Data Sources 1 Transit Agencies

Transit agency data was procured from the National Transit Database (NTD). NTD is overseen by the Federal Transit Administration, one of several divisions under the auspices of the United States Department of Transportation (USDOT).

US Federal law, precisely Title 49 of the United States Code §5335, requires a reporter or transit agency to ‘report’ or file certain vital revenue operational and financial statistics on a monthly basis. Specific agency material was extracted from NTD 2014 Transit Agency Profiles. These profiles furnish monetary measurements, and other performance metrics used to calculate the figures detailed further in the Results and Discussion section. NTD is generally two years behind. As an illustration in the year 2016, it will provide researchers with 2014 data. Thus, the Florida Transit Information System (FTIS) was employed, secondarily, to accompany and support NTD info where incomplete. FTIS is an online database that offers quicker access to the latest TA statistics. To maintain consistency, all data is from the year 2015, except where indicated.

Besides the Profiles dataset, other facts and figures were derived from the 2015 Public Transportation Fact Book Appendix A: Historical Tables. These tables are industry-wide and

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provide aggregated numbers based upon type of mode, and its corresponding expenditures such as operational and capital costs.

The American Public Transportation Association (APTA) is a professional organization that speaks for the public transportation industry. APTA represents the full gamut of modes including, but not limited to, people movers, ferries and funiculars. APTA produces their own datasets from information gleaned by its members. The annually figures are aggregated industry- wide by mode and a variety of operational statistics. APTA also obtains data from NTD.

4.1.2 TNCs

Data was extricated directly, where possible, from the TNCs respective websites. i.e. www.uber.com, www.lyft.com, etc. Due to the proprietary nature, the only open data available are the details of how they approximate their fares. The latter was confirmed either by live telephone conversations with representatives of TNCs or delving every single TNC website. Google Maps furnished routes, route lengths, travel times as well as transit options.

4.2 Metrics

The metrics utilized for determining productivity efficiencies, calculating the aggregated financial performance of TAs, and subsequent subsidization is organized for reference in Table 4.1. In the How Calculated column, the Tables refer to those found in the 2015 APTA Fact Book Appendix A – Historical Tables.22 _{For those items that couldn’t be found in any of the APTA}

Tables or any other of their publications, either were located in FTIS and/or supplementary publications. Furthermore, the calculations regardless of wherever they were found are fully explained.

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Table 4.1 Metrics

Metric Unit of _Measurement How Calculated

Average Trip Length miles Table 5

Vehicle Revenue Miles millions Table 11

Passenger Miles millions Table 3

Vehicle Revenue Hours millions Table 15

Average Occupancy per Passenger Passenger Miles (millions)/Vehicle Revenue _{Miles (millions)} Number of Unlinked Trips millions Table 1

Average Fare (USD) per unlinked trip Fare Revenue (Millions of USD)/Ridership _(Millions) Assumed Average for Vehicle Revenue

Capacity or Quantity of Seats per

Vehicle per Passenger

Number of Active Vehicles in Fleet x Seating Capacity/ Number of Active Vehicles in Fleet Total Expenses (USD) includes Capital

and Operating Costs millions Tables 62 and 68 Fare Revenue (USD) millions Table 92

Subsidization (USD) millions TA Total Expenses - TA Fare Revenue Average Subsidy (USD) per Passenger Subsidization (USD)/Ridership

Average Vehicle Revenue Speed miles per hour _(MPH) Vehicle Revenue Miles (millions)/ Vehicle _{Revenue Hours (millions)} Assumed Average for Vehicle Revenue

Capacity or The Quantity of Seats per

Vehicle (Total = Seating + Standees) Passengers

Number of Active Vehicles in Fleet x (Seating Capacity + Standing Capacity)/Number of Active Vehicles in Fleet

Efficiency (assumed capacity) Percentage Average Occupancy/Assumed Average Revenue _{Vehicle Capacity}

Pre-established is how TNCs do not make their data readily available to the public. Accordingly, some of the data needed is not easily or promptly obtainable other than what is promulgated on their respective websites. Therefore, certain assumptions were made and are systematized in Table 4.2.

Table 4.2 TNC Assumptions in Methodology

TNC Metrics Unit of Measurement TNC Assumptions

TNC: Average Trip Length miles Assumed trip length is the same as TA mode average trip length; Pathway is mirrors route TNC: Average Vehicle Speed miles per hour (MPH) Equal or higher than MB

TNC: Fare per Passenger Regular TNC fare

TNC: Average Trip Duration minutes Time is approximate and was retrieved from Google Maps

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Further assumed are vehicle revenue miles and average trip length remain the same. The metric’s figures stay the same since an apple-to-apple or as close as possible comparison is being presented. Average vehicle revenue speed of the TNC is equal or higher than road-based vehicles. This is mainly due to dedicated bus lanes versus uncommitted lanes for taxis and regularly operated passenger cars. Also, cars may move faster since they do not have to constantly stop to conduct boardings and alighting which can result in increased bus dwell time and revenue service bus travel time.

4.3 Venue

For venue selection, the City of Austin, Texas was chosen. Forbes Magazine named Austin the fastest growing city in the United States.23 _{As of 2016, it had a population growth rate of 3.15%}

and is the capital of the state of Texas [15]. From this it can be inferred that the trend is for anticipated development. This became obvious to Austin’s business leaders and elected representatives as they continue to plan, strategize and prepare accordingly in anticipation for its future’s inevitable expansion. The TA for Austin is Capital Metropolitan Transportation Authority or Cap Metro. It is designated as operating within the 37th _{largest Urbanized Area (UZA). Atypical}

is the quantity of 18 TNCs, Austinites can select from.24

In the United States properties are of various sizes: small, medium, large and very large. Without question, TNCs are already operational in many small, medium and larger sized metropolitan areas. Supposing that TNCs are legally permitted to operate everywhere it could, at the very least, engage parallel to any sized reporter. And what makes this characteristically attractive is how TNCs currently manage to transport thousands of riders. There are 18 TNCs

23 _{Forbes named fastest growing United States city at least twice – 2012 and 2016.}

24 _{Recently, it was ascertained that ScoopMe, the 19th TNC, ended operations November 30, 2016. There are,} currently, 18 TNCs in Austin

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already operating in Austin. Only Lyft Line offers a commuter type of fare. Assumed are these low fares are privately self-subsidized with investor funds. Even if it is highly speculated that TNCs operate under this type of strategic tactic to increase ridership, should a private public partnership be combined with government assistance, that is subsidization, this could lead to a serious contemplation for supplantment.

The origin/destination pair selected was based upon iconic status and proximity. The AMTRAK Station is well-established. The Barton Creek Square shopping mall is also deeply- rooted in the Austin metropolitan area. The route is as close to the TA’s Average Trip Length as can be attained.

4.4 Other

Taking all the above into consideration, this paper’s scope is purposely limited to the United States for a miscellany of reasons listed below:

1. Language Barriers. Even with tools such as Google Translate there is no guarantee that a perfect translation shall occur. Above all, tools such as the latter provide literal interpretation. Those can be problematic for people who do not speak that language as they will not notice the dissimilarity. Also, some languages utilize that type of verbiage for idiomatic functions. Procuring information from foreign transit and government agencies can been challenging especially if the responsible personnel do not speak English fluently.25

2. Legal restrictions. Some foreign properties and/or agencies may not be permitted to release data because of policy or their country’s laws.26

25 _{At least s/he claimed to want to help but could not do so because of a lack of English comprehension or some} other raison d’etre.

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3. Ease of Data Procurement. The Federal government mandates TAs to submit statistical reports making the task of obtaining and examining data straightforward. Especially if, for the most part, it is within a single data source. For reasons stated earlier, this may not be the case for many other countries.

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