Abstract
In disadvantaged neighborhoods residents often must travel longer distances to and from transit stops, which creates challenges for human mobility. Transportation network companies (TNCs) (e.g., Uber) revolutionized mobility by detaching car access from car ownership. However, economic barriers to these services have limited mobility increases in disadvantaged neighborhoods. Here we will investigate the inequalities in TNC and public transit service across the demographic groups under normal and rare events, and create simulation tools to promote equitable mobility.
Description
Increasing mobility for all requires equitable transportation access regardless of location. Within households that do not own personal vehicles, public transit has been instrumental in providing mobility to jobs and other essential services. Now ride-hailing services from transportation network companies (TNCs), like Uber and Lyft, have revolutionized urban transportation by providing an on-demand service option for public transit dependent populations. Yet, the benefits and costs of these changes have been inequitably distributed, widening the gap between those with and without high levels of mobility. This inequity, partially seen in disadvantaged neighborhoods, stems from private ride-hailing firms having profit driven incentives, and less regulation than public transit agencies that need to balance economic and equity objectives in their decision-making. One striking example of this dichotomy can be seen in recent studies, which have found that some neighborhoods and individuals pay more for the same ride-hailing service, even when locations are as little as a few meters apart.1,2 Moreover, if TNCs complement public transportation they could potentially improve access for disadvantaged groups that rely on transit4. On the other hand, if TNCs displace public transit this would lessen mobility, and can severely inhibit disadvantaged neighborhoods that historically have not have many mobility options.
In our first Mobility21 Big Idea grant we assess: 1) how TNCs have impacted wage and employment across metropolitan areas, and 2) how long-term rare evets (i.e., COVID) have impacted the TNC demand patterns across a city. We will extend this work through this proposal by integrating bus ridership into the TNC analysis and identifying how TNC and bus usage shift during extreme weather events. This will help identify opportunities for reducing racial inequity (e.g., do low-income minority groups have sharp decreases in TNC access during heavy rain?), and support more equitable climate change mitigation (e.g., should buses dispatch more frequent routes to vulnerable groups during heavy rain?). This proposal aims to establish assess how transit and TNC demand changes for individuals across low and high-income, as well as minority populations during extreme weather conditions (e.g., heavy rain and heat waves). Our investigation will identify opportunities for public policies that may enhance transportation benefits, while mitigating private costs, social costs, and inequities in disadvantaged neighborhoods. To do this, we will (1) leverage historical data to econometrically estimate the causal impact of extreme weather events on bus and ride-hailing service operation disruptions and how this effect was distributed across riders served; (2) characterize how the level of service varies by demographic community area in multiple cities (i.e., Pittsburgh, Chicago and Austin), and how patterns of bus and ride-hailing service use shifts under extreme weather conditions; and (3) use simulation and optimization models to identify economic and policy incentives that could encourage equitable solutions to transportation disparities.
This work pushes forward Secretary Buttigieg’s priorities of improving mobility for disadvantaged communities and addressing racial inequity by evaluating how rare events (weather and COVID) have impacted ridership. This will be crucial to addressing system inequities as climate change becomes more intense.
Timeline
This proposal is a part of a larger 3-year plan. The first 1.5 years was funded by the Mobility21 Big Idea project funds. Prof. Michalek will serve as the lead advisor for Adam Kolig, and Prof. Nock and Prof Harper will serve as lead advisors for the second PhD student (Hanig). Prof. Davis will support both students, with a focus on survey and data analysis.
Currently, under a Mobility21 Big Idea grant, the team is investigating how the rise of TNCs has affected employment and wage opportunities across geographic areas and occupations. Further, in our current work we leverage mixed methods, which integrate statistical assessment of trends with rigorous qualitative interviews and surveys of users, drivers, and stakeholders to understand perceptions and mechanisms underlying trends. As an extension of our Big Idea grant would propose to test possible interventions (whether imposed by regulators or proactively and voluntarily implemented by TNCs) that could help alleviate these issues, such as “equity pricing,” or various forms of rules, incentives, and regulations. To address these gaps in understanding the economic and equity effects of ride-hailing services and to identify economic, technological, and policy options for improving equity, we propose the following tasks, spanning statistical analysis, qualitative methods, simulation, and optimization for normal operations as well as rare events.
In the first year, Koling began the econometric studies of Task 1 while Hanig studied detailed data on changes to TNC operations and populations served following the COVID-19 outbreak in Chicago and begins to integrate knowledge into a simulation for rare event responses. Both students have made successful progress on these objectives under the Mobility21 Big Idea project.
At the start of this proposal Koling will complete and publish the econometric tasks and the study of detailed data for Chicago and Austin and begin simulation and optimization modeling for normal TNC operations while Hanig completes her analysis of rare events in Chicago and Austin and continues interview work to support simulation/optimization for rare events. To ensure our work is relevant to the policy sphere we plan to receive feedback from the City of Pittsburgh. We will also continue collaborating with the Port Authority, who we have been meeting with monthly since the start of our Mobility21 Big Idea grant.
Following the completion of their analyses, Koling and Hanig will finalize and publish the simulation and optimization studies. The team will plan and execute policy outreach to local, state, and/or federal policymakers, as appropriate, depending on findings. One deliverable will be a policy memo for dissemination of our findings.
The detailed research tasks are as follows:
Task 1 Deep Dive City Data Analysis: Leverage high-resolution origin-destination ride-hailing data in Pittsburgh, Chicago, and Austin to characterize how TNC trips vary by neighborhood (i.e., census tract) geography, income, race, ethnicity, and major occupation. Assess the degree to which costs and benefits of TNCs identified in Task 1 are equitably distributed. [Leads: Nock, Harper, Michalek, Davis]
• Task 1a (normal operations): Identify trends in this high-resolution city data amidst normal operation periods
• Task 1b (rare events): Identify changes in trends during rare events, including extreme weather events before and after the onset of COVID-19 in Pittsburgh and Chicago.
During the project period we will also explore the potential to expand the analysis to other cities, and have started investigating available TNC data in New York. We will use COVID-19 data by county and city from a variety of sources such as C3-AI14 and USAFACTS15. SharedStreets initiative (https://sharedstreets.io/) as data become available.
Task 2 Simulation and Policy Assessment: Simulation modeling to test a variety of economic conditions, TNC policy, and public policy interventions that may help alleviate equity and employment disparity issues. These would incorporate the previously derived economic (ongoing work) and equity (Task 1) model estimates. [Leads: Michalek, Nock]
• Task 2a (normal operations): Leverage and refine existing models at CMU16 to simulate TNC operations and observe how alternative policy proposals affect both TNC operations and outcomes. Such possible interventions could include congestion and air pollution taxation, equity pricing (like surge pricing but for underserved areas), ride quotas, ride subsidies, or a dedicated portion of TNC fleets that purely operate in underserved areas. As part of these simulation models, we would need to consider (1) managing cost, environmental sustainability, and equality objectives, (2) undue burden on minority populations (e.g., greater wait times in low-income and minority neighborhoods), (3) system efficiency (including economic efficiency), and (4) unintended consequences.
• Task 2b (rare events): Expand aforementioned existing models at CMU16 to include current COVID essential worker transportation modeling to simulate TNC operations and observe how alternative policy proposals affect both TNC operations and outcomes amidst the pandemic. Such possible interventions could be subsidies or contract duties for drivers to serve users during emergencies or dedicated fleets of medical professionals taking users with potential symptoms (akin to Germany’s “corona taxis”17). Besides considering the four factors above in simulation models of normal operations, rare events have the added complexity of potential unwillingness of drivers to serve during rare events (i.e., driver compliance). We will capture driver compliance risks associated with rare events using compliance rate estimates from Task 3 as constraints in the model.
Strategic Description / RD&T
Deployment Plan
Addressing urban mobility, particularly in disadvantaged neighborhoods, requires a deep understanding of how transit service varies in both normal and rare conditions. Our deployment partner, the Port Authority of Allegheny County, is interested in understanding the economic and equity issues of ride-hailing services as a piece of urban mobility, as they both complement and compete with transit. While public transit agencies like Port Authority have processes to explicitly consider and balance effectiveness, efficiency, and equity for any service changes considered, private for-profit ride-hailing services do not necessarily need to balance these factors in a similar way, potentially resulting in different outcomes and leaving gaps for disabled riders, lower income communities, and other groups. Port Authority is interested in using the results of this research to help inform strategies for how they interact with ride-hailing services in Pittsburgh and what kinds of policies could potentially improve equity outcomes without severe negative unintended consequences. Port Authority has also experienced massive changes in ridership with the COVID-19 pandemic, and understanding changes in ride-hailing trends during this period can help Port Authority gain a fuller picture of risks and opportunities for the transportation system and to inform planning of fleet schedules.
We have also started conversations with a second deployment partner, the City of Pittsburgh, who is interested in how the service of the TNCs and public buses varies across the city under normal and rare conditions.
Expected Outcomes/Impacts
The goal of this project is to inform policies for improving mobility in disadvantaged neighborhoods. For example our work will push forward Secretary Buttigieg’s priorities of improving mobility for disadvantaged communities and addressing racial inequity by evaluating how rare events (weather and COVID) have impacted TNC and public bus ridership.
Month 1-4 Milestones:
1. Data acquired and cleaned, model specified, initial estimation complete and shared with deployment partners (Port Authority and City of Pittsburgh) for feedback. Submit Task 1a for publication.
2. Obtain, clean, and conduct first analysis of city-level data.
3. Begin constructing simulation models for rare events.
Month 5 - 8 Milestones:
1. Finalize and publish assessment of city-level trends through a policy brief. Receive feedback from City of Pittsburgh prior to publication.
2. Continue development of simulation models for rare events and begin constructing optimization and simulation models for normal operations leveraging prior CMU work in this area.
Month 9 - 12 Milestones:
1. Complete and publish simulation, optimization, and policy analysis tasks. Disseminate findings and recommendations to deployment partners (Porth Authority and City of Pittsburgh), TNCs, media and policymakers.
2. Finalize and publish econometric analyses. Disseminate to deployment partner, TNCs, media, and policymakers through a policy brief.
3. Develop and implement a strategy to disseminate key findings and recommendations from the entire project to decision-makers, which may include policy briefs, videos, media outreach (e.g.: op-ed), and meetings with key decision-makers in industry and government.
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| alexdavis@cmu.edu |
Davis, Alex |
Carnegie Mellon University |
Co-PI |
Faculty - Untenured, Tenure Track |
| cdharper@andrew.cmu.edu |
Harper, Corey |
Carnegie Mellon University |
Co-PI |
Faculty - Untenured, Tenure Track |
| bethannh@andrew.cmu.edu |
Hockenberry, Beth |
CMU |
Other |
Staff - Business Manager |
| akoling@andrew.cmu.edu |
Koling, Adam |
Carnegie Mellon University |
Other |
Student - PhD |
| jmichalek@cmu.edu |
Michalek, Jeremy |
Carnegie Mellon University |
Co-PI |
Faculty - Tenured |
| dnock@andrew.cmu.edu |
Nock, Destenie |
Carnegie Mellon Universirty |
PI |
Faculty - Untenured, Tenure Track |
Budget
Amount of UTC Funds Awarded
$100000.00
Total Project Budget (from all funding sources)
$200004.00
Documents
| Type |
Name |
Uploaded |
| Data Management Plan |
Data_Plan_Equity_Effects_of_Rare_Events_on_TNC.pdf |
Feb. 23, 2022, 7:51 p.m. |
| Presentation |
Slides-BigIdea-Part_2___Nock.pptx |
Feb. 23, 2022, 7:57 p.m. |
| Progress Report |
396_Progress_Report_2022-09-30 |
Sept. 27, 2022, 1:48 p.m. |
| Publication |
COVID-19 public transit precautions: Trade-offs between risk reduction and costs |
March 30, 2023, 6:07 a.m. |
| Publication |
What Stay-At-Home Orders Reveal About Dependence on Transportation Network Companies |
March 30, 2023, 6:08 a.m. |
| Publication |
Ridehailing Service Equity in Normal and Rare Conditions |
March 30, 2023, 6:09 a.m. |
| Progress Report |
396_Progress_Report_2023-03-30 |
March 30, 2023, 11:10 a.m. |
| Final Report |
Final_Report_-_396.pdf |
Aug. 3, 2023, 6:31 a.m. |
Match Sources
No match sources!
Partners
| Name |
Type |
| Port Authority of Allegheny County |
Deployment & Equity Partner Deployment & Equity Partner |
| City of Pittsburgh |
Deployment Partner Deployment Partner |
