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.

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.

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.

Spatial and Temporal Differences in Weekday Travel Durations Between Private-for-Hire Transportation Services and Transit in the City Center

2021 ◽  
pp. 036119812110197
Author(s):  
Baxter Shandobil ◽  
Ty Lazarchik ◽  
Kelly Clifton
Keyword(s):  
Transportation Network ◽  
Central City ◽  
Temporal Analysis ◽  
Time Of Day ◽  
City Center ◽  
Transportation Services ◽  
Regulatory Frameworks ◽  
Transit Services ◽  
Level Information ◽  
The City

There is increasing evidence that ridehailing and other private-for-hire (PfH) services such as taxis and limousines are diverting trips from transit services. One question that arises is where and when PfH services are filling gaps in transit services and where they are competing with transit services that are publicly subsidized. Using weekday trip-level information for trips originating in or destined for the city center of Portland, OR from PfH transportation services (taxis, transportation network companies, limousines) and transit trip data collected from OpenTripPlanner, this study investigated the temporal and spatial differences in travel durations between actual PfH trips and comparable transit trips (the same origin–destination and time of day). This paper contributes to this question and to a growing body of research about the use of ridehailing and other on-demand services. Specifically, it provides a spatial and temporal analysis of the demand for PfH transportation using an actual census of trips for a given 2 week period. The comparison of trip durations of actual PfH trips to hypothetical transit trips for the same origin–destination pairs into or out of the central city gives insights for policy making around pricing and other regulatory frameworks that could be implemented in time and space.

scholarly journals Diabetes Care in the San Francisco County Jail

2006 ◽  
Vol 96 (9) ◽  
pp. 1571-1574 ◽  
Author(s):  
Brinton C. Clark ◽  
Ellie Grossman ◽  
Mary C. White ◽  
Joe Goldenson ◽  
Jacqueline Peterson Tulsky
Keyword(s):  
San Francisco ◽  
Diabetes Care ◽  
County Jail ◽  
San Francisco County

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.

““Breaking Bread with a Spread”” in a San Francisco County Jail

2008 ◽  
Vol 8 (3) ◽  
pp. 17-24 ◽  
Author(s):  
sandra cate
Keyword(s):  
San Francisco ◽  
County Jail ◽  
Generic Term ◽  
San Francisco County ◽  
County Jails ◽  
Ethnic Variations

In many jails and prisons, inmates devise a cuisine that supplements –– or replaces –– the official meals provided them. Nearly every evening in the San Francisco County jails, inmates make ““spread,”” the generic term for this cuisine, out of dried ramen noodles and ingredients saved from their meal trays or purchased on weekly commissary orders. Based on a series of over thirty interviews, inmate's recipes indicate wide ethnic variations in spread, as well as skills in inventing pies and other desserts. Obtaining ingredients and sharing spread establishes bonds between individuals and groups within the jail setting. As both product and practice, spread's significance emerges out of its oppositions –– in appearance, taste, and origins –– to jail food. According to the inmates, despite its adherence to nutritional standards, the jailhouse diet represents monotony, insufficiency, and a lack of autonomy; spreading thus provides a creative and social outlet that counters the constraints of incarceration.

Sign in / Sign up

Export Citation Format

Share Document