Transit Agencies Performance Assessment and Implications

Although most transit systems operate in small urban and rural areas in the United States, these systems have rarely received the same attention as their urban counterparts, both in terms of ensuring the efficiency and effectiveness of their operations and understanding the factors that affect their performance. This thesis\u27s main goals are to assess the performance of rural and small urban public transit agencies and help them evaluate adopting a ridehailing program, thereby improving their performance. We applied operations research and decision-making tools to two public transit projects in small urban and rural areas. The first project focuses on three models developed to evaluate the efficiency, effectiveness, and combined efficiency-effectiveness of rural transit agencies using data envelopment analysis. The models were estimated for the case study of transit systems in rural Appalachia and measured the agencies\u27 performance relative to their peers. Besides, the returns to scale were explored in the context of rural transit management. The second project focused on employing ridehailing programs in small urban and rural areas to improve agencies’ performance and reach. The most relevant criteria were identified to evaluate the performance of different ridehailing programs using multi-criteria decision analysis methodology. To perform a set of MCDA methods, we used the perceived rating of each ridehailing program according to the stakeholders\u27 opinions with respect to each criterion. The framework was estimated for the case study of Mountain Line Transit Authority in Morgantown, WV

Towards A Spatial Model of Rurality

The term rural is used to describe people, places, traditions, and spaces. It is often employed as a setting for study as well as an object of study. People’s perceptions of rural are confused and differ considerably. For over a century researchers have attempted to define more precisely this term using social, economic, and or ecological components. However, problems of interpreting official definitions and measurements exist. These definitions require extensions in order to capture a more objective meaning of the word. This thesis presents the foundations of a new approach to measuring and defining rurality. A spatial based approach is taken in which explicitly spatial data instead of social or economic data are collected and indexed. The index is divided into two clusters, a connectivity cluster and an access-to-service cluster. The indicators in the clusters are chosen based on a list of criteria taken from the Institute for International Development. The model employs mathematical foundations of both topology and metrics. The use of fuzzy measures to determine a degree of rurality, instead of classical set theory, enhances the model. A degree of connectivity, a degree of accessibility, and an overall degree of rurality is determined. The model also incorporates scale. The granularity of an indicator depends on a user-required level of detail. The data are manipulated and analyzed in a GIS. The spatial index is tested on a number of towns throughout Maine. A graphical user interface illustrates the results in an easy to understand format. The results of this thesis show that a spatial approach to defining rural extends formal definitions to capture a different facet of rurality, a degree of rurality. Furthermore, spatial, temporal and attribute queries are possible enabling users a choice given a particular task

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Transportation planning options for elderly mobility

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 46-49).The population of the United States is aging, yet the current transportation system is not designed to accommodate the elderly. Reduced mobility has a profound impact on elderly well-being, and the transportation needs of older people will only increase as the baby boomer generation ages in the suburbs. Where transportation options do exist, few meet the standards of the private car that the baby boomers have come to expect. I explore the mobility attitudes and habits of the baby boomers and the responses of communities and regions to an already apparent mobility gap. I then evaluate a sample of near-term policy options for decision makers, using case studies of public transportation, SilverRideTM, ITNAmerica®, and villages. I argue that the options vary along the criteria of availability, acceptability, and affordability, and within a taxonomy of fiscal and social capacity. Diverse contexts mean that no one option is sufficient, and communities will ultimately decide which options to pursue based on their unique needs and resources. In the future, policy options will likely evolve to better address public funding constraints and build on informal forms of transportation. Both fiscal and social capacity are necessary for transportation policy options to function optimally over time, and communities can leverage existing social capacity to help enhance elderly well-being and address the unpreparedness of regions and individuals.by Holly Chase.M.C.P

Assessing Potential Energy Savings in Household Travel: Methodological and Empirical Considerations of Vehicle Capability Constraints and Multi-day Activity Patterns.

The lack of multi-day data for household travel and vehicle capability requirements is an impediment to evaluations of energy savings strategies, since 1) travel requirements vary from day-to-day, and 2) energy-saving transportation options often have reduced capability. This work demonstrates a survey methodology and modeling system for evaluating the energy-savings potential of household travel, considering multi-day travel requirements and capability constraints imposed by the available transportation resources. A stochastic scheduling model is introduced – the multi-day Household Activity Schedule Estimator (mPHASE) – which generates synthetic daily schedules based on “fuzzy” descriptions of activity characteristics using a finite-element representation of activity flexibility, coordination among household members, and scheduling conflict resolution. Results of a thirty-household pilot study are presented in which responses to an interactive computer assisted personal interview were used as inputs to the mPHASE model in order to illustrate the feasibility of generating complex, realistic multi-day household schedules. Study vehicles were equipped with digital cameras and GPS data acquisition equipment to validate the model results. The synthetically generated schedules captured an average of 60 percent of household travel distance, and exhibited many of the characteristics of complex household travel, including day-to-day travel variation, and schedule coordination among household members. Future advances in the methodology may improve the model results, such as encouraging more detailed and accurate responses by providing a selection of generated schedules during the interview. Finally, the Constraints-based Transportation Resource Assignment Model (CTRAM) is introduced. Using an enumerative optimization approach, CTRAM determines the energy-minimizing vehicle-to-trip assignment decisions, considering trip schedules, occupancy, and vehicle capability. Designed to accept either actual or synthetic schedules, results of an application of the optimization model to the 2001 and 2009 National Household Travel Survey data show that U.S. households can reduce energy use by 10 percent, on average, by modifying the assignment of existing vehicles to trips. Households in 2009 show a higher tendency to assign vehicles optimally than in 2001, and multi-vehicle households with diverse fleets have greater savings potential, indicating that fleet modification strategies may be effective, particularly under higher energy price conditions.Ph.D.Natural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91567/1/kevinb_1.pd

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management