Abstract

div" "p"Public transportation is vital for many people who do not have the means to use other forms of transportation. In small communities, transit service is often limited, due to funding constraints of the transit agency. In order to maximize the use of available funding resources, agencies strive to provide effective and efficient service that meets the needs of as many people as possible. To do this, effective service planning is critical."/p" "p" "/p" "p"Unlike traditional road-based transportation projects, transit service modifications can be implemented over the span of just a few weeks. In planning for these short-term changes, the traditional four-step transportation planning process is often inadequate. Yet, the characteristics of small communities and the resources available to them limit the applicability of existing transit demand models, which are generally intended for larger cities."/p" "p" "/p" "p"This research proposes a methodology for using population and demographic data from the Census Bureau, combined with stop-level ridership data from the transit agency, to develop models for forecasting transit ridership generated by a given geographic area with known population and socioeconomic characteristics. The product of this research is a methodology that can be applied to develop ridership models for transit agencies in small cities. To demonstrate the methodology, the thesis built ridership models using data from Lafayette, Indiana."/p" "p" "/p" "p"A total of four (4) ridership models are developed, giving a transit agency the choice to select a model, based on available data and desired predictive power. More complex models are expected to provide greater predictive power, but also require more time and data to implement. Simpler models may be adequate where data availability is a challenge. Finally, examples are provided to aid in applying the models to various situations. Aggregation levels of the American Community Survey (ACS) data provided some challenge in developing accurate models, however, the developed models are still expected to provide useful information, particularly in situations where local knowledge is limited, or where additional information is unavailable."/p" "/div" "br

Abstract

div"In recent years, there has been considerable growth in the adoption and technology development of electric vehicles (EV), autonomous vehicles (AV), and ride sharing (RS). These technologies have the potential to improve transportation sustainability. Many studies have evaluated the environmental impacts of these technologies but the existing literature has three major gaps: (1) the adoption of these three technologies need to be evaluated considering their impact on each other, (2) many existing models do not evaluate systems on a common ground, and (3) the heterogeneous preferences of riders towards these emerging technologies are not fully incorporated. To address these gaps, this work studies and quantifies the environmental and efficiency gains that can be gained through these emerging transportation technologies by developing a Parameterized Preference-based Shared Autonomous Electric Vehicle (PP-SAEV) agent-based model. The model is then applied to a case study of New York City (NYC) taxis to evaluate the system performance with increasing AV, EV, and RS adoption."/div""div""br""/div""div"The outputs from the PP-SAEV model show that replacing taxi cabs in NYC with AVs along with RS potentially can reduce CO\\textsubscript{2} emissions by 866 metric Tones per day and increase average vehicle occupancy from 1.2 to 3 persons in vehicles with passenger seating capacity of 4. A prediction model based on the PP-SAEV output recommends that 6000 vehicles are needed to maintain the current level of service with 100\\% AV and RS adoption using capacity 4 taxis. Taxi fleets with capacity 4 with high RS and low AV adoption are also found to have the least CO\\textsubscript{2} emissions. Because the heterogeneous sharing preferences of riders have shown as the major limiting factor to ride sharing, these heterogeneous sharing preferences are further modelled. The results show that high service levels are achieved when all the riders are open to sharing, and the maximum service level is reached when 30\\% of riders will only accept shared rides and 70\\% of the riders are either indifferent to sharing or prefer to use ride sharing over riding alone. Additionally, the service level and waiting time of riders that are inflexible (will accept only shared or non-shared rides) are greatly impacted by varying mix of riders with different sharing preference. Finally, an optimization model was built to site charging stations in a system with continually increasing EV adoption. Using the best charging station locations, transforming a fleet of autonomous or traditional vehicles to electric vehicles does not significantly change the system service level. The results show that increasing the EV adoption in fleets with 100\\% RS and AV adoption reduced the daily CO\\textsubscript{2} emissions by about 861 Tones and transforming a fleet of traditional taxi cabs to electric taxi cabs reduced the daily CO\\textsubscript{2} emissions by 1100 Tones."/div""div""br""/div""div"In summary, this dissertation evaluates the potential growth of autonomous vehicles, ride sharing, and electric vehicles in systems where riders may have heterogeneous sharing preferences, from a system performance`s perspective and assesses the environmental impacts. The developed model and the insights gained from this study can inform policy makers to develop sustainable transportation systems incorporating the emerging transportation technologies."/div