Can Transportation Network Companies Replace the Bus? An ...

Can Transportation Network Companies Replace the Bus?

An Evaluation of Shared Mobility Operating Costs

Xavier Harmony

School of Public and International Affairs

Virginia Polytechnic Institute and State University

[email protected]

ORCID ID: 0000-0002-7092-399X

February 6, 2021

Word Count: 7,894

Can TNCs Replace the Bus? An Evaluation of Shared Mobility Operating Costs 2

Xavier Harmony Original Submittal

Abstract 1

Municipal governments struggle with providing accessible mobility for constituents 2 without overburdening them with service costs. While transit offers many advantages, the cost of 3 providing services can be prohibitive. Transportation Network Companies (TNCs) are a mobility 4

alternative. This research answers the following question: Can TNCs be economically feasible as 5 a replacement for bus? U.S. Federal Transit Administration (FTA) National Transit Database 6 (NTD) data was evaluated finding that while TNCs could replace transit in some instances (23% 7 of cases for an exclusive TNC option; 45% of cases for shared TNCs) most of the time bus will be 8 more cost effective. Three agency characteristics were identified to anticipate TNC cost 9

effectiveness: ridership, service area density, and average bus operating speeds. In conclusion, 10 while TNCs are unlikely to be able to replace transit completely in most cases, their flexibility 11

allows public entities to be more creative when making mobility policy and operational decisions. 12

13

Key Words 14 TNC; ride-hail; transit; break-even; operational cost; municipal; decision-making 15



1.0 Introduction 1

One of the struggles of municipal government is the challenge of providing accessible 2

mobility options for constituents without overburdening them with the cost of providing the 3 service. Transit systems offer many advantages as a mobility choice including high efficiency and 4 low user costs, however, criticisms of transit highlight the high cost of providing transit services 5 and the reliance on public subsidies (Litman, 2020; O’Toole, 2010). Consequently, some use these 6 criticisms as an economic argument to justify replacing transit with alternative mobility options. 7

One alternative discussed as a replacement to transit is transportation network companies (TNCs), 8 like Uber, partially subsidized by government (Gordon, 2019; Rauch & Schleicher, 2015; 9 Woodman, 2016). Although TNCs are a form of car travel, the mode’s spatial distribution looks 10 like transit (Brown, 2019) and its operations could be suitable for serving transit-dependent 11 populations (Jiao & Wang, 2020). A recent example tested the feasibility of TNCs as an alternative 12

to transit in Innisfil, an exurb of Toronto, Ontario (Bliss, 2019). In 2015, the town considered 13 implementing a fixed-route bus service to meet the growing mobility needs of the area. However, 14

after an estimate showed the cost of a new transit system, the town opted to provide subsidized 15 Uber rides instead. The Uber alternative was implemented in 2017 but the town soon found costs 16

escalating, from $150,000 in 2017 to an expected $900,000 in 2019, causing the town to reduce 17 the subsidy per trip and cap the total number of trips per month. The success of subsidized Uber 18

made the system less useful and less cost efficient. While this example was not successful, it leads 19 us to ask if and when TNCs could serve as a mobility replacement for transit. Specifically, this 20 research will answer the following question: Can transportation network companies be 21

economically feasible as a replacement for transit (specifically fixed-route bus)? Put another way, 22 can municipalities use TNCs to serve transit demand given the operating costs of both services? I 23

focus on fixed-route bus because it is the most common form of transit in U.S. municipalities, 24

there is less fixed infrastructure investment than other transit modes (lower “sunk costs”), and the 25

flexibility of the systems make it easier to change. Answering this research question could help 26 municipal government with the challenge of providing cost effective mobility. 27

I will answer this question by performing an analysis of transit systems in the United States 28

(U.S.) using univariate and bivariate analyses. The evaluation will rely on data from the Federal 29 Transit Administration’s (FTA) National Transit Database (NTD) to determine what attributes 30 make it feasible for TNCs to replace transit systems. Because the data relies on averaged values, I 31

have included a sensitivity analysis to demonstrate how changes in different metrics could impact 32 the results. Finally, the results of the analysis will be discussed, and conclusions made. 33

2.0 Literature Review 34

TNCs, otherwise known as ridesourcing or e-hailing, connect passengers and drivers 35 through online platforms like mobile applications. These online platforms allow customers to 36 reserve trips, make payments, and give feedback to the driver (Federal Transit Administration, 37

2020). Many TNCs offer different types of services but this paper only focuses on two varieties: 38 an exclusive ride option (e.g. UberX) where a customer makes a trip by themselves, and a shared 39 option (e.g. UberPool), also called ride-splitting, where customers are paired in real-time with 40 other travelers along the route (Federal Transit Administration, 2020). 41

Since the introduction of Uber a decade ago, much research has been dedicated to TNCs 42 with academics and practitioners alike attempting to determine how TNCs fit into the 43 transportation landscape. Do TNCs complement transit or are they are a competitor threatening to 44



make transit obsolete? TNC ability to complement transit has been the basis of many arguments 1 in favor of the service. In particular, researchers have noted TNCs role as a first mile/last mile 2

connection (Blodgett, Khani, Negoescu, & Benjaafar, 2017; Lazarus et al., 2018; Malalgoda & 3 Lim, 2019; Schaller, 2018), a resource to alleviate transit deserts (Jiao & Wang, 2020), and a 4 mobility option when transit service is infrequent or unavailable, like late at night (Feigon & 5 Murphy, 2016, 2018). However, the role of TNCs as a transit complement has been countered by 6 research that shows few TNC trips connect to transit (Schaller, 2018) with the introduction of 7

TNCs even decreasing transit ridership, especially for bus (Clewlow & Mishra, 2017; Malalgoda 8 & Lim, 2019). Considering the negative impact of TNCs on transit ridership, researchers have 9 discussed TNCs as a replacement for transit service. In particular, considering TNCs as a 10 replacement for low ridership routes, replacing transit in lower density and rural areas, and using 11 TNCs for specific needs like for people with disabilities (Alonso-González, Liu, Cats, Van Oort, 12

& Hoogendoorn, 2018; Blodgett et al., 2017). However, existing research has primarily considered 13 replaced a single route or a specific part of a service area with little research considering TNCs as 14 a complete substitute for transit, as some municipalities, like Innisfil, have considered. 15

Merlin (2017) included a systemwide analysis cost in his paper comparing TNC-equivalent 16 automated taxis and conventional bus transit in Ann Arbor, Michigan. They found that, when 17 compared to transit, automated taxis have shorter travel times and lower daily system costs but 18 higher greenhouse gas emissions. Merlin’s analysis assumed highly-autonomous vehicles, 19 excluding labor costs. This makes the results difficult to compare to the realities of current 20

transportation. In addition, Merlin assumed automatic taxis used fuel-efficient vehicles but did not 21 assume the same for buses, potentially inflating the benefit-cost of autonomous taxis. 22

While ample research has focused on TNC operations, comparatively little has focused on 23

cost, a knowledge gap previously identified by Schwieterman (2019). To partially fill this gap, 24

Schwieterman evaluated different origin-destination pairs in Chicago, Illinois, to compare 25 differences in passenger travel time and trip cost for both TNC and transit trips. They found that, 26 compared to transit, TNCs were 2.5-4.2 times more expensive for shared ride services (e.g. 27

UberPool) and 5.7-5.8 times more expensive for exclusive ride services (e.g. UberX). In addition, 28 TNC trips were up to 45.8% faster than transit trips except in downtown areas where results were 29

mixed. This could be because transit can be faster during peak periods (Feigon & Murphy, 2016), 30 which could particularly affect downtown trips. While this research provides a better 31 understanding on the cost of TNC trips, it only does so from a customer’s perspective and does 32 not consider the public costs associated with replacing transit with TNCs. 33

Although the research comparing systemwide costs between TNCs and transit is limited, 34 other research provides an indication of what might cause systemwide operating cost differences. 35

The structure of transit operating costs is one way transit could be more cost competitive than 36 TNCs. While transit has high costs (including vehicle costs, facility costs, and operating costs) 37 many of these costs are fixed. This means “transit services tend to experience scale economies 38

(unit costs decline with increased use), resulting in low marginal costs” (Litman, 2020); the 39 incremental cost of serving an additional passenger is low. While it requires a significant amount 40 of money to put a bus on the road, the change in cost between the first passenger and the fortieth 41 passenger is negligible. TNCs do not have the same scale economies or marginal cost, an issue 42 with all urban car service operators (Horan, 2017). The cost per passenger stays the same as the 43 number of passengers increase. This suggests bus services are more cost competitive when they 44 can take advantage of economies of scale through higher productivity. 45



Land use density is another important factor to consider. Some transit agencies have 1 considered using TNCs to completely replace transit in low density areas (Blodgett et al., 2017). 2

TNCs could be more competitive in this environment for two principal reasons. First, the greater 3 flexibility of TNCs means they might better serve lower density areas (Jiao & Wang, 2020). 4 Second, lower density areas are less financially viable for transit as they are likely to have lower 5 ridership while requiring more resources (Beimborn, Greenwald, & Jin, 2003; Chen, Varley, & 6 Chen, 2011; Jiao & Wang, 2020). However, while TNCs do serve lower density suburban and 7

rural areas (Brown, 2019), they tend to concentrate their services in dense urban areas that are 8 typically well served by transit (Brown, 2019; Button, 2020; Feigon & Murphy, 2018; Grahn, 9 Harper, Hendrickson, Qian, & Matthews, 2019; Jiao & Wang, 2020). So, while TNCs could be 10 more competitive at lower densities, competitiveness is less clear as density increases. 11

Overall, the literature is still not clear on the role of TNCs. While there is evidence TNCs 12

could complement transit, there is also evidence it competes with transit, or even does both 13

(Young, Allen, & Farber, 2020). Although some research has looked at using TNCs to replace a 14 route or part of a service area, research focusing on the fiscal feasibility of completely replacing 15 transit with TNCs is limited, even though this is a decision some municipalities are considering. 16 Finally, low density areas might benefit TNC as an alternative to transit but, overall, the scale 17

economies and marginal cost of transit could be difficult to overcome. 18

3.0 Methods and Data 19

I will answer the research question using an exploratory analysis of U.S. transit systems 20 with the purpose of understanding what system characteristics make it more or less feasible for 21 transit to be replaced with TNCs. To complete this analysis, operational and performance data are 22

first collected from a sample of transit agencies. From these data, metrics comparing TNCs and 23

transit on the basis of operating cost are calculated. This is done for both an exclusive TNC option 24 (e.g. UberX) and a shared TNC option (e.g. UberPool) as the two types of TNCs have different 25 cost structures and operations. Once these metrics are calculated for each transit agency, they are 26

incorporated into univariate and bivariate analyses to determine what transit agency characteristics 27 (e.g. average system density, average hourly ridership) make TNCs more or less competitive 28

compared to bus service. Table 1 provides generalized equations for calculating each metric as 29 well as averages values for bus and TNC operating and performance metrics. 30

Table 1: Average operating and cost metrics and calculations for bus and TNCs 31

Mode Variable Unit Value Equation Source

Bus Average Trip Length L Miles 3.70 =

𝑇𝑜𝑡𝑎𝑙 𝑃𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟 𝑀𝑖𝑙𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑅𝑖𝑑𝑒𝑟𝑠ℎ𝑖𝑝

(Federal Transit

Administration, 2017)

Bus Average Speed V MPH 12.00 =

𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑀𝑖𝑙𝑒𝑠

𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐻𝑜𝑢𝑟𝑠

(American Public

Transportation

Association, 2019)

Bus Average trip time

(in-vehicle)

t Minutes 18.50 =(L/V)60

Bus Cost per hour (UZAs*

<1 million people)

h Dollars 74.92 =

𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑜𝑠𝑡


(Federal Transit


Bus Cost per hour (UZAs >1

million people)

H Dollars 81.57 =

𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑜𝑠𝑡


(Federal Transit


Bus Bus (b) to car (c) speed

ratio**

S Ratio 0.819

*** = 𝐴𝑣𝑔 (

𝑀𝑒𝑑𝑖𝑎𝑛 𝐶𝑎𝑟 𝑇𝑖𝑚𝑒

𝑀𝑒𝑑𝑖𝑎𝑛 𝐵𝑢𝑠 𝑇𝑖𝑚𝑒)

(El-Geneidy, Hourdos,

& Horning, 2009; Kieu,



Bhaskar, & Chung,

2015)

Bus Average passengers/

revenue hour

Z Pax/Hr 30.70 =

𝑇𝑜𝑡𝑎𝑙 𝑅𝑖𝑑𝑒𝑟𝑠ℎ𝑖𝑝


(Federal Transit


TNC Average speed W MPH 14.66 =V/S

TNC Average trip time (in-

vehicle)

T Minutes 15.15 =(L/S)60

TNC Average Cost per trip

(UberX)

X Dollars 14.48 =Max{7.2,(2.2+0.42L+1.6T)} (Uber, n.d.)

TNC Average Cost per trip

(UberPool)

P Dollars 7.65 =Max{7.65,(2.2+1.29L)} (Uber, n.d.)

*UZA = Urbanized Area; ** 𝑆 =[

24.87𝑐𝑖19.11𝑏𝑖

+20.8𝑐𝑗

24.6𝑏𝑗+

17.1𝑐𝑗

20.3𝑏𝑗]

3 where: c=car, b=bus, i= Kieu, Bhaskar, and Chung 2015, j= 1

El-Geneidy, Hourdos, and Horning 2009; *** This is consistent with previous research (Schwieterman, 2019; 2 Schwieterman & Smith, 2018) that found in-vehicle-travel-time (IVTT) between Uber and transit can be comparable 3 with bus transit tending to be a little slower. As this research focuses on operating costs, only IVTT is considered. 4

3.1 Cost Assumptions 5

Mobility operational costs (service costs) are typically funded in two ways: costs paid by 6 the customer (fare) and costs not paid by the customer (subsidy). Equation 1 shows these parts in 7

a generalized form. This analysis is contingent on assumptions on both sides of the equation. 8

𝑆𝑒𝑟𝑣𝑖𝑐𝑒 𝑐𝑜𝑠𝑡 = 𝐹𝑎𝑟𝑒 + 𝑆𝑢𝑏𝑠𝑖𝑑𝑦 (1)

Service cost is calculated differently for transit and TNCs. For transit service, cost is simply 9 the average operating cost per hour reported to the FTA NTD. Service is fixed and does not change 10

based on ridership, trip distance, or trip time. This is not true for TNCs. Service cost for TNCs 11

increases proportionally with ridership. There are two reasons for this. First, like in the pilots 12 referenced earlier (Bliss, 2019; Woodman, 2016), when TNCs are used to replace transit they are 13 assumed to contract with municipalities as a private service. This means the service cost for each 14

passenger is the full cost of an individual TNC trip with the trip cost depending on the type of 15 service used (e.g. exclusive or shared), the time the trip takes, and the trip distance (Button, 2020). 16

(Button (2020) also mentions city-specific impacts to trip cost however, due to lack of data, these 17 will be held constant in this analysis. The impact of this assumption is discussed in the sensitivity 18 analysis.) Hence, each additional passenger adds the service cost of the additional trip, resulting in 19

a direct, linear relationship. Second, the cost structure of TNC trips (see Table 1) show cost does 20 not vary with vehicle occupancy. This means that even when TNCs are shared, like with UberPool, 21 costs do not decrease when occupancy increases. This is because “the user pays the same price 22

regardless of whether another rider is found” (Schwieterman & Smith, 2018). Consequently, for 23

both exclusive and shared TNCs, the service cost increases proportionally with ridership. 24

The analysis also assumes the cost to the passenger is the same regardless of the mobility 25 type. To a passenger, this means a municipally-subsidized TNC trip would cost the equivalent of 26 a bus fare. This assumption is based on the idea local governments could use TNCs as an 27 instrument of economic redistribution, a role speculated by Rauch and Schleicher (2015). This is 28

different to what some pilots (Bliss, 2019; Woodman, 2016) and research (Blodgett et al., 2017) 29 have assumed but is an important assumption because it redistributes the burden of mobility costs 30 away from lower-income populations, maintaining low fares for passengers. This alleviates equity 31 concerns associated with replacing buses with TNCs. This is particularly important considering 32 both that bus ridership has the lowest median income of all transit modes (Taylor & Morris, 2015) 33



and that the high cost of TNCs are a barrier for transit-dependent low-income populations (Jiao & 1 Wang, 2020). As passenger revenue will thus be the same for both forms of mobility, this also 2

means services can be directly compared based on just operating cost. 3

3.2 Key Units of Analysis 4

The first key unit of analysis for the evaluation is the TNC-Transit Passenger Equivalence 5 Threshold (E), measured in passengers per hour (pax/hr). This value, calculated using transit 6 hourly cost data and TNC trip cost data (see Equation 2), gives the passenger breakeven point 7 between the hourly cost of mobility for TNCs and transit. The calculated value is the approximate 8 number of passengers that could be served by TNCs before it becomes more cost effective to use 9

transit. The higher the number, the more cost competitive TNCs are compared to transit. 10

𝑇𝑁𝐶 𝑇𝑟𝑎𝑛𝑠𝑖𝑡 𝑃𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟 𝐸𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 =𝑇𝑟𝑎𝑛𝑠𝑖𝑡 𝐶𝑜𝑠𝑡 𝑝𝑒𝑟 𝐻𝑜𝑢𝑟

𝑇𝑁𝐶 𝐶𝑜𝑠𝑡 𝑝𝑒𝑟 𝑇𝑟𝑖𝑝

Example for UberX in a large urbanized area (values from Table 1):

𝐸 =𝐻

𝑋=

$81.57

$14.48= 5.63 𝑝𝑎𝑥/ℎ𝑟

(2)

The resulting value of 5.63 pax/hr means if a transit service carries an average of fewer than six 11

passengers an hour it could be more cost effective to replace the service with TNCs. 12

The advantage of the TNC-Transit Passenger Equivalence Threshold is that it converts 13 TNC cost effectiveness into the units of a standard transit productivity measure: the average 14 number of passengers per service hour. By dividing the TNC-Transit Passenger Equivalence 15

Threshold by the average number of passengers an hour, we get a unit-less measure of TNC cost 16 competitiveness for a transit agency given the agency’s ridership performance: the TNC-Transit 17

Threshold Unit (see Equation 3). If the value is under 1.0, then bus is more cost effective than a 18 TNC equivalent service. If the value is over 1.0, then TNCs are more cost effective. 19

TN𝐶 𝑇𝑟𝑎𝑛𝑠𝑖𝑡 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 𝑈𝑛𝑖𝑡 =𝑇𝑁𝐶 𝑇𝑟𝑎𝑛𝑠𝑖𝑡 𝑃𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟 𝐸𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟𝑠/𝑟𝑒𝑣𝑒𝑛𝑢𝑒 ℎ𝑜𝑢𝑟

Example for UberX in a large urbanized area (values from Table 1 and Equation 2):

𝑈 =𝐸

𝑍=

5.63

30.70= 0.18

(3)

As U<1.0, UberX would not be a cost effective replacement for transit in this example. 20

3.3 Sensitivity Analysis 21

As many of the variables used in the analysis are averages, and actual values will vary in a 22

case-to-case basis, a sensitivity analysis is required to understand the impact of these variances. 23 This is especially important for changes in cost per TNC trip as the limited information available 24 for TNCs means the trip cost-per-mile and cost-per-minute are assumed to remain constant for the 25 purposes of this analysis, an assumption that does not hold in the real world (Button, 2020). In the 26 sensitivity analysis, independent metrics are increased by 1% to determine the corresponding 27

change in the TNC-Transit Passenger Equivalence Threshold. When the results show a negative 28 delta, it means the change in the independent metric leads to a decrease in the TNC-Transit 29 Passenger Equivalence Threshold, suggesting the change increases the competitiveness of transit. 30 The results of the sensitivity analysis are shown in Table 2. 31

32



Table 2: Sensitivity analysis 1

Independent Metric

1% increase in Metric

TNC-Transit Pax

Equivalence Threshold (E)

Δ Existing New Unit Existing New

Average Trip Length 3.70 3.74 Miles 5.63 5.59 -0.8%

Average Bus Speed 12.00 12.12 MPH 5.63 5.66 0.4%

Average Car Speed 14.66 14.80 MPH 5.63 5.66 0.4%

Bus-Car Speed Ratio 0.819 0.827 Percent 5.63 5.61 -0.4%

Average Hourly Bus Cost 81.57 82.39 Dollars 5.63 5.69 1.0%

UberX Cost-Per-Mile Increased 1.60 1.62 Dollars 5.63 5.61 -0.4%

UberX Cost-Per-Minute Increased 0.420 0.424 Dollars 5.63 5.61 -0.4%

Both UberX Per-Minute and Per-

Mile Costs Increase

- - Dollars 5.63 5.59 -0.8%

The sensitivity analysis indicates that longer trip length improves the competitiveness of 2 transit, which is consistent with what Grahn et al. (2019) speculated. This suggests that, as average 3

rural transit trips are longer than urban transit trips (Pucher & Renne, 2004), transit might still be 4 competitive in a rural environment. Intuitively, the sensitivity analysis also shows that the closer 5 average bus speeds are to average car speeds, the more competitive transit becomes. However, the 6

magnitude of the impact is half as great as trip length. Finally, Button (2020) mentioned TNC trip 7 cost also changes based on the city in which the trip takes place. This could be reflected in the 8

cost-per-distance variables in the fare formula (shown in Table 1). A 1% increase in either the 9 cost-per-minute or cost-per-mile could see a 0.4% increase in transit competitiveness. This is 10 additive so if both metrics are changed the deltas are added together, as shown in Table 2. As TNC 11

per-mile and per-minute costs are likely to be lower in many cities than the values in Table 1 12

suggests (which is calculated based on a trip in San Francisco), TNCs are likely to be more 13 competitive in many places than results suggest. This is an important data limitation, driven by the 14 lack of publicly available TNC data. 15

3.4 Data 16

20181 FTA NTD data is used for the empirical analysis. These data give transit agency 17

specific information on operating performance, fiscal performance, and contextual factors like the 18 size of the agency or the service area. Transit agencies are required to provide FTA NTD data if 19

they are recipients or beneficiaries of U.S. Department of Transportation FTA grants. According 20 to the “APTA 2019 Fact Book” (American Public Transportation Association, 2019) there are 21 1,228 bus systems in the U.S. The FTA’s NTD “2018 Annual Database Agency Mode Service” 22

spreadsheet was used to identify the individual transit systems in this population. As this database 23 includes all transit modes, the total list of agencies was filtered by bus (“MB” under “Mode”) with 24 any duplicate listings for an agency (occurring because a system could have multiple types of 25 service, “TOS”) removed. These filters provided a final list of 1,225 U.S. transit systems with bus 26

service, approximately the same number as the “APTA 2019 Fact Book.” Because the population 27 is so large, a random sampling strategy was used to select a representative sample for analysis. A 28 simple random number generator was used to randomly select transit agencies. Given a population 29

1 Because of different reporting frequency requirements, some transit agencies do not have 2018 data. The latest data

available (2014) was used in these cases affecting 5.6% of the dataset.



of 1,225, and assuming a margin of error of 5% and a confidence interval of 95%, the minimum 1 sample size was calculated as 293 (rounded to 300 for this analysis). 2

The transit agencies included in the randomized sample are geographically diverse with 47 3 states, Puerto Rico, and Guam represented. The sample includes an average of 5.6 transit agencies 4 per state with no transit agencies from Hawaii, North Dakota, Rhode Island, or the Virgin Islands 5 included. The geographical representation of the sample was calculated using a Pearson correlation 6

coefficient. The coefficient (0.96) suggested the sample is highly geographically representative of 7 the population. 8

FTA classifies transit agencies as either “Urban,” “Rural,” or “Tribe.” Urban transit 9

systems are transit systems located in an incorporated urbanized area with a population of 50,000 10 or more. Rural transit systems are those in rural areas with populations of less than 50,000. Tribal 11

transit systems are those located on Indian reservations. As Figure 1 shows, 62% of the sample 12

size were classified as Urban, 33% were classified as Rural, and the remainder were classified as 13 Tribe. These percentages are consistent with the total transit agency population. 14

15

16

Figure 1: Transit agency classifications 17

Figure 2 presents some of the key transit agency characteristics from the sample to show 18 the range and spread of the data. Figure 2e and Figure 2f each have fewer observations as differing 19 FTA NTD reporting requirements means not all transit agencies provide the same data. Smaller 20

agencies have less stringent requirements, meaning some data are biased towards larger transit 21 agencies. For example, average trip lengths are likely longer than Figure 2e suggests and service 22 areas less dense than Figure 2f suggests. The average trip length from Table 1 was used when trip 23 length could not be calculated for the TNC-Transit Passenger Equivalence Thresholds. 24

As Figure 2 shows, all transit characteristics presented are skewed right with long tails. 25 The mode for average cost per hour is consistent with the average from Table 1. However, the 26

mode for average number of passengers per hour is much lower than the average of 30.7 passengers 27 per hour from Table 1, suggesting the national average is possibly skewed by large and heavily 28 used transit systems like the New York Metropolitan Transportation Authority. The mode for 29 average speeds is slightly higher than the average shown in Table 1. This result could also be 30 because the national average is skewed by larger, more urban transit systems where congestion 31 plays more of a role. The mode for average trip length is slightly lower than the average from 32 Table 1, possibly because of the exclusion of some of the smaller transit agencies. 33

Urban

62%

Rural

33%

Tribe

5%



a) Average cost per hour ($/hr), N=300 b) Average number of passengers per hour (pax/hr),

N=300

c) Average fare ($/trip), N=300 d) Average speed: vehicle revenue miles/vehicle

revenue hours (MPH), N=300

e) Average trip length: passenger miles/ridership

(miles), N=108

f) Average service area density: service area/service

area population (Pop./Sq. mile) N=195

1

Figure 2: Histograms of transit agency characteristics 2



4.0 Results 1

The key units of analysis, TNC-Transit Passenger Equivalence Threshold and TNC-Transit 2

Threshold Unit, were calculated for each transit agency. Figures 3 and 4 show box-and-whisker-3 plots for each of these variables2. 4

5 Figure 3: TNC-Transit Passenger Equivalence Threshold box-and-whisker plots (N=300) 6

7 Figure 4: TNC-Transit Threshold Unit box-and-whisker plots (N=300) 8

Both Figures 3 and 4 show UberPool is more cost competitive than UberX while having a 9

larger spread in the data, indicating the cost competitiveness of UberX is more consistent across 10 transit agencies. As a reminder, when the TNC-Transit Threshold Unit exceeds 1.0, TNCs become 11 more cost effective. Figure 4 shows the upper quartile for UberX is below this cutoff while only 12

the median for UberPool is below the threshold. Overall, UberX is more cost effective than 23% 13 of transit agencies while UberPool is more cost effective than 45% of transit agencies. 14

The TNC-Transit Passenger Equivalence Threshold was plotted against transit agencies’ 15 average hourly ridership, shown in Figure 5. Any plotted point above the diagonal black line 16

indicates it would be more cost effective to replace bus service with TNCs. 17 18

2 Figure 4’s y-axis was truncated to better show the box-and-whisker plot shapes. Consequently, some of the outliers

are not shown.

Breakeven

point



1

Figure 5: TNC Passenger Equivalence Threshold and average transit ridership (N=300) 2

As Figure 5 shows, both types of TNC have similar trends. TNCs appear to be more cost 3 effective when transit systems have low average hourly ridership; however, as average ridership 4 per revenue hour increases, both UberX and UberPool quickly become less cost competitive. 5

Figure 6 shows this even more clearly. The figure shows, for a given value of average ridership, 6 the proportion of transit systems where average ridership exceeds TNC-Transit Passenger 7

Equivalence Thresholds, indicating transit is more cost effective. For example, at five passengers 8

per hour, transit agencies are more cost competitive than UberPool in about 10% of cases and more 9

cost competitive than UberX in almost 50% of cases. Pearson correlation coefficients between 10 average ridership and the TNC-Transit Passenger Equivalence Threshold indicated moderate 11

positive correlations between the variables (0.46, UberX; 0.47 UberPool), meaning average transit 12 ridership could be a somewhat decent predictor of TNC competitiveness. 13

14

Figure 6: Transit competitiveness and average transit ridership 15

The literature identified density as a possible indicator for determining when TNCs are 16

more cost effective than transit. The research suggested that, because transit ridership is less 17

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efficient in low-density areas, we could expect TNC competitiveness to increase as density 1 decreases. At higher densities the results could be mixed as both transit and TNCs benefit. Figure 2

6 shows the TNC-Transit Threshold Unit for each transit agency plotted against the agency’s 3 average service area density. 4

5

Figure 7: TNC-Transit Threshold Unit Values and average service area density (N=195) 6

The scatter plot in Figure 7 shows that for lower average densities there is a wide range in 7

values for TNC-Transit Threshold Unit. In most cases bus is still the more cost effective option; 8

however, in many cases TNC could be more cost effective. When average density exceeds 5,000 9 people per square mile, TNCs become much less cost competitive with UberX failing to be more 10 cost effective beyond approximately 4,000 population/square mile. As transit is likely to perform 11

better in higher density areas, it is possible the findings regarding higher ridership and higher 12 density are related. The Pearson correlation coefficient for the two variables (0.42) indicates there 13

is a moderate positive correlation, suggesting a connection between the two findings. 14

Because urban and rural are defined as high and low density areas, respectively, the FTA 15 transit agency classifications could be used as a blunt measure of density. Figure 8 shows the TNC-16

Transit Threshold Unit box-and-whisker plots for each FTA agency classification3. Overall, Figure 17

8 shows TNCs are less likely to be cost effective in urban transit agencies than rural or tribal transit 18 agencies. For urban transit agencies, UberX is only cost effectiveness in outliers while in almost 19 75% of cases transit is more cost effective than UberPool. For rural transit agencies, transit is more 20

cost effective than UberX in just over half of cases and, while UberPool is more cost effective for 21 most rural transit agencies, there is a lot of variation in the results. Finally, TNCs are reliably more 22 cost effective for tribal transit agencies with transit being less cost effective than UberPool in 23

almost all cases; however, it is difficult to make conclusions about tribal transit agencies as the 24 sample is comparatively small. While a blunt measure, the results of Figure 8 support the findings 25 from Figure 7 and suggest FTA classification of transit agencies could also be used as a simple 26 predictor of TNC cost competitiveness. 27

3 Figure 8’s y-axis was truncated to better show the box-and-whisker plot shapes. Consequently, some of the outliers

are not shown.

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1 Figure 8: TNC-Transit Threshold Unit box-and-whisker plots by FTA agency classification 2

The sensitivity analysis demonstrated that changes in average trip length could have a 3

significant impact on the TNC-Transit Passenger Equivalence Threshold, with a longer trip 4 increasing the competitiveness of transit. Consequently, average trip length was also evaluated 5

with the TNC-Transit Threshold Unit. However, the results showed no real relationship between 6 the TNC-Transit Threshold Unit and average trip length. The Pearson correlation coefficients for 7 these variables were very low (-0.03, UberX; -0.04 UberPool), suggesting that average trip length 8

is not closely related to TNC cost competitiveness. 9

Average speeds were another variable the sensitivity analysis indicated might affect TNC 10 cost competitiveness. The analysis found TNC becomes more competitive as transit speeds 11 increase. Consequently, the TNC-Transit Threshold Unit was plotted against average transit 12

speeds, as shown in Figure 9. 13

14 Figure 9: TNC-Transit Threshold Unit Values and average speed (N=300) 15

The figure reveals a somewhat positive relationship between the TNC-Transit Threshold Unit 16

and average speeds with TNCs becoming much more competitive when average speeds reach 17

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approximately 30 miles per hour. Pearson correlation coefficients support this trend with 1

moderate to high values (0.59, UberX; 0.51, UberPool) suggesting average speeds could be a 2

decent predictor for TNC competitiveness. 3

5.0 Discussion 4

The exploratory analysis demonstrated there are several characteristics of transit agencies 5 that can be useful in determining whether fixed-route bus service could be replaced with TNCs. 6

First, the type of TNC scheme used for replacing transit matters. As expected, the shared-7 ride version of TNCs (UberPool) was much more cost effective than exclusive TNC services 8 (UberX). There are additional benefits to prioritizing shared TNCs like UberPool rather than 9 exclusive TNCs like UberX. UberPool is a more efficient use of resources because, while the 10

capacity is much lower than a bus, strangers can still share trips, meaning fewer vehicles would be 11 required (Button, 2020). This is particularly important in environments where there are fewer TNC 12

drivers, like rural areas. Using UberPool could also mean less of an environmental and traffic 13 impact compared to UberX. However, while UberPool does have some capacity advantages, they 14 do not translate to significant cost savings for municipalities. The cost structure of UberPool means 15 the trip cost does not decrease with additional passengers (Schwieterman & Smith, 2018). This 16

means the cost per trip is the same if an UberPool vehicle carried one passenger or three 17 passengers. If the cost structure for shared TNC service varied based on vehicle occupancy, this 18

form of mobility could be more cost effective and be better able to compete with transit. 19

As noted in the literature review, the scale economy advantages of transit mean transit 20 vehicles can carry many people without increasing cost, providing an advantage over car-based 21

mobility. This was supported by the relationship identified between the TNC-Transit Passenger 22

Equivalence Threshold and average ridership. Because TNCs cannot scale like transit, they are 23

less effective at managing higher passenger demand. This explains why UberX and UberPool 24 quickly became less cost competitive as average ridership increased. At an average of just 10 25 pax/hr, Figure 6 showed bus transit is more cost competitive than UberX 90% of the time and more 26

cost competitive than UberPool 60% of the time with very few TNCs of either type being cost 27 competitive above 20 pax/hr. This finding has direct implications for service planning. In transit 28

service planning, planners can design transit networks to focus more on coverage, ensuring 29 everyone has some service, or frequency, which tries to maximize ridership. Most transit systems 30 are somewhere in the middle of the coverage-frequency spectrum. The relationship between 31

ridership and the cost competitiveness of TNCs identified in this paper suggests, for transit 32 agencies to remain cost competitive, there should be more of a focus on frequency. If a 33 municipality has more coverage-orientated goals for mobility, TNCs may be more competitive. 34

The literature also suggested density could play a role in TNC cost competitiveness. TNCs 35 were expected to be more competitive at lower densities with no clear relationship for higher 36 densities. The results from the bivariate analysis showed the opposite to be true. At lower densities, 37

there is a lot of noise with no clear relationship between average density and TNC competitiveness. 38 However, at higher densities, transit is the more cost effective choice. This finding is possibly 39 because transit is more efficient in higher densities. While the relationship between TNC 40 competitiveness and lower densities is difficult to identify, the literature highlights other 41 implications. Specifically, public entities favor TNCs in lower density areas where transit might 42 be less effective, filling the role of a public good. In contrast, evidence of current TNC use suggests 43



the rational-actor goals of TNC drivers favors operations in higher density areas (Feigon & 1 Murphy, 2018) where deadheading and downtime between trips is minimized, maximizing 2

revenue (Jiao & Wang, 2020). This opposition signals a possible principal-agent problem. If TNCs 3 were to replace transit, the ideal operating densities of drivers and public entities might not match, 4 resulting in poorer level of service where it might be needed the most, a concern shared by Alonso-5 Gonzalez et al. (2018). As Jiao and Wang (2020), wrote: “it is difficult to only rely on the private 6 sector to provide equitably shared mobility service to all populations.” Consequently, 7

municipalities subsidizing TNCs would need to develop an incentive program to ensure TNC 8 drivers operate where they are needed most. 9

Although the sensitivity analysis indicated trip length can have a direct impact on the TNC-10 Transit Passenger Equivalence Threshold, the evaluation of transit agencies failed to identify a 11

relationship. However, this non-result could be influenced by bias in the data. When the data were 12

introduced in Section 3.4 it was noted that there were fewer observations for calculated average 13

trip length because of differences in FTA NTD reporting requirements. Because almost two-thirds 14 of transit systems in the dataset do not have passenger mile data, and these are disproportionately 15 smaller and more rural transit agencies, the results could just indicate trip length is less of a factor 16 for larger transit agencies. There may be a relationship between TNC cost effectiveness and 17

average trip length, but further study is required. 18

The final variable considered in the evaluation was average operating speed. The analysis 19 of operating speeds supported the finding from the sensitivity analysis: higher operating speeds 20

increase the cost effectiveness of TNCs. This is because the cost function for TNCs is dependent 21 on time so the faster a trip can be made, the more cost effective TNCs become. Higher operating 22 speeds for transit agencies could also reflect the environment in which transit operates. Dense, 23

urban areas are likely to have lower posted speeds and more traffic congestion, further slowing 24

speeds. As already demonstrated, transit is more cost effective in denser areas. 25

While the higher scale economies and marginal cost of transit makes it difficult for TNCs 26 to completely replace transit, the ridership findings could inform decisions around making TNCs 27 a complement to transit. Figure 10 shows the hourly ridership for an illustrative transit agency 28

while Table 3 shows key operating and cost information. 29 30

31 Figure 10: Example of transit ridership (measured in pax/VRH) peaking characteristics 32

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Table 3: TNCs as a strategic complement to transit 1

Variable Value

Existing Scenario

(Bus service 4am - 11pm)

Average transit cost/hr* $81.57

Span of service (hours) 19

Average ridership/VRH 6.65

TNC-Transit Passenger

Equivalence Threshold 5.63

Total cost $1,549.83

Proposed Alternative

(Bus service 5am - 7pm;

TNC 4am - 5am and 7pm - 11pm)

Average transit cost/hr* $81.57

Span of service (hours) 14

Total transit cost $1,141.98

Average TNC cost/trip* $14.48

Anticipated demand^ 18.5

Total TNC cost $267.91

Total cost $1,409.89

Estimated Savings Cost difference $139.94

Savings 9.0%

* Average values from Table 1; ^ Sum of pax/VRH before 5am and after 7pm from Figure 10 2

The average transit ridership for the example is 6.65 pax/VRH, higher than the TNC-3

Transit Passenger Equivalence Threshold. This means transit is more cost effective overall. 4

However, TNCs can be used strategically to reduce operating costs by shortening the span of 5

service when ridership falls below a certain threshold, 6 pax/VRH in this case. In this scenario, 6

as long as fewer than nine additional people (Table 3 cost difference divided by average cost of a 7

TNC trip) opt to use TNCs (a 52% increase over the existing ridership), the route will have lower 8

operating costs without negatively impacting customers who have mobility needs outside of the 9

new transit span of service. 10

6.0 Conclusions and Limitations 11

The purpose of this paper was to answer the question: Can transportation network 12

companies (TNCs) like Uber be economically feasible as a replacement for transit (specifically 13 fixed-route bus)? Through the use of publicly available FTA NTD transit agency data and basic 14

assumptions about TNC cost structure, I performed an exploratory analysis of U.S. transit agencies 15 leveraging univariate and bivariate evaluation methods. This analysis demonstrated that, while 16 TNCs could be more operationally economical than transit in some instances, most of the time 17

transit will be more cost effective. The paper was able to identify three agency characteristics that 18 could be used to indicate TNC cost effectiveness: transit is more cost competitive when ridership 19 is higher; transit is more cost effective in denser service areas; and, TNCs are more cost 20 competitive when average transit operating speeds are higher. In addition to these three metrics, 21

there is some evidence longer average trip lengths could benefit transit, but further research is 22 required. Finally, the units of analysis developed for these evaluations could be used to 23 strategically identify when TNCs could work as a complement to transit. In conclusion, while 24 TNCs are unlikely to be able to replace transit completely in most cases, at least until vehicles 25 become fully autonomous (Merlin, 2017), their flexibility allows public entities to be more creative 26 when making policy and operational decisions. 27



While this paper was able to answer the original research question, there are some 1 limitations to the results. First, the paper did not consider the availability of TNC vehicles in the 2

analysis. While transit service miles and hours are directly under the control of transit agencies in 3 the U.S., the supply of TNC equivalent service is subject to the availability of drivers in the area. 4 Research is mixed on the significance of this limitation. Button (2020) stated that the supply curve 5 for TNCs is relatively flexible, suggesting they have some capacity to respond to changes in 6 demand, while Jiao and Wang (2020) said the fluctuating supply makes it difficult to difficult for 7

public entities to contract with TNCs. Both Merlin (2017) and Alonso-Gonzalez et al. (2018) also 8 noted that for a demand-response system like TNCs to be successful a large fleet is required. 9 Second, surge pricing, a well-publicized feature of Uber, was not considered and could be a factor 10 for trips in peak periods. Third, externalities of switching completely to TNCs like congestion (as 11 Merlin (2017) and Alonso-Gonzalez et al. (2018) both emphasized) are also not included in this 12

analysis. Fourth, TNCs are not required to meet the same American with Disabilities Act (ADA) 13 standards as transit so people with disabilities and older adults could be negatively impacted by a 14 TNC replacement service. Finally, this paper uses averaged and aggregated data. While this can 15

be useful for showing trends, as shown in this paper, more granular analysis is required for more 16

nuanced strategies, as indicated by the span of hours example in the discussion. 17

Acknowledgements 18

The author thanks Margaret Cowell and Ralph Buehler for providing feedback on earlier drafts of 19 this work. 20

21

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