Matching Items (14)
Filtering by
- All Subjects: Civil Engineering
- Creators: Pendyala, Ram M.
Description
Vehicle type choice is a significant determinant of fuel consumption and energy sustainability; larger, heavier vehicles consume more fuel, and expel twice as many pollutants, than their smaller, lighter counterparts. Over the course of the past few decades, vehicle type choice has seen a vast shift, due to many households making more trips in larger vehicles with lower fuel economy. During the 1990s, SUVs were the fastest growing segment of the automotive industry, comprising 7% of the total light vehicle market in 1990, and 25% in 2005. More recently, due to rising oil prices, greater awareness to environmental sensitivity, the desire to reduce dependence on foreign oil, and the availability of new vehicle technologies, many households are considering the use of newer vehicles with better fuel economy, such as hybrids and electric vehicles, over the use of the SUV or low fuel economy vehicles they may already own. The goal of this research is to examine how vehicle miles traveled, fuel consumption and emissions may be reduced through shifts in vehicle type choice behavior. Using the 2009 National Household Travel Survey data it is possible to develop a model to estimate household travel demand and total fuel consumption. If given a vehicle choice shift scenario, using the model it would be possible to calculate the potential fuel consumption savings that would result from such a shift. In this way, it is possible to estimate fuel consumption reductions that would take place under a wide variety of scenarios.
ContributorsChristian, Keith (Author) / Pendyala, Ram M. (Thesis advisor) / Chester, Mikhail (Committee member) / Kaloush, Kamil (Committee member) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2013
Description
The growing use of synthetic population, which is a disaggregate representation of the population of an area similar to the real population currently or in the future, has motivated the analysis of its sensitivity in the population generation procedure. New methods in PopGen have enhanced the generation of synthetic populations whereby both household-level and person-level characteristics of interest can be matched in a computationally efficient manner. In the process of set up, population synthesis procedures need sample records for households and persons to match the marginal totals with a specific set of control variables for both the household and person levels, or only the household level, for a specific geographic resolution. In this study, an approach has been taken to analyze the sensitivity by changing and varying this number of controls, with and without taking person controls. The implementation of alternative constraints has been applied on a sample of three hundred block groups in Maricopa County, Arizona. The two datasets that have been used in this study are Census 2000 and a combination of Census 2000 and ACS 2005-2009 dataset. The variation in results for two different rounding methods: arithmetic and bucket rounding have been examined. Finally, the combined sample prepared from the available Census 2000 and ACS 2005-2009 dataset was used to investigate how the results differ when flexibility for drawing households is greater. Study shows that fewer constraints both in household and person levels match the aggregate total population more accurately but could not match distributions of individual attributes. A greater number of attributes both in household and person levels need to be controlled. Where number of controls is higher, using bucket rounding improves the accuracy of the results in both aggregate and disaggregates level. Using combined sample gives the software more flexibility as well as a rich seed matrix to draw households which generates more accurate synthetic population. Therefore, combined sample is another potential option to improve the accuracy in matching both aggregate and disaggregate level household and person distributions.
ContributorsDey, Rumpa Rani (Author) / Pendyala, Ram M. (Thesis advisor) / Ahn, Soyoung (Committee member) / Mamlouk, Michael S. (Committee member) / Arizona State University (Publisher)
Created2012
Description
The development of microsimulation approaches to urban systems modeling has occurred largely in three parallel streams of research, namely, land use, travel demand and traffic assignment. However, there are important dependencies and inter-relationships between the model systems which need to be accounted to accurately and comprehensively model the urban system. Location choices affect household activity-travel behavior, household activity-travel behavior affects network level of service (performance), and network level of service, in turn, affects land use and activity-travel behavior. The development of conceptual designs and operational frameworks that represent such complex inter-relationships in a consistent fashion across behavioral units, geographical entities, and temporal scales has proven to be a formidable challenge. In this research, an integrated microsimulation modeling framework called SimTRAVEL (Simulator of Transport, Routes, Activities, Vehicles, Emissions, and Land) that integrates the component model systems in a behaviorally consistent fashion, is presented. The model system is designed such that the activity-travel behavior model and the dynamic traffic assignment model are able to communicate with one another along continuous time with a view to simulate emergent activity-travel patterns in response to dynamically changing network conditions. The dissertation describes the operational framework, presents the modeling methodologies, and offers an extensive discussion on the advantages that such a framework may provide for analyzing the impacts of severe network disruptions on activity-travel choices. A prototype of the model system is developed and implemented for a portion of the Greater Phoenix metropolitan area in Arizona to demonstrate the capabilities of the model system.
ContributorsKonduri, Karthik Charan (Author) / Pendyala, Ram M. (Thesis advisor) / Ahn, Soyoung (Committee member) / Kuby, Michael (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2012
Description
In the middle of the 20th century in the United States, transportation and infrastructure development became a priority on the national agenda, instigating the development of mathematical models that would predict transportation network performance. Approximately 40 years later, transportation planning models again became a national priority, this time instigating the development of highly disaggregate activity-based traffic models called microsimulations. These models predict the travel on a network at the level of the individual decision-maker, but do so with a large computational complexity and processing time requirement. The vast resources and steep learning curve required to integrate microsimulation models into the general transportation plan have deterred planning agencies from incorporating these tools. By researching the stochastic variability in the results of a microsimulation model with varying random number seeds, this paper evaluates the number of simulation trials necessary, and therefore the computational effort, for a planning agency to reach stable model outcomes. The microsimulation tool used to complete this research is the Transportation Analysis and Simulation System (TRANSIMS). The requirements for initiating a TRANSIMS simulation are described in the paper. Two analysis corridors are chosen in the Metropolitan Phoenix Area, and the roadway performance characteristics volume, vehicle-miles of travel, and vehicle-hours of travel are examined in each corridor under both congested and uncongested conditions. Both congested and uncongested simulations are completed in twenty trials, each with a unique random number seed. Performance measures are averaged for each trial, providing a distribution of average performance measures with which to test the stability of the system. The results of this research show that the variability in outcomes increases with increasing congestion. Although twenty trials are sufficient to achieve stable solutions for the uncongested state, convergence in the congested state is not achieved. These results indicate that a highly congested urban environment requires more than twenty simulation runs for each tested scenario before reaching a solution that can be assumed to be stable. The computational effort needed for this type of analysis is something that transportation planning agencies should take into consideration before beginning a traffic microsimulation program.
ContributorsZiems, Sarah Elia (Author) / Pendyala, Ram M. (Thesis advisor) / Ahn, Soyoung (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2010
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
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
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 COVID-19 pandemic has revealed the fault lines in society. Whether it be remote work, remote learning, online shopping, grocery and meal deliveries, or medical care, disparities and inequities among socio-economic and demographic groups leave some segments of society more vulnerable and less adaptable. This thesis aims to identify vulnerable and less adaptable groups in the context of access to food. Using a comprehensive behavioral survey data set collected during the height of the pandemic in 2020, this thesis aims to provide insights on the groups that may have experienced food access vulnerability during the disruption when businesses and establishments were restricted, the risk of contagion was high, and accessing online platforms required technology-savviness and the ability to afford delivery charges. This thesis presents estimation results for a simultaneous equations model of six endogenous choice variables defined by a combination of two food types (groceries and meals) and three access modalities (in-person, online with in-person pickup, and online with delivery). The model estimation results show that attitudes and perceptions play a significant role in shaping pandemic-era access modalities. The model revealed that even after controlling for a host of attitudinal indicators, minorities, those having low household incomes, those living in low-density or rural locations, females, and those with lower educational attainment are particularly vulnerable to being left behind and experiencing challenges in accessing food during a severe and prolonged disruption. Social programs should aim to provide these vulnerable groups with tools and financial resources to leverage online activity engagement and access modalities. Policy recommendations to increase food access for the mostvulnerable in future disruption scenarios are explored.
ContributorsDirks, Abbie Clara (Author) / Pendyala, Ram M. (Thesis advisor) / Chester, Mikhail V. (Committee member) / Polzin, Steven E. (Committee member) / Arizona State University (Publisher)
Created2023
Description
The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population with an array of socio-demographic and socio-economic attributes has drawn remarkable attention due to privacy and cost constraints in collecting and disclosing full scale data. Although, there has been enormous progress in producing synthetic population, there has been less progress in the development of population evolution modeling arena to forecast future year population. The objective of this dissertation is to develop a well-structured full-fledged demographic evolution modeling system, capturing migration dynamics and evolution of person level attributes, introducing the concept of new household formations and apprehending the dynamics of household level long-term choices over time. A comprehensive study has been conducted on demography, sociology, anthropology, economics and transportation engineering area to better understand the dynamics of evolutionary activities over time and their impacts in travel behavior. This dissertation describes the methodology and the conceptual framework, and the development of model components. Demographic, socio-economic, and land use data from American Community Survey, National Household Travel Survey, Census PUMS, United States Time Series Economic Dynamic data and United States Center for Disease Control and Prevention have been used in this research. The entire modeling system has been implemented and coded using programming language to develop the population evolution module named `PopEvol' into a computer simulation environment. The module then has been demonstrated for a portion of Maricopa County area in Arizona to predict the milestone year population to check the accuracy of forecasting. The module has also been used to evolve the base year population for next 15 years and the evolutionary trend has been investigated.
ContributorsPaul, Sanjay (Author) / Pendyala, Ram M. (Thesis advisor) / Kaloush, Kamil (Committee member) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2014
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