Transportation Network Companies (TNCs) and public transit: Examining relationships between TNCs, transit ridership, and neighborhood qualities in San Francisco

2020 ◽  
Vol 8 (4) ◽  
pp. 1233-1246
Author(s):  
Dwayne Marshall Baker
Keyword(s):  
San Francisco ◽  
Public Transit ◽  
Transportation Network ◽  
Transit Ridership ◽  
Transportation Network Companies

Impacts on Vehicle Occupancy and Airport Curb Congestion of Transportation Network Companies at Airports

2018 ◽  
Vol 2672 (23) ◽  
pp. 52-58 ◽  
Author(s):  
Karina Hermawan ◽  
Amelia C. Regan
Keyword(s):  
Los Angeles ◽  
San Francisco ◽  
Public Transit ◽  
Transportation Network ◽  
Light Rail ◽  
International Airport ◽  
Using Data ◽  
Standard Service ◽  
Transportation Network Companies

How does the growth of transportation network companies (TNCs) at airports affect the use of shared modes and congestion? Using data from the 2015 passenger survey from Los Angeles International Airport (LAX), San Francisco International Airport (SFO), and Oakland International Airport (OAK), this research analyzes TNCs’ relationship with shared modes (modes that typically have higher vehicle-occupancy and include public transit such as buses and light rail, shared vans or shuttles) and the demand for their shared vs. standard service at the airport. Because TNCs both replace shared rides and make them possible, the research also measured the net effects at these airports. The results suggest that in 2015, TNCs caused 215,000 and 25,000 passengers to switch from shared to private modes at SFO and OAK, respectively. By 2020, the increase is expected to be about 840,000 and 107,000 passengers per year, respectively.

scholarly journals Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco

2021 ◽  
Author(s):  
Gregory D. Erhardt ◽  
Richard Alexander Mucci ◽  
Drew Cooper ◽  
Bhargava Sana ◽  
Mei Chen ◽  
...  
Keyword(s):  
San Francisco ◽  
Transportation Network ◽  
Transportation Systems ◽  
Light Rail ◽  
Transit Ridership ◽  
Application Programming ◽  
Transit Agencies ◽  
Using Data ◽  
Transit Use ◽  
Transportation Network Companies

AbstractTransportation 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.

Partnerships with Technology-Enabled Mobility Companies

2017 ◽  
Vol 2649 (1) ◽  
pp. 106-112 ◽  
Author(s):  
Marla Westervelt ◽  
Joshua Schank ◽  
Emma Huang
Keyword(s):  
Service Delivery ◽  
Public Transit ◽  
Transportation Network ◽  
Public Agencies ◽  
On Demand ◽  
Transportation Network Companies

The rise and the proliferation of the on-demand economy are creating a new mobility marketplace. This research explored how these new options could be synergistic with public transit models and detailed the experiences of two transit operators that entered into service delivery partnerships with a transportation network company and a micro-transit operator. Based on a series of interviews and the experiences of these two public agencies, this research provides a set of key takeaways and recommendations for transit operators exploring the potential of partnering with new mobility services such as transportation network companies (e.g., Uber or Lyft) and microtransit (e.g., Bridj or Via).

scholarly journals Do transportation network companies decrease or increase congestion?

2019 ◽  
Vol 5 (5) ◽  
pp. eaau2670 ◽  
Author(s):  
Gregory D. Erhardt ◽  
Sneha Roy ◽  
Drew Cooper ◽  
Bhargava Sana ◽  
Mei Chen ◽  
...  
Keyword(s):  
San Francisco ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Time Data ◽  
Travel Time Data ◽  
Application Programming ◽  
Using Data ◽  
Programming Interfaces ◽  
Insight Into ◽  
Transportation Network Companies

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.

scholarly journals Uber vs. Pubic transit friend or foe? : an empirical study of ridership trends across Uber cities in the USA

2021 ◽  
Author(s):  
Dominic Tremblay
Keyword(s):  
Public Transit ◽  
Transportation Network ◽  
Municipal Governments ◽  
Policy Makers ◽  
Highly Educated ◽  
Transit Ridership ◽  
The Usa ◽  
Transit Agencies ◽  
Taxi Industry ◽  
High Level

Uber is a disruptive transportation network company (TNC) that is affecting the way people move in cities. While its effects on the taxi industry are increasingly clear, little research has been conducted to study its effect on public transit ridership. This study uses descriptive statistics to establish demographic and socio-economic profiles of cities that Uber has targeted, and a quasi difference-in-difference analysis to explore Uber's effect on transit ridership levels in order to determine whether the service is acting as a complement or substitute to public transit. The results from this high-level study suggest that Uber my be more of a complement to transit over time, and in large dense regions with highly-educated, affluent, productive labour markets that already have very high transit ridership. Finally, recommendations informed by these findings are offered for transit agencies, policy makers, and municipal governments as they continue to determine how to regulate Uber and similar ride sourcing services

Uber Economics: Evaluating the Monetary and Travel Time Trade-Offs of Transportation Network Companies and Transit Service in Chicago, Illinois

2019 ◽  
Vol 2673 (4) ◽  
pp. 295-304 ◽  
Author(s):  
Joseph P. Schwieterman
Keyword(s):  
Travel Time ◽  
Public Transit ◽  
Transportation Network ◽  
Cost Effective ◽  
Service Levels ◽  
Metropolitan Regions ◽  
Transit Service ◽  
Trade Offs ◽  
The U.S ◽  
Transportation Network Companies

The potential diversion of passengers from public transit to transportation network companies (TNCs) is attracting considerable attention in metropolitan regions. Despite this, relatively little microeconomic analysis has been made available to explore how service attributes affect choices between the services offered by TNCs and public transit. To fill this shortfall, this study evaluates prices and service levels for Lyft, Lyft Line, UberX, UberPool, and Chicago Transit Authority (CTA) services in Chicago. Analysis of 3,075 fares and estimated travel times for 620 trips in the 4- to 11-mile range shows TNCs tend to be relatively costly when expressed in relation to the additional amount spent per unit of time saved. The average traveler using these four TNC services, across the entire sample, spends the equivalent of $42–$108 per hour saved—well above the $14.95/hr. the U.S. Department of Transportation (U.S. DOT) recommends assigning to the average transit passenger when conducting analyses about the value of time. However, for travelers on business and those between locations poorly served by transit, including trips between neighborhoods with less transit service than the downtown district, the analysis shows a significant share of passengers will likely find TNCs cost-effective options based on the U.S. DOT standard. The approach taken illustrates how the mobility benefits and competitive issues associated with TNCs can be systematically evaluated by reviewing the price and travel time characteristics of each trip.

scholarly journals Uber vs. Pubic transit friend or foe? : an empirical study of ridership trends across Uber cities in the USA

2021 ◽  
Author(s):  
Dominic Tremblay
Keyword(s):  
Public Transit ◽  
Transportation Network ◽  
Municipal Governments ◽  
Policy Makers ◽  
Highly Educated ◽  
Transit Ridership ◽  
The Usa ◽  
Transit Agencies ◽  
Taxi Industry ◽  
High Level

Uber is a disruptive transportation network company (TNC) that is affecting the way people move in cities. While its effects on the taxi industry are increasingly clear, little research has been conducted to study its effect on public transit ridership. This study uses descriptive statistics to establish demographic and socio-economic profiles of cities that Uber has targeted, and a quasi difference-in-difference analysis to explore Uber's effect on transit ridership levels in order to determine whether the service is acting as a complement or substitute to public transit. The results from this high-level study suggest that Uber my be more of a complement to transit over time, and in large dense regions with highly-educated, affluent, productive labour markets that already have very high transit ridership. Finally, recommendations informed by these findings are offered for transit agencies, policy makers, and municipal governments as they continue to determine how to regulate Uber and similar ride sourcing services

Profiling Transport Network Company Activity using Big Data

2018 ◽  
Vol 2672 (42) ◽  
pp. 192-202 ◽  
Author(s):  
Drew Cooper ◽  
Joe Castiglione ◽  
Alan Mislove ◽  
Christo Wilson
Keyword(s):  
San Francisco ◽  
Transportation Network ◽  
Time Of Day ◽  
Transport Network ◽  
Objective Data ◽  
Policy Decisions ◽  
Transit Ridership ◽  
Level Data ◽  
Application Programming ◽  
San Francisco County

Transportation network companies (TNCs) provide vehicle-for-hire services. They are distinguished from taxis primarily by the presumption that vehicles are privately owned by drivers. Unlike taxis, which must hold one of approximately 1,800 medallions licensed by the San Francisco Municipal Transportation Agency (SFMTA) to operate in San Francisco, there is no regulatory limit on the supply of TNCs. TNCs have an increasingly visible presence in San Francisco. However, there has been little or no objective data available on TNCs to allow planners to understand the number of trips they provide, the amount of vehicle miles traveled they generate, or their effects on congestion, transit ridership, transit operations, or safety. Without this type of data it is difficult to make informed planning and policy decisions. Discussions with Uber, Lyft, and the California Public Utilities Commission, which collects trip-level data from TNCs in California, requesting information on TNC trips have not resulted in any data being shared. Under increasing pressure from policymakers for objective data to inform policy decisions, the San Francisco County Transportation Authority (SFCTA) partnered with researchers from Northeastern University who developed a methodology for collecting data through Uber’s and Lyft’s application programming interfaces (APIs) with high spatial and temporal resolution. This paper provides a brief literature review on transport network company (TNC) data, and goes one to describe the methodology used to collect data, summarizes the process for converting the raw data into estimated TNC trips, and presents an analysis of the results of the TNC trip estimates. This study determined that TNCs serve a substantial number of trips in San Francisco, over 170,000 on a typical weekday, that these trips follow traditional time of day distributions, and that they tend to take place in the busiest parts of the City.

Sign in / Sign up

Export Citation Format

Share Document