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.

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 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.

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.

Understanding Transit Agency Perceptions about Transportation Network Companies, Shared Mobility, and Autonomous Transit: Lessons from the United States

2019 ◽  
Vol 2673 (5) ◽  
pp. 95-108
Author(s):  
Mengjie Han ◽  
Matthew D. Dean ◽  
Pedro Adorno Maldonado ◽  
Parfait Masungi ◽  
Sivaramakrishnan Srinivasan ◽  
...  
Keyword(s):  
United States ◽  
New Technologies ◽  
Transportation Network ◽  
Critical Role ◽  
The United States ◽  
Transit System ◽  
Emergent Technologies ◽  
Shared Mobility ◽  
Transit Agencies ◽  
Transportation Network Companies

Emergent technologies like autonomous/connected vehicles and shared mobility platforms are anticipated to significantly affect various aspects of the transportation network such as safety, mobility, accessibility, environmental effects, and economics. Transit agencies play a critical role in this network by providing mobility to populations unable to drive or afford personal vehicles, and in some localities carry passengers more efficiently than other modes. As transit agencies plan for the future, uncertainty remains with how to best leverage new technologies. A survey completed by 50 transit agencies across the United States revealed similar yet different perceptions and preparations regarding transportation network companies (TNCs) and autonomous transit (AT) systems. Transit agencies believe TNC market share will grow, either minimally or rapidly (72%), within the next 5 years and have either a negative (43%) or positive (35%) impact on their transit system. Only 30% of agency boards instructed the agency to work with TNCs, despite no perceived transit union support. For AT systems, 22% of agencies are studying them, 64% believe the impacts of AT over the next 10–20 years will be positive, but fewer agencies are influenced to consider new technologies because of AT systems (38%) compared with TNCs (72%). Surprisingly, transit administration is mostly unsure about driver and transit unions’ perceptions of these technologies. In addition, a significant number of transit agencies do not believe they should play a role in ensuring TNCs are safe and equitable and that TNCs should not have to adhere to the same regulations (50%, 28% respectively).

Map-Matching Algorithm for Applications in Multimodal Transportation Network Modeling

2015 ◽  
Vol 2537 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Kenneth Perrine ◽  
Alireza Khani ◽  
Natalia Ruiz-Juri
Keyword(s):  
Public Transportation ◽  
Transportation Network ◽  
Data Representation ◽  
Transportation Systems ◽  
Data Sets ◽  
The Public ◽  
Standard Data ◽  
Planning Models ◽  
Transit Agencies ◽  
Public Transportation Systems

Generalized Transit Feed Specification (GTFS) files have gained wide acceptance by transit agencies, which now provide them for most major metropolitan areas. The public availability GTFSs combined with the convenience of presenting a standard data representation has promoted the development of numerous applications for their use. Whereas most of these tools are focused on the analysis and utilization of public transportation systems, GTFS data sets are also extremely relevant for the development of multimodal planning models. The use of GTFS data for integrated modeling requires creating a graph of the public transportation network that is consistent with the roadway network. The former is not trivial, given limitations of networks often used for regional planning models and the complexity of the roadway system. A proposed open-source algorithm matches GTFS geographic information to existing planning networks and is also relevant for real-time in-field applications. The methodology is based on maintaining a set of candidate paths connecting successive geographic points. Examples of implementations using traditional planning networks and a network built from crowdsourced OpenStreetMap data are presented. The versatility of the methodology is also demonstrated by using it for matching GPS points from a navigation system. Experimental results suggest that this approach is highly successful even when the underlying roadway network is not complete. The proposed methodology is a promising step toward using novel and inexpensive data sources to facilitate and eventually transform the way that transportation models are built and validated.

Partnerships Between Transit Agencies and Transportation Network Companies

2019 ◽  
Author(s):  
Terra Curtis ◽  
Meg Merritt ◽  
Carmen Chen ◽  
David Perlmutter ◽  
Dan Berez ◽  
...  
Keyword(s):  
Transportation Network ◽  
Transit Agencies ◽  
Transportation Network Companies

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

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