Simulating the influence of life trajectory events on transport mode behavior in an agent-based system

this paper describes the results of a study on the impact of lifecycle or life trajectory events on activity-travel decisions. This lifecycle trajectory of individual agents can be easily incorporated in an agent-based simulation system. This paper focuses on two lifecycle events, change in residential location and change in number of household members. An Internet-based survey was designed to collect data concerning structural lifecycle events. Previous papers describe the conceptual framework underlying the model and the temporal effects of lifecycle events on mode choice. This paper focuses on predicting the occurrence of structural lifecycle events at a certain time. Structure and parameter learning are applied to build a Bayesian Belief Network based on the data

Simulating the influence of life trajectory events on transport mode behavior in an agent-based system

this paper describes the results of a study on the impact of lifecycle or life trajectory events on activity-travel decisions. This lifecycle trajectory of individual agents can be easily incorporated in an agent-based simulation system. This paper focuses on two lifecycle events, change in residential location and change in number of household members. An Internet-based survey was designed to collect data concerning structural lifecycle events. Previous papers describe the conceptual framework underlying the model and the temporal effects of lifecycle events on mode choice. This paper focuses on predicting the occurrence of structural lifecycle events at a certain time. Structure and parameter learning are applied to build a Bayesian Belief Network based on the data

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

Mobility Management in Metropolitan Regions

Die zunehmenden Verkehrsprobleme, die vor allem auf den motorisierten Individualverkehr zurückzuführen sind und sich besonders in den wachsenden Metropolregionen manifistieren, verlangen nach komplexen Lösungsstrategien zur Reduzierung des Verkehrswachstums. Gerade auch die Umlandbereiche der Metropolkerne sind zunehmend von den wachsenden Verkehrsproblemen betroffen. Das Handlungsfeld Mobilitätsmanagement, das zur Lösung der Verkehrsprobleme insbesondere auf die individuelle Beratung und Information der Verkehrsteilnehmer zur Förderung einer umweltverträglichen Mobilität setzt, wird in jüngerer Vergangenheit sowohl auf kommunaler als auch auf regionaler Ebene als wesentlicher Maßnahmenbereich neben den klassischen Handlungsfeldern wie Infrastrukturausbau oder Verkehrsmanagement eingesetzt. Wichtiger Teilaspekt des Mobilitätsmanagements ist die zielgruppenspezifische, individuelle Ansprache und Information bzw. Beratung der Verkehrsteilnehmer mittels spezieller Marketinginstrumente. Als Zielgruppe werden im Rahmen der Dissertation Neubürger fokussiert, da aufgrund ihrer veränderten Lebensumstände davon ausgegangen werden kann, dass sie besonders offen für eine neue Verkehrsmittelwahl in ihrem neuen Mobilitätskontext sind und damit Informations- und Anreizinstrumente zur Stabilisierung und Förderung einer umweltverträglichen Mobilität besonders erfolgversprechend eingesetzt werden können. Vor dem Hintergrund des weiterhin zunehmenden regionalen Verkehrs, von dem insbesondere die prosperierenden Metropolregionen betroffen sind, konzentriert sich die vorliegende Arbeit aus räumlicher Sicht auf die Umlandbereiche der Metropolkerne, da diese einerseits in besonderem Maße zu den geschilderten Verkehrsproblemen beitragen und andererseits selbst von diesen betroffen sind. Als Beispielraum wird dazu die Metropolregion München als Wachstumsraum herausgegriffen. Ferner hat sich die Landeshauptstadt München in den letzten Jahren intensiv im Bereich des Mobilitätsmanagements engagiert und als einen festen Bestandteil in ihrer Stadt- und Verkehrspolitik integriert und institutionalisiert. Dabei spielt die Vermarktung alternativer Mobilitätsdienstleistungen eine zentrale Rolle. Ein Leitprojekt der Landeshauptstadt München ist in diesem Zusammenhang das sogenannte „Münchner Neubürgerpaket“. Erste Schritte in Richtung eines regionalen Ansatzes im Sinne eines „Regionalen Neubürgerpakets“ sind bereits unternommen worden, um die Verlagerungspotenziale vom Autoverkehr auf umweltverträgliche Verkehrsmittel räumlich auf die Region zu erweitern. Bisher fehlt es allerdings an institutionellen Strukturen und der flächendeckenden Umsetzung. Vor diesem Hintergrund konzentriert sich die Arbeit auf den Maßnahmenbereich „Regionales Neubürgerpaket“ als ein Handlungsfeld zur Lösung der regionalen Verkehrsprobleme. Dazu werden Potenziale zur konkreten Umsetzung der Maßnahme in den Umlandgemeinden näher untersucht und Vorschläge zur institutionellen und organisatorischen Verankerung der Maßnahme gemacht