This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find 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.
Transportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger Count data on transit use and other supporting data. Using a panel data model of the change in bus ridership in San Francisco between 2010 and 2015, and confirming the result with a separate time-series model, we find that TNCs are responsible for a net ridership decline of about 10%, offsetting net gains from other factors such as service increases and population growth. We do not find a statistically significant effect on light rail ridership. Cities and transit agencies should recognize the transit-competitive nature of TNCs as they plan, regulate and operate their transportation systems.
Credible forecasts of long-distance travel are an important tool for evaluating proposed intercity transportation improvements, including intercity highway and transit projects. Although researchers have studied the topic and have developed frameworks for modeling long-distance travel behavior, these research models have not been integrated into comprehensive model systems used for a wide range of applications. This paper presents a long-distance travel model that bridges the gap between research and practice. It is based on a rigorous behavioral framework that models the unique aspects of long-distance travel, such as a less regular frequency of trips and a different set of modal alternatives. The model structure includes the choice of whether to travel, the selection of the days on which to travel, scheduling to a specific time of day, destination choice, and mode choice. The model is sensitive to important descriptive variables, including the demographic characteristics of travelers, the attractiveness of possible destinations, and the levels of service of air, transit, and highway networks. It has been successfully implemented as part of the Ohio statewide model, which also features an advanced tour-based model of short-distance travel. Through this integration, it allows for behavioral consistency within the entire model system and competition among all travelers for transportation capacity. Lessons are learned about the data needs and research needs to further improve long-distance travel models.
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