01659712

Component

https://doi.org/10.1177/0361198118798459

https://doi.org/10.1177/0361198118798459

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.

18-05899

English

pp 192-202

2018

Transportation Research Record: Journal of the Transportation Research Board

Print

Figures (1) ; References (11) ; Tables (1)

Application programming interfaces; Data collection; Information processing; Methodology; Paratransit services; Ridesourcing; Travel

Lyft; Uber

San Francisco (California)

Data and Information Technology; Public Transportation

TRIS, TRB, ATRI

Jan 8 2018 11:31AM

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• A Methodology to Assess the Quality of Travel Time Estimation and Incident Detection Based on Connected Vehicle Data
• Application of the “Foot-in-the-Door” Compliance Technique for Traveler Intercept Surveys
• Carsharing Trip Characteristic Analysis: Do Users Choose Carsharing Rather Than Taxi to Economize?
• Characteristics and Temporal Stability of Recurring Bottlenecks
• Closing the Loop: Improving Transit through Crowdsourced Information
• Cross-Vendor and Cross-State Analysis of GPS Probe Data Latency
• Discovering Regularity in Mobility Patterns to Identify Predictable Aggregate Supply for Ridesharing
• e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel Data
• Enriching Activity-Based Models using Smartphone-Based Travel Surveys
• Exploring the Relationships between the Non-Work Trip Frequency and Accessibility Based on Mobile Phone Data
• Exploring the Spatial Dependence and Selection Bias of Double Parking Citations Data
• Factors Affecting Interview Duration in Web-Based Travel Surveys
• Identifying Temporal and Spatial Characteristics of Residents’ Trips from Cellular Signaling Data: Case Study of Beijing
• Integrated Incident Decision-Support using Traffic Simulation and Data-Driven Models
• Interstate Speed Profiles
• Is There a Limit to Adoption of Dynamic Ridesharing Systems? Evidence from Analysis of Uber Demand Data from New York City
• Network Level Clearance Assessment using LiDAR to Improve the Reliability and Efficiency of Issuing Over-Height Permits on Highways
• Performance Measures for Characterizing Regional Congestion using Aggregated Multi-Year Probe Vehicle Data
• Reconstructing Vehicle Trajectories to Support Travel Time Estimation
• Resampling Methods for Estimating Travel Time Uncertainty: Application of the Gap Bootstrap
• Using National Performance Management Research Data Set for Corridor Performance Measures: A US-281N Corridor Case Study
• Wind Data Collection and Analysis of Topographical Features along a Highway for Traffic Safety Assessment Based on Mobile Mapping Technology