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- All Subjects: Transportation
- Genre: Academic theses
- Creators: Pendyala, Ram M.
Description
Contrary to many previous travel demand forecasts there is increasing evidence that vehicle travel in developed countries may be peaking. The underlying causes of this peaking are still under much debate and there has been a mobilization of research, largely focused at the national scale, to study the explanatory drivers but research focused at the metropolitan scale, where transportation policy and planning are frequently decided, is relatively thin. Additionally, a majority of this research has focused on changes within the activity system without considering the impact transportation infrastructure has on overall travel demand. Using Los Angeles County California, we investigate Peak Car and whether the saturation of automobile infrastructure, in addition to societal and economic factors, may be a suppressing factor. After peaking in 2002, vehicle travel in Los Angeles County in 2010 was estimated at 78 billion and was 20.3 billion shy of projections made in 2002. The extent to which infrastructure saturation may contribute to Peak Car is evaluated by analyzing social and economic factors that may have impacted personal automobile usage over the last decade. This includes changing fuel prices, fuel economy, population growth, increased utilization of alternate transportation modes, changes in driver demographics , travel time and income levels. Summation of all assessed factors reveals there is at least some portion of the 20 billion VMT that is unexplained in all but the worst case scenario. We hypothesize that the unexplained remaining VMT may be explained by infrastructure supply constraints that result in suppression of travel. This finding has impacts on how we see the role of hard infrastructure systems in urban growth and we explore these impacts in the research.
ContributorsFraser, Andrew (Author) / Chester, Mikhail V (Thesis advisor) / Pendyala, Ram M. (Committee member) / Seager, Thomas P (Committee member) / Arizona State University (Publisher)
Created2014
Description
Concern regarding the quality of traffic data exists among engineers and planners tasked with obtaining and using the data for various transportation applications. While data quality issues are often understood by analysts doing the hands on work, rarely are the quality characteristics of the data effectively communicated beyond the analyst. This research is an exercise in measuring and reporting data quality. The assessment was conducted to support the performance measurement program at the Maricopa Association of Governments in Phoenix, Arizona, and investigates the traffic data from 228 continuous monitoring freeway sensors in the metropolitan region. Results of the assessment provide an example of describing the quality of the traffic data with each of six data quality measures suggested in the literature, which are accuracy, completeness, validity, timeliness, coverage and accessibility. An important contribution is made in the use of data quality visualization tools. These visualization tools are used in evaluating the validity of the traffic data beyond pass/fail criteria commonly used. More significantly, they serve to educate an intuitive sense or understanding of the underlying characteristics of the data considered valid. Recommendations from the experience gained in this assessment include that data quality visualization tools be developed and used in the processing and quality control of traffic data, and that these visualization tools, along with other information on the quality control effort, be stored as metadata with the processed data.
ContributorsSamuelson, Jothan P (Author) / Pendyala, Ram M. (Thesis advisor) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2011
Description
As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
ContributorsOshan, Taylor Matthew (Author) / Fotheringham, A. S. (Thesis advisor) / Farmer, Carson J.Q. (Committee member) / Rey, Sergio S.J. (Committee member) / Nelson, Trisalyn (Committee member) / Arizona State University (Publisher)
Created2017
Description
Rapid developments are occurring in the arena of activity-based microsimulation models. Advances in computational power, econometric methodologies and data collection have all contributed to the development of microsimulation tools for planning applications. There has also been interest in modeling child daily activity-travel patterns and their influence on those of adults in the household using activity-based microsimulation tools. It is conceivable that most of the children are largely dependent on adults for their activity engagement and travel needs and hence would have considerable influence on the activity-travel schedules of adult members in the household. In this context, a detailed comparison of various activity-travel characteristics of adults in households with and without children is made using the National Household Travel Survey (NHTS) data. The analysis is used to quantify and decipher the nature of the impact of activities of children on the daily activity-travel patterns of adults. It is found that adults in households with children make a significantly higher proportion of high occupancy vehicle (HOV) trips and lower proportion of single occupancy vehicle (SOV) trips when compared to those in households without children. They also engage in more serve passenger activities and fewer personal business, shopping and social activities. A framework for modeling activities and travel of dependent children is proposed. The framework consists of six sub-models to simulate the choice of going to school/pre-school on a travel day, the dependency status of the child, the activity type, the destination, the activity duration, and the joint activity engagement with an accompanying adult. Econometric formulations such as binary probit and multinomial logit are used to obtain behaviorally intuitive models that predict children's activity skeletons. The model framework is tested using a 5% sample of a synthetic population of children for Maricopa County, Arizona and the resulting patterns are validated against those found in NHTS data. Microsimulation of these dependencies of children can be used to constrain the adult daily activity schedules. The deployment of this framework prior to the simulation of adult non-mandatory activities is expected to significantly enhance the representation of the interactions between children and adults in activity-based microsimulation models.
ContributorsSana, Bhargava (Author) / Pendyala, Ram M. (Thesis advisor) / Ahn, Soyoung (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2010
Description
To reduce the environmental burden of transport, previous studies have resorted on solutions that accentuate towards techno-economical pathways. However, there is growing evidence that transport behaviors, lifestyle choices, and the role of individuals' attitudes/perceptions are considered influential factors in shaping households’ engagement with sustainable technologies in the face of environmental crises. The objective of this dissertation is to develop multidimensional econometric model systems to explore complex relationships that can help us understand travel behaviors' implications for transport and household energy use. To this end, the second chapter of this dissertation utilizes the latent segmentation approach to quantify and unravel the relationship between attitudes and behaviors while recognizing the presence of unobserved heterogeneity in the population. It was found that two-thirds of the population fall in the causal structure where behavioral experiences are shaping attitudes, while for one-third attitudes are shaping behaviors. The findings have implications on the energy-behavior modeling paradigm and forecasting household energy use. Building on chapter two, the third chapter develops an integrated modeling framework to explore the factors that influence the adoption of on-demand mobility services and electric vehicle ownership while placing special emphasis on attitudes/perceptions. Results indicated that attitudes and values significantly affect the use of on-demand transportation services and electric vehicle ownership, suggesting that information campaigns and free trials/demonstrations would help advance towards the sustainable transportation future and decarbonize the transport sector. The integrated modeling framework is enhanced, in chapter four, to explore the interrelationship between transport and residential energy consumption. The findings indicated the existence of small but significant net complimentary relationships between transport and residential energy consumption. Additionally, the modeling framework enabled the comparison of energy consumption patterns across market segments. The resulting integrated transport and residential energy consumption model system is utilized, in chapter fifth, to shed light on the overall household energy footprint implications of shifting vehicle/fuel type choices. Results indicated that electric vehicles are driven as much as gasoline vehicles are. Interestingly, while an increase in residential energy consumption was observed with the wide-scale adoption of electric vehicles, the total household energy use decreased, indicating benefits associated with transportation electrification.
ContributorsSharda, Shivam (Author) / Pendyala, Ram M. (Thesis advisor) / Khoeini, Sara (Committee member) / Grimm, Kevin J. (Committee member) / Chester, Mikhail V. (Committee member) / Garikapati, Venu M. (Committee member) / Arizona State University (Publisher)
Created2021
Description
The primary objective of this dissertation is to advance the existing empirical literature on the relationship between transportation and quality of life, with a specific focus on wellbeing indicators and their applicability in the transportation sector. To achieve this, the dissertation is structured around four primary areas of inquiry. Firstly, it introduces a subjective wellbeing scoring method that generates episode-level wellbeing scores, which can be aggregated to produce daily person-level wellbeing scores. This method can be utilized as a post-processor of activity-based travel demand model outputs to assess equity implications in various planning scenarios. Secondly, the dissertation examines the intricate relationships between mobility poverty, time poverty, and subjective wellbeing. It compares the rates of time poverty and zero-trip making among different socio-demographic groups and evaluates their alignment with subjective wellbeing. Thirdly, this research investigates the association between automobile use and satisfaction with daily travel routines (thus, wellbeing). This analysis aims to provide an understanding of why automobile use remains the primary mode of transportation, despite attempts to shift towards alternative modes of transportation. The fourth area of investigation focuses on the wellbeing impacts of the COVID-19 pandemic. Specifically, the chapter examines the resurgence in travel and discretionary out-of-home activities, as well as the slow return of workers to workplaces by using the subjective wellbeing indicator and time poverty. Additionally, the chapter identifies groups that were disproportionately impacted and provides strategies to mitigate adverse consequences for vulnerable socio-economic and demographic groups in future disruptions. Overall, this dissertation contributes to the literature on transportation and quality of life by introducing a reliable subjective wellbeing scoring method that can be used to evaluate the quality of life implications of transportation systems. It also offers practical applications of wellbeing indicators in identifying differences in wellbeing across the population and provides opportunities for targeted interventions and the development of transportation policies to address equity and sustainability issues. Furthermore, to demonstrate the practicality of the generated knowledge in this dissertation, a web-based wellbeing platform is developed to track changes in the wellbeing of individuals that arise from their daily activity and travel patterns.
ContributorsBatur, Irfan (Author) / Pendyala, Ram M. (Thesis advisor) / Chester, Mikhail V. (Committee member) / Polzin, Steven E. (Committee member) / Zhou, Xuesong S. (Committee member) / Arizona State University (Publisher)
Created2023
Description
Environmental heat is a growing concern in cities as a consequence of rapid urbanization and climate change, threatening human health and urban vitality. The transportation system is naturally embedded in the issue of urban heat and human heat exposure. Research has established how heat poses a threat to urban inhabitants and how urban infrastructure design can lead to increased urban heat. Yet there are gaps in understanding how urban communities accumulate heat exposure, and how significantly the urban transportation system influences or exacerbates the many issues of urban heat. This dissertation focuses on advancing the understanding of how modern urban transportation influences urban heat and human heat exposure through three research objectives: 1) Investigate how human activity results in different outdoor heat exposure; 2) Quantify the growth and extent of urban parking infrastructure; and 3) Model and analyze how pavements and vehicles contribute to urban heat.
In the urban US, traveling outdoors (e.g. biking or walking) is the most frequent activity to cause heat exposure during hot periods. However, outdoor travel durations are often very short, and other longer activities such as outdoor housework and recreation contribute more to cumulative urban heat exposure. In Phoenix, parking and roadway pavement infrastructure contributes significantly to the urban heat balance, especially during summer afternoons, and vehicles only contribute significantly in local areas with high density rush hour vehicle travel. Future development of urban areas (especially those with concerns of extreme heat) should focus on ensuring access and mobility for its inhabitants without sacrificing thermal comfort. This may require urban redesign of transportation systems to be less auto-centric, but without clear pathways to mitigating impacts of urban heat, it may be difficult to promote transitions to travel modes that inherently necessitate heat exposure. Transportation planners and engineers need to be cognizant of the pathways to increased urban heat and human heat exposure when planning and designing urban transportation systems.
ContributorsHoehne, Christopher Glenn (Author) / Chester, Mikhail V (Thesis advisor) / Hondula, David M. (Committee member) / Sailor, David (Committee member) / Pendyala, Ram M. (Committee member) / Arizona State University (Publisher)
Created2019