Transportation Network Companies Services Research Articles

Greenhouse Gas Emissions and Potential for Electrifying Transportation Network Companies in Toronto

This study investigates the spatial and temporal patterns of greenhouse gas (GHG) emissions from transportation network companies (TNCs) in Toronto, Canada. TNC services primarily consist of short-distance trips in the central business district. Deadheading—driving without passengers—contributes 40% of TNC GHG emissions, and part-time drivers had a higher deadheading proportion than full-time drivers. Pooled TNC trips account for 10% of total trips, and just 27% of pooled trips resulted in multiple passengers sharing a vehicle on the same route. The GHG implications of pooled trips were compared with other TNC services and with a consumer driving their own private vehicle, through the estimation of median trip emission intensities of each TNC service in grams of CO2eq per passenger-km, while accounting for deadheading. Non-pooled internal combustion engine (ICE) ride-hailing trips have a median emission intensity 61% higher than that of a single-occupancy private vehicle. The median emission intensity for a pooled ICE ride-hailing trip is 20% higher than that of driving one’s own vehicle. Vehicle electrification provides a 91% daily GHG emission reduction for this fleet. This reduction is mainly achieved through full-time drivers, who, on average, achieve three times as much GHG savings per electrified vehicle compared with part-time drivers.

Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs

Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve the efficiency of passenger pick-ups, thereby reducing traffic congestion and contributing to the overall sustainability of the program. Previous research has proposed sophisticated machine learning and neural-network-based models to predict the short-term demand for taxi or TNC services. However, few of them jointly consider both modes, even though the short-term demand for taxis and TNCs is closely related. By enabling information sharing between the two modes, it is possible to reduce the prediction errors for both. To improve the prediction accuracy for both modes, this study proposes a multi-task learning (MTL) model that jointly predicts the short-term demand for taxis and TNCs. The model adopts a gating mechanism that selectively shares information between the two modes to avoid negative transfer. Additionally, the model captures the second-order spatial dependency of demand by applying a graph convolutional network. To test the effectiveness of the technique, this study uses taxi and TNC demand data from Manhattan, New York, as a case study. The prediction accuracy of single-task learning and multi-task learning models are compared, and the results show that the multi-task learning approach outperforms single-task learning and benchmark models.

Integrating transit and TNC services to improve job accessibility: Scenario analysis with an equity lens

With the rapid growth of Transportation Network Company (TNC) services and the continued decline of transit ridership, existing research has proposed and some transit agencies have implemented programs that integrate transit and TNC services. This paper expands the research area to examine the equity implications of such integrations, focusing on job accessibility improvements for low-income workers. We develop an analytical framework that compares improvements in accessibility to jobs under different hypothetical scenarios in which TNC travel serves as the last-mile connection of transit services. Using the city of Chicago for the case study, this research confirms that such transit-TNC integration increases job accessibility for all low-income workers throughout the city, but it also pinpoints nuanced differences in the accessibility improvements among workers of different races, ethnicities, and sexes during peak and off-peak hours.

Results of the First Large-Scale Survey of Transportation Network Companies Use in the Bay Area

Transportation network companies (TNCs) such as Uber and Lyft have grown tremendously over the last decade, particularly in the San Francisco Bay Area. Nonetheless, relatively little publicly available data exist about the users of these services, their travel behaviors, volume of use, the times and locations of TNC trips, and how TNC services are affecting transportation system performance overall. This paper describes the methods and descriptive results of the first large-scale smartphone-based TNC user survey conducted in the California Bay Area in the fall 2018 and spring of 2019.

Integrating shared mobility services with public transit in areas of low demand

The increasing demand for shared mobility services has led to multiple Public-Private Partnerships (PPP), where public transit agencies and mobility providers work together to increase transit service coverage. However, several partnerships have failed due to budget constraints and lack of ridership. Furthermore, the limited data-sharing agreements restrict the analysis and dissemination of outcomes. This paper analyzes the integration of shared-mobility services into the public transit system using data from pilot programs in Austin, Texas. The use of Transportation Network Companies (TNCs) and microtransit service is evaluated as first-mile-last-mile (FMLM) and door-to-door solutions. The associated TNC, microtransit, and bus transit demand are analyzed using ridership data, and the trip characteristics are discussed and compared. Then, after controlling for confounding variables through matching, a difference-in-differences (DID) model is used to infer the effect of microtransit on bus transit demand. Specifically, by evaluating bus ridership before and after implementing the microtransit program, the DID model estimates the change in bus demand attributed to microtransit. The results suggest that the TNC services, focusing only on FMLM, were not as popular as microtransit services offering FMLM and intrazonal trips. Even though the microtransit trips were longer and, in some cases, the driver response times were higher than for TNC trips. Also, the DID model demonstrates that the microtransit service did not substantially impact the bus transit ridership, suggesting that the trips were mainly used for intrazonal door-to-door trips and not for FMLM trips. A further discussion highlights the need for performance metrics beyond demand and costs to capture the benefits of these partnerships for improving mobility, safety, and customer experience.

The relationship between ridehailing and public transit in Chicago: A comparison before and after COVID-19

As Transportation Network Companies (TNCs) have expanded their role in U.S. cities recently, their services (i.e. ridehailing) have been subject to scrutiny for displacing public transit (PT) ridership. Previous studies have attempted to classify the relationship between transit and TNCs, though analysis has been limited by a lack of granular TNC trip records, or has been conducted at aggregated scales. This study seeks to understand the TNC-PT relationship in Chicago at a spatially and temporally granular level by analyzing detailed individual trip records. An analysis framework is developed which enables TNC trips to be classified according to their potential relationship with transit: complementary (providing access to/from transit), substitutive (replacing a transit alternative), or independent (not desirably completable by transit). This framework is applied to both regular operating conditions and to early stages of the COVID-19 pandemic, to identify the TNC-PT relationship in these two contexts. We find that complementary TNC trips make up a small fraction of trips taken (approximately 2%), while potential independent trips represent 48% to 53% and potential substitution trips represent 45% to 50%. The percentage of substitution trips drops substantially following COVID-19 shutdowns (to around 14%). This may be attributed to a reduction in work-based TNC trips from Chicago's north side, indicated by changes in spatial distributions and flattening of trips occurring during peak hours. Furthermore, using spatial regression, we find that an increased tendency of TNC trips to substitute transit is related to a lower proportion of elderly people, greater proportion of peak-period TNC travel, greater transit network availability, a higher percentage of white population, and increased crime rates. Our findings identify spatial and temporal trends in the tendency to use TNC services in place of public transit, and thus have potential policy implications for transit management, such as spatially targeted service improvements and safety measures to reduce the possibility of public transit being substituted by TNC services.

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.

Transportation Users’ Attitudes and Choices of Ride-Hailing Services in Two Cities with Different Attributes

The rapid technological developments in the 21st century created new opportunities for shared-use economy applications around the globe. Among other services, Transportation Network Companies (TNCs) like Uber and Lyft emerged in the US as a transportation alternative that offered a higher level of availability, reliability, and convenience than traditional modes. However, TNCs deployment was also blamed for increases in vehicle miles traveled (VMT) in large cities that embraced TNC services early on. Concerns about TNC adoption are also magnified by the current controversy in policy and legislation as to the regulation of TNCs. These new realizations create a need to examine the transportation users’ attitudes and perceptions regarding ride-hailing service, after nearly a decade of service in the Unites States market. In doing so, this paper compares and contrasts results from two recently completed studies aiming at creating links between socio-demographic factors and TNC use. The paper describes the methods employed to collect the data and presents findings from the analysis of 790 users’ responses in the Birmingham, AL and Miami Beach, FL markets. The study documents preferences and attitudes toward TNCs and highlights similarities and differences in travel behaviors related to local considerations. Moreover, the study uses the Least Absolute Shrinkage and Selection Operator (Lasso) method to identify predictors for TNC use based on the users’ responses in Birmingham and Miami Beach case studies. Vehicle availability and waiting time emerged as the only significant predictors for the Birmingham region whereas vehicle ownership, vehicle use, residency, and prior use of transit and TNC where some of the predictors identified for the Miami Beach area. Understanding the characteristics of TNC users and the leading reasons that drive people towards the use of TNCs services is expected to help transportation agencies and TNC providers in their efforts to plan for transportation services that meet customer needs in the future.

Mode choice behavior for access and egress connection to transit services

In the aims to investigate the potential market of Transportation Network Companies (TNCs) to serve as first/last mile connection for transit services, this study investigated the influential factors that affect transit users’ choices of access and egress modes, including TNC or taxi, drive alone (park and ride), carpool (kiss and ride, carpool or shuttle), Micromobility modes (bike-sharing, scooters), walking, and wheelchair. The data for this study comes from a transit on-board survey conducted in Spring 2017 for the Orlando metropolitan area. Moreover, the Smart Location Database was also integrated to investigate how land use characteristics may contribute to users' choice for access and egress modes. Separate multinomial logit models were developed to investigate the mode choice for access and egress links, respectively. Various personal, trip, and land use characteristics showed significant impacts. Particularly, trips going to airports or universities/colleges had much higher probabilities of using TNC for access and egress purposes, while sports events and medical visits were less likely to be connected through TNC services. Visitors were also less likely to start their trips with TNCs. A longer distance between the origin and the transit service showed positive impacts on the use of TNC services. Higher household income also showed a positive influence on TNC usage. In view of land use characteristics, higher employment and household entropy, and high diversity at the origin reduced the probability of using motorized modes, including TNCs, for access purposes. On the destination side, high regional diversity seemed to encourage the use of TNCs and drive alone modes for egress purposes.

Are drivers cool with pool? Driver attitudes towards the shared TNC services UberPool and Lyft Shared

The transportation network companies (TNCs) Uber and Lyft have introduced shared ride services, called “UberPool” and “Lyft Shared,” which use real-time cyber-connectivity to match travelers with similar origins and destinations so they can share discounted rides. It is hoped this will reduce the amount of vehicle miles traveled that TNCs are adding to the roads. However, previous evidence suggests that drivers and passengers have some dissatisfaction with these services. This paper uses a survey of 309 TNC drivers to examine the driver experience with providing shared rides. We find that in the aggregate drivers are considerably less satisfied with providing shared trips compared to solo trips with services such as UberX and Lyft Classic. There are a number of sources of dissatisfaction. Although some feel shared services add to their earnings, more drivers perceive their compensation for shared rides to be unfair. Further, although drivers sometimes enjoy the social interactions that Pool and Shared generate, many drivers complain that customers are often unhappy due to issues like trips taking longer than expected and friction between passengers. Finally, drivers feel serving shared trips is difficult and stressful work, due to things like routing and pick-up instructions that sometimes seem irrational and change frequently, difficult pick-ups and drop-offs, and long trips. We offer suggestions for improving driver satisfaction with Pool/Shared by raising and restructuring driver compensation, better publicizing the ways in which Pool/Shared increase driver incomes, increasing ridership through means such as better advertising of shared services and raising the price of solo travel, improving the information given to drivers, incentivizing good passenger behavior, and improving the passenger experience.

Eliciting preferences of TNC users and drivers: Evidence from the United States

Transportation Network Companies (TNCs) are changing the transportation ecosystem, but micro-decisions of travelers and TNC drivers need to be better understood to assess their system-level impacts. Using a unique sample (N = 11,902) of the U.S. population residing in TNC-served areas, we estimate preferences of TNC travelers for a) being a rider, a driver, or a non-user of TNC services; and b) to use pooled ridesourcing. In addition, using a large sample of TNC drivers, we estimate their inclination to a) switch to vehicles with better fuel economy; and b) buy, rent or lease a new vehicle with driving for TNCs being a major contributing factor. The population-weighted statistical analysis indicates that TNCs are mainly attracting personal vehicle users as riders, without substantially affecting demand for transit. Moreover, around 10% of TNC users reported postponing the purchase of a new car due to the availability of TNCs. Using multinomial logistic regression, we find that the likelihood of being a TNC user increases with age until 44 years and decreases afterwards. Older suburban TNC users with higher vehicle ownership are less likely to pool rides. Among TNC drivers who work daily, 65% indicated that driving for TNCs was a consideration in vehicle purchase decisions. Households with postgraduate drivers who drive daily and live in metropolitan regions are more likely to switch to fuel-efficient vehicles. These findings can inform transportation planners and TNCs in developing policies to encourage pooled ridesourcing and to improve the average fuel economy of the TNC fleet.

Do transportation network companies reduce public transit use in the U.S.?

The rise of transportation network companies (TNCs) in recent years has revolutionized urban transportation across the globe. Most noticeably, the increased popularity of TNC services coincides with the declining trend of public transit ridership in the U.S. The impact of TNCs on urban public transit needs to be carefully evaluated in light of the potential implications of any future policy changes in transit funding and planning. Using top 50 U.S. agency-level data from 2007 through 2017, this study examines the effect of TNCs and transit effectiveness on public transit ridership in the U.S. We find that (1) transit effectiveness of both bus and rail transits declined over the study period; (2) TNC availability increased rail transit ridership in 2015; (3) transit effectiveness was highly significant for public transit, and when examining its effect year-by-year, rail transit effectiveness trumped TNC availability; (4) TNCs are neither a complement nor a substitute of bus transit.

Deep ${Q}$ -Network-Based Route Scheduling for TNC Vehicles With Passengers’ Location Differential Privacy

The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers’ revenues. From the TNC drivers’ side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings by using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning-based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment, such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep ${Q}$ -network-based route scheduling algorithm for vacant TNC vehicles based on distributed framework, which makes the server closer to the terminal users and accelerates the training speed. Furthermore, we apply the geo-indistinguishability scheme based on differential privacy to preserve the sensitive location information uploaded by the passengers. We evaluate the proposed algorithm’s performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

Socioeconomic and usage characteristics of transportation network company (TNC) riders

The widespread adoption of smartphones followed by an emergence of transportation network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.

Expanding Seniors’ Mobility through Phone Apps: Potential Responses from the Private and Public Sectors

Seniors (over 64 years) are a growing demographic. Some seniors risk social exclusion given the lack of transportation options. Using qualitative methods, I investigate how transportation network company (TNC) services such as Lyft and Uber present an alternative for seniors’ mobility in comparison to taxis. I use technology acceptance models as a lens and explore the challenges that TNC services present for seniors. This analysis suggests that emerging transportation services—including self-driving cars—may present a boon for seniors if stakeholders from the private and public sectors can increase the ease of use of these new forms of mobility.

Legislating Transportation Network Companies

The rapid expansion of transportation network companies (TNCs), such as Uber and Lyft, has led policy makers across the country to consider legislation to legalize and regulate TNC operations. In 2012, TNCs introduced a new travel option that used digital technology to provide an on-demand and highly automated private ride service, which has received support from consumers and investors. The emergence of TNCs has generated uncertainty about the legal status of TNC services, criticism from the taxicab industry, and concerns about public safety. In almost every U.S. state, policy makers have considered TNC legislation to address these issues, but there is no comprehensive source of information on the content of these policies. In this study, researchers systematically compiled a database of state TNC legislation and evaluated a set of policy issues addressed in the legislation. Thirty-four states and Washington, D.C., enacted legislation to authorize TNC operations through May 2016. These laws addressed policy areas including permits and fees, insurance and financial responsibility, driver and vehicle requirements, operational requirements, passenger protections, data reporting, and regulatory and rulemaking authority. This database can help policy makers navigate the evolving policy considerations presented by the rising popularity—and accompanying controversy—of TNCs. Key policy questions that emerged from this review of state TNC legislation include whether to regulate TNCs; if so, at what level of government; how to harmonize TNC policies with existing taxi and transportation policies; and how to address public safety without suppressing market competition.

Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators

Transportation network companies (TNC) which use smartphone apps to connect travelers to drivers for point to point, intra-city trips have expanded rapidly in recent years, yet the impacts of their services on urban transportation systems, for good or ill, are not fully understood. These services may increase access to transportation for some individuals, but it is unclear whether these benefits are equitably distributed across different neighborhoods. In this paper, we explore the spatial variability in wait times for a TNC vehicle throughout the Seattle, WA region, testing whether areas with lower average income or a greater percentage of minorities experience different waiting times than other areas. We collected approximately 1 million observations of estimated waiting times, quasi-randomly sampled over approximately two months in 2015. We analyzed spatial and temporal patterns using local regression, which suggested lower wait times in densely populated areas of the Seattle region, and the lowest wait times during midday hours. We developed a spatial error regression model to investigate relationships between wait times and socioeconomic indicators at the census block group (CBG) level. We find that conditional on other covariates, expected waiting times are longer in CBGs with higher average income, and shorter in CBGs with greater population density and employment density. After adjusting for differences in density and income, a higher percentage of minorities in a CBG is associated with longer waiting times late at night and shorter waiting times during the day, with an average effect of close to zero. Geographically weighted regression indicates that the strength, and in some cases the sign, of these relationships varies throughout the Seattle region. Overall, the results suggests that transportation network companies offer higher performance in dense urban areas, and that adequate access to TNC services is not necessarily restricted to areas that are “white and wealthy.”