TNC partnerships

This paper identifies major aspects of ridesourcing services provided by Transportation Network Companies (TNCs) which influence vehicles miles traveled (VMT) and energy use. Using detailed data on approximately 1.5 million individual rides provided by RideAustin in Austin Texas, we quantify the additional miles TNC drivers travel: before beginning and after ending their shifts, to reach a passenger once a ride has been requested, and between consecutive rides (all of which is referred to as deadheading); and the relative fuel efficiency of the vehicles that RideAustin drivers use compared to the average vehicle registered in Austin.

Investigating emerging transportation services is critical to forecasting mode choice and providing appropriate infrastructure. One such infrastructure is parking, as parking demand may shift with the availability of ride-hailing services. This study uses passenger surveys collected when driving for Uber and Lyft in the Denver, Colorado, region—to gather quantitative and qualitative data on ride-hailing and analyze the impacts of ridehailing on parking, including changes in parking demand and parking as a reason to deter driving. The study also examines relationships between parking time and cost. This includes building a classification tree-based model to predict the replaced driving trips as a function of car ownership, destination land type, parking stress, and demographics.

Ride-hailing is a climate problem for two primary reasons. First, a typical ride-hailing trip is more polluting than a trip in a personal car, mainly as a result of “deadheading”the miles a ride-hailing vehicle travels without a passenger between hired rides. The second reason is that ride-hailing is not just replacing personal car trips; instead, it is increasing the total number of car trips. In the absence of ride-hailing, many would-be ride-hailing passengers would take mass transit, walk, bike, or forgo the trip. This report focuses on ride-hailing, but many of its findings and recommendations apply to taxis as well. For example, electrification, increased pooling, and improved coordination with mass transit would lessen the negative impacts of taxi service on transportation systems and the environment.

Ridesharing holds promise as a more efficient and sustainable version of emergent ride-hailing services. However, the adoption of pooled services in which individuals pay a reduced fare to share a portion of their ridehailing trip with other passengers has substantially lagged in popularity to the standard single-party services offered by Uber and Lyft in many American cities. To help guide policies and programs targeted at increasing pooling shares, this study analyzes data collected during fall 2017 from an in-vehicle intercept survey of 944 ride-hailing passengers in the Greater Boston region. These data, which describe the socioeconomic background, mobility options, and trip context of single-party and pooled ride-hailing survey respondents, were used to identify differences in the trip patterns and individual characteristics of passengers adopting the two service types and then estimate the individual-level social and trip-related predictors of ridesharing for different purposes.

The impacts of ride-hailing services on the transportation system have been immediate and major. Yet, public agencies are only beginning to understand their magnitude because the private ride-hailing industry has provided limited amounts of meaningful data. Consequently, public agencies responsible for managing congestion and providing transit services are unable to clearly determine who uses ride-hailing services and how their adoption influences established travel modes, or forecast the potential growth of this emergent mode in the future. To address these pressing questions, an intercept survey of ride-hailing passengers was conducted in the Greater Boston region in fall 2017. The responses, which enabled a robust description of ride-hailing passengers for the region, were used to analyze how new on-demand mobility services such as Uber and Lyft may be substituting travel by other modes.

On-demand ridesourcing services from transportation network companies (TNCs), such as Uber and Lyft, have reshaped urban travel and changed externality costs from vehicle emissions, congestion, crashes, and noise. To quantify these changes, we simulate replacing private vehicle travel with TNCs in six U.S. cities.

This report builds on an on-going research effort that investigates emerging mobility patterns and the adoption of new mobility services. In this report, the authors focus on the environmental impacts of various modality styles and the frequency of ridehailing use among a sample of millennials (i.e., born from 1981 to 1997) and members of the preceding Generation X (i.e., born from 1965 to 1980). The total sample for the analysis included in this report includes 1,785 individuals who participated in a survey administered in Fall 2015 in California. In this study, the researchers focus on the vehicle miles traveled, the energy consumption, and greenhouse gas (GHG) emissions for transportation purposes of various groups of travelers. 

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.

Ride-haling such as Uber and Lyft are changing the ways people travel. Despite widespread claims that these services help reduce driving, there is little research on this topic. This research paper uses a quasi-natural experiment in the Denver, Colorado, region to analyze basic impacts of ride-hailing on transportation efficiency in terms of deadheading, vehicle occupancy, mode replacement, and vehicle miles traveled (VMT).

This report builds on an on-going research effort that investigates emerging mobility patterns and the adoption of new mobility services. In this report, the authors focus on the environmental impacts of various modality styles and the frequency of ridehailing use among a sample of millennials (i.e., born from 1981 to 1997) and members of the preceding Generation X (i.e., born from 1965 to 1980). The total sample for the analysis included in this report includes 1,785 individuals who participated in a survey administered in Fall 2015 in California. In this study, the researchers focus on the vehicle miles traveled, the energy consumption and greenhouse gas (GHG) emissions for transportation purposes of various groups of travelers. 

From June to October 2019, researchers at Urbanism Next identified 249 new mobility and AV delivery pilot projects, completed and in-progress, in the United States and Canada. Relevant information about all 249 pilot projects, including sponsoring organizations, key dates, and geographic area, are recorded in this file. This data set provided the foundation of the report Perfecting Policy with Pilots. Ultimately, Urbanism Next used information from 220 of the pilots in the report. The new mobility modes included in this data set include shared micromobility devices such as e-scooters and bikes, transportation network company partnerships, microtransit, autonomous passenger vehicle pilots, autonomous delivery pilots, and non-autonomous goods delivery pilots. The information collected by Urbanism Next researchers is limited to publicly available information collected from online resources, such as reports, government websites, public and private press releases, and news articles as well as a limited number of follow-up phone calls requesting information.

The purpose of this study is to go beyond cataloging pilot projects to determine the lessons learned, emerging trends and considerations, and examples of promising practices from pilot projects in the United States and Canada. Researchers assessed 220 pilot projects and 11 case studies. Based on that assessment, they recommend 10 actions for pilot projects generally. The study resulted in 31 lessons learned organized by pilot goals, evaluation, implementation, outcomes, and policy and infrastructure implications.

In order to ease congestion downtown and relieve pressure on parking during the holiday season, the city of Boulder, Colorado engaged in a partnership with Lyft, Uber, and a taxi company zTrip. The pilot project, which ran for 11 weeks, involved the city subsidizing rides for residents of Boulder who travelled downtown using one of the partnership companies. This report presents the motivation, design, operation, and results of the pilot.

The Go Centennial pilot was the first pilot project in the country where a government or transit agency fully subsidized first and last-mile rides provided by a transportation network company (in this case Lyft). The Go Centennial pilot was launched in Centennial, Colorado on August 2016 and ran for six months until February 2017. This final report is one of the most comprehensive evaluations of a TNC partnership pilot, and details the goals, preexisting conditions, and procurement and design of the pilot. The report concludes with a qualitative and quantitative analysis of the pilot and a set of lessons learned and key takeaways.

This report evaluates the Massachusetts Bay Transportation Authority's "The RIDE" pilot project. The pilot project, which is still in operation today, is an example of a public-private partnership, where the MBTA subsidizes ADA paratransit rides provided by Uber, Lyft, and Curb their traditional ADA paratransit customers. The analysis and modeling in the report is based off of data provided by the MBTA stretching from the pilot's start date in October 2016 through March 2018.

"The Pinellas Suncoast Transit Authority (PSTA), in Pinellas County, FL, was the first transit agency in the US to sign a service provision agreement with a transportation network company (TNC) to offer joint first/last-mile service subsidized by public dollars. PSTA’s “Direct Connect” pilot allows riders to get to and from bus stops in a taxi, wheelchair-accessible vehicle (WAV), or Uber TNC vehicle at a subsidized rate. PSTA’s overall experience developing, managing, and adapting the Direct Connect pilot provides insight into what transit agencies can expect when working with on-demand service providers. While operating on a larger scale, in a denser environment, or with a different ridership base may have offered different lessons in implementation, the Direct Connect pilot’s service design shows what is necessary for a successful launch of a pilot program: good data and transparency from all parties, as well as concrete plans for outreach and evaluation."

With a pilot program in Summit, New Jersey, the ride-hail giant is looking to replace commuter parking lots.

"This report’s findings, draw on a thorough investigation of active and inactive partnerships between transit agencies and TNCs, designed to enhance understanding of project development and structure and how those were achieved. While partnerships between transit agencies and private mobility providers are not new, partnerships with TNCs create unique opportunities and challenges as both parties work toward mutually beneficial program models. This research is informed by dozens of transit agency surveys and follow-up interviews, past literature, and interviews with TNC policy staff and industry experts as well as FTA representatives, and provides a Partnership Playbook so that the transit industry can be more deliberate in its approach to working with TNCs."

A San Francisco judge ruled that Motivate, the bike-share operator that Lyft purchased one year ago, has exclusive rights to rent both docked and dockless bikes in the city.

Driverless vehicles have unveiled in New York City. For now, they can be found shuttling people around a loop at the Brooklyn Navy Yard.

Uber Eats will now deliver food to customers in the most unexpected of places—restaurants. The food delivery and pick-up app's "Dine-in" feature is now being pilot-tested in Dallas, Austin, Phoenix and San Diego, according to an Uber spokesperson.

Cities that turn to technology companies to save their transit systems are bound to be disappointed by the outcome. This article looks at Pinellas County, Fla., whose transit authority was the first in the country to supplement its bus service with taxpayer-subsidized rides from Uber in February, 2016.

New York City’s cap on the number of for-hire vehicles that can operate on its streets will continue—and a new provision will, in theory, limit the amount of time FHVs can spend idling without passengers.

The purpose of this report is to provide information on TNC activity in San Francisco, in order to help the San Francisco County Transportation Authority (Transportation Authority) fulfill its role as the Congestion Management Agency for San Francisco County. The report is also intended to inform the Transportation Authority board which is comprised of the members of the San Francisco Board of Supervisors, as well as state and local policy-makers in other arenas, and the general public, on the size, location and time-of-day characteristics of the TNC market in San Francisco.

The next time you need to book an Uber home from Pearson Airport, you won’t need your phone to do so. Toronto’s largest airport has just implemented a new Uber pilot that makes it easier for travellers to get home, as smartphones are no longer needed to book a ride.