In an attempt to answer the question of ‘who runs the red lights’ a study was undertaken to answer this question in terms of age and sex of motor vehicle drivers. On three days during the workweek, at three
times per day, a randomly chosen major intersection was observed for 20 minutes. This intersection was the same for the whole study. Traffic was observed in all direction. The term “running the red light” was defined as any driver who was in the intersection while their light was red; this included those who “rush” the left hand turns. For any who met these conditions their sex and approximate age or age category was recorded. It was concluded that
2.0 Introduction
We are all familiar with the ways that vehicle insurance companies differentiate between cohorts in terms of what they charge for insurance. The logic is in the idea that some demographics of people (i.e. 16-year-old males) are more likely to be involved in a motor vehicle accident than others due to specific characteristics of that group. This assumption is based on motor vehicle accident studies that look at the age and sex of people involved in collisions that cause damage to the vehicle. We can see though that their data is somewhat biased in the sense that the insurance companies that conduct these studies are only interested in the accidents that require insurance money to pay for the damage. As we all have experienced a fender-bender at some point in our lives we know that most vehicle collisions are minor; it is likely that many do not even get reported.
This study assumes that the people who run red lights are more likely in general to be involved in some sort of collision, expensive or otherwise. This means that this study leans in the direction behavioral research; the data collected is meant to draw conclusions on the tendencies of populations to run red lights which in turn are used to infer about the likeliness of populations to be involved in collisions. The statistical analysis told us if there is a difference in the amount of people in different groups that run the red light, and this was used to draw behavioral conclusions on people in general.
Some sources of variance include the fact that I sampled in only one city, only one intersection. The intersection chosen was a major intersection and represents a massive area of the city, and randomness is maintained through the fact that I had no control over the people that chose to travel through the intersection that I observed. This intersection was also randomly chosen. That only one intersection was chosen does not mean more error, partly because of the volume of traffic that was observed and also because the traffic that traveled through the intersection was randomly observed; I had no control. It might have been ideal to observe more intersections, and possible more days of the wee, but under the circumstances this was not felt to be necessary or feasible. Another possible problem was in the fact that sex and age are not equally distributed among drivers, that the average driver may not just as likely to be male as female. This would have an affect on the outcome of my data but I do not think that it affects my conclusions on the behavior of drivers in a negative way. This study was designed to answer the question of who runs the red lights and if part of that reason is based on the makeup of the overall population of drivers my conclusions are still valid because I did not attempt to ask why certain groups run the red light more frequently.
3.0 Materials and Methods
Data were collected on three days of the week to observe the variance as the week progressed. The days were Monday, Wednesday and Friday. During each day the intersection chosen was observed for 20 minutes at three different times during the day; 800, 130 and 500. These times were kept the same for all three days. The intersection was randomly chosen by dropping a coin onto a city map blind folded. If the chosen area did not contain a major intersection then this procedure was repeated to finally settle upon the intersection of 113th st. and 72nd ave. While collecting data, all directions were observed coming into the intersection.
The second and third factors studied in this experiment were the sex and the age of the drivers. The drivers were defined as running a red light for the purposes of this study when they were in the intersection while the lights were red. While the sex of the driver was ascertained easily, the exact age could not be. Also, exact ages would be hard to test statistically because there would be too many categories. So the age was divided into categories that were more general and that would be easy to judge. These categories were ages 16-25, 26-35, 36-45, 46-55, and 56 and older.
4.0 Statistical Analysis
5.0 Discussion
As mentioned in the introduction I did not investigate the reasons for my conclusions, I simply attempted to make a model for the likeliness of the running of the red light with the population of drivers in this city. Another study that might start to answer that “why” of this could look at equal, set numbers of male, female and all age classes of drivers. This would tell us if the collision rates among different groups of drivers was due to their proportions in the overall driver population or if there is something inherent in the behavior of males over females, for example, that causes them to run the red light more often.
6.0 Literature Cited
7.0 Appendix
Raw Data Table 7.1
Monday
Sex800 Age800 Sex230 Age230 Sex500 Age500 Legend
1 1 1 1 1 1 Sex: 1=F 2=M
1 1 1 1 1 1 Age: 1=16-25
1 1 1 2 1 1 2=26-35
1 1 1 4 1 1 3=36-45
1 2 2 1 1 2 4=46-55
1 2 2 1 1 2 5=56 and up
1 2 2 2 1 2
1 2 2 2 1 2
1 2 2 3 1 2
1 2 2 4 1 2
1 2 2 4 1 2
1 3 1 2
1 3 1 2
1 3 1 2
2 1 1 2
2 1 1 2
2 1 1 3
2 1 1 3
2 2 1 3
2 2 1 3
2 2 1 4
2 2 2 1
2 2 2 1
2 2 2 1
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 3 2 2
2 3 2 2
2 3 2 2
2 4 2 2
2 2
2 3
2 3
2 3
2 4
2 4
Wednesday
Sex800 2 Age800 2 Sex230 2 Age230 2 Sex500 2 Age500 2
1 1 1 1 1 1
1 2 1 1 1 1
1 2 1 2 1 1
1 2 1 2 1 2
1 2 1 2 1 2
1 3 2 2 1 2
1 3 2 2 1 2
1 3 2 2 1 2
2 1 2 2 1 2
2 1 2 2 1 3
2 1 2 3 2 1
2 1 2 1
2 1 2 1
2 2 2 1
2 2 2 1
2 2 2 1
2 2 2 1
2 2 2 1
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 3 2 2
2 3 2 2
2 3 2 3
2 3 2 3
2 4 2 3
2 4
Friday
Sex800 3 Age800 3 Sex230 3 Age230 3 Sex500 3 Age500 3
1 1 1 1 1 1
1 1 1 2 1 1
1 1 1 2 1 1
1 1 1 2 1 1
1 1 1 3 1 1
1 2 1 3 1 1
1 2 2 1 1 1
1 2 2 1 1 1
1 2 2 2 1 2
1 2 2 2 1 2
1 3 2 2 1 2
1 3 2 3 1 2
2 1 2 3 1 2
2 1 1 2
2 1 1 2
2 1 1 2
2 1 1 3
2 1 2 1
2 1 2 1
2 1 2 1
2 2 2 1
2 2 2 1
2 2 2 1
2 2 2 1
2 2 2 1
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 2 2 2
2 3 2 2
2 3 2 2
2 3 2 2
2 3 2 2
2 3 2 2
2 2
2 3
2 3
2 3
2 3
Mon Age
1 2 3 4 5 6 Total
Sex 1 10 20 7 2 39
2 9 25 7 5 46
Total 19 45 14 7 85
Wed Age
1 2 3 4 5 6 Total
Sex1 6 13 4 23
2 13 36 8 57
Total 19 46 12 80
Fri Age
1 2 3 4 5 6 Total
Sex1 14 16 5 35
2 18 42 11 71
Total 32 58 16 106