problems.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2020

Is Travel Demand Actually Deep? An Application in Event Areas Using Semantic Information.

[BibT_eX]

[DOI]

Ioulia Markou

IEEE Trans. Intell. Transp. Syst., 2020

Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach.

[BibT_eX]

[DOI]

Georges Sfeir

Maya Abou-Zeid

Isam Kaysi

CoRR, 2020

Recurrent Flow Networks: A Recurrent Latent Variable Model for Spatio-Temporal Density Modelling.

[BibT_eX]

[DOI]

Daniele Gammelli

CoRR, 2020

Scaling Bayesian inference of mixed multinomial logit models to very large datasets.

[BibT_eX]

[DOI]

CoRR, 2020

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation.

[BibT_eX]

[DOI]

Kristian Henrickson

IEEE Trans. Intell. Transp. Syst., 2019

Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach.

[BibT_eX]

[DOI]

Ioulia Markou

Niklas Christoffer Petersen

Inf. Fusion, 2019

Multi-output bus travel time prediction with convolutional LSTM neural network.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2019

Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures.

[BibT_eX]

[DOI]

Carlos Lima Azevedo

Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, 2019

2018

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data.

[BibT_eX]

[DOI]

CoRR, 2018

Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems.

[BibT_eX]

[DOI]

CoRR, 2018

Real-Time Taxi Demand Prediction using data from the web.

[BibT_eX]

[DOI]

Ioulia Markou

Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018

Deep Learning from Crowds.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Learning Supervised Topic Models for Classification and Regression from Crowds.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2017

A Bayesian Additive Model for Understanding Public Transport Usage in Special Events.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2017

2016

Can Topic Modelling benefit from Word Sense Information?

[BibT_eX]

[DOI]

Adriana Ferrugento

Hugo Gonçalo Oliveira

Ana Alves

Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016, 2016

Using internet search queries to predict human mobility in social events.

[BibT_eX]

[DOI]

Proceedings of the 19th IEEE International Conference on Intelligent Transportation Systems, 2016

2015

Mining point-of-interest data from social networks for urban land use classification and disaggregation.

[BibT_eX]

[DOI]

Comput. Environ. Urban Syst., 2015

Why so many people? Explaining Nonhabitual Transport Overcrowding With Internet Data.

[BibT_eX]

[DOI]

IEEE Trans. Intell. Transp. Syst., 2015

Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios.

[BibT_eX]

[DOI]

Moshe E. Ben-Akiva

J. Intell. Transp. Syst., 2015

Learning Supervised Topic Models from Crowds.

[BibT_eX]

[DOI]

Proceedings of the Third AAAI Conference on Human Computation and Crowdsourcing, 2015

Towards the Improvement of a Topic Model with Semantic Knowledge.

[BibT_eX]

[DOI]

Adriana Ferrugento

Ana Alves

Hugo Gonçalo Oliveira

Proceedings of the Progress in Artificial Intelligence, 2015

2014

Sequence labeling with multiple annotators.

[BibT_eX]

[DOI]

Bernardete Ribeiro

Mach. Learn., 2014

ASAP: Automatic Semantic Alignment for Phrases.

[BibT_eX]

[DOI]

Proceedings of the 8th International Workshop on Semantic Evaluation, 2014

Gaussian Process Classification and Active Learning with Multiple Annotators.

[BibT_eX]

[DOI]

Bernardete Ribeiro

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Learning from multiple annotators: Distinguishing good from random labelers.

[BibT_eX]

[DOI]

Bernardete Ribeiro

Pattern Recognit. Lett., 2013

2011

Tagging Space from Information Extraction and Popularity of Points of Interest.

[BibT_eX]

[DOI]

Ana Oliveira Alves