A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data
The emergence of data-driven demand analysis has led to the increased use of generative
modelling to learn the probabilistic dependencies between random variables. Although their …
modelling to learn the probabilistic dependencies between random variables. Although their …
Rethinking travel behavior modeling representations through embeddings
FC Pereira - arXiv preprint arXiv:1909.00154, 2019 - arxiv.org
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 …
representing discrete variables that are typically used in travel demand modeling, such as …
A generative model of urban activities from cellular data
Activity-based travel demand models are becoming essential tools used in transportation
planning and regional development scenario evaluation. They describe travel itineraries of …
planning and regional development scenario evaluation. They describe travel itineraries of …
Imputing qualitative attributes for trip chains extracted from smart card data using a conditional generative adversarial network
Abstract Travel Diary Survey (TDS) collects comprehensive attributes such as
sociodemographic attributes, trip purpose, and trip chain attributes of the trips taken by a …
sociodemographic attributes, trip purpose, and trip chain attributes of the trips taken by a …
Model-based machine learning for transportation
This chapter describes the Model-Based approach to Machine Learning (MBML). When
faced with a modeling problem, the first step in MBML is to formulate the uncertainty …
faced with a modeling problem, the first step in MBML is to formulate the uncertainty …
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
Researchers have compared machine learning (ML) classifiers and discrete choice models
(DCMs) in predicting travel behavior, but the generalizability of the findings is limited by the …
(DCMs) in predicting travel behavior, but the generalizability of the findings is limited by the …
Forecasting travel behavior using Markov Chains-based approaches
Recent advances in agent-based micro-simulation modeling have further highlighted the
importance of a thorough full synthetic population procedure for guaranteeing the correct …
importance of a thorough full synthetic population procedure for guaranteeing the correct …
[HTML][HTML] Gaussian process latent class choice models
Abstract We present a Gaussian Process–Latent Class Choice Model (GP-LCCM) to
integrate a non-parametric class of probabilistic machine learning within discrete choice …
integrate a non-parametric class of probabilistic machine learning within discrete choice …
Neural network analysis of travel behavior: evaluating tools for prediction
This article explores the application of neural networks to a behavioral transportation
planning problem. The motivation for adding neural networks as a new modeling …
planning problem. The motivation for adding neural networks as a new modeling …
Machine learning fundamentals
FC Pereira, SS Borysov - Mobility patterns, big data and transport analytics, 2019 - Elsevier
This chapter aims to be a smooth introduction to the basic concepts of machine learning,
and, building on them, explain some to the latest advanced techniques. After a brief …
and, building on them, explain some to the latest advanced techniques. After a brief …