Using machine learning to predict activity chains and mode choice on transportation models

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2020.Considerando as viagens como demanda derivada da necessidade das pessoas de executar suas atividades, fica claro que um melhor entendimento de como as pessoas organizam essas atividades durante o dia leva a uma modelagem de demanda por transportes mais sólida. Replicando decisões desagregadas (individuais) de transporte, os modelos baseados em atividades podem produzir melhores previsões de demanda por viagens comparados às gerações anteriores de abordagens de modelagem (a modelagem baseada em viagens, por exemplo). Um artigo publicado em 2019 se destaca entre as produções científicas recentes relacionadas à modelagem baseada em atividades por propor um modelo composto para geração de diários detalhados de atividades para agentes, com base em suas características socioeconômicas, o Agendador de Atividades Baseado em Dados (Data-Driven Activity Scheduler – DDAS). O objetivo deste trabalho foi desenvolver uma replicação comentada da abordagem metodológica de dois módulos do DDAS: o Modelo de Tipo de Atividade (Activity Type Model – ATM) e o Modelo de Escolha Modal (Mode Choice Model – MCM). Objetivos específicos incluíam a replicação destes módulos do DDAS usando dados da Pesquisa de Mobilidade Urbana do Distrito Federal, que é significativamente maior que a base de dados utilizada no artigo original. Além disso, pretendia-se investigar possíveis melhorias a serem feitas aos modelos do DDAS ou ao seu método de validação. Os resultados obtidos indicaram que uma modificação no método de treino dos modelos poderia compensar o desbalanço de frequência entre as classes. Assim, foi desenvolvida uma segunda implementação usando a técnica de SMOTE (Synthetic Minority Oversampling Technique – Técnica de Sobreamostragem Sintética de Minoria) para treinar os módulos ATM e MCM. Apesar de terem sido obtidas cadeias de atividades mais realistas a partir dessa segunda implementação, o score de validação para o módulo ATM foi baixo. Dessa forma, uma terceira implementação foi desenvolvida, com os modelos treinados como classificadores Random Forest no lugar de classificadores de árvore de decisão isoladas. Foi observada melhoria significativa nos resultados desse terceiro modelo, tanto no treinamento quanto na validação, para ambos os módulos ATM e MCM. Além disso, outra contribuição desse trabalho foi a disponibilização pública de todos os códigos desenvolvidos durante sua condução.When travel is considered a demand derived from people’s need to perform activities, it becomes clear that a better understanding of how people organize their activities during a day must provide a more solid basis for travel demand modeling. By replicating disaggregate travel decisions (at the individual level), activity-based models may produce better travel demand predictions, compared to the previous generations of modeling approaches (tripbased approaches, for instance). A paper published in 2019 stands out among the most recent activity-based modeling research as the authors propose a comprehensive framework for generating full and detailed activity schedules for given agents depending on their sociodemographic features, called Data-Driven Activity Scheduler (DDAS). The aim of this research was to develop a commented replication of the methodological approach of two modules of the DDAS: the Activity Type Model (ATM) and the Mode Choice Model (MCM). Specific objectives included replicating these two modules of the DDAS framework using data from the Federal District Urban Mobility Survey, which is significantly larger than the dataset used in the original DDAS study. Moreover, it was intended to investigate possible improvements to be made on the DDAS framework, including its validation procedure. The obtained results from the replication of the DDAS framework indicated that there was improvement to be made on the manner how models were being trained, in order to better deal with class imbalance. Therefore, a second implementation was made by using the SMOTE technique (Synthetic Minority Oversampling Technique) for training the ATM and MCM modules. Although activity chains seemed more realistic in this second set of results, the overall validation score for the ATM module was low. Therefore, a third model was developed by training the models as Random Forest classifiers instead of isolated Decision Tree classifiers as it was defined in the original DDAS framework. Significant improvement was observed in the results of this third model, both in training and test, for both ATM and MCM modules. Furthermore, another contribution of this study is the public availability of all scripts that were developed during its conduction

Development of predictive energy management strategies for hybrid electric vehicles

2017 Fall.Includes bibliographical references.Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. This study seeks to research the effect of prediction error on FE. First, a speed prediction method is developed, and trained with real-world driving data gathered only from the subject vehicle (a local data collection method). This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. Results demonstrate that 60-90 second predictions resulted in the highest FE improvement over the baseline, achieving up to a 4.8% FE increase. A second speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication was developed to understand if incorporating near-term technologies could be utilized to further improve prediction fidelity. This prediction method produced lower variation in speed prediction error, and was able to realize a larger FE improvement over the local prediction method for longer prediction durations, achieving up to 6% FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability, as up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction methods

Organising a photograph collection based on human appearance

This thesis describes a complete framework for organising digital photographs in an unsupervised manner, based on the appearance of people captured in the photographs. Organising a collection of photographs manually, especially providing the identities of people captured in photographs, is a time consuming task. Unsupervised grouping of images containing similar persons makes annotating names easier (as a group of images can be named at once) and enables quick search based on query by example. The full process of unsupervised clustering is discussed in this thesis. Methods for locating facial components are discussed and a technique based on colour image segmentation is proposed and tested. Additionally a method based on the Principal Component Analysis template is tested, too. These provide eye locations required for acquiring a normalised facial image. This image is then preprocessed by a histogram equalisation and feathering, and the features of MPEG-7 face recognition descriptor are extracted. A distance measure proposed in the MPEG-7 standard is used as a similarity measure. Three approaches to grouping that use only face recognition features for clustering are analysed. These are modified k-means, single-link and a method based on a nearest neighbour classifier. The nearest neighbour-based technique is chosen for further experiments with fusing information from several sources. These sources are context-based such as events (party, trip, holidays), the ownership of photographs, and content-based such as information about the colour and texture of the bodies of humans appearing in photographs. Two techniques are proposed for fusing event and ownership (user) information with the face recognition features: a Transferable Belief Model (TBM) and three level clustering. The three level clustering is carried out at “event” level, “user” level and “collection” level. The latter technique proves to be most efficient. For combining body information with the face recognition features, three probabilistic fusion methods are tested. These are the average sum, the generalised product and the maximum rule. Combinations are tested within events and within user collections. This work concludes with a brief discussion on extraction of key images for a representation of each cluster

Proceedings of the ECMLPKDD 2015 Doctoral Consortium

ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic