The aim of this thesis was to study determinants of a readiness for pro-environmental travel behavior in households. Four empirical studies were conducted examining reduction in car use (Study I), acceptability of transport policy measures (Study II and III), and behavioral adaptations in response to travel demand management (TDM) measures (Study IV). In Study I, the aim was to interrupt habitual car use by means of a deliberation intervention and to examine the importance of moral motivation (i.e., personal norm) for car use reduction. Results showed that, as a result of the intervention, car use was mainly reduced among car users with a strong car use habit and a strong moral motivation to reduce car use. The aim of Study II was to examine factors important for the acceptability of three TDM measures: raised tax on fossil fuel, improved public transport, and an information campaign. The results demonstrated the importance of general environmental beliefs (i.e., pro-environmental orientation, problem awareness, personal norm, and willingness to reduce car use) and policy specific beliefs (i.e., perceived impact on freedom to choose travel mode and own car use, perceived effectiveness, and perceived fairness) for the acceptability of the measures. Furthermore, personal norm was found to be particularly important for the acceptability of raised tax and the information campaign, whereas problem awareness was more important for the acceptability of improved public transport. Following up on Study II, the purpose of Study III was to examine the acceptability of single and combined transport policy measures, more specifically, raised tax on fossil fuel, improved public transport, subsidies of renewable fuel, a package of raised tax on fossil fuel and improved public transport, and a package of raised tax on fossil fuel and subsidies of renewable fuel. General environmental beliefs (i.e., pro-environmental orientation, problem awareness, personal norm, and willingness to act) and policy specific beliefs (i.e., perceived effectiveness and perceived fairness) were found to be important for the acceptability of the measures. Moreover, personal norm was particularly important for the acceptability of raised tax on fossil fuel and the packages, while problem awareness was more important for the acceptability of improved public transport and subsidies of renewable fuel. The aim of Study IV was to examine the behavioral adaptations, more specifically, the expected car use reduction, in response to three hypothetical TDM measures: raised tax on fossil fuel, improved public transport, and a package of raised tax on fossil fuel and improved public transport. Furthermore, factors important for the expected car use reduction were analyzed. Results showed that a combination of the measures was expected to lead to a larger car use reduction compared to the single measures, and the most commonly chosen reduction strategies were more efficient car use and changing travel mode. Moreover, internal motivational factors, such as personal norm, and the perceived personal impact of the measures were important for expected car use reduction in response to the measures.

For most kind of analyses in the field of traffic planning, there is a need for origin--destination (OD) matrices, which specify the travel demands between the origin and destination nodes in the network. This thesis concerns the OD-matrix estimation problem, that is, the calculation of OD-matrices using observed link flows. Both time-independent and time-dependent models are considered, and we also study the placement of link flow detectors. Many methods have been suggested for OD-matrix estimation in time-independent models, which describe an average traffic situation. We assume a user equilibrium to hold for the link flows in the network and recognize a bilevel structure of the estimation problem. A descent heuristic is proposed, in which special attention is given to the issue of calculating the change of a link flow with respect to a change of the travel demand in a certain pair of origin and destination nodes. When a time-dimension is considered, the estimation problem becomes more complex. Besides the problem of distributing the travel demand onto routes, the flow propagation in time and space must also be handled. The time-dependent OD-matrix estimation problem is the subject for two studies. The first is a case study, where the conventional estimation technique is improved through introducing pre-adjustment schemes, which exploit the structure of the information contained in the OD-matrix and the link flow observations. In the second study, an algorithm for time-independent estimation is extended to the time-dependent case and tested for a network from Stockholm, Sweden. Finally, we study the underlying problem of finding those links where traffic flow observations are to be performed, in order to ensure the best possible quality of the estimated OD-matrix. There are different ways of quantifying a common goal to cover as much traffic as possible, and we create an experimental framework in which they can be evaluated. Presupposing that consistent flow observations from all the links in the network yields the best estimate of the OD-matrix, the lack of observations from some links results in a relaxation of the estimation problem, and a poorer estimate. We formulate the problem to place link flow detectors as to achieve the least relaxation with a limited number of detectors.

Several research projects all over the world have considered transport pricing measures and policies as promising attempts to solve urgent traffic problems in urban areas. One important precondition for the successful implementation of pricing strategies is public acceptability. However, empirical findings have shown that the acceptability of such strategies in general is very low. Aim of this dissertation is to analyse the reasons for the low acceptability of road pricing. The dissertation is organized as follows: Firstly, the problems related to transport and the measures to cope with these problems are outlined. The focus is thereby on economic measures such as road user fees, whose economic and traffic-related assumptions are discussed. Then, a definition of the terms used is given and main European results concerning the current acceptability level of various travel demand management (TDM) strategies are reported. Moreover, a theoretical framework is developed which attempts to identify the essential factors determining transport pricing acceptability. In two empirical studies (N1 = 923, N2 = 667), it is investigated whether and to what extent these factors contribute to the explanation of acceptability. Finally, conclusions are drawn and some recommendations for future research are given. / Straßenbenutzungsgebühren für den privaten Autoverkehr sind vielfach als sinnvoll und wünschenswert vor allem zur Lösung der zahlreichen Verkehrsprobleme in städtischen Ballungsräumen begründet worden. Bisher ist es jedoch nicht zu einer nennenswerten Umsetzung dieser Maßnahmen gekommen. Es herrscht in der Literatur Einigkeit, daß die geringe Akzeptanz von Straßenbenutzungsgebühren in der Bevölkerung und die deutliche Ablehnung bei den betroffenen Autofahrern die wesentlichen Gründe für den fehlenden politischen Willen sind, road pricing einzuführen. Ziel dieser Arbeit war es, die Ursachen und Bedingungen für die mangelnde Akzeptanz von Straßenbenutzungsgebühren bei den betroffenen Autofahrern zu untersuchen. Die Arbeit ist wie folgt aufgebaut: Im ersten Abschnitt wird die Ausgangslage und der Problemhintergrund dargestellt. Es wird erläutert, welche negativen Konsequenzen des motorisierten Individualverkehrs vorliegen, und mit welchen Maßnahmen ihnen begegnet werden kann. Der Fokus liegt dabei auf preispolitischen Maßnahmen wie Straßenbenutzungsgebühren, dessen ökonomische und verkehrswissenschaftliche Annahmen dargestellt werden. Dem gegenübergestellt wird die gesellschaftspolitische Realität: zum einen in Form der massiven Ablehnung von Straßenbenutzungsgebühren durch Bevölkerung und betroffene Autofahrer und zum anderen in Form der zahlreichen, erfolglosen und gescheiterten Versuche, Straßenbenutzungsgebühren einzuführen. Dies mündet in die Ableitung erster Forschungsfragen und in die Entwicklung des Untersuchungsansatzes. Im darauf folgenden Abschnitt werden die theoretischen Grundlagen zur Untersuchung der Akzeptanz von Straßenbenutzungsgebühren gelegt. Dabei wird zunächst geklärt, wie sich der Begriff "Akzeptanz" definieren läßt und in welche relevanten psychologischen Theorien er sich einordnet. Im Anschluß wird das Modell zur Akzeptanz von Schlag vorgestellt und analysiert. Darauf aufbauend werden die zentralen Variablen des Modells auf ihre theoretische und empirische Relevanz und unter Berücksichtigung potentiell konkurrierender Ansätze dargestellt und untersucht. Ziel ist die Klärung der logischen und kausalen Struktur des Modells und der Beziehungen der Variablen untereinander. Dies führt schließlich in die Ableitung überprüfbarer Hypothesen in Form eines erweiterten Akzeptanzmodells. Zur Überprüfung des Modells, der aufgestellten Hypothesen und Forschungsfragen werden zwei Untersuchungen berichtet (N1 = 923, N2 = 667), die inhaltlich aufeinander aufbauen. Diese wurden im Rahmen der EU-Forschungsprojekte TransPrice und AFFORD durchgeführt. Generelles Ziel ist die Validierung der Modellkonstrukte und ihrer angenommenen Beziehungen. Im letzten Abschnitt werden zusammenfassend und basierend auf den beiden Studien die wesentlichen Ergebnisse rekapituliert, sowie offene Forschungsfragen und Anforderungen abgeleitet, die sich zukünftigen Studien stellen. Schließlich wird diskutiert, welche Implikationen sich aus dieser Untersuchung ziehen lassen und welche Aspekte zu berücksichtigen sind, wenn Straßenbenutzungsgebühren der Öffentlichkeit präsentiert werden. - Die Druckexemplare enthalten jeweils eine CD-ROM als Anlagenteil: 490 KB - Nutzung: Referat Informationsvermittlung der SLUB

This study contributes to the literature on national long-distance travel demand modeling by providing an analysis of households' annual destination choices and time allocation patterns for long-distance leisure travel purposes. An annual vacation destination choice and time allocation model is formulated to simultaneously predict the different destinations that a household visits and the time it spends on each of these visited destinations, in a year. The model takes the form of a Multiple Discrete-Continuous Extreme Value (MDCEV) structure (Bhat, 2005; Bhat, 2008). The model assumes that households allocate their annual vacation time to visit one or more destinations in a year to maximize the utility derived from their choices. The model framework accommodates variety-seeking in households' vacation destination choices in that households can potentially visit a variety of destinations rather than spending all of their annual vacation time for visiting a single destination. At the same time, the model accommodates corner solutions to recognize that households may not necessarily visit all available destinations. An annual vacation time budget is also considered to recognize that households may operate under time budget constraints. Further, the paper proposes a variant of the MDCEV model that avoids the prediction of unrealistically small amounts of time allocation to the chosen alternatives. To do so, the continuously non-linear utility functional form in the MDCEV framework is replaced with a combination of a linear and non-linear form. The empirical data for this analysis comes from the 1995 American Travel Survey Data, with the U.S. divided into 210 alternative destinations. The empirical analysis provides important insights into the determinants of households' leisure destination choice and time allocation patterns. An appealing feature of the proposed model is its applicability in a national, long-distance leisure travel demand model system. The annual destination choices and time allocations predicted by this model can be used for subsequent analysis of the number of trips made (in a year) to each destination and the travel choices for each trip. The outputs from such a national travel modeling framework can be used to obtain national-level Origin-Destination demand tables for long-distance leisure travel.

Spatial and social dependence shape human activity-travel pattern decisions and their antecedent choices. Although the transportation literature has long recognized the importance of considering spatial and social dependencies in modeling individuals’ choice behavior, there has been less research on techniques to accommodate these dependencies in discrete choice models, mainly because of the modeling complexities introduced by such interdependencies. The main goal of this dissertation, therefore, is to propose new modeling approaches for accommodating flexible spatial and social dependency structures in discrete choice models within the broader context of activity-based travel demand modeling. The primary objectives of this dissertation research are three-fold. The first objective is to develop a discrete choice modeling methodology that explicitly incorporates spatial dependency (or correlation) across location choice alternatives (whether the choice alternatives are contiguous or non-contiguous). This is achieved by incorporating flexible spatial correlations and patterns using a closed-form Generalized Extreme Value (GEV) structure. The second objective is to propose new approaches to accommodate spatial dependency (or correlation) across observational units for different aspatial discrete choice models, including binary choice and ordered-response choice models. This is achieved by adopting different copula-based methodologies, which offer flexible dependency structures to test for different forms of dependencies. Further, simple and practical approaches are proposed, obviating the need for any kind of simulation machinery and methods for estimation. Finally, the third objective is to formulate an enhanced methodology to capture the social dependency (or correlation) across observational units. In particular, a clustered copula-based approach is formulated to recognize the potential dependence due to cluster effects (such as family-related effects) in an ordered-response context. The proposed approaches are empirically applied in the context of both spatial and aspatial choice situations, including residential location and activity participation choices. In particular, the results show that ignoring spatial and social dependencies, when present, can lead to inconsistent and inefficient parameter estimates that, in turn, can result in misinformed policy actions and recommendations. The approaches proposed in this research are simple, flexible and easy-to-implement, applicable to data sets of any size, do not require any simulation machinery, and do not impose any restrictive assumptions on the dependency structure. / text

This thesis presents a prism- and gap-based approach for modelling shopping destination choice in the Travel/Activity Scheduler for Household Agents (TASHA). The gap-location choice model improves upon TASHA’s existing destination choice model in 3 key ways: 1) Shifting from a zone-based to a disaggregate location choice model, 2) Categorizing shopping trips into meaningful types, and 3) Accounting for scheduling constraints in choice set generation and location choice. The model replicates gap and location choices reasonably well at an aggregate level and shows that a simple yet robust model can be developed with minimal changes to TASHA’s existing location choice model. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic activity scheduling environment.

This thesis presents a prism- and gap-based approach for modelling shopping destination choice in the Travel/Activity Scheduler for Household Agents (TASHA). The gap-location choice model improves upon TASHA’s existing destination choice model in 3 key ways: 1) Shifting from a zone-based to a disaggregate location choice model, 2) Categorizing shopping trips into meaningful types, and 3) Accounting for scheduling constraints in choice set generation and location choice. The model replicates gap and location choices reasonably well at an aggregate level and shows that a simple yet robust model can be developed with minimal changes to TASHA’s existing location choice model. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic activity scheduling environment.

Das Thema der vorliegenden Bachelorarbeit ist, das Vier-Stufen-Modell der Verkehrsnachfrage nach Lohse, auch als Kennwertmodell bekannt, auf die Stadt Brandenburg an der Havel anzuwenden, um die Verkehrserzeugung, die Verkehrsverteilung und die Verkehrsmittelwahl zu berechnen. Dies ist für alle Leser interessant, die damit beginnen, sich mit der integrierten Verkehrsplanung zu beschäftigen, denn diese Arbeit stellt das theoretische, rechnerische und praktische Vorgehen formal vor. Die Berechnungen wurden mit dem Programm „Excel 2013“ realisiert. Zudem ist der Arbeit ein USB-Stick beigelegt, aus dem Sie die formalen Rechnungen aus der Bachelorarbeit besser nachvollziehen können, da auf dem USB-Stick alle Rechnungen hinterlegt sind, die dem Verfahren zugrunde liegen. Zusätzlich enthält der USB-Stick einige Grafiken, welche die Verteilung der Verkehrsmittel in der Stadt Brandenburg an der Havel darstellen.

Dans le cadre du protocole de Kyoto, la France s’est engagée à diviser par quatre ses émissions de gaz à effet de serre (GES) de 1990 à l'horizon 2050. Le Mexique a pour objectif d’atteindre une réduction de 50% en 2050 par rapport à l’année 2000. En vue du poids croissant du secteur des transport dans les villes d’environ 1 million d’habitants dans le total des émissions de CO2, nous souhaitons vérifier dans quelles mesures les expériences observées dans le Nord (plafonnement de la mobilité, diffusion de nouvelles technologies sur les véhicules) peuvent se répéter dans le Sud. Nous nous sommes pour cela appuyé sur des études de cas en France (Paris et Lille) et au Mexique (Juarez et Puebla). Le premier objectif de cette thèse a été d’identifier les déterminants de la mobilité urbaine. Le deuxième objectif a été d’appliquer le modèle âge-cohorte pour la prévision de la demande de transport, afin de prendre en compte l’évolution de la structure de la population (vieillissement) et les changements de comportement. Finalement, nous avons développé des diagnostics des émissions de GES. En France, nous avons observé des tendances vers une réduction des émissions de GES due à la baisse de la mobilité et aux nouvelles technologies, mais qui est encore loin d'être suffisante pour atteindre les objectifs fixés. Les études de cas du Mexique montrent l’incapacité à inverser la tendance à l'augmentation des émissions de GES ; par conséquent les objectifs de réduction seront difficilement atteints. Le cas du Mexique peut nous donner un aperçu des tendances dans les pays émergents, qui sont très loin d'atteindre un développement durable et resteront face à un grand défi dans le futur. / In the context of the Kyoto Protocol, France has set Greenhouse Gas (GHG) emission reduction targets of 75% below 1990 levels by 2050. More recently, Mexico has set the objective to achieve a 50% reduction by 2050 with respect to the base year 2000. Since the transport sector in urban areas with approximately 1 million inhabitants accounts for most CO2 emissions and will continue to increase its share, we wanted to determine to what extent the experiences observed in cities from developed countries (peak travel, dissemination of new vehicle technologies) may be repeated in urban areas from developing nations. For this purpose, we focus on case studies in France (Paris and Lille) and Mexico (Juarez and Puebla). The first objective of this thesis was to identify the determinants of mobility on each urban region. The second objective was to apply the age-cohort model for the development of long-term travel demand forecasts in order to take into account changes in the population structure (ageing) and in travel behaviour. The last objective was to develop GHG emissions assessments from observed travel demand. The decline in mobility and the dissemination of new vehicle technologies in France led to a reduction in GHG emissions. However, these changes are not sufficient to achieve the GHG reduction targets. The case studies in Mexico show the inability to reverse the trend towards the increase of GHG emissions; therefore the reduction targets will be hardly achieved. The case of Mexico give us an overview of trends in emerging countries, which are very far from achieving sustainable development and will face a major challenge in the coming decades.

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825