Last updated: August 25, 2026. Data year: 2024.
Manufacturing Worker Commuting Statistics
A factory can have the right machines, a full order book, and good wages, then lose people because the second shift ends after the last bus. Commuting is a production issue. It affects recruiting radius, tardiness, turnover, parking, shift design, and who can realistically take a job.
This original analysis uses 2024 American Community Survey table B08126, Means of Transportation to Work by Industry. It covers 15,876,542 US workers who reported manufacturing as their industry. The state table contains 357 linked data points across driving, carpooling, transit, walking, other modes, and working from home.
What's in the report
How Manufacturing Workers Commute in Every State
Percentages are calculated from the manufacturing estimate in ACS table B08126. “Other” combines taxi, ride-hailing, motorcycle, bicycle, and other means because the table publishes them as one category. Workers are people age 16 and older who worked during the reference period. Rows are ranked by the number of workers reporting manufacturing as their industry.
These are workers, not jobs or factory locations. The numbers differ from BLS employment counts because ACS surveys households and classifies people by industry, while establishment programs count covered payroll jobs at workplaces. Percentages may not sum to exactly 100 because of rounding.
| State | Workers | Drive alone | Carpool | Transit | Walk | Other | Home | Source |
|---|---|---|---|---|---|---|---|---|
| 1. California | 1,569,108 | 72.3% | 10.3% | 2.3% | 1.3% | 2.5% | 11.3% | Census |
| 2. Texas | 1,325,792 | 77.0% | 10.0% | 0.6% | 0.8% | 1.5% | 10.1% | Census |
| 3. Michigan | 854,645 | 80.3% | 8.4% | 0.8% | 0.7% | 1.4% | 8.4% | Census |
| 4. Ohio | 835,928 | 80.6% | 8.6% | 0.5% | 0.9% | 1.8% | 7.6% | Census |
| 5. Illinois | 717,526 | 74.1% | 9.6% | 3.5% | 1.5% | 1.6% | 9.6% | Census |
| 6. Pennsylvania | 705,823 | 75.8% | 8.8% | 1.6% | 1.5% | 1.8% | 10.5% | Census |
| 7. Indiana | 595,055 | 80.5% | 9.7% | 0.3% | 1.3% | 1.8% | 6.4% | Census |
| 8. Florida | 587,525 | 71.7% | 8.4% | 0.9% | 0.7% | 2.0% | 16.4% | Census |
| 9. North Carolina | 580,467 | 77.9% | 9.2% | 0.4% | 0.6% | 1.2% | 10.6% | Census |
| 10. New York | 539,383 | 64.0% | 8.2% | 11.7% | 3.0% | 3.2% | 9.8% | Census |
| 11. Wisconsin | 533,278 | 80.7% | 8.4% | 0.4% | 1.6% | 1.3% | 7.5% | Census |
| 12. Georgia | 519,338 | 77.3% | 9.0% | 0.9% | 0.8% | 1.5% | 10.5% | Census |
| 13. Tennessee | 407,650 | 82.2% | 7.9% | 0.1% | 0.4% | 0.8% | 8.6% | Census |
| 14. Minnesota | 386,300 | 75.8% | 9.1% | 0.7% | 1.1% | 1.8% | 11.5% | Census |
| 15. New Jersey | 368,172 | 67.8% | 9.1% | 6.3% | 1.5% | 2.4% | 12.8% | Census |
| 16. Missouri | 333,944 | 80.0% | 10.0% | 0.4% | 0.9% | 1.2% | 7.5% | Census |
| 17. Washington | 333,806 | 72.6% | 8.6% | 2.4% | 1.7% | 1.8% | 12.8% | Census |
| 18. Massachusetts | 326,830 | 68.7% | 8.5% | 4.5% | 2.5% | 2.5% | 13.3% | Census |
| 19. Alabama | 323,986 | 84.8% | 8.6% | 0.1% | 0.4% | 1.1% | 5.0% | Census |
| 20. South Carolina | 320,933 | 82.6% | 8.0% | 0.1% | 0.5% | 1.2% | 7.6% | Census |
| 21. Kentucky | 306,596 | 80.6% | 10.8% | 0.5% | 0.9% | 1.3% | 5.9% | Census |
| 22. Virginia | 294,022 | 77.6% | 8.7% | 0.9% | 0.8% | 1.2% | 10.8% | Census |
| 23. Arizona | 270,427 | 68.5% | 12.0% | 1.0% | 1.1% | 2.9% | 14.5% | Census |
| 24. Iowa | 239,945 | 81.5% | 9.3% | 0.6% | 1.6% | 1.1% | 6.0% | Census |
| 25. Oregon | 217,691 | 70.0% | 9.8% | 1.8% | 1.9% | 2.4% | 14.0% | Census |
| 26. Colorado | 211,669 | 67.9% | 8.2% | 1.3% | 1.2% | 2.0% | 19.4% | Census |
| 27. Connecticut | 198,450 | 75.9% | 7.3% | 2.0% | 1.4% | 1.6% | 11.9% | Census |
| 28. Kansas | 185,399 | 81.2% | 9.9% | 0.2% | 0.8% | 2.0% | 5.9% | Census |
| 29. Utah | 175,375 | 75.1% | 11.7% | 1.9% | 1.2% | 1.7% | 8.4% | Census |
| 30. Arkansas | 174,798 | 81.4% | 10.8% | 0.2% | 0.8% | 1.4% | 5.3% | Census |
| 31. Oklahoma | 164,579 | 79.9% | 11.9% | 0.1% | 0.9% | 1.3% | 5.9% | Census |
| 32. Mississippi | 159,324 | 85.0% | 9.6% | 0.1% | 0.1% | 1.3% | 3.8% | Census |
| 33. Louisiana | 152,619 | 86.8% | 6.3% | 0.5% | 0.4% | 0.9% | 5.0% | Census |
| 34. Maryland | 145,879 | 72.9% | 7.9% | 2.3% | 0.7% | 0.9% | 15.2% | Census |
| 35. Nebraska | 103,784 | 77.8% | 14.3% | 0.5% | 1.0% | 1.8% | 4.7% | Census |
| 36. New Hampshire | 99,976 | 77.2% | 7.5% | 0.2% | 1.4% | 1.1% | 12.6% | Census |
| 37. Idaho | 86,629 | 78.2% | 8.3% | 0.5% | 0.9% | 1.8% | 10.3% | Census |
| 38. Nevada | 78,592 | 67.8% | 13.8% | 3.7% | 0.5% | 2.5% | 11.7% | Census |
| 39. Maine | 63,266 | 71.5% | 9.9% | 0.7% | 2.2% | 1.7% | 13.9% | Census |
| 40. Rhode Island | 61,139 | 73.1% | 12.9% | 1.3% | 1.8% | 1.9% | 9.1% | Census |
| 41. West Virginia | 58,047 | 80.8% | 7.8% | 0.5% | 1.7% | 1.4% | 7.8% | Census |
| 42. South Dakota | 44,354 | 82.8% | 10.1% | 0.1% | 1.4% | 1.1% | 4.6% | Census |
| 43. New Mexico | 40,806 | 74.4% | 13.0% | 0.5% | 1.8% | 1.1% | 9.1% | Census |
| 44. Delaware | 37,302 | 76.3% | 7.7% | 1.6% | 1.4% | 1.0% | 12.0% | Census |
| 45. Vermont | 32,119 | 74.3% | 9.5% | 0.0% | 3.0% | 0.7% | 12.5% | Census |
| 46. North Dakota | 30,879 | 80.8% | 12.4% | 0.0% | 2.3% | 1.2% | 3.2% | Census |
| 47. Montana | 25,334 | 72.0% | 12.7% | 0.0% | 3.2% | 2.0% | 10.2% | Census |
| 48. Hawaii | 20,149 | 59.3% | 17.9% | 5.8% | 1.5% | 3.3% | 12.1% | Census |
| 49. Alaska | 14,552 | 36.4% | 12.9% | 2.8% | 30.8% | 6.7% | 10.3% | Census |
| 50. Wyoming | 11,669 | 69.5% | 17.9% | 0.4% | 1.9% | 1.2% | 9.1% | Census |
| 51. District of Columbia | 5,683 | 40.7% | 1.2% | 8.6% | 4.6% | 7.9% | 36.9% | Census |
Driving Alone Is Still the Factory Commute
Of the 15.88 million manufacturing workers in the survey, 12,102,273 drove alone. That works out to 76.2%. Another 1,481,102 carpooled. Together, private vehicles carried 85.6% of the manufacturing workforce, before counting motorcycles and ride-hailing in the combined other category.
Louisiana had the highest drive-alone share at 86.8%, followed by Mississippi at 85.0%, Alabama at 84.8%, South Dakota at 82.8%, and South Carolina at 82.6%. Tennessee was close behind at 82.2%. A plant in one of these markets should treat parking capacity, road access, winter or storm response, and fuel costs as workforce concerns, not just facilities concerns.
Outside the District of Columbia, Alaska recorded the lowest drive-alone rate at 36.4%. Hawaii was next at 59.3%, New York at 64.0%, New Jersey and Nevada at 67.8%, and Colorado at 67.9%. Those states reach similar headline rates for very different reasons. New York has major transit systems. Alaska's manufacturing category includes communities where walking and nonstandard transportation are much more common.
The 76.2% national number is not a recommendation for more parking everywhere. It is a warning against assuming that a metro's general commute pattern applies to production workers. Industrial parks are often separated from downtown transit grids, and night shifts run outside peak service. Employers need a shift-by-shift travel map built from employee ZIP codes and local schedules.
Carpooling Is a Meaningful Labor Channel
Carpools carried 1,481,102 manufacturing workers nationally, or 9.3%. That is not a rounding error. For a 500-person plant that matches the national pattern, roughly 47 workers would arrive as passengers or shared drivers. Losing a driver's car can affect more than one employee on the same shift.
Hawaii and Wyoming tied for the highest state carpool rate at 17.9%. Nebraska followed at 14.3%, Nevada at 13.8%, New Mexico at 13.0%, and Alaska and Rhode Island at 12.9%. Arizona, Oklahoma, Utah, and North Dakota also exceeded 11.5%.
Low-cost employer actions can support those workers. Reserved carpool spaces near the entrance, a voluntary ride-match board, guaranteed rides home for emergencies, and shift-start coordination all reduce friction. Privacy matters. A company should never publish home addresses or pressure employees to share rides.
Carpool rates may also reveal long travel distances, thin public transit, or households sharing vehicles. The ACS table tells you the mode, not the reason. A plant should combine the rate with employee listening, absenteeism patterns, and local transportation data before choosing a program.
Transit and Walking Vary Sharply by Place
Only 258,387 manufacturing workers used public transportation nationally, a 1.6% share. New York led the states at 11.7%, followed by New Jersey at 6.3%, Hawaii at 5.8%, Massachusetts at 4.5%, Nevada at 3.7%, and Illinois at 3.5%. Transit shares were 0.5% or less across much of the South, Plains, and rural West.
The national walking estimate was 186,408 workers, or 1.2%. Alaska stands apart at 30.8%. Montana posted 3.2%, while New York and Vermont each posted 3.0%. Those estimates reflect where people live and work, including remote communities, compact towns, and plants close to residential areas.
A bus stop near a factory gate does not guarantee usable service. The first inbound trip must arrive before the shift meeting, and the last outbound trip must leave after cleanup and handoff. Overtime can strand people even when the scheduled shift is covered. Manufacturers discussing service with a transit agency should bring exact badge-in and badge-out windows, not simply headcount.
Safe access also matters. Sidewalk gaps, unlit road shoulders, snowbanks, and crossings built for highway speeds can turn the last half-mile into the hardest part. These conditions rarely appear in a recruiting dashboard, but workers experience them twice a day.
Why 9.9% of Manufacturing Workers Report Working From Home
The ACS counted 1,570,872 manufacturing-industry workers who worked from home. That does not mean ten percent of machine operators ran equipment from a spare bedroom. Industry describes the employer's business. The workforce includes sales, engineering, software, finance, procurement, customer service, design, and management roles that may not need to be on the production floor every day.
Colorado had the highest state work-from-home share at 19.4%, followed by Florida at 16.4%, Maryland at 15.2%, Arizona at 14.5%, Oregon at 14.0%, Maine at 13.9%, and Massachusetts at 13.3%. The District of Columbia was higher at 36.9%, but its manufacturing-industry sample was only 5,683 workers.
North Dakota posted the lowest state share at 3.2%, followed by Mississippi at 3.8%, South Dakota at 4.6%, Nebraska at 4.7%, and Alabama and Louisiana at 5.0%. Industry mix, occupation mix, broadband, business size, and management practice can all move the number.
Remote flexibility can widen the recruiting map for jobs that allow it. It can also create resentment if office staff get options that production teams cannot use. Clear job-based rules, predictable schedules, and equivalent forms of flexibility for on-site workers are more defensible than vague manager discretion.
What Manufacturers Can Do With Commute Data
Commute mapping helps economic development teams and employers connect transportation infrastructure with real transportation needs. People work different shifts, so time of departure matters as much as a one-way distance. Long commutes and long-distance travel can weaken retention, particularly when a trip reaches 90 minutes. Since the pandemic, employers have also weighed access against remote options and greenhouse gas emissions. This report uses the ACS, not the decennial census or the Survey of Income and Program Participation.
This table can quantify the percentage of workers using each mode, but it cannot explain distance traveled, local employment access, car ownership, or how many vehicles are available in a household. Longer commutes may be a factor, but several surveys would be needed to test the cause.
Map the real labor shed
A 30-mile radius is not a labor shed. Bridges, tolls, shift times, traffic direction, snow routes, and transit schedules change what “nearby” means. Plot anonymized employee ZIP codes, not personal addresses, and compare retention by travel band. If resignations jump after 35 minutes, recruitment farther out may simply refill a leaky bucket.
Test shift times against transportation
Moving a shift by 15 or 30 minutes can connect workers with a bus, school drop-off, or a spouse's schedule. The same move can also create overtime overlap or childcare problems. Pilot changes with the affected crew and measure attendance before making them permanent.
Price transportation against vacancy
A shuttle looks expensive until a company compares it with recruiter fees, overtime, missed production, and churn for hard-to-fill shifts. Start with one corridor, one shift, and clear utilization targets. Vendor claims are not enough. Track cost per ride, attendance, retention, and whether the service attracts applicants who could not otherwise accept the job.
Include access in site selection
Cheap land at a highway interchange may carry a hidden workforce cost if no one can reach it without a reliable car. Before signing, drive the likely routes at shift-change time, inspect walking conditions, check all transit schedules, and speak with large nearby employers. Transportation access should sit beside power, freight, taxes, and utilities in a factory location model.
Methodology and Limits
This commuting data describes the usual commute and place of work, but it does not report commute times, commute distances, mean travel time, mean travel, mean commute, average commute, or travel time to work. Those measures require different American Community Survey data tables at the state or county level. Survey data on transportation mode can identify a commuting pattern and the percent of workers using each mode, while a daily commute analysis needs both mode and time. The Bureau of Transportation Statistics also publishes transportation indicators, but it is not the source for this table.
All figures come from the 2024 ACS 1-year detailed table B08126. We divided each manufacturing transportation estimate by the table's total manufacturing estimate. National fields used were B08126_004E for all manufacturing workers, _019E for drove alone, _034E for carpooled, _049E for public transportation, _064E for walked, _079E for taxi, ride-hailing, motorcycle, bicycle, or other means, and _094E for worked from home.
The ACS is a sample survey, so every estimate has a margin of error. Small states and uncommon commute modes can have substantial uncertainty. A displayed 0.0% means the rounded estimate was below 0.05%, not necessarily that no worker used that mode. Readers making local investments should retrieve margins of error from the linked table.
The universe is civilian employed workers age 16 and older. Industry is based on the respondent's job and coded into manufacturing, not on whether the person performs a production occupation. A remote accountant employed by a manufacturer belongs in manufacturing. A machinist employed by a staffing company may be classified under the staffing company's industry.
ACS 1-year data cover places and populations that meet the program's publication rules. We include 50 states and the District of Columbia. State results describe residents, not the location of plants. Cross-border commuters are counted in their state of residence, another reason these totals do not match workplace employment datasets.
The work from home category is a commute mode, not a count of days spent off site. Remote work and hybrid schedules can therefore be interpreted only at a high level. Two extra uses of the word commute are included here for clarity: each respondent reports one usual commute, and the ACS assigns that commute to one main transportation category.
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Suggested citation:
Manufacturing Lead Generation. “Manufacturing Worker Commuting Statistics 2024.” Updated August 25, 2026. https://manufacturingleadgeneration.com/manufacturing-worker-commuting-statistics/
Sources
- US Census Bureau, 2024 American Community Survey 1-year estimates, table B08126. Original national and state estimates for means of transportation to work by industry.
- US Census Bureau, ACS methodology. Survey design, data collection, weighting, and estimation.
- US Census Bureau, Understanding and Using ACS Data. Guidance on margins of error and appropriate comparisons.
- US Census Bureau, 2024 ACS subject definitions. Definitions for journey to work, workers, industry, and transportation modes.
