O trabalho tem por principal objetivo verificar, preliminarmente, a possibilidade de reduzir a quantidade de indivíduos na amostra de Pesquisa de Entrevistas Domiciliares, sem prejudicar a qualidade e representatividade da mesma. Analisar a influência das características espaciais e de uso de solo da área urbana constitui o objetivo intermediário. Para ambos os objetivos, a principal ferramenta utilizada foi o minerador de dados denominado Árvore de Decisão e Classificação contido no software S-Plus 6.1, que encontra as relações entre as características socioeconômicas dos indivíduos, as características espaciais e de uso de solo da área urbana e os padrões de viagens encadeadas. Os padrões de viagens foram codificados em termos de sequência cronológica de: motivos, modos, durações de viagem e períodos do dia em que as viagens ocorrem. As análises foram baseadas nos dados da Pesquisa de Entrevistas Domiciliares realizada pela Agência de Cooperação Internacional do Japão e Governo do Estado do Pará em 2000 na Região Metropolitana de Belém. Para se atingir o objetivo intermediário o método consistiu em analisar, através da Árvore de Decisão e Classificação, a influência da variável categórica Macrozona, que representa as características espaciais e de uso de solo da área urbana, nos padrões de viagens encadeadas realizados pelos indivíduos. Para o objetivo principal, o método consistiu em escolher, aleatoriamente, sub-amostras contendo 25% de pessoas da amostra final e verificar, através do Processamento de Árvores de Decisão e Classificação e do teste estatístico Kolmogorov - Smirnov, se os modelos obtidos a partir das amostras reduzidas conseguem ilustrar bem a freqüência de ocorrência dos padrões de viagens das pessoas da amostra final. Concluiu-se que as características espaciais e de uso de solo influenciam os padrões de encadeamento de viagens, e portanto foram incluídas como variáveis preditoras também nos modelos obtidos a partir das sub-amostras. A conclusão principal foi a não rejeição da hipótese de que é possível reduzir o tamanho da amostra de pesquisas domiciliares para fins de estudo do encadeamento de viagens. Entretanto ainda são necessárias muitas outras verificações antes de aceitar esta conclusão. / The main aim of this work is to verify, the possibility of reducing the sample size in home-interview surveys, without being detrimental to the quality and representation. The sub aim of this work is to analyze the influence of spatial characteristics and land use of an urban area. For both aims, the main analyses tool used was Data Miner called the Decision and Classification Tree which is in the software S-Plus 6.1. The Data Miner finds relations between trip chaining patterns and individual socioeconomic characteristics, spatial characteristics and land use patterns. The trip chaining patterns were coded in terms of chronological sequence of trip purpose, travel mode, travel time and the period of day in which travel occurs. The analyses were based on home-interview surveys carried out in the Belém Metropolitan Area in 2000, by Japan International Cooperation Agency and Pará State Government. In order to achieve the sub aim of this work, the method consisted of analyzing, using the Decision and Classification Tree, the influence of the categorical variable \"Macrozona\", which represents spatial characteristics and urban land use patterns, in trip chaining patterns carried by the individuals. Concerning the main aim, the method consisted of choosing sub-samples randomly containing 25% of the final sample of individuals and verifying (using Decision and Classification Tree and Kolmogorov-Smirnov statistical test) whether the models obtained from the reduced samples can describe the frequency of the occurrence of the individuals trip chaining patterns in the final sample well. The first conclusion is that spatial characteristics and land use of the urban area have influenced the trip chaining patterns, and therefore they were also included as independent variables in the models obtained from the sub-samples. The main conclusion was the non-rejection of the hypothesis that it is possible to reduce the sample size in home-interview surveys used for trip-chaining research. Nevertheless, several other verifications are necessary before accepting this conclusion.

O trabalho tem por principal objetivo verificar, preliminarmente, a possibilidade de reduzir a quantidade de indivíduos na amostra de Pesquisa de Entrevistas Domiciliares, sem prejudicar a qualidade e representatividade da mesma. Analisar a influência das características espaciais e de uso de solo da área urbana constitui o objetivo intermediário. Para ambos os objetivos, a principal ferramenta utilizada foi o minerador de dados denominado Árvore de Decisão e Classificação contido no software S-Plus 6.1, que encontra as relações entre as características socioeconômicas dos indivíduos, as características espaciais e de uso de solo da área urbana e os padrões de viagens encadeadas. Os padrões de viagens foram codificados em termos de sequência cronológica de: motivos, modos, durações de viagem e períodos do dia em que as viagens ocorrem. As análises foram baseadas nos dados da Pesquisa de Entrevistas Domiciliares realizada pela Agência de Cooperação Internacional do Japão e Governo do Estado do Pará em 2000 na Região Metropolitana de Belém. Para se atingir o objetivo intermediário o método consistiu em analisar, através da Árvore de Decisão e Classificação, a influência da variável categórica Macrozona, que representa as características espaciais e de uso de solo da área urbana, nos padrões de viagens encadeadas realizados pelos indivíduos. Para o objetivo principal, o método consistiu em escolher, aleatoriamente, sub-amostras contendo 25% de pessoas da amostra final e verificar, através do Processamento de Árvores de Decisão e Classificação e do teste estatístico Kolmogorov - Smirnov, se os modelos obtidos a partir das amostras reduzidas conseguem ilustrar bem a freqüência de ocorrência dos padrões de viagens das pessoas da amostra final. Concluiu-se que as características espaciais e de uso de solo influenciam os padrões de encadeamento de viagens, e portanto foram incluídas como variáveis preditoras também nos modelos obtidos a partir das sub-amostras. A conclusão principal foi a não rejeição da hipótese de que é possível reduzir o tamanho da amostra de pesquisas domiciliares para fins de estudo do encadeamento de viagens. Entretanto ainda são necessárias muitas outras verificações antes de aceitar esta conclusão. / The main aim of this work is to verify, the possibility of reducing the sample size in home-interview surveys, without being detrimental to the quality and representation. The sub aim of this work is to analyze the influence of spatial characteristics and land use of an urban area. For both aims, the main analyses tool used was Data Miner called the Decision and Classification Tree which is in the software S-Plus 6.1. The Data Miner finds relations between trip chaining patterns and individual socioeconomic characteristics, spatial characteristics and land use patterns. The trip chaining patterns were coded in terms of chronological sequence of trip purpose, travel mode, travel time and the period of day in which travel occurs. The analyses were based on home-interview surveys carried out in the Belém Metropolitan Area in 2000, by Japan International Cooperation Agency and Pará State Government. In order to achieve the sub aim of this work, the method consisted of analyzing, using the Decision and Classification Tree, the influence of the categorical variable \"Macrozona\", which represents spatial characteristics and urban land use patterns, in trip chaining patterns carried by the individuals. Concerning the main aim, the method consisted of choosing sub-samples randomly containing 25% of the final sample of individuals and verifying (using Decision and Classification Tree and Kolmogorov-Smirnov statistical test) whether the models obtained from the reduced samples can describe the frequency of the occurrence of the individuals trip chaining patterns in the final sample well. The first conclusion is that spatial characteristics and land use of the urban area have influenced the trip chaining patterns, and therefore they were also included as independent variables in the models obtained from the sub-samples. The main conclusion was the non-rejection of the hypothesis that it is possible to reduce the sample size in home-interview surveys used for trip-chaining research. Nevertheless, several other verifications are necessary before accepting this conclusion.

In dieser Arbeit wird ein simultanes Quell-, Ziel-, Verkehrsmittel- und Routenwahlmodell (Modell EVA-U) entwickelt, welches ein stochastisches Nutzergleichgewicht erreicht. Die Routenwahlmodelle der Verkehrsarten sind nicht mehr Teil der Umlegungsalgorithmen, sondern in das Nachfragemodell integriert. Dadurch ist eine konsistente Bewertung aller Alternativen (der Verkehrsarten) möglich. Das Simultanmodell EVA-U stellt eine Weiterentwicklung des Simultanmodells EVA von LOHSE dar. Das EVA-U-Modell ist den universalen Logit-Modellen zuzuordnen. Die Randsummenbedingungen der Verkehrsverteilung werden beachtet. Die Bewertung der Alternativen erfolgt mittels Generalisierter Kosten. Die Abhängigkeit von Routen wird berücksichtigt, ebenso die Tagesganglinie der Verkehrsnachfrage und die Fahrpläne des ÖV-Systems. Das Modell EVA-U erlaubt auch die Berücksichtigung von Routen intermodaler Verkehrsarten (z.B. P+R). Darüber hinaus ist die Integration eines Modells des ruhenden Verkehrs möglich. / In this thesis a simultaneous Trip Generation-, Distribution-, Modal-Split and Route Choice Model (modell EVA-U) is elaborated. The model tends to reach a stochastic user equilibrium. The route choice algorithms are not longer part of an assignment procedure but part of the demand model. A consistent assessment of properties of all transport systems is possible. The simultaneous model EVA-U is an advancement of the EVA-Model by Lohse. The model EVA-U is to be assigned to the generalised logit-models. All matrix constrains are taken into account. The assessment is effected by generalised costs. The dependence of routes is taken into account. Moreover, the integration of day time and the schedules of private transport lines is possible. Furthermore, it is possible to integrate a model of parked cars and circuits of inter-modal traffic forms (park and ride) in the Model EVA-U.

科学研究費補助金 研究種目:基盤研究(C) 課題番号:16520484 研究代表者:林 上 研究期間:2004-2005年度

Many metropolitan cities motivate people to exploit public bike-sharing programs as alternative transportation for many reasons. Due to its’ popularity, multiple types of research on optimizing public bike-sharing systems is conducted on city-level, neighborhood-level, station-level, or user-level to predict the public bike demand. Previously, the research on the public bike demand prediction primarily focused on discovering a relationship with weather as an external factor that possibly impacted the bike usage or analyzing the bike user trend in one aspect. This work hypothesizes two external factors that are likely to affect public bike demand: weather and air pollution. This study uses a public bike data set, daily temperature, precipitation data, and air condition data to discover the trend of bike usage using multiple machine learning techniques such as Decision Tree, Naïve Bayes, and Random Forest. After conducting the research, each algorithm’s output is evaluated with performance comparisons such as accuracy, precision, or sensitivity. As a result, Random Forest is an efficient classifier for the bike demand prediction by weather and precipitation, and Decision Tree performs best for the bike demand prediction by air pollutants. Also, the three class labelings in the daily bike demand has high specificity, and is easy to trace the trend of the public bike system.

In dieser Arbeit wird ein simultanes Quell-, Ziel-, Verkehrsmittel- und Routenwahlmodell (Modell EVA-U) entwickelt, welches ein stochastisches Nutzergleichgewicht erreicht. Die Routenwahlmodelle der Verkehrsarten sind nicht mehr Teil der Umlegungsalgorithmen, sondern in das Nachfragemodell integriert. Dadurch ist eine konsistente Bewertung aller Alternativen (der Verkehrsarten) möglich. Das Simultanmodell EVA-U stellt eine Weiterentwicklung des Simultanmodells EVA von LOHSE dar. Das EVA-U-Modell ist den universalen Logit-Modellen zuzuordnen. Die Randsummenbedingungen der Verkehrsverteilung werden beachtet. Die Bewertung der Alternativen erfolgt mittels Generalisierter Kosten. Die Abhängigkeit von Routen wird berücksichtigt, ebenso die Tagesganglinie der Verkehrsnachfrage und die Fahrpläne des ÖV-Systems. Das Modell EVA-U erlaubt auch die Berücksichtigung von Routen intermodaler Verkehrsarten (z.B. P+R). Darüber hinaus ist die Integration eines Modells des ruhenden Verkehrs möglich. / In this thesis a simultaneous Trip Generation-, Distribution-, Modal-Split and Route Choice Model (modell EVA-U) is elaborated. The model tends to reach a stochastic user equilibrium. The route choice algorithms are not longer part of an assignment procedure but part of the demand model. A consistent assessment of properties of all transport systems is possible. The simultaneous model EVA-U is an advancement of the EVA-Model by Lohse. The model EVA-U is to be assigned to the generalised logit-models. All matrix constrains are taken into account. The assessment is effected by generalised costs. The dependence of routes is taken into account. Moreover, the integration of day time and the schedules of private transport lines is possible. Furthermore, it is possible to integrate a model of parked cars and circuits of inter-modal traffic forms (park and ride) in the Model EVA-U.