289 citations · 849 across the 19 of their papers we have counts for
20 papers
The Fragility Of Synthetic Populations Over Time: An Evaluation Of Forecasting Strategies For Agent-Based Models
Vianey Darsel, Etienne C{ô}me, Jeppe Rich +2
Agent-based simulation models are widely used to assess the future mobility projects. However, their effectiveness hinges on the accuracy of synthetic populations, which are often…
Attitudes and Latent Class Choice Models using Machine learning
Lorena Torres Lahoz, Francisco Camara Pereira, Georges Sfeir +3
Latent Class Choice Models (LCCM) are extensions of discrete choice models (DCMs) that capture unobserved heterogeneity in the choice process by segmenting the population based on…
Choice modelling in the age of machine learning -- discussion paper
S. Van Cranenburgh, S. Wang, A. Vij +2
Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling…
Population synthesis for urban resident modeling using deep generative models
Martin Johnsen, Oliver Brandt, Sergio Garrido +1
The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned…
On the Quality Requirements of Demand Prediction for Dynamic Public Transport
Inon Peled, Kelvin Lee, Yu Jiang +2
As Public Transport (PT) becomes more dynamic and demand-responsive, it increasingly depends on predictions of transport demand. But how accurate need such predictions be for effec…
Estimating Causal Effects with the Neural Autoregressive Density Estimator
Sergio Garrido, Stanislav S. Borysov, Jeppe Rich +1
Estimation of causal effects is fundamental in situations were the underlying system will be subject to active interventions. Part of building a causal inference engine is defining…
Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
Georges Sfeir, Maya Abou-Zeid, Filipe Rodrigues +2
This study presents a semi-nonparametric Latent Class Choice Model (LCCM) with a flexible class membership component. The proposed model formulates the latent classes using mixture…
QTIP: Quick simulation-based adaptation of Traffic model per Incident Parameters
Inon Peled, Raghuveer Kamalakar, Carlos Lima Azevedo +1
Current data-driven traffic prediction models are usually trained with large datasets, e.g. several months of speeds and flows. Such models provide very good fit for ordinary road…
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
Daniele Gammelli, Inon Peled, Filipe Rodrigues +3
Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available sup…
Uncovering life-course patterns with causal discovery and survival analysis
Bojan Kostic, Romain Crastes dit Sourd, Stephane Hess +3
We provide a novel approach and an exploratory study for modelling life event choices and occurrence from a probabilistic perspective through causal discovery and survival analysis…
Prediction of rare feature combinations in population synthesis: Application of deep generative modelling
Sergio Garrido, Stanislav S. Borysov, Francisco C. Pereira +1
In population synthesis applications, when considering populations with many attributes, a fundamental problem is the estimation of rare combinations of feature attributes. Unsurpr…
Rethinking travel behavior modeling representations through embeddings
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, tr…
Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network
Niklas Christoffer Petersen, Filipe Rodrigues, Francisco Camara Pereira
Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport…
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
Inon Peled, Kelvin Lee, Yu Jiang +2
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand…
A Bayesian Additive Model for Understanding Public Transport Usage in Special Events
Filipe Rodrigues, Stanislav S. Borysov, Bernardete Ribeiro +1
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although…
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data
Filipe Rodrigues, Francisco C. Pereira
Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has be…
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Filipe Rodrigues, Kristian Henrickson, Francisco C. Pereira
Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of cro…
Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems
Filipe Rodrigues, Francisco C. Pereira
Spatio-temporal problems are ubiquitous and of vital importance in many research fields. Despite the potential already demonstrated by deep learning methods in modeling spatio-temp…
Learning Supervised Topic Models for Classification and Regression from Crowds
Filipe Rodrigues, Mariana Lourenço, Bernardete Ribeiro +1
The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables,…
Scalable Population Synthesis with Deep Generative Modeling
Stanislav S. Borysov, Jeppe Rich, Francisco C. Pereira
Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transport where the…