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 study intends to improve understanding of the characteristics of early adopters of TNC services and contribute towards understanding travel choices made by individuals in alcohol-related situations. Data for this study came from a telephone survey of just over 3000 respondents across three metropolitan regions of Virginia; Northern Virginia, Hampton Roads/Tidewater and the Richmond urban area. 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. Based on the surveys, ordinal logit models were developed to predict the degree of familiarity and use frequency of TNCs. The results showed that income was significantly associated with both increased familiarity and increased use frequency of TNCs. Educational attainment was also significant and positively associated with familiarity and use frequency. Age was significantly and negatively associated with TNC familiarity and use frequency. This may be important in understanding TNC use in locations with older populations. Individuals located in Northern Virginia were associated with increased TNC familiarity and use frequency. Individuals who used multiple modes to commute had a higher likelihood of being familiar with and using TNCs more frequently. Use of an app for sourcing taxi services was associated with increased TNC familiarity and use frequency. Similarly, using an app for hotel reservations and/or air transportation arrangements was associated with increased TNC use frequency. In addition, individuals using their phone for entertainment were more likely to be familiar with and use TNCs. Use of mobile wallet was associated with increased TNC familiarity and use frequency. Employment status “student” was significantly associated with TNC familiarity which suggests that information is easily accessible for this group of people. Also, individuals self-identifying their race as white had a higher probability of using TNCs. The second part of the research analysis included multinomial logistic regression models which identified factors associated with respondents’ travel choices in alcohol-related situations: (1) the last time the respondent consumed alcohol, (2) when avoiding driving after drinking, and (3) when avoiding riding with a driver who had been drinking. From the model results, it was found v that consumption of alcohol at a bar was statistically associated with use of TNC services in all three alcohol-related situations. TNCs were more likely to be used by younger people in all three alcohol-related situations examined in this study. Older people were more likely to ride with designated drivers than to use TNCs when avoiding driving after drinking and the last instance of consuming alcohol. Familiarity with, and regular use of TNCs increased the likelihood of using TNCs in all three alcohol-related situations in this study.

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.

<p dir="ltr">Transportation Network Companies (TNCs) have increased significantly over the last decade, changing the urban mobility dynamics by shifting people from other modes of transportation, potentially affecting safety. While TNC companies promised to enhance urban mobility with more convenient end-to-end services, they were found to contribute to externalities like traffic congestion and safety issues. A deeper analysis is required to test the promise of TNC services and their impacts on cities. This study investigated the safety implications of the surge of TNC services in New York City (NYC) from 2017 to 2019. Specifically, we analyzed the changes in traffic safety performances using surrogate safety measures (SSMs) from 2017 to 2019 based on large-scale GPS trajectories generated by TNC vehicles in NYC.</p><p dir="ltr">This research utilized the twenty-eight days of high-quality and large-scale GPS-based trajectories of Uber vehicles to determine the critical surrogate safety measures (SSMs). To determine the potential traffic conflict and safety from SSMs, this research determined the SSMs based on evasive actions. In addition, this research also utilized real-world historical crash events, traffic flow, road conditions, land use, and congestion index to explore the relationship between critical SSMs and accidents. Additionally, this research extends to assess the socioeconomic inequalities from the perspective of increased TNCs and accidents.</p><p dir="ltr">Our findings indicate a significant increase in critical SSM events such as harsh braking and jerking citywide. These increases are particularly pronounced during off-peak hours and in peripheral areas of Manhattan and transportation hubs. Moreover, we observed stronger correlations between SSMs of TNC vehicles and injury/motorist accidents, compared to those involving pedestrians and cyclists. Despite the evident deterioration in SSMs, we noticed that the overall number of accidents in NYC from 2017 to 2019 has remained relatively stable possibly due to the reduction of traffic speeds. As such, a clustering analysis was conducted to unfold the nuanced patterns of SSMs/accident changes. Also, we find the existence of inequality in the increase in accidents and critical SSMs, and Manhattan is higher in inequality, especially in upper Manhattan. Moreover, individuals disadvantaged from low socioeconomic status and those living in deprived areas are experiencing more inequality from accidents and critical SSMs due to increased TNCs and accidents. This research enriches the understanding of how TNC services impact urban traffic safety. The findings of this research may help to get a holistic understanding of the road safety situations due to increased TNCs and accidents and help the policymakers and authorities to make informed decisions to develop a transportation system prioritizing all road users. Additionally, the methodology employed can be adapted for broader traffic safety applications or real-time monitoring of traffic safety performances using anonymous GPS trajectory segments.</p>

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.