57770 Zip Code Historical Commuting to Work Data
ACS 2010-2014 data
| | 57770 Zip Code | South Dakota | U.S. |
| Workers 16 Years and Over | 1,386, 100% | 420,700 | 141,337,148 |
| Car, Truck, or Van—Drove Alone | 774, 55.84%, see rank | 78.81% | 76.41% |
| Car, Truck, or Van—Carpooled | 339, 24.46%, see rank | 9.52% | 9.59% |
| Public Transportation | 9, 0.65%, see rank | 0.50% | 5.06% |
| Walked | 171, 12.34%, see rank | 4.13% | 2.78% |
| Other Means | 23, 1.66%, see rank | 1.58% | 1.79% |
| Worked at Home | 70, 5.05%, see rank | 5.46% | 4.37% |
Mean Travel Time to Work, see rank| 57770 Zip Code | 9.7 minutes |
| South Dakota | 16.2 minutes |
| U.S. | 25.7 minutes |
ACS 2008-2012 data
| | 57770 Zip Code | South Dakota | U.S. |
| Workers 16 Years and Over | 1,670, 100% | 412,161 | 139,893,639 |
| Car, Truck, or Van—Drove Alone | 1,021, 61.14%, see rank | 77.87% | 76.14% |
| Car, Truck, or Van—Carpooled | 299, 17.90%, see rank | 9.90% | 10.03% |
| Public Transportation | 31, 1.86%, see rank | 0.56% | 4.98% |
| Walked | 141, 8.44%, see rank | 4.27% | 2.82% |
| Other Means | 42, 2.51%, see rank | 1.71% | 1.76% |
| Worked at Home | 136, 8.14%, see rank | 5.69% | 4.27% |
Mean Travel Time to Work, see rank| 57770 Zip Code | 13.9 minutes |
| South Dakota | 16.1 minutes |
| U.S. | 25.4 minutes |
US Census 2000 data
| | 57770 Zip Code | South Dakota | U.S. |
| Workers 16 Years and Over | 1,127, 100% | 372,648 | 128,279,228 |
| Car, Truck, or Van—Drove Alone | 727, 64.51%, see rank | 77.35% | 75.70% |
| Car, Truck, or Van—Carpooled | 173, 15.35%, see rank | 10.41% | 12.19% |
| Public Transportation | 23, 2.04%, see rank | 0.46% | 4.73% |
| Walked | 155, 13.75%, see rank | 4.50% | 2.93% |
| Other Means | 13, 1.15%, see rank | 0.80% | 1.19% |
| Worked at Home | 36, 3.19%, see rank | 6.48% | 3.26% |
Mean Travel Time to Work, see rank| 57770 Zip Code | 10.3 minutes |
| South Dakota | 15.7 minutes |
| U.S. | 25.1 minutes |
* ACS stands for U.S. Census American Community Survey. According to the U.S. Census, if the date is a range, you can interpret the data as an average of the period of time.
