SOURCES, DISCLOSURES & REGULATORY
The detail behind
the opportunity.
Sources, definitions, company status and model assumptions in one place. Numbered references across the website link directly to the relevant section below.
[1] Company facts and development stage
Cyber Vehicle Operations LLC is a Pennsylvania LLC operating under the CYVO brand. Stephen Kovach is CEO and Rob Nagy is COO; each holds a 50% ownership interest. Company facts, leadership information and plans are supplied by the owners. CYVO is developing a proposed electric taxi business. The website does not represent an operating fleet, paying riders, revenue, a confirmed launch date or granted operating approvals. The roadmap shows evaluation markets unless a location is expressly marked otherwise.
CYVO is in development. Rides are not currently available. Vehicle acquisitions, operating locations and launch dates remain subject to commercial agreements, vehicle availability and regulatory approvals.
[2] Vehicle acquisition and manufacturer independence
CYVO intends to evaluate Tesla Cybercab and other suitable vehicles. Vehicle availability, delivery timing, prices, commercial-use rights, insurance and network or dispatch access must be confirmed in separate agreements. CYVO has not represented a confirmed purchase allocation or delivery. CYVO is independent of Tesla, Waymo, Uber and other referenced businesses; references do not imply endorsement, affiliation or authorized dealer status. Third-party names belong to their owners.
[3] Concept imagery and leadership portraits
The vehicle and fictional-city artwork is AI-generated. It is a design concept, not a production specification, a purchased CYVO vehicle or an operating location. Placeholder leadership portraits are illustrative silhouettes, not photographs of the executives. A real authorized portrait can be supplied by each owner.
[4] Regulatory and operating considerations
City markers identify locations for commercial evaluation. They do not indicate CYVO offices, service boundaries, operating permits or committed launches. Commercial operations depend on applicable federal, state and local requirements, passenger-service licensing, vehicle capability, insurance and suitable manufacturer terms. Requirements must be evaluated for each jurisdiction and operating model. This page is not a jurisdiction-specific legal opinion.
Safety performance, geofencing, weather, charging, maintenance, labor, customer support, competition, pricing, financing and platform fees can materially change viability. A technically capable vehicle does not by itself establish the right to operate a taxi business.
[5] Private financing and indications of interest
CYVO is a privately owned LLC. This website supports information requests and, when enabled, nonbinding indications of interest. It does not accept payments, securities subscriptions or binding investment commitments. Any future investment would require appropriate documentation and an applicable lawful offering pathway. A website disclaimer alone does not establish an exemption.
Nonbinding interest only. No money or other consideration is being solicited, and anything sent will not be accepted. No sale or commitment to purchase securities can be accepted until an appropriate offering pathway is established and applicable requirements are met. An indication of interest creates no obligation or commitment of any kind.
There are no investment terms, guaranteed returns or reserved allocations on this website. You may lose your entire investment in any future offering. This form does not establish investor eligibility.
Early-stage private investment can involve complete loss, illiquidity, dilution, conflicts of interest and uncertain distributions. No valuation, return, allocation or future financing is promised. Expressions of interest do not establish eligibility.
[6] City data and opportunity assessment
The profiles use the U.S. Census Bureau’s 2024 American Community Survey (ACS) 1-year Detailed Tables, downloaded October 24, 2026 from the official table-based Summary File. The same vintage and geography type are used across profiles. These are Census places, not metropolitan areas: Honolulu uses Urban Honolulu CDP; Indianapolis, Nashville and Louisville use their Census “balance” geographies. Adjacent city markers may share a regional travel economy and should not be treated as independent metro markets.
Population (B01003) describes resident scale. Median household income (B19013, in 2024 dollars) provides fare-testing context. Household counts and no-vehicle households (B08201) describe vehicle access. Transportation-to-work counts (B08301) describe the usual commuting mode of resident workers aged 16 and over, including those working at home in the denominator. Commuting shares do not measure all trips, taxi demand or visitor travel.
The opportunity text is CYVO’s screening interpretation of these indicators, not a Census forecast. Population, income, transit use and vehicle access do not establish willingness to pay or adoption. The scenario treats adoption as a separate, adjustable assumption rather than mechanically converting income or commuting into customers.
A decision-grade forecast would additionally need origin/destination demand by time of day; tourism, airport and employment-center flows; local fares and competitor capacity; trip lengths and empty miles; service-area permissions and vehicle capability; charging and fleet-staging capacity; insurance, energy, maintenance, financing and overhead quotes; customer acquisition and retention; and pilot utilization. Airport boarding volumes and an explicit airport transfer scenario are provided separately. Tourism and local trip-level demand still require further validation.
ACS estimates have sampling and nonsampling error. Individual source sections show available 90% margins of error (MOE). Shares are calculated from point estimates; ratio MOEs are not calculated. A missing MOE indicates the published value is controlled or not applicable, not zero uncertainty. Small differences should not be read as meaningful rankings. Data is a bundled dated snapshot, not a live feed.
Census 2024 ACS Summary File and documentation ↗ · ACS data-user guidance ↗
[7] Household ride-spend scenarios
The city calculator explores annual gross passenger fares for a scenario-defined household pool. Let H = total households, N = households with no available vehicle, A = assumed adoption among no-vehicle households, B = assumed adoption among other households, R = local non-airport paid rides per participating household per month and F = average fare. Participating households = N × A + (H − N) × B. Annual rides = participating households × R × 12. Annual gross fares = annual rides × F.
Initial assumptions are 5% adoption for no-vehicle households, 1% for other households, 6 monthly rides per participating household and a $18 fare. These are editable scenario inputs chosen for exploration, not survey-derived adoption estimates, observed demand, market share predictions or a valuation. Household segments are mutually exclusive. All household members’ rides must fit within the selected household ride frequency.
Outputs are potential gross ride spending under those assumptions, not CYVO revenue, profit or an investment return. They are not limited by fleet capacity, competition, geographic coverage or launch timing. A lower-adoption sensitivity uses half the two selected adoption rates; an upper sensitivity doubles them, capped at 100%. This range is an arithmetic sensitivity, not a statistical confidence interval or a forecast range. The separate fleet calculator models capacity and selected costs. A business forecast must reconcile the two and include all costs.
[8] Fleet economics and exclusions
Fleet = cities × vehicles per city. Annual gross passenger fares = fleet × paid rides per vehicle per operating day × average fare × operating days. After-fee receipts = gross fares × (1 − platform fee percentage). Illustrative operating cash contribution = after-fee receipts − fleet × operating days × daily cash costs.
Cash contribution is not net profit or an investment return. It excludes vehicle acquisition, depreciation, financing, taxes, central overhead and other unmodeled costs. Daily cash costs are an aggregate input for items such as energy, insurance, cleaning, maintenance and local support. Operating days and paid rides must account for charging and downtime. No Tesla fee, utilization, fare or operating cost has been confirmed. Gross fares may differ from accounting revenue depending on commercial arrangements. All inputs are illustrative and editable.
[9] Map geography
The map uses U.S. Census Bureau 2024 1:20-million cartographic state boundaries, rendered with the D3 Albers USA projection. Alaska is reduced in scale; Alaska and Hawaii are inset. Markers locate approximate city centers and do not define service areas. The map and profile data are served locally by the website.
Census cartographic boundary files ↗ · Albers USA projection documentation ↗
[10] Waymo trips during 2025 through its December update
14M+ — Waymo reported over 14 million trips through its December 2025 update, with public rides more than tripling year over year.
Waymo · December 10, 2025 ↗ · Source date: 2025-12-10.
Waymo’s figure is trips through the December 10, 2025 update, not the full calendar year. Public rides more than tripled from the prior year. It is an operator-reported benchmark, not an industry total or CYVO performance.
[11] Uber Mobility gross bookings growth, year over year
22% — Uber’s global Mobility business generated approximately $29.0 billion in gross bookings in Q2 2026, a 22% increase from the same quarter in 2025.
Uber investor relations · August 5, 2026 ↗ · Source date: 2026-08-05.
Uber’s figure is global Mobility gross bookings in Q2 2026: $28.988 billion, up 22% as reported (20% in constant currency). Gross bookings is ride value, not company revenue; this is neither U.S.-only nor autonomous-only activity.
[12] U.S. cities with at least 100,000 residents in 2024
342 — The Census Bureau identified 342 incorporated places with at least 100,000 residents in its 2024 population estimates: a broad landscape for city-by-city expansion.
U.S. Census Bureau · May 15, 2025 ↗ · Source date: 2025-05-15.
The Census count refers to incorporated places with at least 100,000 residents in the 2024 population estimates. It is a different product from ACS survey profiles. City boundaries do not equal metro areas or an addressable taxi market.
City source records
Open a city for exact Census geography, estimates, margins of error and source tables. Dollar estimates are in 2024 dollars. All counts describe residents or households, rather than visitor demand.
[100] New York city, New York
2024 ACS 1-year · Census geography 1600000US3651000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 8,478,072 | Not applicable |
| Median household income ($) | 81,228 | 908 |
| Households | 3,379,651 | 15,364 |
| No-vehicle households | 1,915,214 | 23,042 |
| Workers 16+ | 4,070,607 | 20,036 |
| Public-transit commuters | 1,981,663 | 23,605 |
| Walking commuters | 389,200 | 13,121 |
| Working at home | 509,852 | 12,449 |
No-vehicle share: 56.7% of households. Public-transit share: 48.7% of workers. Walking: 9.6% of workers.
[101] Los Angeles city, California
2024 ACS 1-year · Census geography 1600000US0644000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 3,878,718 | 69 |
| Median household income ($) | 82,263 | 1,125 |
| Households | 1,480,869 | 9,988 |
| No-vehicle households | 178,146 | 7,370 |
| Workers 16+ | 1,987,728 | 17,280 |
| Public-transit commuters | 122,542 | 7,534 |
| Walking commuters | 72,039 | 5,230 |
| Working at home | 318,737 | 10,239 |
No-vehicle share: 12% of households. Public-transit share: 6.2% of workers. Walking: 3.6% of workers.
[102] Chicago city, Illinois
2024 ACS 1-year · Census geography 1600000US1714000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 2,721,326 | 60 |
| Median household income ($) | 80,613 | 1,364 |
| Households | 1,172,455 | 9,440 |
| No-vehicle households | 321,432 | 10,192 |
| Workers 16+ | 1,430,663 | 21,052 |
| Public-transit commuters | 293,874 | 11,635 |
| Walking commuters | 102,022 | 7,871 |
| Working at home | 251,656 | 12,845 |
No-vehicle share: 27.4% of households. Public-transit share: 20.5% of workers. Walking: 7.1% of workers.
[103] Houston city, Texas
2024 ACS 1-year · Census geography 1600000US4835000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 2,387,910 | 2,025 |
| Median household income ($) | 64,361 | 1,735 |
| Households | 943,336 | 9,915 |
| No-vehicle households | 102,278 | 7,054 |
| Workers 16+ | 1,152,431 | 17,793 |
| Public-transit commuters | 38,913 | 4,663 |
| Walking commuters | 26,482 | 3,876 |
| Working at home | 125,576 | 6,589 |
No-vehicle share: 10.8% of households. Public-transit share: 3.4% of workers. Walking: 2.3% of workers.
[104] Phoenix city, Arizona
2024 ACS 1-year · Census geography 1600000US0455000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 1,673,122 | 151 |
| Median household income ($) | 85,246 | 2,218 |
| Households | 631,270 | 8,668 |
| No-vehicle households | 46,819 | 4,597 |
| Workers 16+ | 877,083 | 13,823 |
| Public-transit commuters | 18,270 | 3,935 |
| Walking commuters | 12,357 | 3,460 |
| Working at home | 147,552 | 8,280 |
No-vehicle share: 7.4% of households. Public-transit share: 2.1% of workers. Walking: 1.4% of workers.
[105] Philadelphia city, Pennsylvania
2024 ACS 1-year · Census geography 1600000US4260000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 1,573,916 | Not applicable |
| Median household income ($) | 60,521 | 1,851 |
| Households | 702,646 | 5,320 |
| No-vehicle households | 196,339 | 9,712 |
| Workers 16+ | 762,230 | 13,514 |
| Public-transit commuters | 153,150 | 9,566 |
| Walking commuters | 54,621 | 4,806 |
| Working at home | 109,484 | 7,780 |
No-vehicle share: 27.9% of households. Public-transit share: 20.1% of workers. Walking: 7.2% of workers.
[106] San Antonio city, Texas
2024 ACS 1-year · Census geography 1600000US4865000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 1,526,621 | 96 |
| Median household income ($) | 66,176 | 1,375 |
| Households | 568,979 | 7,359 |
| No-vehicle households | 49,419 | 4,191 |
| Workers 16+ | 742,336 | 12,273 |
| Public-transit commuters | 17,867 | 3,831 |
| Walking commuters | 16,938 | 3,433 |
| Working at home | 82,573 | 6,674 |
No-vehicle share: 8.7% of households. Public-transit share: 2.4% of workers. Walking: 2.3% of workers.
[107] San Diego city, California
2024 ACS 1-year · Census geography 1600000US0666000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 1,404,461 | 82 |
| Median household income ($) | 111,032 | 2,696 |
| Households | 542,590 | 6,726 |
| No-vehicle households | 38,114 | 4,291 |
| Workers 16+ | 751,867 | 12,087 |
| Public-transit commuters | 19,409 | 3,180 |
| Walking commuters | 28,327 | 3,328 |
| Working at home | 135,017 | 6,433 |
No-vehicle share: 7% of households. Public-transit share: 2.6% of workers. Walking: 3.8% of workers.
[108] Dallas city, Texas
2024 ACS 1-year · Census geography 1600000US4819000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 1,326,093 | 76 |
| Median household income ($) | 74,323 | 2,841 |
| Households | 537,317 | 9,138 |
| No-vehicle households | 50,771 | 5,411 |
| Workers 16+ | 693,351 | 14,298 |
| Public-transit commuters | 14,058 | 3,056 |
| Walking commuters | 16,716 | 3,228 |
| Working at home | 93,946 | 7,089 |
No-vehicle share: 9.4% of households. Public-transit share: 2% of workers. Walking: 2.4% of workers.
[109] San Jose city, California
2024 ACS 1-year · Census geography 1600000US0668000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 997,395 | 145 |
| Median household income ($) | 148,226 | 6,193 |
| Households | 331,653 | 4,603 |
| No-vehicle households | 20,595 | 2,592 |
| Workers 16+ | 511,080 | 9,073 |
| Public-transit commuters | 15,024 | 2,977 |
| Walking commuters | 10,104 | 2,166 |
| Working at home | 65,423 | 5,283 |
No-vehicle share: 6.2% of households. Public-transit share: 2.9% of workers. Walking: 2% of workers.
[110] Austin city, Texas
2024 ACS 1-year · Census geography 1600000US4805000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 993,771 | 945 |
| Median household income ($) | 90,430 | 2,569 |
| Households | 484,550 | 8,058 |
| No-vehicle households | 35,915 | 5,208 |
| Workers 16+ | 598,399 | 10,748 |
| Public-transit commuters | 17,697 | 4,663 |
| Walking commuters | 19,302 | 3,625 |
| Working at home | 148,176 | 8,412 |
No-vehicle share: 7.4% of households. Public-transit share: 3% of workers. Walking: 3.2% of workers.
[111] Jacksonville city, Florida
2024 ACS 1-year · Census geography 1600000US1235000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 1,009,831 | 22 |
| Median household income ($) | 72,389 | 2,436 |
| Households | 413,620 | 4,742 |
| No-vehicle households | 35,969 | 4,606 |
| Workers 16+ | 513,150 | 8,198 |
| Public-transit commuters | 4,090 | 1,758 |
| Walking commuters | 8,353 | 1,956 |
| Working at home | 76,748 | 6,312 |
No-vehicle share: 8.7% of households. Public-transit share: 0.8% of workers. Walking: 1.6% of workers.
[112] Fort Worth city, Texas
2024 ACS 1-year · Census geography 1600000US4827000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 1,014,376 | 6,611 |
| Median household income ($) | 82,503 | 2,414 |
| Households | 362,403 | 7,055 |
| No-vehicle households | 24,808 | 3,254 |
| Workers 16+ | 502,371 | 11,370 |
| Public-transit commuters | 3,647 | 1,520 |
| Walking commuters | 5,245 | 1,365 |
| Working at home | 52,871 | 4,736 |
No-vehicle share: 6.8% of households. Public-transit share: 0.7% of workers. Walking: 1% of workers.
[113] Columbus city, Ohio
2024 ACS 1-year · Census geography 1600000US3918000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 931,551 | 3,936 |
| Median household income ($) | 67,084 | 2,199 |
| Households | 396,650 | 6,126 |
| No-vehicle households | 34,965 | 3,086 |
| Workers 16+ | 487,910 | 9,089 |
| Public-transit commuters | 9,394 | 2,202 |
| Walking commuters | 13,043 | 2,425 |
| Working at home | 79,600 | 5,835 |
No-vehicle share: 8.8% of households. Public-transit share: 1.9% of workers. Walking: 2.7% of workers.
[114] Charlotte city, North Carolina
2024 ACS 1-year · Census geography 1600000US3712000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 943,474 | 59 |
| Median household income ($) | 86,416 | 2,366 |
| Households | 381,274 | 5,009 |
| No-vehicle households | 27,414 | 2,955 |
| Workers 16+ | 521,849 | 9,176 |
| Public-transit commuters | 11,832 | 2,420 |
| Walking commuters | 10,618 | 1,818 |
| Working at home | 130,426 | 7,547 |
No-vehicle share: 7.2% of households. Public-transit share: 2.3% of workers. Walking: 2% of workers.
[115] Indianapolis city (balance), Indiana
2024 ACS 1-year · Census geography 1600000US1836003.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 890,315 | 6,484 |
| Median household income ($) | 66,900 | 2,061 |
| Households | 370,768 | 5,268 |
| No-vehicle households | 32,991 | 5,012 |
| Workers 16+ | 458,357 | 8,254 |
| Public-transit commuters | 5,393 | 1,856 |
| Walking commuters | 10,128 | 2,164 |
| Working at home | 54,812 | 5,824 |
No-vehicle share: 8.9% of households. Public-transit share: 1.2% of workers. Walking: 2.2% of workers.
[116] San Francisco city, California
2024 ACS 1-year · Census geography 1600000US0667000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 827,526 | Not applicable |
| Median household income ($) | 139,801 | 6,180 |
| Households | 371,841 | 4,793 |
| No-vehicle households | 122,680 | 4,669 |
| Workers 16+ | 468,430 | 7,060 |
| Public-transit commuters | 117,279 | 6,864 |
| Walking commuters | 46,917 | 4,689 |
| Working at home | 100,347 | 5,261 |
No-vehicle share: 33% of households. Public-transit share: 25% of workers. Walking: 10% of workers.
[117] Seattle city, Washington
2024 ACS 1-year · Census geography 1600000US5363000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 780,992 | 54 |
| Median household income ($) | 118,745 | 5,861 |
| Households | 383,698 | 5,364 |
| No-vehicle households | 75,811 | 5,122 |
| Workers 16+ | 476,426 | 9,123 |
| Public-transit commuters | 71,683 | 5,122 |
| Walking commuters | 50,984 | 4,830 |
| Working at home | 116,087 | 6,912 |
No-vehicle share: 19.8% of households. Public-transit share: 15% of workers. Walking: 10.7% of workers.
[118] Denver city, Colorado
2024 ACS 1-year · Census geography 1600000US0820000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 729,019 | Not applicable |
| Median household income ($) | 92,504 | 3,897 |
| Households | 349,785 | 3,874 |
| No-vehicle households | 37,150 | 4,138 |
| Workers 16+ | 426,844 | 5,700 |
| Public-transit commuters | 14,843 | 2,500 |
| Walking commuters | 17,642 | 3,091 |
| Working at home | 105,633 | 5,876 |
No-vehicle share: 10.6% of households. Public-transit share: 3.5% of workers. Walking: 4.1% of workers.
[119] Washington city, District of Columbia
2024 ACS 1-year · Census geography 1600000US1150000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 702,250 | Not applicable |
| Median household income ($) | 109,707 | 4,149 |
| Households | 329,687 | 4,510 |
| No-vehicle households | 120,469 | 6,178 |
| Workers 16+ | 388,136 | 6,178 |
| Public-transit commuters | 103,317 | 6,796 |
| Walking commuters | 41,417 | 4,817 |
| Working at home | 88,726 | 4,752 |
No-vehicle share: 36.5% of households. Public-transit share: 26.6% of workers. Walking: 10.7% of workers.
[120] Boston city, Massachusetts
2024 ACS 1-year · Census geography 1600000US2507000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 673,822 | 3,086 |
| Median household income ($) | 97,791 | 5,438 |
| Households | 294,977 | 3,333 |
| No-vehicle households | 100,402 | 5,267 |
| Workers 16+ | 378,248 | 7,465 |
| Public-transit commuters | 95,880 | 6,523 |
| Walking commuters | 55,134 | 4,987 |
| Working at home | 58,574 | 4,464 |
No-vehicle share: 34% of households. Public-transit share: 25.3% of workers. Walking: 14.6% of workers.
[121] Nashville-Davidson metropolitan government (balance), Tennessee
2024 ACS 1-year · Census geography 1600000US4752006.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 704,965 | 24 |
| Median household income ($) | 80,090 | 2,958 |
| Households | 331,090 | 4,462 |
| No-vehicle households | 23,778 | 3,590 |
| Workers 16+ | 405,721 | 6,428 |
| Public-transit commuters | 6,673 | 2,049 |
| Walking commuters | 8,012 | 1,804 |
| Working at home | 72,909 | 5,734 |
No-vehicle share: 7.2% of households. Public-transit share: 1.6% of workers. Walking: 2% of workers.
[122] Oklahoma City city, Oklahoma
2024 ACS 1-year · Census geography 1600000US4055000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 713,014 | 298 |
| Median household income ($) | 70,040 | 2,362 |
| Households | 290,311 | 3,962 |
| No-vehicle households | 19,223 | 2,573 |
| Workers 16+ | 357,046 | 7,388 |
| Public-transit commuters | 1,172 | 531 |
| Walking commuters | 4,501 | 1,251 |
| Working at home | 36,150 | 3,311 |
No-vehicle share: 6.6% of households. Public-transit share: 0.3% of workers. Walking: 1.3% of workers.
[123] El Paso city, Texas
2024 ACS 1-year · Census geography 1600000US4824000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 681,724 | 47 |
| Median household income ($) | 59,932 | 2,820 |
| Households | 246,570 | 3,658 |
| No-vehicle households | 18,526 | 2,705 |
| Workers 16+ | 318,270 | 6,553 |
| Public-transit commuters | 3,002 | 1,078 |
| Walking commuters | 4,226 | 1,262 |
| Working at home | 23,633 | 3,035 |
No-vehicle share: 7.5% of households. Public-transit share: 0.9% of workers. Walking: 1.3% of workers.
[124] Portland city, Oregon
2024 ACS 1-year · Census geography 1600000US4159000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 636,958 | 1,441 |
| Median household income ($) | 91,478 | 1,981 |
| Households | 294,419 | 3,813 |
| No-vehicle households | 40,570 | 3,553 |
| Workers 16+ | 363,501 | 5,381 |
| Public-transit commuters | 25,629 | 3,540 |
| Walking commuters | 15,451 | 2,567 |
| Working at home | 89,364 | 4,876 |
No-vehicle share: 13.8% of households. Public-transit share: 7.1% of workers. Walking: 4.3% of workers.
[125] Las Vegas city, Nevada
2024 ACS 1-year · Census geography 1600000US3240000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 678,924 | 71 |
| Median household income ($) | 78,556 | 3,539 |
| Households | 253,523 | 4,746 |
| No-vehicle households | 22,444 | 2,843 |
| Workers 16+ | 323,293 | 7,234 |
| Public-transit commuters | 8,251 | 2,228 |
| Walking commuters | 3,423 | 1,443 |
| Working at home | 39,360 | 4,317 |
No-vehicle share: 8.9% of households. Public-transit share: 2.6% of workers. Walking: 1.1% of workers.
[126] Detroit city, Michigan
2024 ACS 1-year · Census geography 1600000US2622000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 645,702 | 43 |
| Median household income ($) | 39,209 | 2,683 |
| Households | 268,048 | 4,923 |
| No-vehicle households | 52,030 | 4,041 |
| Workers 16+ | 242,743 | 6,751 |
| Public-transit commuters | 9,539 | 2,452 |
| Walking commuters | 6,227 | 1,443 |
| Working at home | 23,818 | 3,418 |
No-vehicle share: 19.4% of households. Public-transit share: 3.9% of workers. Walking: 2.6% of workers.
[127] Memphis city, Tennessee
2024 ACS 1-year · Census geography 1600000US4748000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 610,936 | 64 |
| Median household income ($) | 52,679 | 2,423 |
| Households | 250,830 | 4,397 |
| No-vehicle households | 27,480 | 3,189 |
| Workers 16+ | 271,695 | 7,607 |
| Public-transit commuters | 1,499 | 890 |
| Walking commuters | 5,513 | 1,499 |
| Working at home | 22,881 | 3,327 |
No-vehicle share: 11% of households. Public-transit share: 0.6% of workers. Walking: 2% of workers.
[128] Louisville/Jefferson County metro government (balance), Kentucky
2024 ACS 1-year · Census geography 1600000US2148006.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 640,793 | 48 |
| Median household income ($) | 67,251 | 3,208 |
| Households | 269,328 | 3,297 |
| No-vehicle households | 27,485 | 3,676 |
| Workers 16+ | 311,529 | 6,378 |
| Public-transit commuters | 3,947 | 1,387 |
| Walking commuters | 5,308 | 1,343 |
| Working at home | 38,134 | 3,824 |
No-vehicle share: 10.2% of households. Public-transit share: 1.3% of workers. Walking: 1.7% of workers.
[129] Baltimore city, Maryland
2024 ACS 1-year · Census geography 1600000US2404000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 568,271 | Not applicable |
| Median household income ($) | 64,778 | 3,447 |
| Households | 265,998 | 3,452 |
| No-vehicle households | 74,075 | 4,505 |
| Workers 16+ | 283,337 | 6,170 |
| Public-transit commuters | 27,465 | 4,283 |
| Walking commuters | 16,130 | 2,392 |
| Working at home | 39,632 | 3,629 |
No-vehicle share: 27.8% of households. Public-transit share: 9.7% of workers. Walking: 5.7% of workers.
[130] Milwaukee city, Wisconsin
2024 ACS 1-year · Census geography 1600000US5553000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 563,512 | 72 |
| Median household income ($) | 56,792 | 2,550 |
| Households | 235,240 | 4,635 |
| No-vehicle households | 40,566 | 4,157 |
| Workers 16+ | 264,985 | 6,214 |
| Public-transit commuters | 14,312 | 2,033 |
| Walking commuters | 13,066 | 2,594 |
| Working at home | 28,762 | 3,389 |
No-vehicle share: 17.2% of households. Public-transit share: 5.4% of workers. Walking: 4.9% of workers.
[131] Albuquerque city, New Mexico
2024 ACS 1-year · Census geography 1600000US3502000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 560,333 | 51 |
| Median household income ($) | 71,494 | 2,759 |
| Households | 243,739 | 3,040 |
| No-vehicle households | 12,669 | 2,152 |
| Workers 16+ | 273,610 | 5,834 |
| Public-transit commuters | 1,307 | 579 |
| Walking commuters | 5,699 | 1,649 |
| Working at home | 36,665 | 4,184 |
No-vehicle share: 5.2% of households. Public-transit share: 0.5% of workers. Walking: 2.1% of workers.
[132] Tucson city, Arizona
2024 ACS 1-year · Census geography 1600000US0477000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 554,011 | 53 |
| Median household income ($) | 60,483 | 2,076 |
| Households | 233,314 | 4,795 |
| No-vehicle households | 22,913 | 3,333 |
| Workers 16+ | 271,727 | 6,177 |
| Public-transit commuters | 6,904 | 1,790 |
| Walking commuters | 8,170 | 2,192 |
| Working at home | 33,619 | 3,591 |
No-vehicle share: 9.8% of households. Public-transit share: 2.5% of workers. Walking: 3% of workers.
[133] Fresno city, California
2024 ACS 1-year · Census geography 1600000US0627000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 550,091 | 72 |
| Median household income ($) | 74,491 | 4,065 |
| Households | 181,398 | 3,497 |
| No-vehicle households | 16,540 | 2,015 |
| Workers 16+ | 240,387 | 5,927 |
| Public-transit commuters | 3,521 | 1,143 |
| Walking commuters | 3,201 | 1,302 |
| Working at home | 21,405 | 2,921 |
No-vehicle share: 9.1% of households. Public-transit share: 1.5% of workers. Walking: 1.3% of workers.
[134] Sacramento city, California
2024 ACS 1-year · Census geography 1600000US0664000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 535,787 | 75 |
| Median household income ($) | 91,387 | 3,799 |
| Households | 198,965 | 5,283 |
| No-vehicle households | 17,908 | 2,325 |
| Workers 16+ | 259,107 | 6,141 |
| Public-transit commuters | 5,780 | 1,709 |
| Walking commuters | 5,640 | 1,468 |
| Working at home | 41,939 | 3,782 |
No-vehicle share: 9% of households. Public-transit share: 2.2% of workers. Walking: 2.2% of workers.
[135] Atlanta city, Georgia
2024 ACS 1-year · Census geography 1600000US1304000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 520,066 | 55 |
| Median household income ($) | 88,165 | 4,451 |
| Households | 244,482 | 5,448 |
| No-vehicle households | 36,517 | 3,847 |
| Workers 16+ | 289,601 | 7,300 |
| Public-transit commuters | 16,646 | 3,255 |
| Walking commuters | 13,858 | 2,637 |
| Working at home | 74,207 | 5,521 |
No-vehicle share: 14.9% of households. Public-transit share: 5.7% of workers. Walking: 4.8% of workers.
[136] Kansas City city, Missouri
2024 ACS 1-year · Census geography 1600000US2938000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 516,045 | 88 |
| Median household income ($) | 69,958 | 3,137 |
| Households | 222,248 | 4,314 |
| No-vehicle households | 21,409 | 3,167 |
| Workers 16+ | 273,307 | 6,824 |
| Public-transit commuters | 5,419 | 1,295 |
| Walking commuters | 7,355 | 2,185 |
| Working at home | 41,944 | 4,166 |
No-vehicle share: 9.6% of households. Public-transit share: 2% of workers. Walking: 2.7% of workers.
[137] Miami city, Florida
2024 ACS 1-year · Census geography 1600000US1245000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 487,006 | 67 |
| Median household income ($) | 66,337 | 3,795 |
| Households | 202,757 | 5,197 |
| No-vehicle households | 37,765 | 3,905 |
| Workers 16+ | 265,512 | 7,798 |
| Public-transit commuters | 20,372 | 3,148 |
| Walking commuters | 10,755 | 1,997 |
| Working at home | 41,627 | 4,947 |
No-vehicle share: 18.6% of households. Public-transit share: 7.7% of workers. Walking: 4.1% of workers.
[138] Raleigh city, North Carolina
2024 ACS 1-year · Census geography 1600000US3755000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 499,637 | 651 |
| Median household income ($) | 85,060 | 7,119 |
| Households | 219,004 | 4,451 |
| No-vehicle households | 14,371 | 2,598 |
| Workers 16+ | 281,989 | 7,118 |
| Public-transit commuters | 4,776 | 1,939 |
| Walking commuters | 3,802 | 1,294 |
| Working at home | 61,431 | 5,343 |
No-vehicle share: 6.6% of households. Public-transit share: 1.7% of workers. Walking: 1.3% of workers.
[139] Omaha city, Nebraska
2024 ACS 1-year · Census geography 1600000US3137000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 489,263 | 35 |
| Median household income ($) | 71,640 | 2,145 |
| Households | 202,635 | 2,818 |
| No-vehicle households | 16,225 | 2,566 |
| Workers 16+ | 250,525 | 4,934 |
| Public-transit commuters | 2,124 | 824 |
| Walking commuters | 5,985 | 1,443 |
| Working at home | 30,830 | 3,116 |
No-vehicle share: 8% of households. Public-transit share: 0.8% of workers. Walking: 2.4% of workers.
[140] Minneapolis city, Minnesota
2024 ACS 1-year · Census geography 1600000US2743000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 428,572 | 46 |
| Median household income ($) | 77,732 | 4,594 |
| Households | 194,480 | 4,873 |
| No-vehicle households | 34,772 | 3,960 |
| Workers 16+ | 240,144 | 5,800 |
| Public-transit commuters | 17,161 | 3,061 |
| Walking commuters | 17,375 | 3,137 |
| Working at home | 50,185 | 3,909 |
No-vehicle share: 17.9% of households. Public-transit share: 7.1% of workers. Walking: 7.2% of workers.
[141] Cleveland city, Ohio
2024 ACS 1-year · Census geography 1600000US3916000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 365,391 | 56 |
| Median household income ($) | 43,383 | 3,068 |
| Households | 171,450 | 4,094 |
| No-vehicle households | 40,273 | 3,834 |
| Workers 16+ | 167,021 | 6,108 |
| Public-transit commuters | 11,994 | 2,278 |
| Walking commuters | 8,566 | 2,203 |
| Working at home | 16,963 | 2,179 |
No-vehicle share: 23.5% of households. Public-transit share: 7.2% of workers. Walking: 5.1% of workers.
[142] New Orleans city, Louisiana
2024 ACS 1-year · Census geography 1600000US2255000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 362,701 | Not applicable |
| Median household income ($) | 58,821 | 2,885 |
| Households | 157,535 | 3,023 |
| No-vehicle households | 26,772 | 2,816 |
| Workers 16+ | 170,171 | 5,552 |
| Public-transit commuters | 6,934 | 1,617 |
| Walking commuters | 10,349 | 2,041 |
| Working at home | 21,330 | 2,403 |
No-vehicle share: 17% of households. Public-transit share: 4.1% of workers. Walking: 6.1% of workers.
[143] Tampa city, Florida
2024 ACS 1-year · Census geography 1600000US1271000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 414,575 | 160 |
| Median household income ($) | 84,114 | 6,239 |
| Households | 176,004 | 4,971 |
| No-vehicle households | 16,103 | 2,653 |
| Workers 16+ | 223,498 | 6,509 |
| Public-transit commuters | 2,612 | 1,214 |
| Walking commuters | 4,794 | 1,642 |
| Working at home | 44,517 | 4,545 |
No-vehicle share: 9.1% of households. Public-transit share: 1.2% of workers. Walking: 2.1% of workers.
[144] Orlando city, Florida
2024 ACS 1-year · Census geography 1600000US1253000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 334,871 | 86 |
| Median household income ($) | 77,597 | 5,979 |
| Households | 137,393 | 5,667 |
| No-vehicle households | 9,137 | 1,982 |
| Workers 16+ | 192,400 | 6,603 |
| Public-transit commuters | 2,874 | 1,195 |
| Walking commuters | 2,696 | 1,278 |
| Working at home | 25,344 | 3,461 |
No-vehicle share: 6.7% of households. Public-transit share: 1.5% of workers. Walking: 1.4% of workers.
[145] Pittsburgh city, Pennsylvania
2024 ACS 1-year · Census geography 1600000US4261000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 307,670 | 38 |
| Median household income ($) | 66,954 | 3,968 |
| Households | 142,997 | 4,350 |
| No-vehicle households | 27,349 | 3,130 |
| Workers 16+ | 159,557 | 5,357 |
| Public-transit commuters | 22,052 | 3,332 |
| Walking commuters | 14,348 | 2,035 |
| Working at home | 28,096 | 3,265 |
No-vehicle share: 19.1% of households. Public-transit share: 13.8% of workers. Walking: 9% of workers.
[146] Cincinnati city, Ohio
2024 ACS 1-year · Census geography 1600000US3915000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 314,914 | 39 |
| Median household income ($) | 56,910 | 4,437 |
| Households | 145,964 | 3,698 |
| No-vehicle households | 27,087 | 3,845 |
| Workers 16+ | 158,835 | 5,219 |
| Public-transit commuters | 6,255 | 2,129 |
| Walking commuters | 9,890 | 1,779 |
| Working at home | 18,907 | 2,429 |
No-vehicle share: 18.6% of households. Public-transit share: 3.9% of workers. Walking: 6.2% of workers.
[147] St. Louis city, Missouri
2024 ACS 1-year · Census geography 1600000US2965000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 279,695 | Not applicable |
| Median household income ($) | 53,374 | 4,332 |
| Households | 148,637 | 2,621 |
| No-vehicle households | 29,580 | 3,195 |
| Workers 16+ | 146,665 | 3,657 |
| Public-transit commuters | 11,520 | 2,601 |
| Walking commuters | 5,902 | 1,587 |
| Working at home | 20,457 | 3,268 |
No-vehicle share: 19.9% of households. Public-transit share: 7.9% of workers. Walking: 4% of workers.
[148] Salt Lake City city, Utah
2024 ACS 1-year · Census geography 1600000US4967000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 217,783 | 33 |
| Median household income ($) | 77,186 | 4,499 |
| Households | 98,916 | 3,919 |
| No-vehicle households | 12,789 | 2,642 |
| Workers 16+ | 129,670 | 5,295 |
| Public-transit commuters | 7,597 | 1,908 |
| Walking commuters | 7,311 | 2,138 |
| Working at home | 22,631 | 3,962 |
No-vehicle share: 12.9% of households. Public-transit share: 5.9% of workers. Walking: 5.6% of workers.
[149] Richmond city, Virginia
2024 ACS 1-year · Census geography 1600000US5167000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 233,655 | Not applicable |
| Median household income ($) | 63,390 | 5,113 |
| Households | 108,990 | 1,943 |
| No-vehicle households | 12,757 | 2,131 |
| Workers 16+ | 129,395 | 3,436 |
| Public-transit commuters | 5,435 | 2,041 |
| Walking commuters | 6,438 | 1,840 |
| Working at home | 21,918 | 2,876 |
No-vehicle share: 11.7% of households. Public-transit share: 4.2% of workers. Walking: 5% of workers.
[150] Buffalo city, New York
2024 ACS 1-year · Census geography 1600000US3611000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 276,618 | 43 |
| Median household income ($) | 52,211 | 3,012 |
| Households | 119,620 | 3,310 |
| No-vehicle households | 23,813 | 2,933 |
| Workers 16+ | 125,756 | 5,151 |
| Public-transit commuters | 9,269 | 1,620 |
| Walking commuters | 6,734 | 1,991 |
| Working at home | 13,889 | 2,517 |
No-vehicle share: 19.9% of households. Public-transit share: 7.4% of workers. Walking: 5.4% of workers.
[151] Hartford city, Connecticut
2024 ACS 1-year · Census geography 1600000US0937000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 122,136 | 42 |
| Median household income ($) | 50,418 | 4,925 |
| Households | 49,316 | 2,736 |
| No-vehicle households | 15,172 | 2,184 |
| Workers 16+ | 54,042 | 3,106 |
| Public-transit commuters | 8,634 | 2,163 |
| Walking commuters | 3,377 | 1,147 |
| Working at home | 4,507 | 1,185 |
No-vehicle share: 30.8% of households. Public-transit share: 16% of workers. Walking: 6.2% of workers.
[152] Birmingham city, Alabama
2024 ACS 1-year · Census geography 1600000US0107000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 198,477 | 2,719 |
| Median household income ($) | 51,355 | 2,944 |
| Households | 91,130 | 3,356 |
| No-vehicle households | 10,306 | 1,443 |
| Workers 16+ | 95,603 | 5,041 |
| Public-transit commuters | 3,028 | 3,320 |
| Walking commuters | 2,926 | 1,300 |
| Working at home | 8,589 | 2,034 |
No-vehicle share: 11.3% of households. Public-transit share: 3.2% of workers. Walking: 3.1% of workers.
[153] Charleston city, South Carolina
2024 ACS 1-year · Census geography 1600000US4513330.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 159,011 | 3,519 |
| Median household income ($) | 98,085 | 6,046 |
| Households | 71,361 | 3,579 |
| No-vehicle households | 5,270 | 1,424 |
| Workers 16+ | 86,771 | 4,323 |
| Public-transit commuters | 1,091 | 917 |
| Walking commuters | 3,400 | 1,148 |
| Working at home | 14,428 | 2,222 |
No-vehicle share: 7.4% of households. Public-transit share: 1.3% of workers. Walking: 3.9% of workers.
[154] Boise City city, Idaho
2024 ACS 1-year · Census geography 1600000US1608830.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 237,959 | 34 |
| Median household income ($) | 81,102 | 4,347 |
| Households | 106,045 | 2,312 |
| No-vehicle households | 7,925 | 1,766 |
| Workers 16+ | 125,025 | 3,833 |
| Public-transit commuters | 297 | 196 |
| Walking commuters | 3,207 | 931 |
| Working at home | 18,311 | 2,270 |
No-vehicle share: 7.5% of households. Public-transit share: 0.2% of workers. Walking: 2.6% of workers.
[155] Spokane city, Washington
2024 ACS 1-year · Census geography 1600000US5367000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 230,604 | 35 |
| Median household income ($) | 78,583 | 5,004 |
| Households | 99,109 | 2,493 |
| No-vehicle households | 8,054 | 1,467 |
| Workers 16+ | 111,797 | 4,182 |
| Public-transit commuters | 3,375 | 1,041 |
| Walking commuters | 3,421 | 1,011 |
| Working at home | 12,235 | 1,916 |
No-vehicle share: 8.1% of households. Public-transit share: 3% of workers. Walking: 3.1% of workers.
[156] Anchorage municipality, Alaska
2024 ACS 1-year · Census geography 1600000US0203000.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 289,600 | Not applicable |
| Median household income ($) | 105,356 | 5,596 |
| Households | 109,479 | 2,451 |
| No-vehicle households | 6,904 | 1,555 |
| Workers 16+ | 154,450 | 4,321 |
| Public-transit commuters | 1,402 | 683 |
| Walking commuters | 3,837 | 1,229 |
| Working at home | 13,856 | 2,190 |
No-vehicle share: 6.3% of households. Public-transit share: 0.9% of workers. Walking: 2.5% of workers.
[157] Urban Honolulu CDP, Hawaii
2024 ACS 1-year · Census geography 1600000US1571550.
| Indicator | Estimate | 90% MOE (±) |
|---|---|---|
| Population | 344,977 | 47 |
| Median household income ($) | 86,169 | 4,741 |
| Households | 136,908 | 3,974 |
| No-vehicle households | 18,154 | 1,596 |
| Workers 16+ | 183,248 | 5,061 |
| Public-transit commuters | 14,968 | 2,302 |
| Walking commuters | 15,375 | 2,367 |
| Working at home | 14,442 | 1,894 |
No-vehicle share: 13.3% of households. Public-transit share: 8.2% of workers. Walking: 8.4% of workers.
[15] Accredited-investor screening and verification
The inquiry form screens investor interest using self-reported eligibility under Regulation D Rule 501(a). Individual pathways include income above $200,000 individually or $300,000 jointly with a spouse or spousal equivalent in each of the prior two years, with a reasonable expectation of reaching that level in the current year; net worth above $1 million excluding the primary residence under the applicable rules; or a Series 7, 65 or 82 license in good standing.
Primary-residence debt treatment matters: generally, mortgage debt up to the residence’s fair market value is excluded, while excess mortgage debt and certain increases within 60 days count as liabilities. Entity categories have separate conditions. Eligible corporations, LLCs, partnerships and certain organizations may qualify with assets above $5 million and a restriction against formation for the specific investment. A trust pathway also requires direction by a sophisticated person. Other paths include all equity owners being accredited, qualifying institutions, eligible entities with investments above $5 million, and qualifying family offices with more than $5 million under management and experienced direction.
The form’s minimum indicated amount is a CYVO screening setting, not an SEC accreditation threshold or a binding investment term. Self-reports are stored as “verification pending,” not proof of accredited status. A public-facing form does not itself establish compliance with any offering exemption. If Rule 506(c) is used, purchasers must be accredited and the issuer must take reasonable steps to verify status, along with meeting the rule’s other requirements. Verification and any offering documentation must be handled separately; financial statements, tax returns and identity documents should not be submitted here.
SEC accredited-investor criteria ↗ · Rule 501(a) definitions ↗ · SEC primary-residence and net-worth rules ↗ · SEC Rule 506(c) verification requirements ↗
[14] Airport ride demand, fares and allocations
Airport passenger inputs come from the FAA’s calendar-year 2025 commercial-service enplanement workbook, updated September 24, 2026 and retrieved October 24, 2026. Enplanements count departing boardings, including connecting passengers; they are not taxi trips, unique travelers, or arrivals plus departures. The model approximates both ground-travel directions as twice annual boardings, assuming balanced passenger flows.
Estimated annual hired-vehicle trips = allocated boardings × 2 × assumed local origin/destination passenger share × assumed taxi/ride-hail passenger share ÷ passengers per hired vehicle. CYVO airport trips = that pool × assumed CYVO capture. The share and occupancy inputs are editable scenario assumptions, not measured local market shares. Connecting passengers are excluded through the origin/destination share; employee trips, noncommercial activity and secondary airports not listed in the profile are excluded.
Airport coordinates come from OurAirports’ public-domain dataset. Distance is the great-circle distance from airport to the mapped city center, multiplied by an assumed 1.3 road factor. The city fare default is the larger of $27 or $5 plus $2.50 per estimated road mile, weighted by allocated boardings where multiple airports are listed. It is an approximate route/fare scenario, not a published tariff or live route quote. The financial model adds a separate editable airport-fare multiplier. For context, PHL publishes a $32 Center City base flat fare, with direction/passenger adjustments; that specific route is not an average for all Philadelphia journeys.
Airport catchments cross city boundaries. DFW boardings are allocated between Dallas and Fort Worth in proportion to their 2024 ACS city populations; the weights sum to one. DAL is assigned to Dallas. Other listed airports occur once across the default city set. These are scenario allocations, not surveyed airport origins. Selecting both Dallas and Fort Worth does not duplicate DFW’s passenger total. A regional model could use different surveyed catchment weights.
The household model’s monthly ride input now means local, non-airport rides. Airport rides are modeled separately to avoid counting the same assumed ride in both revenue streams. On the city explorer, demand scenarios are not fleet-capacity constrained. On the financial page, local and airport demand compete for the same available fleet hours, with airport trips given their own duration, distance and access-cost inputs.
FAA CY2025 commercial-service boarding workbook ↗ · FAA data definitions and release ↗ · OurAirports coordinates and public-domain terms ↗ · PHL published taxi fare reference ↗
[13] Financial projections: accounting, mechanics and limits
The projections are an annual, accrual-based planning model for a business beginning with no modeled assets, liabilities or retained earnings. They are not historical results, an audited forecast, or complete financial statements prepared in accordance with U.S. GAAP. Dollar amounts, cost estimates, fleet growth, capacity and funding inputs are assumptions until supported by contracts and operating evidence. The model uses the linked structure of an income statement, balance sheet, cash-flow statement and members’ equity schedule.
Operations and revenue
For each selected city, local household demand and allocated airport demand begin in its selected launch year. Demand is adjusted for growth, economic shock, launch delay and initial ramp. Local and airport rides use different cycle times; empty travel adds time and mileage. Available trip-cycle capacity equals vehicles × operating days × available daily hours × availability × target utilization, reduced by launch-year delay. If demand exceeds capacity, both ride streams are scaled proportionally. No guaranteed utilization is assumed.
Local fare revenue and airport fare revenue are tracked separately. The default assumes CYVO is the principal operator and recognizes gross passenger fares, with platform expense shown separately. A net-remittance sensitivity recognizes the fare amount after the selected platform deduction and removes that same deduction from expenses, preserving earnings and avoiding double counting. Actual revenue presentation depends on the contracts and control of the service under ASC 606; selecting a sensitivity does not make that accounting policy appropriate. Fares exclude pass-through taxes, tips and tolls.
Assets, costs and debt
Vehicles and infrastructure are capitalized at acquisition cost. Straight-line depreciation uses the entered useful lives and vehicle residual percentage. Assets are assumed acquired at the start of the relevant model year and receive a full year of depreciation. Vehicles are retired and replaced after their useful life; disposal proceeds equal the assumed original-cost residual. The model separately tracks purchase cost, accumulated depreciation, book value and disposal gains or losses. Startup, payroll, maintenance and operating costs are expensed. Depot/service payments are modeled as service arrangements; lease right-of-use assets and lease liabilities are not modeled.
Vehicle debt uses the specified financing fraction, equal annual principal amortization and interest on the beginning balance including new borrowing. Each loan fixes its rate when originated; economic rate shocks affect new loans only. The next year’s scheduled principal is classified as current debt. Payments include principal and interest separately. All purchase funding is assumed available at the start of the year; launch delays reduce revenue capacity but do not defer committed asset purchases or fixed expenses.
Working capital, tax and cash
Receivables equal recognized revenue × collection days ÷ 365. Operating payables equal cash operating expenses × payable days ÷ 365. Taxes and interest are assumed paid in the year accrued. Cash flow from operations starts with net income, adds depreciation, removes disposal gains and adjusts for receivable and payable changes. Asset purchases/disposals are investing flows; borrowing, principal repayment, equity and distributions are financing flows. No borrowing proceeds or member contributions are revenue.
The default entity income-tax rate is zero, reflecting a two-member LLC modeled as a pass-through entity. Owner taxes and distributions are distinct from entity expenses. The optional entity tax rate is a simplified effective current-tax sensitivity on positive pretax income; it does not model tax-basis differences, deferred taxes, credits or net-operating-loss benefits. State/local franchise or gross-receipts charges must be included in overhead or a revised cost model where applicable. Tax classification must reflect the company’s actual elections.
Funding, returns and reconciliation
Additional equity required is calculated whenever projected cash would fall below the selected minimum reserve after operations, asset purchases and debt payments. This is uncommitted funding needed to complete the modeled plan—not money already raised. Displayed ending cash assumes that funding is obtained. Annual calculations do not measure the peak intrayear funding requirement. Distributions are limited to the selected fraction of positive net income and available cash above reserves before additional equity, so new equity does not fund distributions.
EBITDA and free cash flow are supplemental metrics. Debt-service coverage uses operating cash flow plus interest divided by principal plus interest. Break-even rides cover fixed operating cash costs, excluding depreciation and interest, at the modeled average contribution per ride. Airport fees, variable costs and platform deductions enter contribution. Balance-sheet, cash, fixed-asset, debt and equity roll-forwards are checked independently.
Terminal enterprise value equals positive final-year EBITDA times the selected multiple; terminal equity value adds cash and subtracts debt, floored at zero. Business-equity MOIC, IRR and NPV include all modeled equity contributions, distributions and terminal equity value. Contributions are placed at the start of the applicable year; distributions and terminal value occur at year-end. IRR is blank when no unique meaningful root is detected in the search range. Terminal value is an unsold scenario value, not promised cash, an offer valuation or an investor-specific return. Ownership percentages, future dilution, investor rights, liquidation preferences and transaction costs require separately agreed terms.
Economic presets change stated growth, inflation, demand, pricing and new-loan-rate assumptions. They are sensitivity cases, not predictions. The demand/fare grid may show limited revenue response when fleet capacity is binding. A complete accounting and diligence model would require actual opening balances, commercial contracts, tax policies, impairment and credit-loss analysis, lease terms and financing documents. The website does not certify GAAP compliance.
SEC financial-statement relationships ↗ · SEC cash-flow statement guide ↗ · FASB principal-versus-agent revenue guidance ↗ · IRS LLC tax-classification guidance ↗
Airport source records by market
[200] New York airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| JFK — John F Kennedy International | 30,792,806 | 100.00% | 13.03 |
| LGA — Laguardia | 16,278,532 | 100.00% | 8.56 |
| EWR — Newark Liberty International | 23,464,492 | 100.00% | 8.53 |
Allocated boardings: 70,535,830. Weighted estimated road miles: 13.65. Distance-based average fare scenario: $39.13.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[201] Los Angeles airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| LAX — Los Angeles International | 36,497,303 | 100.00% | 12.16 |
Allocated boardings: 36,497,303. Weighted estimated road miles: 15.81. Distance-based average fare scenario: $44.52.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[202] Chicago airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| ORD — Chicago O'Hare International | 40,680,735 | 100.00% | 15.68 |
| MDW — Chicago Midway International | 9,367,712 | 100.00% | 9.05 |
Allocated boardings: 50,048,447. Weighted estimated road miles: 18.77. Distance-based average fare scenario: $51.93.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[203] Houston airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| IAH — George Bush Intcntl/Houston | 23,265,028 | 100.00% | 15.6 |
| HOU — William P Hobby | 6,790,274 | 100.00% | 9.7 |
Allocated boardings: 30,055,302. Weighted estimated road miles: 18.55. Distance-based average fare scenario: $51.37.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[204] Phoenix airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| PHX — Phoenix Sky Harbor International | 25,094,817 | 100.00% | 3.83 |
Allocated boardings: 25,094,817. Weighted estimated road miles: 4.98. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[205] Philadelphia airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| PHL — Philadelphia International | 14,779,849 | 100.00% | 6.9 |
Allocated boardings: 14,779,849. Weighted estimated road miles: 8.97. Distance-based average fare scenario: $27.43.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[206] San Antonio airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| SAT — San Antonio International | 5,237,816 | 100.00% | 7.95 |
Allocated boardings: 5,237,816. Weighted estimated road miles: 10.34. Distance-based average fare scenario: $30.84.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[207] San Diego airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| SAN — San Diego International | 12,675,684 | 100.00% | 1.98 |
Allocated boardings: 12,675,684. Weighted estimated road miles: 2.57. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[208] Dallas airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| DFW — Dallas-Fort Worth International | 41,297,009 | 56.66% | 16 |
| DAL — Dallas Love Field | 8,151,354 | 100.00% | 5.26 |
Allocated boardings: 31,549,944. Weighted estimated road miles: 17.19. Distance-based average fare scenario: $47.98.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[209] San Jose airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| SJC — Mineta San Jose International | 5,257,421 | 100.00% | 2.65 |
Allocated boardings: 5,257,421. Weighted estimated road miles: 3.44. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[210] Austin airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| AUS — Austin-Bergstrom International | 10,651,727 | 100.00% | 6.84 |
Allocated boardings: 10,651,727. Weighted estimated road miles: 8.89. Distance-based average fare scenario: $27.23.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[211] Jacksonville airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| JAX — Jacksonville International | 3,719,723 | 100.00% | 11.35 |
Allocated boardings: 3,719,723. Weighted estimated road miles: 14.76. Distance-based average fare scenario: $41.89.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[212] Fort Worth airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| DFW — Dallas-Fort Worth International | 41,297,009 | 43.34% | 19.41 |
Allocated boardings: 17,898,419. Weighted estimated road miles: 25.23. Distance-based average fare scenario: $68.08.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[213] Columbus airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| CMH — John Glenn Columbus International | 4,396,625 | 100.00% | 6.3 |
Allocated boardings: 4,396,625. Weighted estimated road miles: 8.19. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[214] Charlotte airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| CLT — Charlotte/Douglas International | 25,873,555 | 100.00% | 5.92 |
Allocated boardings: 25,873,555. Weighted estimated road miles: 7.7. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[215] Indianapolis airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| IND — Indianapolis International | 5,221,447 | 100.00% | 8.02 |
Allocated boardings: 5,221,447. Weighted estimated road miles: 10.43. Distance-based average fare scenario: $31.07.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[216] San Francisco airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| SFO — San Francisco International | 26,251,850 | 100.00% | 10.67 |
Allocated boardings: 26,251,850. Weighted estimated road miles: 13.87. Distance-based average fare scenario: $39.68.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[217] Seattle airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| SEA — Seattle-Tacoma International | 25,417,071 | 100.00% | 11.23 |
Allocated boardings: 25,417,071. Weighted estimated road miles: 14.6. Distance-based average fare scenario: $41.5.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[218] Denver airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| DEN — Denver International | 39,976,695 | 100.00% | 18.72 |
Allocated boardings: 39,976,695. Weighted estimated road miles: 24.34. Distance-based average fare scenario: $65.84.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[219] Washington airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| DCA — Ronald Reagan Washington Ntl | 12,003,594 | 100.00% | 4 |
| IAD — Washington Dulles International | 13,859,569 | 100.00% | 22.48 |
Allocated boardings: 25,863,163. Weighted estimated road miles: 18.07. Distance-based average fare scenario: $50.18.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[220] Boston airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| BOS — General Edward Lawrence Logan International | 21,021,153 | 100.00% | 2.66 |
Allocated boardings: 21,021,153. Weighted estimated road miles: 3.46. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[221] Nashville airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| BNA — Nashville International | 12,549,304 | 100.00% | 6.19 |
Allocated boardings: 12,549,304. Weighted estimated road miles: 8.05. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[222] Oklahoma City airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| OKC — Okc Will Rogers International | 2,242,888 | 100.00% | 6.89 |
Allocated boardings: 2,242,888. Weighted estimated road miles: 8.96. Distance-based average fare scenario: $27.39.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[223] El Paso airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| ELP — El Paso International | 1,940,314 | 100.00% | 7.55 |
Allocated boardings: 1,940,314. Weighted estimated road miles: 9.81. Distance-based average fare scenario: $29.54.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[224] Portland airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| PDX — Portland International | 9,054,394 | 100.00% | 6.19 |
Allocated boardings: 9,054,394. Weighted estimated road miles: 8.05. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[225] Las Vegas airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| LAS — Harry Reid International | 26,531,765 | 100.00% | 6.02 |
Allocated boardings: 26,531,765. Weighted estimated road miles: 7.83. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[226] Detroit airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| DTW — Detroit Metro Wayne County | 16,294,768 | 100.00% | 17.48 |
Allocated boardings: 16,294,768. Weighted estimated road miles: 22.72. Distance-based average fare scenario: $61.81.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[227] Memphis airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| MEM — Frederick W Smith International/Memphis | 2,368,014 | 100.00% | 8.43 |
Allocated boardings: 2,368,014. Weighted estimated road miles: 10.96. Distance-based average fare scenario: $32.4.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[228] Louisville airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| SDF — Louisville Muhammad Ali International | 2,237,842 | 100.00% | 5.65 |
Allocated boardings: 2,237,842. Weighted estimated road miles: 7.35. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[229] Baltimore airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| BWI — Baltimore/Washington International Thurgood Marshall | 12,274,134 | 100.00% | 8.51 |
Allocated boardings: 12,274,134. Weighted estimated road miles: 11.06. Distance-based average fare scenario: $32.66.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[230] Milwaukee airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| MKE — General Mitchell International | 2,889,040 | 100.00% | 6.45 |
Allocated boardings: 2,889,040. Weighted estimated road miles: 8.38. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[231] Albuquerque airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| ABQ — Albuquerque International Sunport | 2,562,028 | 100.00% | 3.61 |
Allocated boardings: 2,562,028. Weighted estimated road miles: 4.69. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[232] Tucson airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| TUS — Tucson International | 1,891,548 | 100.00% | 7.49 |
Allocated boardings: 1,891,548. Weighted estimated road miles: 9.74. Distance-based average fare scenario: $29.34.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[233] Fresno airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| FAT — Fresno Yosemite International | 1,394,397 | 100.00% | 4.69 |
Allocated boardings: 1,394,397. Weighted estimated road miles: 6.1. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[234] Sacramento airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| SMF — Sacramento International | 6,805,910 | 100.00% | 9.66 |
Allocated boardings: 6,805,910. Weighted estimated road miles: 12.56. Distance-based average fare scenario: $36.39.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[235] Atlanta airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| ATL — Hartsfield/Jackson Atlanta International | 51,459,786 | 100.00% | 8.13 |
Allocated boardings: 51,459,786. Weighted estimated road miles: 10.57. Distance-based average fare scenario: $31.42.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[236] Kansas City airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| MCI — Kansas City International | 5,613,974 | 100.00% | 15.67 |
Allocated boardings: 5,613,974. Weighted estimated road miles: 20.37. Distance-based average fare scenario: $55.93.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[237] Miami airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| MIA — Miami International | 26,450,972 | 100.00% | 6.69 |
Allocated boardings: 26,450,972. Weighted estimated road miles: 8.7. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[238] Raleigh airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| RDU — Raleigh-Durham International | 7,670,956 | 100.00% | 10.7 |
Allocated boardings: 7,670,956. Weighted estimated road miles: 13.91. Distance-based average fare scenario: $39.77.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[239] Omaha airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| OMA — Eppley Airfield | 2,565,135 | 100.00% | 3.82 |
Allocated boardings: 2,565,135. Weighted estimated road miles: 4.97. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[240] Minneapolis airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| MSP — Minneapolis-St Paul International/Wold-Chamberlain | 17,488,261 | 100.00% | 7.3 |
Allocated boardings: 17,488,261. Weighted estimated road miles: 9.49. Distance-based average fare scenario: $28.73.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[241] Cleveland airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| CLE — Cleveland-Hopkins International | 4,855,943 | 100.00% | 10.28 |
Allocated boardings: 4,855,943. Weighted estimated road miles: 13.36. Distance-based average fare scenario: $38.41.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[242] New Orleans airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| MSY — Louis Armstrong New Orleans International | 6,221,146 | 100.00% | 12.03 |
Allocated boardings: 6,221,146. Weighted estimated road miles: 15.64. Distance-based average fare scenario: $44.1.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[243] Tampa airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| TPA — Tampa International | 12,110,427 | 100.00% | 4.8 |
Allocated boardings: 12,110,427. Weighted estimated road miles: 6.24. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[244] Orlando airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| MCO — Orlando International | 27,999,709 | 100.00% | 8.77 |
Allocated boardings: 27,999,709. Weighted estimated road miles: 11.4. Distance-based average fare scenario: $33.5.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[245] Pittsburgh airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| PIT — Pittsburgh International | 4,792,035 | 100.00% | 13.25 |
Allocated boardings: 4,792,035. Weighted estimated road miles: 17.23. Distance-based average fare scenario: $48.06.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[246] Cincinnati airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| CVG — Cincinnati/Northern Kentucky International | 4,366,865 | 100.00% | 9.17 |
Allocated boardings: 4,366,865. Weighted estimated road miles: 11.92. Distance-based average fare scenario: $34.8.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[247] St. Louis airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| STL — St Louis Lambert International | 7,483,526 | 100.00% | 12.3 |
Allocated boardings: 7,483,526. Weighted estimated road miles: 15.99. Distance-based average fare scenario: $44.98.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[248] Salt Lake City airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| SLC — Salt Lake City International | 13,429,128 | 100.00% | 5.11 |
Allocated boardings: 13,429,128. Weighted estimated road miles: 6.64. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[249] Richmond airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| RIC — Richmond International | 2,481,863 | 100.00% | 7.02 |
Allocated boardings: 2,481,863. Weighted estimated road miles: 9.13. Distance-based average fare scenario: $27.81.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[250] Buffalo airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| BUF — Buffalo Niagara International | 2,533,180 | 100.00% | 8.25 |
Allocated boardings: 2,533,180. Weighted estimated road miles: 10.72. Distance-based average fare scenario: $31.81.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[251] Hartford airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| BDL — Bradley International | 3,283,397 | 100.00% | 12.34 |
Allocated boardings: 3,283,397. Weighted estimated road miles: 16.04. Distance-based average fare scenario: $45.11.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[252] Birmingham airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| BHM — Birmingham-Shuttlesworth International | 1,615,940 | 100.00% | 4.1 |
Allocated boardings: 1,615,940. Weighted estimated road miles: 5.33. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[253] Charleston airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| CHS — Charleston AFB/International | 3,142,402 | 100.00% | 10.19 |
Allocated boardings: 3,142,402. Weighted estimated road miles: 13.25. Distance-based average fare scenario: $38.12.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[254] Boise airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| BOI — Boise Air Trml/Gowen Field | 2,579,647 | 100.00% | 4.01 |
Allocated boardings: 2,579,647. Weighted estimated road miles: 5.21. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[255] Spokane airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| GEG — Spokane International | 2,106,307 | 100.00% | 5.58 |
Allocated boardings: 2,106,307. Weighted estimated road miles: 7.25. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[256] Anchorage airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| ANC — Ted Stevens Anchorage International | 2,729,285 | 100.00% | 4.19 |
Allocated boardings: 2,729,285. Weighted estimated road miles: 5.45. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
[257] Honolulu airport inputs
| Airport | 2025 boardings | Allocation | Air miles to city center |
|---|---|---|---|
| HNL — Daniel K Inouye International | 10,430,353 | 100.00% | 4.27 |
Allocated boardings: 10,430,353. Weighted estimated road miles: 5.55. Distance-based average fare scenario: $27.
FAA workbook — locate the airport codes above ↗ · Airport-coordinate dataset ↗
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