The Coronavirus pandemic changed the normal lives across the country as strategies for mitigating the spread of the virus were put in place. Daily life was moved to a virtual setting as much as possible and typical mobility purposes changed or were eliminated. Shared transportation ridership declined dramatically in response to the pandemic, with reported drops of up to 90% across the United States. Mobility providers were tasked with determining strategies to encourage ridership during the risky time. The main research question that was explored in this study was, "What is the impact of the Coronavirus pandemic on public transit and micromobility ridership?" The study aimed to determine important factors that potential riders considered and emphasized in their decision making. The research approach was to use a custom-developed stated preference survey. The survey collected opinions about public transit and micromobility ridership during and emerging from the Coronavirus pandemic. The study focused on Blacksburg, VA as it has both public transit and micromobility services. Personal characteristics and stated important factors that influenced potential rider decisions were determined to understand what is most important to potential riders. Mobility providers can use these findings to better address rider concerns and make informed decisions on provided service. Therefore, encouraging an increase in shared transportation ridership. / Master of Science / The Coronavirus pandemic changed the normal lives across the country as strategies for mitigating the spread of the virus were put in place. Daily life was moved to a virtual setting as much as possible and typical mobility purposes changed or were eliminated. Shared transportation ridership declined dramatically in response to the pandemic, with reported drops of up to 90% across the United States. Mobility providers were tasked with determining strategies to encourage ridership during the risky time. The main research question that was explored in this study was, "What is the impact of the Coronavirus pandemic on public transit and micromobility ridership?" The study aimed to determine important factors that potential riders considered and emphasized in their decision making. The research approach was to use a custom developed stated preference survey. The survey collected opinions about public transit and micromobility ridership during and emerging from the Coronavirus pandemic. The study focused on Blacksburg, VA as it has both public transit and micromobility services. Factors that influenced potential rider decisions were determined. Mobility providers can use these factors to better address rider concerns and make informed decisions on provided service.

Anticipating changes to public transportation ridership demand is important to planning for and meeting service goals and maintaining system viability. These changes may occur in the short- or long-term; extensive academic work has focused on bettering long-term forecasting procedures while improvements to short-term forecasting techniques have not received significant academic attention. This dissertation combines traditional forecasting approaches with multivariate regression to develop a transferable short-term public transportation ridership forecasting model that incorporates fuel price as a prediction parameter. The research herein addresses 254 US transit systems from bus, light rail, heavy rail, and commuter rail modes, and uses complementary methods to account for seasonal and non-seasonal ridership fluctuations. Models were built and calibrated using monthly data from 2002 to 2007 and validated using a six-month dataset from early 2008. Using variable transformations, classical data decomposition techniques, multivariate regression, and a variety of forecasting model validation measures, this work establishes a benchmark for future research into transferable transit ridership forecasting model improvements that may aid public transportation system planners in an era when, due to fuel price concerns, global warming and green initiatives, and other impetuses, transit use is seeing a resurgence in popularity. / text

Master of Regional and Community Planning / Department of Landscape Architecture/Regional and Community Planning / Gregory L. Newmark / At Kansas State University, there is not an issue of opposition to public bus services. Instead, there is a perception that taking the bus is too complicated to figure out or cannot appropriately serve the community’s needs. This, combined with dependence and attachment to the automobile, caused local buses to become unpopular mode choices. Since the service is still relatively new, it has not been engrained into campus culture and ridership is very low. This study looked at the local bus system, ATA bus, used to access the Manhattan, KS campus of Kansas State University (KSU). According to the ATA Annual Report from 2014, 75% of off-campus students at KSU and 35% of employees live within five minutes of ATA city-wide routes (FHATA 2014). Ideally, all those students and employees would take the bus to class or work, but in reality, most walk or drive. A very small minority of students use the fixed route service, and many are unfamiliar with how the system works. A campus access survey distributed in March 2017 to the KSU community aimed to ascertain familiarity with the system, current level of use, as well as attitudes towards the existing public transportation system. The data recovered from the survey contradicts the hypothesis that the disuse of the bus system was due to an active opposition to public buses. Conversely, it pointed to the conclusion that disuse resulted from a lack of information about the bus service and a deep-set attachment to private automobiles. This research aimed to increase ridership by identifying attitudes towards transit among the KSU community and suggesting strategies for improving service.

Cycling, and micromobility tools like bike share, have increasingly been recognized for their health, economic and environmental benefits, and municipalities have recently made encouraging the use of these modes of urban transportation both a policy and a financial priority. Many studies, using varying methods, have identified and confirmed an association between an increased presence and connectivity of cycling infrastructure (bike lanes, cycle tracks, etc.) and cycling or bike share ridership. Determining a more explicit causal link between infrastructure and ridership, however, often proves challenging to researchers, due to data limitations and a variety of simultaneous, exogenous, factors that abound within complex urban transportation systems. Given the financial impacts of capital investment in infrastructure, more closely establishing this causal link, and identifying infrastructure’s ability to generate cycling and bike share traffic, is of growing importance to municipal governments and taxpayers. Using Hamilton Bike Share (HBS) trip logs and GPS trajectories occurring between January, 2019 and August, 2022 (n = 741,369 and 609,746, respectively), this thesis constructs individual shapefiles of each HBS trip for GIS analysis through Dalumpines and Scott’s (2011) GIS-Based Map-Matching Algorithm. It investigates the impact of ten separated cycling infrastructure projects in Hamilton, constructed between 2019 and 2022, on HBS ridership along the respective intervention segments. The thesis also holistically analyzes the spatial and ridership impacts of one infrastructure intervention, the Victoria Avenue cycle track, on the distribution of riders using the segment of interest, a more precise classification of post-intervention trip natures (‘induced’ or ‘diverted’) using a novel categorization process, and maps the impact of the iv segment on trip diversion to use the cycle track. Results indicate that five of the ten interventions have had significant, positive, impacts on monthly HBS ridership along their respective segments, with others having nearly statistically significant results as well. Moreover, the Victoria Avenue cycle track lessened the cost of distance associated with using Victoria Avenue, and 46.9% of trips along the cycle track post-intervention, were determined to be ‘induced’ trips. Finally, of the streets in the Victoria Avenue cycle track’s neighborhood, the cycle track segments were the only segments to experience ridership increases post-intervention, which indicates a significant level of trip diversion and funneling of trips to use the cycle track. These results enhance findings from the literature and more concretely quantify the direct impacts of infrastructure investments. Investments in infrastructure appear to make a significant difference in increasing ridership and serve to benefit more than just existing riders. This thesis can have an important impact on municipal active transportation planning, policy, and financing, through its results and by providing a methodological foundation for future research into infrastructure’s impacts on a variety of users. / Thesis / Master of Science (MSc)

<p> In the spring of 1983, when this project was in its most preliminary stages, a simple hypothesis was put forward. This hypothesis suggested that auto users would react to rising retail gasoline prices by switching to an alternative mode of transportation, such as public transit. It was thought that, since any increase in fuel costs could be spread out among all transit users, public transit would become an attractive alternative to the private automobile in an individual's transportation mode decision as retail gasoline prices increased. Therefore, a positive relationship was anticipated-to exist between public transit ridership and retail gasoline prices. </p> <p> Having established the hypothesis to be investigated, an extensive review of current literature associated with the hypothesis was completed. This review presented conflicting opinions concerning the hypothesis, and also suggested that other variables were more important than the price of retail gasoline in affecting an individual's transportation mode decision. </p> <p> Unfortunately, the literature review did not suggest any relevant method of analysis for this project. It was decided that, for reasons to be discussed later, linear regression would be the method of analysis. The results of the application of a number of linear regression models to data obtained for the Hamilton study area indicated that no definitive statement could be made with respect to the hypothesis of this project. This lack of significant results was attributed to extraneous variance created by certain variables that could not be controlled. </p> <p> However, as a contribution to knowledge, this project provides a basis on which future studies can be built. If the extraneous variance that is discussed in this project can be eliminated in future studies, then- it may be possible to obtain more significant results with respect to the hypothesis that public transit ridership is positively related to retail gasoline prices. </p> / Thesis / Bachelor of Arts (BA)

Master of Science / Department of Civil Engineering / Sunanda Dissanayake / Public transportation in rural America has existed for decades. Its providers are challenged with low populations and long distances in rural areas. Many of these rural transit providers have been in existence for many years, but ridership still remains low. These providers usually operate in a demand-response format, as opposed to large cities, where buses run on fixed routes. This research was conducted to see if any type of service improvements or enhancements could be found in order to increase ridership of demand-response transit service in rural Kansas. In order to determine if ridership of public transportation in rural Kansas can be increased, customer satisfaction surveys were conducted. One survey was distributed to current riders of demand-response systems, one survey distributed to non-riders of public transportation, and the last survey given to providers to obtain basic system information throughout Kansas. Ridership is significantly skewed toward the elderly, disabled, and those who either choose not to drive or are unable to drive. Those who do not fall into one of these three categories often do not use public transportation in rural areas. For most of the riders, public transportation is their only reliable method of mobility as they are transit dependent. Only 35% of the riders had a personal vehicle they could use to make the trip had public transportation not been available. Riders of demand-response transit systems in rural Kansas are pleased with the service provided as a whole. Non-riders are ambivalent toward demand-response transit service. They appreciate the fact that in many cases general public transportation service exist, but they are also generally unwilling to use it themselves. These are typically choice riders, and are unlikely to switch to demand-response transit due to their other mobility options. It was found that the more vehicles a person has access to in their household, the less knowledge they have about public transportation in their area. These people are content to use the vehicles they have, because it is more convenient than using public transportation in rural Kansas. Improvements to the provider’s system, like extending operating hours and days, along with implementing GIS-assisted scheduling may bring higher ridership. However, this may only increase the number of rides by the same current riders with few new riders grained. Increasing the usage of demand response ridership will continue to be a challenge in the future with the increasing number of elderly in the years to come.

The benefits of cycling are well known and many communities are investing in cycling infrastructure in order to encourage and promote ridership. Safety is a primary concern for new cyclists and remains a barrier for increasing ridership. Understanding what influences cyclist safety requires knowing how many cyclists are riding in an area. Lack of ridership data is a common challenge for cycling research and limits our ability to properly assess safety and risk. The goal of our research was to incorporate new data available through crowdsourcing applications to advance cycling research on ridership and safety in the Capital Regional District (CRD), British Columbia (BC), Canada. To meet our goal, our first analysis assessed how crowdsourced fitness app data can be used to map and to quantify the spatial and temporal variation of ridership. Using a dataset from a popular fitness app Strava, we compared how manual cycling counts conducted at intersections during peak commuting hours in Victoria compared to the number of crowdsourced cyclists during these same count periods. In order to estimate ridership at unsampled manual count locations, we used Poisson regression to model the association between manual counts and infrastructure variables found to influence ridership. Our results found that there was a linear association (r2 between 0.4 and 0.58) between crowdsourced cyclists and manual count cyclists, which amounted to one crowdsourced cyclist representing 51 riders. Crowdsourced cyclist volumes, traffic speeds, on street parking, slope, and time of year were found to significantly influence the amount of cyclists in different count locations with a predictive accuracy of 62%. Overall, crowdsourced data from fitness apps are a biased sample of ridership; however, in urban areas in mid-size North American cities, cyclists using fitness apps may choose similar routes as commuter cyclists. Our second analysis used crowdsourced data on cyclist incidents to determine the factors that influence incident reporting at multiuse trail and roadway intersections. Using incident reports from BikeMaps.org, we characterized attributes of reported incidents at intersections between multiuse trails and roads and also examined infrastructure features at these intersections that are predictors of incident frequency. We conducted site observations at 32 multiuse trail-road intersections in the CRD to determine infrastructure characteristics that influence safety. Using Poisson regression we modeled the relationship between the number of incidents (collision and near misses) and the infrastructure characteristics at multiuse trail-road intersections. We found that collisions were more commonly reported (over near misses) at multiuse trail-road intersections than road-road intersections (38% versus 27%), and incidents involving an injury were more common (35% versus 21%). Cycling volumes, vehicle volumes, and lack of vehicle speed reduction factors were associated with incident frequency. Our analysis was able to use crowdsourced cycling incident data to provide valuable evidence on the factors that influence safety at intersections between multiuse trails and roadways where diverse transportation modes converge. Through this thesis we help to overcome limitations for cycling research and planning by demonstrating how crowdsourced ridership and safety data can help fill gaps and supplement available data. Our methodology integrates the high spatial and temporal resolution of crowdsourced cycling data with the detailed attributes provided by traditional ridership counts. We also demonstrate how volunteered safety data can allow new questions on safety to be explored. Improving data available for cycling research allows for a more comprehensive understanding of the factors that influence ridership and safety and, in turn, informs decisions targeted at increasing cycling. / Graduate

The 4-step process has been the standard procedure for transit forecasting for over 50 years. In recent decades, researchers have developed ridership forecasting regression models as alternatives to the costly and time consuming 4-step process. The model created by Lane, DiCarlantonio, and Usvyat in 2006 is among the most recent and most widely accepted. It includes station area demographics, central business district (CBD) employment, and the station areas’ built environments to estimate ridership. This report applies the Lane, DiCarlantonio, and Usvyat model to the North Line of Houston’s Metropolitan Transit Authority of Harris County (METRO). The report compares the 2030 ridership forecast created by METRO using the 4-step process with the LDU model forecasts. For the 2030 projections, this report obtained population and employment estimates from the Houston-Galveston Area Council and analyzed the data using Esri ArcMap and Caliper TRANSCadGIS software programs. The LDU model produced unrealistically high ridership numbers for the North Line. It estimated 108,430,481 daily boardings. METRO’s 4-step process predicted 29,900 daily boardings. The results suggest that the LDU model is not applicable to the Houston light rail system and is not a viable alternative to the 4-step process for this specific metropolitan area. The LDU method for defining Houston’s CBD was the main problem in applying the model. It calculated an extremely high CBD employment density compared to other cities of similar size. Even when the CBD size was manipulated to decrease employment density, the model still predicted 212,210 daily boardings for the North Line, nearly 10 times higher than METRO’s 4-step process estimate. In addition to the problems with the definition of the CBD, the creators of the LU model did not specifically explain how to define a metropolitan area. Multiple inconsistent and subjective definitions of a metro area can be used. This report employs three different definitions of the Houston metro, all of which produced three significantly different ridership forecasts in the LDU model. As a result of these flaws, the LDU model does not accurately apply to METRO’s North Line, and it does not serve as a viable alternative to METRO’s 4-step process. / text

Transit agencies at all levels of government monitor trends in services, operations, and ridership using performance indicators. Federal and state agencies use these performance indicators in the appropriation of funds to transit agencies. Public transportation is subsidized through federal, state and local programs while only a portion of the operating expenses are covered through rider fares. In order to gather information on riders and travel patterns, transit agencies primarily focus on current transit riders, many of which are transit dependent populations. By definition, these populations use public transit services as the primary or only means of transportation. As a result, this offers limited opportunity for ridership growth among transit dependent populations. One segment of a population that offers high opportunity for ridership growth is commuters. A commuter is considered a worker that travels from home to work on a regular basis. However, in the case of commuter oriented transit services, it is important to survey non-riders so that any new services will have the greatest potential of increasing ridership among commuters. This report explores the potential commuter demand for additional or express bus services provided by San Luis Obispo Regional Transit Authority (RTA). RTA operates countywide fixed-route bus services and para-transit services for San Luis Obispo County. This report focuses on RTA’s Route 9, which operates between the North County and the Central County. In order to collect data from non-riders, electronics survey instruments were created and distributed using employer e-mail addresses. The survey instruments were sent to three major employers in San Luis Obispo County: California State University, San Luis Obispo (Cal Poly), the City of San Luis Obispo, and the County of San Luis Obispo. A link to one of the surveys instruments was also included on San Luis Obispo Council of Government’s (SLOCOG) Rideshare’s March 2013 e-newsletter as a way to reach additional non-riders. Analysis of the survey responses resulted in the recommendations to RTA. Recommendations are separated into two categories: (1) Expansion of RTA Route 9 services and (2) Future RTA non-rider outreach.

This study looks at the demographic, urban form and transit service characteristics that influence ridership in a college community. It acknowledges both the internal (those that a transit operator has control over) and external (variables that the transit operator cannot control) factors that influence ridership. A literature review shows that income, unemployment levels, densities, age, urban form, headway and coverage correlated to ridership. The study area used is the Five-College community that is serviced by UMass Transit, the dominant operator in the area. To perform analysis census data is collated at the block and block group levels regarding income, unemployment, vehicle ownership, population, density, college age population and housing age. Additional data about urban form and transit service characteristics is obtained. Exploratory data for all variables support the literatures finding except unemployment and land use diversity. Modeling is done in three stages using different scales of census data. A final model, combining scales is created. The highest indicators of ridership are found to be direction of travel, level of service, the percent of college age students and population density.