This study deals with a) Understanding familiarity with transportation network companies (TNCs) and their use frequency b) Understanding travel choices in alcohol-related situations in Virginia. Ordered logistic regression models were used to identify factors associated with the respondents perceived familiarity with transportation network companies (TNCs) and use frequency. Based on the two models, the consistent factors were using a mobile wallet, a cell phone for entertainment, an app for taxi services, or an app for hotel booking/air transport arrangements, living in Northern Virginia, normally using multiple transportation modes for a single trip, higher education levels, and higher household income which were associated with increased TNC familiarity and use frequency. Self-identifying as White/Caucasian was also associated with increased TNC use frequency. Increased age was associated with decreasing TNC familiarity and use frequency. Subsequently, travel choices in alcohol related situations were studied with the objective of understanding the role of Transportation Network Companies (TNCs) in these situations and whether they have an impact on DUIs. For this objective, this study analyzes travel-choices associated with three scenarios alcohol related situations: (a) the last time the respondent consumed alcohol, (b) when avoiding driving after drinking, and (c) when avoiding riding with a driver who had been drinking. Multinomial Logistic Regression models were developed for all the three scenarios. For model (a), significant factors included use of a personal vehicle to arrive at the location where last consuming alcohol, being comfortable with having a credit card tied to a cell phone app, age, income, travelling alone when leaving the location where last consuming alcohol, having the highest educational attainment of high school graduate (GED), consumption of alcohol at bar/tavern/club, consumption of alcohol at home of friends/acquaintance place, and transportation network company (TNC – e.g., Uber, Lyft) weekly use frequency. For (b), use of a personal vehicle to arrive at the location where last consuming alcohol, consumption of alcohol at a bar/tavern/club, consumption of alcohol at the home of friends/acquaintance place, comfort with tying of credit card to apps, age, gender, income, multi-modal travel for a regular trip, TNC weekly use frequency, and use of an app for hotel reservations and/or air transportation arrangements are significant factors. For (c), use of a personal vehicle to arrive at the location where last consuming alcohol, walking to the location where last consuming alcohol, consumption of alcohol at a bar/tavern/club, comfort with tying a credit card to apps, age, income, TNC weekly use frequency, previously riding in a car with a driver who may have drunk too much to drive safely, and being employed full time are the significant factors. / Master of Science

The emergence and rapid growth of Transportation Network Companies (TNCs), such as Uber and Lyft, has challenged the transportation industry by offering a new mode of transportation to consumers. It is imperative that transit agencies and cities understand the effect of TNCs on public transit usage to make informed decisions. This study analyzes the impact of TNCs on Bay Area Rapid Transit (BART) ridership at the San Francisco International Airport (SFO) to measure the effect of TNCs on public transit. Using a fixed effects model to analyze hourly BART and TNC ridership data from 2011 to 2018, these findings suggest that TNCs are a substitute to BART. Before the entrance of TNCs, BART ridership at the BART SFO station increases. However, with the presence of TNCs, BART ridership at the SFO station decreases. Further research will proxy for transportation demand using hourly air traffic data at SFO and an instrumental variable for TNC supply to reduce endogeneity.

This research investigates whether Transportation Network Companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion by complementing transit and reducing car ownership in major cities. The objective of this research study is to answer the question: are TNCs are correlated to traffic congestion in the city of San Francisco? If found to be so, do they increase or decrease traffic congestion for the case of San Francisco? If and how TNC pickups and drop-offs impact traffic congestion within San Francisco? And finally, how does the magnitude of this measured command of TNCs on congestion compare to that caused by pre-existing conventional drivers of traffic and congestion change? Apart from answering these questions, it is also sought to establish a framework to be able to include TNCs, a seemingly fledgling mode of transportation but one that is demonstrably shaping and modifying extant transportation and mode choice trends, as part of the travel demand models estimated by any geographic jurisdiction. Traffic congestion has worsened noticeably in San Francisco and other major cities over the past few years. Part of this change could reasonably be explained by strong economic growth or other standard factors such as road and transit network changes. The sharp increase in travel times and congestion also corresponds to the emergence of TNCs, raising the question of whether the two trends may be related. Existing research has produced conflicting results and been hampered by a lack of data. Using data scraped from the Application Programming Interfaces (APIs) of two TNCs, combined with observed travel time data, this research finds that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62%, compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system. This research also decomposes the contributors to increased congestion in San Francisco between 2010 and 2016, considering contributions from five incremental effects: road and transit network changes, population growth, employment growth, TNC volumes, and the effect of TNC pick-ups and Drop-offs. It is so done through a series of controlled travel demand model runs, supplemented with observed TNC data. The results show that road and transit network changes over this period have only a small effect on congestion, population and employment growth are important contributors, and that TNCs are the biggest contributor to growing congestion over this period, contributing about half of the increase in vehicle hours of delay, and adding to worsening travel time reliability. This research contradicts several studies that suggest TNCs may reduce congestion and adds evidence in support of a recent empirical analysis showing that their net effect is to increase congestion. This research gives transportation planners a better understanding of the causes of growing congestion, allowing them to more effectively craft strategies to mitigate or adapt to it.

I examine whether Uber and Lyft are currently complements or substitutes of public transit, and how partnerships between cities and ride sharing companies can increase their complementary relationship and solve parking and mobility issues. The results suggest that transportation network companies (TNCs) like Uber and Lyft do not have a statistically significant effect on public transit ridership overall, but are complements of public transit for certain populations. Policies that give discounts for TNC rides taken to and from transit stops help solve the first mile / last mile problem, which consequently help increase transit ridership.