This thesis is based on four studies that explore accessibility for older adults during whole trips by public transport. The overall goal was to gain knowledge of the interrelationships among key variables and to develop a conceptual model of the overall accessibility of public transport. More specifically, the research goals were: (a) to explore links among the key variables postulated to be involved in overall accessibility and to explore the links between these variables and railway accessibility; (b) to gain a deeper understanding of links between critical incidents in traveling and travel behavior decisions; and (c) to develop a conceptual model of overall accessibility. The key variables contributing to overall accessibility are functional ability (depending partly on the person’s functional limitation or disease), travel behavior, and barriers encountered during whole-trip traveling involving train. Respondents with more than one functional limitation or disease reported lower functional ability than did those with only one such limitation and respondents with low functional ability were less frequent travelers than were those with high functional ability. Frequent travelers reported railway accessibility to be better than did those who traveled less frequently. The main barriers were ticket cost and poor punctuality, but respondents with the lowest functional ability attributed the barriers encountered to their own health. The critical incidents most frequently reported were found in the categories “physical environment onboard vehicles” and “physical environment at stations or stops”, as well as in the “pricing and planning during ticketing” phase of the trip. Five themes of reactions to critical incidents were identified that had resulted in behavior change: firm restrictions, unpredictability, unfair treatment, complicated trips, and earlier adverse experiences. A conceptual model of overall accessibility was developed, grounded in the empirical research results. This model is summarized in the following propositions: Overall accessibility is a reciprocal relationship among the barriers/facilitators encountered, functional ability, and travel behavior. Accessibility emerges in the person–environment interaction. To understand accessibility, past experiences and future expectations should both be considered, because both will guide travel decisions. / Measurements enable future train travelling for everybody

In den letzten Jahren hat die Verbreitung von stationsungebundenem Carsharing (Free- Floating-Carsharing) weltweit stark zugenommen. Aufgrund dessen wurden verschiedene Studien, welche die verkehrliche Wirkung von Free-Floating-Carsharing beschreiben, erstellt. Bisher unzureichend unter-sucht wurden jedoch die Nutzer von Free-Floating-Carsharing- Systemen. Im Rahmen der Mitarbeit der TU Dresden am Evaluationsbericht Carsharing in der Landeshauptstadt München standen für sämtliche Münchener Carsharinganbieter Daten zu Buchungen und Kunden zur Verfügung. Ziel dieser Arbeit war es nun, für die zwei Anbieter von Free-Floating-Carsharing eine Typisierung der Nutzer vorzunehmen. Für die Einteilung der Nutzer in Gruppen wurden zunächst Input-Variablen ausgewählt und erzeugt. Neben den zeitlichen Häufigkeiten der Nutzung für Monate, Wochentage und Zeitscheiben wurden zudem Gini-Faktoren berechnet, welche die Regelmäßigkeit der Nutzung abbilden. Außerdem wurden verschiedene Variablen aus den Buchungsdaten erzeugt. Dazu zählen Untersuchungen wie viele Fahrten amWohnort der Nutzer beginnen und/oder enden, ob Fahrten am gleichen Ort beginnen und enden und bei wie vielen Fahrten der Parktarif der Anbieter zum Einsatz kommt. Des Weiteren wurde untersucht, wie viele Fahrten den Flughafen als Start oder Ziel haben, wie der Einfluss des Wetters auf die Anzahl der Buchungen ist und wie hoch die mittlere Fahrtzeit pro Buchung je Nutzer ist. Alle Variablen dienten nun als Input für die Typisierung der Nutzer. Für die Typisierung wurde das Verfahren der Clusteranalyse ausgewählt. Dabei sind jedoch 30 Variablen eine zu große Anzahl, weswegen zuerst eine Verdichtung der Input-Variablen durchgeführt wurde. Dabei kam eine sogenannte Hauptkomponentenanalyse zum Einsatz. Diese bietet die Möglichkeit, verschieden stark korrelierende Variablen zusammenzufassen und dabei den Informationsgehalt dieser zu erhalten. Aus den 30 einfließenden Variablen ergaben sich mit Hilfe der Hauptkomponentenanalyse vier Faktoren, welche anschließend für die Clusteranalyse genutzt wurden. Jeder Nutzer lässt sich durch die vier Faktoren in einem vierdimensionalen Koordinatensystem ein-tragen. Anschließend kann in diesem Raum eine Clusterung durchgeführt werden. Für diese Arbeit wurde sich für das k-Means-Verfahren entschieden. Mit diesem wurden fünf Cluster bestimmt, welche die 13 000 Nutzer abbilden. Jeder Cluster lässt sich durch die Mittelwerte der eingeflossenen sowie durch soziodemografische Variablen wie Alter und Geschlecht und die Wohnorte der Nutzer hinsichtlich seiner Aussage interpretieren. Die fünf Cluster können in zwei Cluster mit einer niedrigen (Nr. 1 und 2), einen mit einer mittleren (Nr. 3) und zwei mit einer hohen Nutzungsintensität einteilen werden (Nr. 4 und 5). Cluster 1 vereint Nutzer, die selten aber spontane Fahrten unternehmen. Dabei sind überdurchschnittliche viele Fahrten am Wochenende und abends zu verzeichnen. In Cluster 2 finden sich Nutzer, die vorwiegend Fahrten mit langen Fahrtzeiten unternehmen. Dabei werden innerhalb einer Buchung mehrere Wege zurückgelegt, was sich an der hohen Nutzung des Parktarifs zeigt und daran, dass der größte Teil der Fahrten am Ausgangsort wieder enden. Diese Gruppe besitzt unter allen Gruppen einen überdurchschnittlich hohen Anteil an Frauen. Cluster 3 beschreibt den normalen Nutzer hinsichtlich der Nutzungsintensität und der zeitlichen Nutzung. Er ist mit 41,4% der Kunden der größte aller Cluster. Cluster 4 und 5 vereinen Kunden mit einer hohen Nutzungsintensität. Obwohl nur ca. 5% der Kunden in diesen beiden Gruppen zu finden sind, werden jedoch ein Drittel aller Fahrten von diesen Nutzern zurückgelegt. Cluster 4 beschreibt Nutzer mit einem typischen Pendlerverhalten. Dabei werden Fahrten vorwiegend Werktags und während der Hauptverkehrszeiten unternommen. Eine abnehmende Nutzung von Januar zu Juni lässt vermuten, dass andere Verkehrsmittel wie das Fahrrad genutzt werden. In Cluster 5 finden sich Kunden, die häufig Carsharing in der Nacht nutzen. Dies lässt vermuten, dass Aktivitäten des Nachtlebens besucht werden. Dieser Cluster hat im Vergleich zum Durchschnitt den geringsten Anteil an Frauen. Da die Ergebnisse ausschließlich auf den Anbieterdaten basieren, ist es nicht möglich, konkrete Aus-sagen über Effekte und Wirkungen von Free-Floating-Carsharing zu treffen und zu bewerten. Dafür wäre weitere Daten zum Beispiel aus Umfragen notwendig. Die klar abgrenzbaren und gut interpre-tierbaren Nutzergruppen zeigen jedoch, dass die gewählte Methodik sich zur Typisierung von Carsha-ringnutzern eignet. Eine Wiederholung des Verfahrens mit anderen Daten, zum Beispiel aus einem späteren Untersuchungszeitraum oder einer anderen Stadt, ist zu empfehlen.

The transportation sector is a largest emitter of greenhouse gases in the U.S., accounting for 28.6% of all 2016 emissions, the majority of which come from the passenger vehicle fleet [1,2]. One major technology that is being investigated by researchers, planners, and policy makers to help lower the emissions from the transportation sector is the plug-in electric vehicle (PEV). The focus of this work is to investigate and model the impacts of increased levels of PEVs on the regional electric power grid and on the net change in CO2 emissions due to the decrease tailpipe emissions and the increase in electricity generation under current emissions caps. The study scope includes all of New England and New York state, modeled as one system of electricity supply and demand, which includes the estimated 2030 baseline demand and the cur- rent generation capacity plus increased renewable capacity to meet state Renewable Portfolio Standard targets for 2030. The models presented here include fully electric vehicles and plug-in hybrids, public charging infrastructure scenarios, hourly charging demand, solar and wind generation and capacity factors, and real-world travel derived from the 2016-2017 National Household Travel Survey. We make certain assumptions, informed by the literature, with the goal of creating a modeling methodology to improve the estimation of hourly PEV charging demand for input into regional electric sector dispatch models. The methodology included novel stochastic processes, considered seasonal and weekday versus weekend differences in travel, and did not force the PEV battery state-of-charge to be full at any specific time of day. The results support the need for public charging infrastructure, specifically at workplaces, with the “work” infrastructure scenario shifting more of the unmanaged charging demand to daylight hours when solar generation could be utilized. Workplace charging accounted for 40% of all non-home charging demand in the scenario where charging infrastructure was “universally” available. Under the increased renewable fuel portfolio, the reduction in average CO2 emissions ranged from 90 to 92% for the vehicles converted from ICEV to PEV. The total emissions reduced for 15% PEV penetration and universally available charging infrastructure was 5.85 million metric tons, 5.27% of system-wide emissions. The results support the premise of plug-in electric vehicles being an important strategy for the reduction of CO2 emissions in our study region. Future investigation into the extent of reductions possible with both the optimization of charging schedules through pricing or other mechanisms and the modeling of grid level energy storage is warranted. Additional model development should include a sensitivity analysis of the PEV charging demand model parameters, and better data on the charging behavior of PEV owners as they continue to penetrate the market at higher rates.

Recent developments in microsimulation modeling of activity and travel demand have called for the explicit recognition of time-space constraints under which individuals perform their activity and travel patterns. The estimation of time-space prism vertex locations, i.e., the perceived time constraints, is an important development in this context. Stochastic frontier modeling methodology offers a suitable framework for modeling and identifying the expected vertex locations of time space prisms within which people execute activity-travel patterns. In this work, stochastic frontier models of time space prism vertex locations are estimated for samples drawn from a household travel survey conducted in 2001 in the city of Thane on the west coast of India and National Household Travel Survey 2001, United States. This offers an opportunity to study time constraints governing activity travel patterns of individuals in a developing as well as developed country context. The work also includes comparisons between males and females, workers and non-workers, and developed and developing country contexts to better understand how socio-economic and socio-cultural norms and characteristics affect time space prism constraints. It is found that time space prism constraints in developing country data set can be modeled using the stochastic frontier modeling methodology. It is also found that significant differences exist between workers and non-workers and between males and females,possibly due to the more traditional gender and working status roles in the Indian context. Finally, both differences and similarities were noticed when comparisons were made between results obtained from the data set of India and United States. Many of these differences can be explained by the presence of other constraints including institutional, household, income, and transportation accessibility constraints that are generally significantly greater in the developing country context.

This research examined if travelers are paying for travel on managed lanes (MLs) as they indicated that they would in a 2008 survey. The other objectives of this research included estimating travelers’ value of travel time savings (VTTS) and their value of travel time reliability (VOR), and examining the multiple survey designs used in a 2008 survey to identify which survey design better predicted ML traveler behavior. To achieve the objectives, an Internet-based follow-up stated preference (SP) survey of Houston’s Katy Freeway travelers was conducted in 2010. Three survey design methodologies—Db-efficient, random level generation, and adaptive random—were tested in this survey. A total of 3,325 responses were gathered from the survey, and of those, 869 responses were from those who likely also responded to the previous 2008 survey. Mixed logit models were developed for those 869 previous survey respondents to estimate and compare the VTTS to the 2008 survey estimates. It was found that the 2008 survey estimates of the VTTS were very close to the 2010 survey estimates. In addition, separate mixed logit models were developed from the responses obtained from the three different design strategies in the 2010 survey. The implied mean VTTS varied across the design-specific models. Only the Db-efficient design was able to estimate a VOR. Based on this and several other metrics, the Db-efficient design outperformed the other designs. A mixed logit model including all the responses from all three designs was also developed; the implied mean VTTS was estimated as 65 percent ($22/hr) of the mean hourly wage rate, and the implied mean VOR was estimated as 108 percent ($37/hr) of the mean hourly wage rate. Data on actual usage of the MLs were also collected. Based on actual usage, the average VTTS was calculated as $51/hr. However, the $51/hr travelers are paying likely also includes the value travelers place on travel time reliability of the MLs. The total (VTTS+VOR) amount estimated from the all-inclusive model from the survey was $59/hr, which is close to the value estimated from the actual usage. The Db-efficient design estimated this total as $50/hr. This research also shows that travelers have a difficulty in estimating the time they save while using a ML. They greatly overestimate the amount of time saved. It may well be that even though travelers are saving a small amount of time they value that time savings (and avoiding congestion) much higher – possibly similar to their amount of perceived travel time savings. The initial findings from this study, reported here, are consistent with the hypothesis that travelers are paying for their travel on MLs, much as they said that they would in our previous survey. This supports the use of data on intended behavior in policy analysis.

Understanding joint and causal relationships among multiple endogenous variables has been of much interest to researchers in the field of activity and travel behavior modeling. Structural equation models have been widely developed for modeling and analyzing the causal relationships among travel time, activity duration, car ownership, trip frequency and activity frequency. In the model, travel time and activity duration are treated as continuous variables, while car ownership, trip frequency and activity frequency as ordered discrete variables. However, many endogenous variables of interest in travel behavior are not continuous or ordered discrete but unordered discrete in nature, such as mode choice, destination choice, trip chaining pattern and time-of-day choice (it can be classified into a few categories such as AM peak, midday, PM peak and off-peak). A modeling methodology with involvement of unordered discrete variables is highly desired for better understanding the causal relationships among these variables. Under this background, the proposed dissertation study will be dedicated into seeking an appropriate modeling methodology which aids in identifying the causal relationships among activity and travel variables including unordered discrete variables. In this dissertation, the proposed modeling methodologies are applied for modeling the causal relationship between three pairs of endogenous variables: trip chaining pattern vs. mode choice, activity timing vs. duration and trip departure time vs.mode choice. The data used for modeling analysis is extracted from Swiss Travel Microcensus 2000. Such models provide us with rigorous criteria in selecting a reasonable application sequence of sub-models in the activity-based travel demand model system.

The health and well-being of individuals is related to their activity-travel patterns. Individuals who undertake physically active episodes such as walking and bicycling are likely to have improved health outcomes compared to individuals with sedentary auto-centric lifestyles. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, models of activity-travel demand do not account for the attitudinal factors and lifestyle preferences that affect activity-travel and mode use patterns. Such attitude and preference variables are virtually never collected explicitly in travel surveys, rendering it difficult to include them in model specifications. This paper applies Bhat’s (2014) Generalized Heterogeneous Data Model (GHDM) approach, whereby latent constructs representing the degree to which individuals are health conscious and inclined to pursue physical activities may be modeled as a function of observed socio-economic and demographic variables and then included as explanatory factors in models of activity-travel outcomes and walk and bicycle use. The model system is estimated on the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample, demonstrating the efficacy of the approach and the importance of including such latent constructs in model specifications that purport to forecast activity and time use patterns. / text

The overall goal of the dissertation is to contribute to the growing literature on the activity-based framework by focusing on the modeling of choices that are influenced by land-use and travel environment attributes. An accurate characterization of activity-travel patterns requires explicit consideration of the land-use and travel environment (referred to as travel environment from here on). There are two important categories of travel environment influences: direct (or causal) and indirect (or self-selection) effects. The direct effect of travel environment refers to how travel environment attributes causally influence travel choices. This direct effect may be captured by including travel environment variables as exogenous variables in travel models. Of course, determining if a travel environment variable has a direct effect on an activity/travel choice of interest is anything but straightforward. This is because of a potential indirect effect of the influence of the travel environment, which is not related to a causal effect. That is, the very travel environment attributes experienced by a decision maker (individual or household) is a function of a suite of a priori travel related choices made by the decision maker. The specific emphasis of the current dissertation is on moving away from considering travel environment choices as purely exogenous determinants of activity-travel models, and instead explicitly modeling travel environment decisions jointly along with activity-travel decisions in an integrated framework. Towards this end, the current dissertation formulates econometric models to analyze multidimensional choices. The multidimensional choice situations examined (and the corresponding model developed) in the research effort include: (1) reason for residential relocation and associated duration of stay (joint multinomial logit model and a grouped logit model), (2) household residential location and daily vehicle miles travelled (Copula based joint binary logit and log-linear regression model), (3) household residential location, vehicle type and usage choices (copula based Generalized Extreme Value and log-linear regression model) and (4) activity type, travel mode, time period of day, activity duration and activity location (joint multiple discrete continuous extreme value (MDCEV) model and multinomial logit model (MNL) with sampling of alternatives). The models developed in the current dissertation are estimated using actual field data from Zurich and San Francisco. A variety of policy exercises are conducted to illustrate the advantages of the econometric models developed. The results from these exercises clearly underline the importance of incorporating the direct and indirect effects of travel environment on these choice scenarios. / text

The transportation community is exploring how new "big'' databases constructed by companies or public administrative agencies can be used to better understand travelers' behaviors and better predict travelers' responses to various transportation policies. This thesis explores how a large targeted marketing database containing information about individuals’ socio-demographic characteristics, current residence attributes, and previous residential locations can be used to investigate research questions related to individuals' transportation preferences and the built environment. The first study examines how household vehicle ownership may be shaped by, or inferred from, previous behavior. Results show that individuals who have previously lived in dense ZIP codes or ZIP codes with more non-automobile commuting options are more likely to own fewer vehicles, all else equal. The second study uses autoregressive models that control for spatial dependence, correlation, and endogeneity to investigate whether investments in public transit infrastructure are associated with higher home values. Results show that willingness-to-pay estimates obtained from the general spatial Durbin model are less certain than comparable estimates obtained through ordinary least squares. The final study develops an empirical framework to examine a housing market's resilience to price volatility as a function of transportation accessibility. Two key modeling frameworks are considered. The first uses a spatial autoregressive model to investigate the relationship between a home's value, appreciation, and price stability while controlling for endogenous missing regressors. The second uses a latent class model that considers all these attributes simultaneously, but cannot control for endogeneity.

abstract: Institutions of higher education, particularly those with large student enrollments, constitute special generators that contribute in a variety of ways to the travel demand in a region. Despite the importance of university population travel characteristics in understanding and modeling activity-travel patterns and mode choice behavior in a region, such populations remain under-studied. As metropolitan planning organizations continue to improve their regional travel models by incorporating processes and parameters specific to major regional special generators, university population travel characteristics need to be measured and special submodels that capture their behavior need to be developed. The research presented herein begins by documenting the design and administration of a comprehensive university student online travel and mode use survey that was administered at Arizona State University (ASU) in the Greater Phoenix region of Arizona. The dissertation research offers a detailed statistical analysis of student travel behavior for different student market segments. A framework is then presented for incorporating university student travel into a regional travel demand model. The application of the framework to the ASU student population is documented in detail. A comprehensive university student submodel was estimated and calibrated for integration with the full regional travel model system. Finally, student attitudes toward travel are analyzed and used as explanatory factors in multinomial logit models of mode choice. This analysis presents an examination of the extent to which attitudes play a role in explaining mode choice behavior of university students in an urban setting. The research provides evidence that student travel patterns vary substantially from those of the rest of the population, and should therefore be considered separately when forecasting travel demand and formulating transport policy in areas where universities are major contributors to regional travel. / Dissertation/Thesis / Doctoral Dissertation Civil Engineering 2014