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Some notes: Data is displayed right as it comes off the spreadsheet. With the programs I have available to me I don't think it is possible for me to present my good looking excel pie graphs, so I'm afraid you all are stuck with the percentages for now.
Upcoming info includes departure and arrival times and some general comments on the data.
First some background. Many people work in downtown San Francisco. Many people live in the East Bay. While there is the commuter train (BART - Bay Area Rapid Transit) it costs almost \$3 one way and can take a while. Bus and ferries exist also, but are not popular for various reasons. Being a big city, people love to drive. That can be difficult with all the other people who want to drive and only the 5 lane (each way) Bay Bridge to support all those cars. To deter logjams of cars a measly two dollar toll is required and when the traffic is heavy, metering lights prevent too many cars jamming the five lanes. These two factors can cause the driver to wait about 45 minutes at the beginning of the bridge.
However if a car has a minimum of three people (or two if it is only a two seater) the driver can bypass the toll stations and metering lights and cruise right onto the bridge. Hence a strong desire to carpool. Ok. It is the friendly Bay Area after all, so there are various places around the East Bay where a person can stand and cars will be lining up to pick you up (or sometimes the people line up and the occasional car picks them up). Basically I get into a stranger's car with another stranger. It works quite well to the surprise of people who have not tried it.
The following data was compiled by not asking any questions, just by obsevations.
| Gender | Females | Males |
|---|---|---|
| Drivers | 43.5% | 56.5% |
| Passengers | 64.0% | 36.0% |
Here's the breakdown by gender of everyone. The percentages were created from 92 driver entries and 86 passenger entries. Notice males are form the clear majority of drivers while among passengers females are an even larger majority.
| Approximate Age | <20 | 20's | 30's | 40's | 50's | >60 |
|---|---|---|---|---|---|---|
| Drivers | 0% | 8.7% | 29.3% | 42.4% | 16.3% | 3.3% |
| Passengers | 1.2% | 16.3% | 19.8% | 39.5% | 23.3% | 0% |
All three of these tables are from subjective observational data but none so obviously as this one. The only pattern easily detectable in the data is how the drivers tend to cluster in their 30's and 40's (in my eyes) compared to the greater distribution in age among the passengers.
| Ethnicity | Asian | Black | Hispanic | White |
|---|---|---|---|---|
| Drivers | 21.7% | 30.4% | 28.3% | 19.6% |
| Passengers | 29.2% | 47.9% | 12.5% | 10.4% |
My poor ability to guess people's ethnic background is put on display, but at least I like to think I am consistent. More importantly while there are again 92 entries for drivers I didn't think of recording the ethnicity of my fellow passengers until later so there are only 48 entries. Given the relatively small numbers any extrapolations must be taken with greater care. However note the decrease in the percentage of White and Hispanic passengers compared to the drivers with an accompanying reversal in the percentages of black drivers.
| Station | Frequency | Description | |
|---|---|---|---|
| 680am | 1 | 1.1% | |
| 740am | 7 | 7.6% | |
| 810am | 4 | 4.3% | KGO news/talk radio |
| 1010am | 1 | 1.1% | |
| 88.5 | 7 | 7.6% | KQED public radio (NPR, PRI) |
| 91.7 | 2 | 2.2% | |
| 94.9 | 4 | 4.3% | |
| 95.7 | 4 | 4.3% | KZQZ |
| 96.5 | 8 | 8.7% | KOIT |
| 98.1 | 4 | 4.3% | KISS R&B and soul |
| 99.7 | 3 | 3.3% | KFRC |
| 101.3 | 3 | 3.3% | K101 |
| 102.1 | 3 | 3.3% | |
| 102.9 | 10 | 10.9% | KBLX hip-hop |
| 103.3 | 1 | 1.1% | |
| 103.7 | 8 | 8.7% | KKSF |
| 104.5 | 1 | 1.1% | |
| 106.1 | 2 | 2.2% | KMEL |
| 107.7 | 2 | 2.2% | KSAN |
| cd | 5 | 5.4% | |
| tape | 2 | 2.2% | |
| none | 10 | 10.9% | |
| total | 92 | 100% |
Here is the rather alarmingly wide distribution of the various radio stations. I should have better descriptions and of course I wish I had more data. One cannot claim much when only six entries have occurred more than five times. Any possible combinations can easily be calculated (what stations do men listen to? How about Asians? Or Chevrolet drivers - I've got that data also.)
The caveat "Primary" in the heading denotes that this was the station that was on when I entered the car or at least the station that was on the most during the drive. The data on the secondary stations is fairly rare and hence not very useful.
| Company | Totals | |
|---|---|---|
| GM | 13 | 14.1% |
| Ford | 14 | 15.2% |
| Daimler/Chrysler | 12 | 13.0% |
| BMW | 6 | 6.5% |
| VW | 3 | 3.3% |
| Audi | 1 | 1.1% |
| Toyota | 13 | 14.1% |
| Honda | 21 | 22.8% |
| Mitsubishi | 3 | 3.3% |
| Isuzu | 1 | 1.1% |
| Nissan | 5 | 5.4% |
| Total | 92 | 100.0% |
Here are the cars everyone drove by company. I have the breakdowns by division (all 19 of them) but this seemed quite enough. I tried to figure out what companies own what other companies but I'm sure I missed a few. Any information would be appreciated.
Many combinations of data is available, I'm not sure what would be the most interesting however. With less than 100 entries I'd love for other casual carpoolers to start recording similar data for much better views of the area.