Air transportation demand forecasting is a core element in aviation planning and policy decision making. NASA Langley Research Center addressed the need of a global forecast model to be integrated into the Transportation Systems Analysis Model (TSAM) to fulfil the vision of the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters to develop a picture of future demand worldwide. Future forecasts can be performed using a range of techniques depending on the data available and the scope of the forecast. Causal models are widely used as a forecasting tool by looking for relationships between historical demand and variables such as economic and population growth. The Global Demand Model is an econometric regression model that predicts the number of air passenger seats worldwide using the Gross Domestic Product (GDP), population, and airlines market share as the explanatory variables. GDP and Population are converted to 2.5 arc minute individual cell resolution and calculated at the airport level in the geographic area 60 nautical miles around the airport. The global demand model consists of a family of models, each airport is assigned the model that best fits the historical data. The assignment of the model is conducted through an algorithm that uses the R2 as the measure of Goodness-of-Fit in addition to a sanity check for the generated forecasts. The output of the model is the projection of the number of seats offered at each airport for every year up to the year 2040. / Master of Science

Freight demand models have not been developed that much as passenger demand models. The reason is existence of too many complexities in this area. To estimate a disaggregate freight transport models large input data is required. The Swedish Flow Commodity survey 2004/2005 (CFS) which is a unique data source at the level of individual firms made it possible to estimate a disaggregate model to analyze the choice of transport chains and shipment size for the domestic metal products. The output of logistics module of the Swedish national freight transport (SAMGODS) is used as an auxiliary database to incorporate logistics decisions which CFS lacks in the model. The model comprises logistics perspective by considering both shipment size and transport chains as endogenous choices. Characteristics of shippers, shipments and transport chains are included in the model to analyze the choice of transport chain and shipment size. It has been tried to include as many transport chains as possible in the choice sets to consider their effects on decision making. Transport costs have been included in the model as shipment size specific to incorporate the concept of logistics more precisely in the model. From the results it can be seen that the freight transport demand is almost inelastic to the cost. The model gives a positive sign for the coefficient of the transport time which can be explained as the storage cost is so high that shippers prefer to use transport modes as the moving inventories instead. Finally, it is suggested to estimate panel discrete choice models on this dataset.

Die vorliegende Arbeit beinhaltet die Entwicklung eines Verfahrens zur Berücksichtigung wetterabhängiger Nachfrageszenarien im ÖPNV. Mittels statistischer Methoden wird unter Einbeziehung sachlogischer Kenntnisse zunächst die Nachfrage hinsichtlich ihrer Wetterabhängigkeit analysiert. Damit werden Schichtungen identifiziert, bei denen die bisher aufgetretenen Nachfrageschwankungen einer Wetterabhängigkeit unterlagen. Anhand der ermittelten Ergebnisse können sowohl kapazitätsrelevante als auch kapazitätsunabhängige Maßnahmen zur Verbesserung der Dienstleistungsqualität oder der Effizienz des Ressourceneinsatzes abgeleitet werden. Ob die Wetterabhängigkeit der Nachfrage eine Veränderung des Platzangebotes erfordert, und ob diesen Erfordernissen entsprochen werden kann, wird ebenfalls geprüft. Das Verfahren wurde anhand von Nachfragedaten für fünf Eisenbahnlinien der Usedomer Bäderbahn getestet. Detaillierte Untersuchungen zu Möglichkeiten und Grenzen kurzfristiger Kapazitätsanpassungen ergänzen die Verfahrensentwicklung.:GELEITWORT ABSTRACT TABELLENVERZEICHNIS ABBILDUNGSVERZEICHNIS BEGRIFFE UND ABKÜRZUNGEN FORMELVERZEICHNIS 1 Motivation, Zielstellung und Aufbau der Arbeit 2 Wetterabhängigkeit der Verkehrsnachfrage 2.1 Verkehrsbedarf und Verkehrsnachfrage 2.2 Wetterabhängigkeit der Verkehrsnachfrage 2.3 Verkehrsnachfrage durch den Freizeitverkehr 2.4 Wetterabhängigkeit von Freizeitaktivitäten 2.5 Zusammenfassung zum Stand der Forschung in Bezug auf die Wetterabhängigkeit der Verkehrsnachfrage 3 Kapazitätsplanung als wesentliche Planungsaufgabe im ÖPNV 3.1 Begriff Kapazität 3.2 Aufgabe der Kapazitätsplanung im Handlungsumfeld des ÖPNV 3.3 Verantwortlichkeiten für die Kapazitätsplanung 3.4 Anspruchsgruppen gegenüber der Kapazitätsplanung 3.5 Einordnung der Kapazitätsplanung in die Abfolge der Planungsaufgaben bei der Erstellung von Verkehrsangeboten 3.6 Allgemeiner Prozessablauf bei der Kapazitätsplanung 3.7 Beschreibung der Nachfrage als Basis für die Kapazitätsplanung 3.8 Nachfrageunabhängige Einflussgrößen und Randbedingungen 3.9 Fahrtgenaue Kapazitätsdimensionierung je Betriebstag als Input weiterer Planungsschritte 3.10 Disposition von Fahrzeugen und Personal 3.11 Schlussfolgerungen für die Berücksichtigung wetterabhängiger Nachfrageschwankungen bei der Kapazitätsplanung 4 Möglichkeiten und Grenzen kurzfristiger Kapazitätsanpassungen in einem Verkehrsunternehmen 4.1 Vorbemerkungen 4.2 Kapazitätsrelevante Produktionsfaktoren 4.3 Handlungszeitraum 4.4 Formen der Kapazitätsanpassung 4.5 Vergaberechtliche und vertragliche Rahmenbedingungen 4.6 Arbeitsrecht und Sozialvorschriften 4.7 Anforderungen durch System- und Fahrgastinformation 4.8 Inanspruchnahme von Reserven für kurzfristige Kapazitätsanpassungen 4.9 Steuerung der Fahrgastnachfrage 4.10 Resultierende Maßnahmen zur kurzfristigen Kapazitätsanpassung 5 Verfahren zur Ermittlung wetterabhängiger Nachfrageszenarien für die Kapazitätsplanung 5.1 Verfahrensaufbau und erforderliche Datengrundlage 5.2 Datenermittlung 5.3 Datenaufbereitung (Verfahrensstufe 1) 5.4 Ermittlung von Zusammenhängen zwischen Wetter und Nachfrage (Verfahrensstufe 2) 5.5 Ableitung kapazitätsrelevanter Szenarien (Verfahrensstufe 3) 6 Anwendungsbeispiel 6.1 Untersuchungsregion 6.2 Datengrundlage 6.3 Datenaufbereitung (Verfahrensstufe 1) 6.4 Ermittlung von Zusammenhängen zwischen Wetter und Nachfrage (Verfahrensstufe 2) 6.5 Ableitung kapazitätsrelevanter Szenarien (Verfahrensstufe 3) 6.6 Fazit zur Verfahrensanwendung 7 Zusammenfassung und Ausblick 7.1 Zusammenfassung 7.2 Ausblick QUELLENVERZEICHNIS / The present work contains the development of a procedure for the consideration of weather-dependent demand scenarios in public transport. Using statistical methods and factual knowledge, the demand is first analysed with regard to its weather dependency. In this way, cases are identified in which the fluctuations in demand that have occurred so far were subject to weather dependency. Based on the results, both capacity-relevant and capacity-independent measures for improving service quality or the efficiency of resource utilization can be derived. It will also be examined, whether the weather-dependency of demand requires a change in the supply of capacity, and whether these requirements can be met. The method was tested on the basis of demand data for five railroad lines of the Usedomer Bäderbahn. Detailed researches on the possibilities and limits of short-term capacity adjustments complement the development of the process.:GELEITWORT ABSTRACT TABELLENVERZEICHNIS ABBILDUNGSVERZEICHNIS BEGRIFFE UND ABKÜRZUNGEN FORMELVERZEICHNIS 1 Motivation, Zielstellung und Aufbau der Arbeit 2 Wetterabhängigkeit der Verkehrsnachfrage 2.1 Verkehrsbedarf und Verkehrsnachfrage 2.2 Wetterabhängigkeit der Verkehrsnachfrage 2.3 Verkehrsnachfrage durch den Freizeitverkehr 2.4 Wetterabhängigkeit von Freizeitaktivitäten 2.5 Zusammenfassung zum Stand der Forschung in Bezug auf die Wetterabhängigkeit der Verkehrsnachfrage 3 Kapazitätsplanung als wesentliche Planungsaufgabe im ÖPNV 3.1 Begriff Kapazität 3.2 Aufgabe der Kapazitätsplanung im Handlungsumfeld des ÖPNV 3.3 Verantwortlichkeiten für die Kapazitätsplanung 3.4 Anspruchsgruppen gegenüber der Kapazitätsplanung 3.5 Einordnung der Kapazitätsplanung in die Abfolge der Planungsaufgaben bei der Erstellung von Verkehrsangeboten 3.6 Allgemeiner Prozessablauf bei der Kapazitätsplanung 3.7 Beschreibung der Nachfrage als Basis für die Kapazitätsplanung 3.8 Nachfrageunabhängige Einflussgrößen und Randbedingungen 3.9 Fahrtgenaue Kapazitätsdimensionierung je Betriebstag als Input weiterer Planungsschritte 3.10 Disposition von Fahrzeugen und Personal 3.11 Schlussfolgerungen für die Berücksichtigung wetterabhängiger Nachfrageschwankungen bei der Kapazitätsplanung 4 Möglichkeiten und Grenzen kurzfristiger Kapazitätsanpassungen in einem Verkehrsunternehmen 4.1 Vorbemerkungen 4.2 Kapazitätsrelevante Produktionsfaktoren 4.3 Handlungszeitraum 4.4 Formen der Kapazitätsanpassung 4.5 Vergaberechtliche und vertragliche Rahmenbedingungen 4.6 Arbeitsrecht und Sozialvorschriften 4.7 Anforderungen durch System- und Fahrgastinformation 4.8 Inanspruchnahme von Reserven für kurzfristige Kapazitätsanpassungen 4.9 Steuerung der Fahrgastnachfrage 4.10 Resultierende Maßnahmen zur kurzfristigen Kapazitätsanpassung 5 Verfahren zur Ermittlung wetterabhängiger Nachfrageszenarien für die Kapazitätsplanung 5.1 Verfahrensaufbau und erforderliche Datengrundlage 5.2 Datenermittlung 5.3 Datenaufbereitung (Verfahrensstufe 1) 5.4 Ermittlung von Zusammenhängen zwischen Wetter und Nachfrage (Verfahrensstufe 2) 5.5 Ableitung kapazitätsrelevanter Szenarien (Verfahrensstufe 3) 6 Anwendungsbeispiel 6.1 Untersuchungsregion 6.2 Datengrundlage 6.3 Datenaufbereitung (Verfahrensstufe 1) 6.4 Ermittlung von Zusammenhängen zwischen Wetter und Nachfrage (Verfahrensstufe 2) 6.5 Ableitung kapazitätsrelevanter Szenarien (Verfahrensstufe 3) 6.6 Fazit zur Verfahrensanwendung 7 Zusammenfassung und Ausblick 7.1 Zusammenfassung 7.2 Ausblick QUELLENVERZEICHNIS

Past political regimes and socio-economic imbalances have led to the formation of a transport system in the Republic of South Africa (RSA) that is unique to the developing world. Affluent communities in metropolitan cities are situated close to economic activity, whereas the people in need of public transport are situated on the periphery of the cities. This demographic structure is opposite to that of developed countries and complicates both the provision of transport services and the planning process thereof. Multi-Agent Transport Simulation (MATSim) has been identified as an Agent-Based Simulation (ABS) approach that models individual travellers as autonomous entities to create large scale traffic simulations. The initial implementation of MATSim in the RSA successfully simulated private vehicle trips between home and work in the province of Gauteng, proving that there is enough data available to create a realistic multi-agent transport model. The initial implementation can be expanded to further enhance the simulation accuracy, but this requires the incorporation of additional primary and secondary activities into the initial transport demand. This study created a methodology to expand the initial implementation in the midst of limited data, and implemented this process for Gauteng. The first phase constructed a 10% synthetic population that represents the demographic structure of the actual population and identified various socio-demographic attributes that can influence an individual's travel behaviour. These attributes were assigned to the synthetic agents by following an approach that combines probabilistic sampling and rule-based models. The second phase used agents' individual attributes, and census, National Household Travel Survey (NHTS) and geospatial data to transform the synthetic population into a set of daily activity plans - one for every agent. All the agents' daily plans were combined into a plans.xml file that was used as input to MATSim, where the individuals' activity plans were executed simultaneously to model the transport decisions and behaviour of agents. Data deficiencies were overcome by contemplating various scenarios and comparing the macroscopic transport demand patterns thereof to the results of the initial implementation and to actual counting station statistics. This study successfully expanded the initial home-work-home implementation of MATSim by including additional non-work activities in the transport demand. The addition of non-work activities improved the simulation accuracy during both peak and off-peak periods, and the initial demand therefore provides an improved representation of the travel behaviour of individuals in Gauteng. / Dissertation (MEng)--University of Pretoria, 2011. / Industrial and Systems Engineering / unrestricted

Quel impératif pousse Guilloux à se tourner vers les autres? Provient-il d'un appel au sentiment ? D'un appel à la raison ? Est-il la réponse à une éthique ? Pourquoi cette nécessité de rendre la vie valable ? La valeur d'un homme se juge d'aprés ses actes semble dire Guilloux. Qu'est-ce qu'écrire en regard de l'action ? Comment écrire dans un monde en guerre ? L'écriture peut-elle être agissante ? Guilloux a voulu partager ses interrogations avec ses lecteurs, esquisser tous les cheminements possibles jusqu'à la contradiction. D'abord, montrer le processus de mythification entourant les actions des hommes qui ont précédé Guilloux dans cette vie. Ceux d'avant savaient agir ensemble et pour le bien de tous, ce savoir s'est perdu laissant l'individu seul face à ses doutes. Les tourments qui agitent le début du XXème siècle n'ont fait qu'exacerber le questionnement existentiel. Les divers degrés de responsabilité des hommes se dévoilent ainsi dans leurs manières de se conduire vis-à-vis d'autrui, autant d'engagements concrets ou de retraits qui attestent de l'humanité ou de l'inhumanité: ? Trop et pas assez d'intelligence ? trop et pas assez d'amour ?. Ni la famille, ni la société, jaugée au travers de ses institutions, l'école, la justice, le clergé, ne répondent plus à l'exigence d'équité. Alors c'est aux hommes de s'unir. Agir, pour Guilloux, semble une forme de révolte et d'indignation contre la souffrance et l'injustice. Lutter, c'est aussi veiller sur la vie des autres. En refusant toutes compromissions, les personnages que Guilloux met en scène voient leur champ d'action se réduire à l'action humanitaire, quant à l'auteur lui revient le devoir de témoigner. Il se lance alors dans une écriture labyrinthique, un incessant jeu de miroir qui rapproche les hommes les plus différents : l'homme d'action, l'homme de lettres, l'homme militant, l'homme pas cru, l'homme en difficulté, l'homme perdu? chacun explore, estime, selon son itinéraire, la valeur accordée à sa vie et à celles des autres, hanté toujours par le devoir et la volonté de changer le monde / What is writing compared to action ? How to write in a state of war ? Guilloux wanted to share his ethical questioning with his readers, try every possible ways to the point of contradiction. First, he tried to show the mythification process of the people who lived before Guilloux's lifetime. The people who lived in days of old knew how to act together, this knowledge is now lost and individuals remain all alone facing their doubts. The troubles of the beginning of the 20th century could but magnify the existencial questioning. The different degrees of responsibility of the people highlight their commitments or retreats and give evidence of the humanity or the inhumanity. It is now up to men to unite against injustice. Acting to Guilloux is a form of revolt. Guilloux's characters refuse to compromise themselves and as a consequence their field of action becomes barely humaniatrian. The author has the duty to give his own account

The economic evaluation of a new airport investment requires the use of estimated future air passenger demand.Today it is well known that air passenger demand is basicly dependent on various socioeconomic factors of the country and the region where the planned airport would serve. This study is focused on estimating the future air passenger demand for planned airports in Turkey where the historical air passsenger data is not available.For these purposses, neural networks and multi-linear regression were used to develop forecasting models. As independent variables,twelve socioeconomic parameters are found to be significant and used in models. The available data for the selected indicators are statistically analysed and it is observed that most of the data is highly volatile, heteroscedastic and show no definite patterns. In order to develop more reliable models, various methods like data transformation, outlier elimination and categorization are applied to the data.Only seven of total twelve indicators are used as the most significant in the regression model whereas in neural network approach the best model is achieved when all the twelve indicators are included. Both models can be used to predict air passenger demand for any future year for Or-Gi and Zafer Airports and future air passenger demand for similar airports. Regression and neural models are tested by using various statistical test methods and it is found that neural network model is superior to regression model for the data used in this study.

A modelagem clássica da demanda por transportes ignora um importante aspecto normalmente presente na estrutura das variáveis de interesse: a autocorrelação espacial. Pesquisas recentes reconhecem e incluem tal característica à estimativa da demanda, mas há limitações referentes aos elementos básicos de tratamento utilizados nas abordagens. No intuito de superar alguns problemas e restrições associados aos estudos anteriores, o presente trabalho utiliza a dependência espacial entre as observações de viagens no intuito de gerar estimativas do volume de Embarques e Desembarques por ponto de parada, bem como Carregamento nos trechos, ao longo de uma linha de transporte público. Dados de uma pesquisa sobe/desce, realizada em oito linhas de ônibus da cidade de São Paulo, foram disponibilizados pela SPTrans, sendo que, selecionou-se a linha 856R-10 para compor uma análise do desempenho da Geoestatística, ferramental adequado para a estimativa de valores de variáveis em locais não amostrados, sobretudo em bancos de dados espacialmente dependentes. O processo de estimação foi realizado por meio da Krigagem Ordinária com distâncias euclidianas, técnica de interpolação geoestatística que, para sua aplicação, exige apenas o valor da variável em pontos do espaço e suas respectivas coordenadas geográficas. Posteriormente, tal método foi comparado às estimativas obtidas por meio da utilização de distâncias em rede ao longo da linha de ônibus. Os resultados obtidos, a partir da validação cruzada e métricas de aderência, apontaram um ajuste considerável dos valores observados e estimados para ambos os tipos de distância. Dessa forma, a investigação proposta confirmou, com sucesso, a viabilidade de aplicação da Geoestatística às variáveis de demanda por transporte público ao longo de uma linha de ônibus. Além disso, uma vez que a pesquisa sobe/desce exige recursos elevados para sua realização, os resultados destacam o potencial dos interpoladores geoestatísticos em eliminar a necessidade de execução do referido levantamento em todo o percurso da linha de ônibus, já que a Geoestatística garante estimativas aproximadas aos valores da pesquisa completa. / The classical modeling of travel demand overlooks an important aspect normally found in the variables of interest: spatial autocorrelation. Recent researches recognize and include this characteristic in travel demand forecasting, but there are limitations regarding the basic elements of treatment used in the approaches. In order to overcome some of the problems and constraints associated with previous researches, the present study relies on the spatial dependence between the observations of trips in order to generate estimates of the Boarding and Alighting passenger volume per bus stop and Loading in the stretches, along a public transport line, that would not be sampled for the survey occasion. Data from a passenger Boarding and Alighting survey carried out on eight bus lines in the city of São Paulo were made available by SPTrans, and line 856R-10 was selected to compose an analysis of the performance of Geostatistics, a tool that deals with the problem of scarce data and estimation in non-sampled places in the forecast of variables of public transport demand, especially in spatially dependent databases. The estimation process was performed by means of Ordinary Kriging with Euclidian distances, a geostatistical interpolation technique that, for its application, only requires the value of the variable in points of the space and their respective geographical coordinates. Subsequently, this method was compared to the estimates obtained through the use of network distances along the bus line. The results obtained, from cross-validation and measures of goodness-of-fit, indicated a considerable adjustment of the observed values to the estimated ones for both types of distance. Thus, the proposed research successfully confirmed the feasibility of applying Geostatistics to the variables of public transport demand along a bus transit line. Moreover, since the boarding/alighting survey requires high resources for its realization, the results highlight the potential of the geostatistical interpolators in eliminating the need to carry out the survey in the whole course of the bus line, since Geostatistics provides estimates similar to the values of the complete survey.

No description available.

Transport demand planning in South Africa is a neglected field of study, using obsolete methods to model an extremely complex, dynamic system composed of an eclectic mix of First and Third World transport technologies, infrastructure and economic participants. We identify agent-based simulation as a viable modelling paradigm capable of capturing the effects emerging from the complex interactions within the South African transport system, and proceed to implement the Multi-Agent Transport Simulation Toolkit (MATSim) for South Africa's economically important Gauteng province. This report describes the procedure followed to transform household travel survey, census and Geographic Information System (GIS) data into an activity-based transport demand description, executed on network graphs derived from GIS shape files. We investigate the influence of network resolution on solution quality and simulation time, by preparing a full network representation and a small version, containing no street-level links. Then we compare the accuracy of our data-derived transport demand with a lower bound solution. Finally the simulation is tested for repeatability and convergence. Comparisons of simulated versus actual traffic counts on important road network links during the morning and afternoon rush hour peaks show a minimum mean relative error of less than 40%. Using the same metric, the small network differs from the full representation by a maximum of 2% during the morning peak hour, but the full network requires three times as much memory to execute, and takes 5.2 times longer to perform a single iteration. Our census- and travel survey-derived demand performs significantly better than uniformly distributed random pairings of home- and work locations, which we took to be analogous to a lower bound solution. The smallest difference in corresponding mean relative error between the two cases comes to more than 50%. We introduce a new counts ratio error metric that removes the bias present in traditional counts comparison error metrics. The new metric shows that the spread (standard deviation) of counts comparison values for the random demand is twice to three times as large as that of our reference case. The simulation proves highly repeatable for different seed values of the pseudo-random number generator. An extended simulation run reveals that full systematic relaxation requires 400 iterations. Departure time histograms show how agents 'learn' to gradually load the network while still complying with activity constraints. The initial implementation has already sparked further research. Current priorities are improving activity assignment, incorporating commercial traffic and public transport, and the development and implementation of the minibus taxi para-transit mode. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Industrial and Systems Engineering / unrestricted

Esta pesquisa objetivou uma melhor compreensão do processo de escolha do modo de viagem. Empregou-se a abordagem indutiva dirigida a dados livre de suposições a priori da mineração em banco de dados (Data Mining), utilizando redes neurais artificiais (RNA) como ferramenta mineradora, à procura de conhecimento, ou informação útil, a respeito de escolha e capaz de indicar qual das estruturas de decisão subjacentes aos modelos de escolha modal considerados mais se aproximaria ao do observado. Partindo-se da ideia de que nesse processo exista um padrão o qual pode ser captado por uma RNA, ajustou-se um modelo de RNA aos dados e extraiu-se então o conhecimento contido no modelo de RNA ajustado através de um algoritmo de extração de árvore de decisão de RNA chamado Trepan (Trees parroting network), que foi analisado e interpretado à luz dos objetivos desta pesquisa. Os dados que foram utilizados nesse processo de descoberta de conhecimento são provenientes de uma pesquisa de entrevista domiciliar realizada na cidade de Bauru - SP, para fins de estimativa da matriz de deslocamentos origem-destino dessa cidade. Obteve-se quatro árvores de decisão com estruturas simples e com a araucária preditiva de 75% aproximadamente para os três modos de viagem estudados. Embora o conhecimento extraído dos modelos neurais ajustados não tenham proporcionado a indicação de qual das estruturas de decisão subjacentes aos modelos de escolha modal mais se aproxima da obtida com o modelo neural, foi constatada nas árvores resultantes do processo de descoberta do conhecimento uma relação de compensação entre o atributo sexo e os atributos relacionados à capacidade econômica do domicílio na decisão de escolha do modo carro para a realização de uma viagem. Os resultados também sugerem a não necessidade de mais um atributo de entrada referente ao deslocamento realizado em uma viagem para modelagem por RNA do processo de escolha do modo de viagem no contexto estudado. / This research aimed at a better understanding of the mode travel choice process. The inductive data driven free from a priori assumptions of the data mining approach was employed, using artificial neural networks (ANN) as a mining tool, looking for knowledge or useful information, concerning the choice process and capable of indicating which of the underlying decision structures to the considered modal choice models would come closer to the observed one. Taking into consideration that there is a pattern in this process that can be captured by ANN, an ANN model was fitted (trained) to the data, and the knowledge contained in the trained ANN model was extracted by employing an ANN decision tree extraction algorithm called Trepan (Trees parroting network), which was analysed and interpreted in the light of the object of this research. The data which was employed in this knowledge discovery process come from a household survey carried out in Bauru - SP in order to estimate the O-D matrix in this city. Four decision trees with simple structures and predicting accuracy of approximately 75% for the three travel modes studied were obtained. Even though the knowledge extracted from the trained ANN model has not yielded the indication of which of the underlying decision structures to the modal choice models was closer to the neural model, a compensating relation between the sex attribute and the household economic-related attribute in the decision of choosing the car mode in order to travel was evidenced in the trees resulting from the process of knowledge discovery. The results also suggest the lack of necessity of more than one input travel attribute concerning the displacement performed in a trip for the ANN modelling of the mode travel choice process in the studied context.