Partnerships between Agencies and Transportation Network Companies for Transportation-Disadvantage Populations: Benefits, Problems, and Challenges

Transportation network companies (TNCs), such as Uber and Lyft, offer a new mobility option to consumers. An increasing number of transit agencies work with TNCs, and different types of partnerships have formed. While these service models may serve the general population well, their implications for transportation-disadvantage populations, including older adults, individuals with disabilities, and low-income people, have not received enough attention. These populations are highly dependent on public transit services. Additionally, we have limited firsthand knowledge of challenges that hinder transit agencies and related human service agencies from building partnerships with TNCs. Can these agency/TNC partnerships accommodate the needs of transportation-disadvantage populations? This study explores these issues through a literature review and interviews with 16 related organizations in the State of Florida, where transportation-disadvantage populations are served through a coordinated system but the partnerships with TNCs are still limited. The paper first categorizes the existing agency/TNC partnership service models into three types and examines their benefits and problems in serving transportation-disadvantage populations. It then identifies different organizations’ perceptions of TNCs and the challenges for some agencies to work with TNCs. The general challenges include difficulty in estimating service demand, data sharing problems, hidden costs and staff efforts, training and safety issues, and the need of complementary vendors. The challenges specifically in rural areas are a lack of motivation and commitment among TNCs, affordability issues, and TNCs’ adaptation to the rural geography. These challenges in agency/TNC partnerships need to be addressed to serve the public better, including transportation-disadvantage populations.

Transportation Network Companies: Drivers’ Perceptions of Ride-Sharing Regarding Climate Change and Extreme Weather

The transportation sector is a major factor contributing to climate change. Transportation Network Companies (TNC) may become part of solutions to reduce emissions and their drivers play an important role in doing so. This study aims to understand TNC driver’s perceptions of climate change, to understand how climate change and extreme weather affects their business and how they see their role in contributing to or mitigating climate change. We conducted an in-person survey of TNC drivers in Nevada, USA, and analyzed the derived information with descriptive statistics and content analysis. Among the 75 TNC drivers, almost half believe climate change is happening and is caused by human activities. We found TNC drivers and their business are affected by extreme weather events. Currently the drivers do not see their role in mitigating climate change and lack the awareness of green initiatives already in place by TNCs’. We conclude that TNCs could increase their climate change responsibility by providing driver incentives for cars with reduced emissions or by geographically expanding customer incentives for using sustainable TNC options such as car-pooling. By doing so, TNC may play a role in reducing global greenhouse gas emissions and traffic congestion; thus, contributing to improved sustainable transportation practices.

Impact of Transportation Network Companies on Ground Access to Airports: A Case Study in Austin, Texas

This study proposes using intelligent transportation systems (ITS) and open-data sources to evaluate the impact of transportation network companies (TNCs) on ground access to airports. The unexpected interruption of the TNCs services in Austin, Texas, U.S., in 2016, is used as a natural experiment to provide a before-and-after analysis of the changes in the traffic conditions of the access area to the Austin-Bergstrom International Airport (ABIA). An analysis of variance (ANOVA) is implemented to determine whether the difference in speeds across periods is statistically significant, and the value of time for TNC-induced delay is estimated, using values of passengers’ willingness to pay for airport access travel time savings. Furthermore, a speed linear model is developed to assess the impact of TNC demand on ground access areas using trip information from an Austin-based TNC service. The main results suggest that airport ground access speeds were higher during the period that the TNCs were out of the city. The re-introduction of the services resulted in a speed reduction of 9% for the airport morning and 18% for the afternoon peak hours, translating to a total passenger cost of approximately $150+ (morning) and $400+ (afternoon) per hour. Furthermore, it was found that the number of TNC pick-up trips is a predictor of airport access speed and that the flight schedule can potentially be used to develop predictive speed models.

Associating ridesourcing with road safety outcomes: Insights from Austin, Texas

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nations sustainable development goals and worldwide vision zero efforts. The advent of transportation network companies and ridesourcing expands mobility options in cities and may impact road safety outcomes. We analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County, Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed-effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes, a 0.25% decrease in road injuries, and a 0.36% decrease in DWI offenses in Travis County. On the other hand, ridesourcing use is not significantly associated with road fatalities. This study augments existing work because it moves beyond binary indicators of ridesourcing availability and analyzes crash and ridesourcing trips patterns within an urbanized area rather than their metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on the transportation system’s safety, which may serve as a template for future analyses for other cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety and uncover the potential to achieve safer mobility systems with transportation network companies.

Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco

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.

Longitudinal Analysis of Transit-Integrated Ridesourcing Users and Their Trips

Several municipal transit agencies have partnered with transportation network companies to provide a range of services, but data restrictions have limited research on trip-level observations of transit-integrated ridesourcing users. The goal of this study was to understand how users’ trip-making behaviors adapted to a transit-integrated ridesourcing pilot in Waterloo, Ontario. This research conducted a longitudinal analysis of 178 unique users and temporal analyses of their 4,536 ridesourcing trips (rides) taken throughout the pilot from November 2018 to December 2019. Trip type and frequency changes over time were measured for frequent, average, and infrequent users. Transit, walking, and cycling alternatives to the pilot rides were generated and characterized based on their complementarity with transit. The number of unique users and daily ridership increased over time, as new users made their first trips and existing users made trips more frequently. Frequent users shifted toward less transit-competitive trip types whereas average and infrequent users had a sporadic but larger share of more transit-competitive trip types. The pilot was mostly used during off-peak service periods, when transit was less frequent, which suggests these systems are valuable for nonwork trips. Transit trip alternatives were not temporally competitive with rides. Cycling was competitive with 5% to 10% of rides and was consistently faster than walking and transit alternatives. Walking was not a practical alternative to rides in most cases. This analysis may inform other agencies of performance evaluation techniques for their transit-integrated ridesourcing pilots and the unique characteristics of trips taken by users of this mode.