1 code implementation • 20 Jul 2023 • Jesper Hauch, Christoffer Riis, Francisco C. Pereira
The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are described by polynomials.
1 code implementation • 16 May 2023 • Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems.
no code implementations • 24 Feb 2022 • Valentino Servizi, Dan R. Persson, Francisco C. Pereira, Hannah Villadsen, Per Bækgaard, Jeppe Rich, Otto A. Nielsen
To close the gap and enhance smartphones towards MaaS, we developed a proprietary smartphone-sensing platform collecting contemporary Bluetooth Low Energy (BLE) signals from BLE devices installed on buses and Global Positioning System (GPS) locations of both buses and smartphones.
no code implementations • 24 Feb 2022 • Valentino Servizi., Dan R. Persson, Francisco C. Pereira, Hannah Villadsen, Per Bækgaard, Inon Peled, Otto A. Nielsen
Passenger flow allows the study of users' behavior through the public network and assists in designing new facilities and services.
1 code implementation • 15 Feb 2022 • Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs.
1 code implementation • 25 Jan 2022 • Mathias Niemann Tygesen, Francisco C. Pereira, Filipe Rodrigues
Our approach has several advantages: 1) a Variational Auto Encoder structure allows for the graph to be dynamically determined by the data, potentially changing through time; 2) the encoder structure allows the use of external data in the generation of the graph; 3) it is possible to place Bayesian priors on the generated graphs to encode domain knowledge.
1 code implementation • 24 Sep 2021 • Ioanna Arkoudi, Carlos Lima Azevedo, Francisco C. Pereira
The novelty of our work lies in enforcing interpretability to the embedding vectors by formally associating each of their dimensions to a choice alternative.
1 code implementation • 21 Jun 2021 • Frederik Boe Hüttel, Inon Peled, Filipe Rodrigues, Francisco C. Pereira
To meet this requirement, accurate forecasting of the charging demand is vital.
no code implementations • 31 May 2021 • Haizheng Zhang, Ravi Seshadri, A. Arun Prakash, Constantinos Antoniou, Francisco C. Pereira, Moshe Ben-Akiva
Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control.
1 code implementation • 23 Apr 2021 • Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles.
no code implementations • 2 Apr 2021 • Frederik Boe Hüttel, Inon Peled, Filipe Rodrigues, Francisco C. Pereira
We address this gap by extending current Censored Quantile Regression models to learn multiple quantiles at once and apply these to synthetic baseline datasets and datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark.
no code implementations • 13 Nov 2020 • Martin Johnsen, Oliver Brandt, Sergio Garrido, Francisco C. Pereira
The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level.
no code implementations • 31 Aug 2020 • Inon Peled, Kelvin Lee, Yu Jiang, Justin Dauwels, Francisco C. Pereira
Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors.
1 code implementation • 17 Aug 2020 • Sergio Garrido, Stanislav S. Borysov, Jeppe Rich, Francisco C. Pereira
Estimation of causal effects is fundamental in situations were the underlying system will be subject to active interventions.
1 code implementation • 9 Mar 2020 • Inon Peled, Raghuveer Kamalakar, Carlos Lima Azevedo, Francisco C. Pereira
In a nutshell, QTIP performs real-time simulations of the affected road for multiple scenarios, analyzes the results, and suggests a change to an ordinary prediction model accordingly.
no code implementations • 30 Jan 2020 • Bojan Kostic, Romain Crastes dit Sourd, Stephane Hess, Joachim Scheiner, Christian Holz-Rau, Francisco C. Pereira
In the lower level, for the pairs of life events, time-to-event modelling through survival analysis is applied to model time-dependent transition probabilities.
1 code implementation • 21 Jan 2020 • Daniele Gammelli, Inon Peled, Filipe Rodrigues, Dario Pacino, Haci A. Kurtaran, Francisco C. Pereira
Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited.
no code implementations • 24 Dec 2019 • Valentino Servizi, Francisco C. Pereira, Marie K. Anderson, Otto A. Nielsen
To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys.
no code implementations • 17 Sep 2019 • Sergio Garrido, Stanislav S. Borysov, Francisco C. Pereira, Jeppe Rich
In this paper, two machine learning algorithms, from the family of deep generative models, are proposed for the problem of population synthesis and with particular attention to the problem of sampling zeros.
2 code implementations • 31 Aug 2019 • Francisco C. Pereira
This paper introduces the concept of travel behavior embeddings, a method for re-representing discrete variables that are typically used in travel demand modeling, such as mode, trip purpose, education level, family type or occupation.
no code implementations • 26 Feb 2019 • Inon Peled, Kelvin Lee, Yu Jiang, Justin Dauwels, Francisco C. Pereira
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements.
no code implementations • 20 Dec 2018 • Filipe Rodrigues, Stanislav S. Borysov, Bernardete Ribeiro, Francisco C. Pereira
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise.
no code implementations • 20 Dec 2018 • Filipe Rodrigues, Kristian Henrickson, Francisco C. Pereira
Traffic speed data imputation is a fundamental challenge for data-driven transport analysis.
no code implementations • 20 Dec 2018 • Filipe Rodrigues, Francisco C. Pereira
Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems.
1 code implementation • 27 Aug 2018 • Filipe Rodrigues, Francisco C. Pereira
Spatio-temporal problems are ubiquitous and of vital importance in many research fields.
3 code implementations • 21 Aug 2018 • Stanislav S. Borysov, Jeppe Rich, Francisco C. Pereira
It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models.