In this study, travel behavior for the users of E-scooter is analyzed based on survey questions. The main purpose of the study is to understand the nature of E-scooters users and their preferences as well as analyzing their trip parameters. The survey was designed to cover most ofthe behavioral influential factors. To represent the motives and barriers towards E-scooter adoption in greater Stockholm. it was sectioned into demographic data, general travel preferences, E-scooter choice preferences, last trip parameters and general discussion questions. Findings of this study suggested that E-scooters are mainly used by residents aged between 18-24. In Stockholm, public transportation facilities are very accessible and affordable than other private transportation modes. Travel time and travel cost were highly evaluated by the users and were considered as deciding criteria prior to making any trip in Stockholm. Findings suggested that trips made by E-scooters are mainly for fun and leisure rather than commuting and services. However, regarding normal trips, it was found that walking replaced the use of E-scooters more often. Historical data was used to validate and support the survey findings. Survey questions did not consider questions regarding impacts of COVID-19 on travel behaviours as well as the changes in travel patterns. Furthermore, the absence of highlights on research areas such as safety which covers the adoption of risky behaviours (i.e., driving on sidewalks, driving opposing traffic flow) and helmet use. The tool was used for research purposes was ‘Questionpro’. / I denna studie analyseras resebeteendet för användarna av E-scooter baserat på enkätfrågor. Huvudsyftet med studien är att förstå E-scooters användares natur och deras preferenser samt att analysera deras tripparametrar. Undersökningen var utformad för att täcka de flesta av debeteendemässiga inflytelserika faktorerna. Att representera motiv och barriärer mot e-scooteradoption i större Stockholm. den delades in i demografisk data, allmänna resepreferenser, val av E-scooter-val, parametrar för sista resan och allmänna diskussionsfrågor. Resultaten av denna studie föreslog att E-skotrar huvudsakligen används av invånare i åldrarna 18-24. I Stockholm är anläggningar för kollektivtrafik mycket tillgängliga och prisvärda än andra privata transportsätt. Restid och resekostnader utvärderades mycket av användarna och ansågs vara avgörande kriterier innan de gjorde en resa i Stockholm. Resultaten föreslog att resor med e-skotrar främst är för skoj och fritid snarare än pendling och tjänster. När det gäller normala resor fann man dock att promenader ersatte användningen av E-skotrar oftare. Historiska data användes för att validera och stödja undersökningsresultaten.

Macroscopic changes in the urban environment and in the built transportation infrastructure, as well as changes in household demographics and socio-economics, can lead to spatio-temporal variations in household travel patterns and therefore regional travel demand. Dynamics in travel behavior may also simply arise from the randomness associated with values, perceptions, attitudes, needs, preferences and decision-making process of the individual travelers. Most urban travel behavior models and analysis seek to explain variations in travel behavior in terms of characteristics of the individuals and their environment. Spatial extents and temporal variation in an individual’s travel pattern may represent a measure of the individual’s spatial appetite for activity and the variability-seeking nature on his/her travel behavior. The objective of this dissertation effort is to develop a methodology to predict activity participation using revealed spatial extents and temporal variability as variables that represent the spatial appetite and variability-seeking nature associated with individual household. Activity participation is defined as a set of activities in which an individual or household takes part, to satisfy the sustenance, maintenance and discretionary needs of the household. To accomplish the goals of the dissertation, longitudinal travel data collected from the Commute Atlanta Study are used. The raw Global Positioning Systems (GPS) data are processed to summarize trip data by household travel day and individual travel day data. A methodology was developed to automatically identify the activity at the end of each trip. Methods were then developed to estimate travel behavior variability that can represent the variability-seeking nature of the individual. Existing methods to estimate activity space were reviewed and a new Modified Kernel Density area method was developed to address issues with current methods. Finally activity participation models using structural equation modeling methods were developed and the effects of the variability-seeking nature and spatial extent of activities were applied to the models. The variability-seeking nature was presented in the activity participation model as a latent variable with coefficient of variation of trips and distance as indicator variables. The dissertation research found that inclusion of activity space variables can improve the activity participation modeling process to better explain travel behavior.

The carhops and drive-ins of the 1950s are symbolic of the freedom that the automobile has granted Americans. What the general public has gained from the automobile, however, may come at the expense of independent mobility and choices for today's adolescents, particularly those not yet old enough to drive or those from lower income families. Sprawl land use development patterns and limited transportation choices in most American cities often hold teenagers and their chauffeuring parents captive to the automobile. At the same time, information and communication technology is fast evolving and changing the ways in which teenagers live, interact, and communicate with others; easier transportation coordination is one potential outcome. This study seeks to examine teenagers' travel behavior for their most common destination ' going to and from school ' and how the use of technology influences this behavior. Survey data from five high schools, three in Northern California and two in Vermont, are used to identify the mode choice to and from school, socio-demographic characteristics, and technology use of the sampled teenagers. The built environment of the teenagers' home surroundings is determined by data obtained from the 2010 Census. Logistic regression analysis is used to describe the most significant variables influencing both mode choice to and from school, and the factors associated with the use of technology. Those variables with a family income component, such as high family education, access to a car and smartphone ownership have a positive effect on teenagers driving more to and from school. Similarly, those teens who travel longer distances depend more on rides and choose active modes of transportation than teens living in more populated neighborhoods. When it comes to technology use for transportation among teenagers, those living farther away from school, in worse connected neighborhoods are more likely to depend more on technology for arranging transportation, whereas those teens who choose active transportation modes to school depend less on. High density development policies seem the right recommendation to ensure teenagers choose active transportation alternatives to school and depend less on their parents, family, and friends to move around. Due to the strong influence of attitudes in teenagers' behavior, social education and culture adaptation programs could be suggested to encourage teens to become more confident on active transportation modes, as well as promote safe routes to school for both genders.

Travel behavior of Prague suburb population Abstract The hinterland of Prague is formed by the process of suburbanization, which influences the spatial structure of whole region. Changed spatial structure puts greater demand on the mobility of the population. The main aim of this work is the research of travel behavior of the Prague suburb population, who makes his movements primarily by the car. The work focuses on the factors influencing travel behavior, which are discussed in theoretical section. The next section discusses the measures against individual transport. The empirical part presents the results of survey in Prague southeast hinterland. Travel behavior is examined in terms of modal split and trip purpose. Further investagation is focused on citizens' satisfaction with the traffic situation in their municipality and reveals their sensitivity to various measures against car use. Key words: mobility, travel behavior, suburbanization, car use limiting, Prague

The elderly and persons residing in zero vehicle households require better transportation services by virtue of their need for special care and lack of mobility, respectively. An analysis of the travel trends of these population cohorts is essential to determine the best ways to improve transportation facilities to better serve them. Information about location of residence, life-cycle, differences by gender, employment status, driver status, highest level of education coupled with trip information like trip rate, travel times and distances trip purpose, modal split and percent of people immobile will facilitate understanding the factors that influence trip making among these people and help predict travel trends for the future. This work attempts to analyze the elderly and persons residing in zero vehicle households in the United States by using NHTS 2001 and NPTS 1990 datasets for the purpose of analysis and comparison.

Modeling travel demand by time of day is gaining increasing attention in travel demand forecasting practice. This is because time of day choice has important implications for mode choice and for quantifying potential modal and time of day shifts in response to traffic congestion and peak period travel demand management strategies. In this context, understanding the causal relationship between time of day (departure time) choice and mode choice behavior would be useful in the development of time of day based travel demand modeling systems both within the four-step modeling paradigm and within newer tour-based and activity-based microsimulation paradigms. This thesis investigates the relationship between departure time choice and mode choice for non-work trips as work trips tend to be constrained with respect to time of day choice. Two alternative causal structures are considered in this thesis: one structure in which departure time choice is determined first and mode choice is subsequently influenced by departure time choice and a second structure in which mode choice is determined first and affects departure time choice. These two causal structures are analyzed in a recursive bivariate probit modeling framework that allows random error covariance. The estimation is performed separately for worker and non-worker samples drawn from the 1999 Southeast Florida Regional Household Travel Survey. For workers, model estimation results show that the causal structure in which departure time choice precedes mode choice performs significantly better. For non-workers, the reverse causal relationship in which mode choice precedes departure time choice is found to be a more suitable joint modeling structure. These two findings can be reasonably explained from a travel behavior perspective and have important implications for advanced travel demand model development and application.

This thesis investigates the relationship between mode choice and the complexity of trip chaining patterns. An understanding of the causality between these two choice behaviors may aid in the development of tour-based travel demand modeling systems that attempt to incorporate models of trip chaining and mode choice. The relationship between these two aspects of travel behavior is represented in this thesis by considering three different causal structures: one structure in which the trip chaining pattern is determined first and influences mode choice, another structure in which mode choice is determined first and influences the complexity of the trip chaining pattern, and a third structure in which neither is predetermined but both are determined simultaneously. The first two structures are estimated within a recursive bivariate probit modeling framework that accommodates error covariance. The simultaneous logit model is estimated for the third structure that allows a bidirectional simultaneous causality. The analysis and model estimation are performed separately for work tour and non-work tour samples drawn from the 2000 Swiss Microcensus travel survey. Model estimation results show that the causal structure in which trip chaining precedes mode choice performs best for the non-work tour sample. For the work-tour sample, the findings were less conclusive because two causal structures, one in which trip chaining affects mode choice and the other in which both are determined simultaneously, gave virtually identical goodness-of-fit measures. But the structure in which mode choice precedes trip chaining pattern choice gave significantly inferior goodness-of-fit measures for the work tour sample. These findings should be reflected in the development of activity-based and tour-based modeling systems.

This thesis evaluates truck route choice set generation algorithms and derives guidance on using the algorithms for effective generation of choice sets for modeling truck route choice. Specifically, route choice sets generated from a breadth first search link elimination (BFS-LE) algorithm are evaluated against observed truck routes derived from large streams of GPS traces of a sizeable truck fleet in the Tampa Bay region of Florida. A systematic evaluation approach is presented to arrive at an appropriate combination of spatial aggregation and minimum number of trips to be observed between each origin-destination (OD) location for evaluating algorithm-generated choice sets. The evaluation is based on both the ability to generate relevant routes that are typically considered by the travelers and the generation of irrelevant (or extraneous) routes that are seldom chosen. Based on this evaluation, the thesis offers guidance on effectively using the BFS-LE approach to maximize the generation of relevant routes. It is found that carefully chosen spatial aggregation can reduce the need to generate large number of routes for each trip. Further, estimation of route choice models and their subsequent application on validation datasets revealed that the benefits of spatial aggregation might be harnessed better if irrelevant routes are eliminated from the choice sets. Lastly, a comparison of route attributes of the relevant and irrelevant routes shed light on presence of systematic differences in route characteristics of the relevant and irrelevant routes.

Suburban growth in the U.S. urban regions has been defined by large subdivisions of single-family detached units. This growth is made possible by the mobility supported by automobiles and an extensive highway network. These dispersed and highly automobile-dependent developments have generated a large body of work examining the socioeconomic and environmental impacts of suburban growth on cities. The particular debate that this study addresses is whether suburban residents are more energy intensive in their travel behavior than central city residents. If indeed suburban residents have needs that are not satisfied by the amenities around them, they may be traveling farther to access such services. However, if suburbs are becoming like cities with a wide range of services and amenities, travel might be contained and no different from the travel behavior of residents in central areas. This paper will compare the effects of long term suburban growth on travel behavior, energy consumption, and GHG emissions through a case study of neighborhoods in central Phoenix and the city of Gilbert, both in the Phoenix metropolitan region. Motorized travel patterns in these study areas will be generated using 2001 and 2009 National Household Travel Survey (NHTS) data by developing a four-step transportation demand model in TransCAD. Energy consumption and GHG emissions, including both Carbon Dioxide (CO₂) and Nitrous Oxide (N₂O) for each study area will be estimated based on the corresponding trip distribution results. The final normalized outcomes will not only be compared spatially between Phoenix and Gilbert within the same year, but also temporally between years 2001 and 2009 to determine how the differential land use changes in those places influenced travel. The results from this study reveal that suburban growth does have an impact on people's travel behaviors. As suburbs grew and diversified, the difference in travel behavior between people living in suburban and urban areas became smaller. In the case of shopping trips the average length of trips for suburban residents in 2009 was slightly shorter than that for central city residents. This convergence was substantially due to the faster growth in trip lengths for central city compared to suburban residents in the 8-year period. However, suburban residents continue to be more energy intensive in their travel behavior, as the effect of reduction in trip length is likely to be offset by the more intensive growth in trip frequency. Additionally, overall energy consumption has grown significantly in both study areas over the period of study.

How do land use characteristics affect individual and household travel behavior in a regional context? Can the investigation justify the land use policies to reduce automobile dependence and achieve the goals of sustainable development in the metropolitan areas? Previous research enhanced our understanding of the connections between land use and travel behavior. It also provided implications for managing automobile-dependent travel behavior. However, there are questions still left unanswered about the causal connections between them, and the effectiveness of the land use policies to manage travel demand. To address the issues, attention is focused on the effects of land use measures on travel behavior outcomes from different modeling perspectives. The travel demand modeling explores the associations between land use and travel behavior. In addition, the causal modeling helps clarify the causal connections between them. It includes the structural equation models (SEMs) and the directed acyclic graphs (DAGs). The study focuses on six counties of the Houston-Galveston Area Council (HGAC) area. Travel behavior outcomes contain individual mode choice, household automobile trip generation and household total vehicle miles traveled (VMT). Three dimensions (i.e., density, diversity and design) of six land use measures are considered, which are computed using quarter-mile buffers for both trip origins and destinations. Different travel outcomes and modeling strategies are examined for different travel purposes. The significance of land use measures in affecting travel behavior is found to be evident, while varying to a certain degree according to trip purposes, travel outcomes and methodologies. For individual model choice, multinomial logit (MNL) models, the SEMs and the DAGs for different trip purposes support the hypothesis that land use measures directly affect individual mode choice behavior when other factors are kept constant. There is also evidence from causal models that land use factors indirectly influence it through travel time. For household automobile trip generation, there is no evidence to assert that land use measures at origin significantly affect household automobile trip rates when travel cost and socioeconomic variables are controlled. However, it is confirmed that land use measures have indirect causal connections with automobile trips through travel costs for all trip purposes. For household total VMT, it is found that land use patterns around residential locations are not only significantly associated, but also causally connected with household VMT. To summarize, compact development with high density and improved network design generally contribute to the reduction in automobile dependent travel patterns in the HGAC region.