Matching Items (4)
Filtering by
- All Subjects: Civil Engineering
- Genre: Masters Thesis
- Resource Type: Text
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
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