Spot Speed Study
Engineering 191H
Autumn, 2011
Frederic Carrier, Seat 36
Erick Dommer, Seat 33
Nathan Kidder, Seat 34
Darren Nash, Seat 35
Instructor: Brooke C. Morin Class Section: 9:30
Lab Section: Wednesday, 9:30-11:18
Date of Experiment: 10/5/11
Date of Submission: 10/12/11
1. Introduction
The purpose of this lab was to see if current speed limit enforcement is enough to keep
drivers going the speed limit. To do this, cars were timed going through a 176-foot long speed
trap. The resulting times were then used to find the average speed of cars.
The data was gathered using the experiment described in Experimental Methodology in
Section 2. The data that was gathered is shown in Results and Description in Section 3 and
discussed in Discussion in Section 4. An overview of the experiment and a conclusion can be
found in the Conclusion in Section 5.
2. Experimental Methodology
This experiment only required a stop watch, a measuring device, and something used to
mark the sidewalk. It also needed 3 people to run as smoothly as possible: a flagger, a timer,
and a recorder. First off, a 176 foot long speed trap was measured and marked on the sidewalk.
A diagram of the proper setup is shown in Figure 1 at the beginning of the next page. The
flagger stood at the beginning of the speed trap and signaled to start the stop watch whenever
a car drove by. The timer, who stood at the other end of the speed trap, had to use the stop
watch to time how long it took for each car to cross the speed trap. The recorder marked all the
times on the field sheet in order to find out the speed. This process was repeated many times
to ensure good data. The experiment was performed under fair weather and dry roads from
roughly 10 AM to 11 AM on October 5th, 2011 along northbound Olengtangy River Road which
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is a 35 mph zone.times
Figure 1: Diagram showing the proper way to set up the experiment.
3. Results and Descriptions
All the times recorded were written down on the field sheet where they were converted
from time groups into speed groups using the calculations shown in Equation 1.
𝑆𝑝𝑒𝑒𝑑 (𝑚𝑝ℎ) =
176 𝑓𝑒𝑒𝑡
1 𝑚𝑖𝑙𝑒
3600 𝑠𝑒𝑐
∗
∗
𝑡 𝑠𝑒𝑐
5280 𝑓𝑒𝑒𝑡 1 ℎ𝑜𝑢𝑟
Equation 1: Conversion of time into speed
The complete set of gathered data can be seen in Table A1 in Appendix A and in Figure
A1 and A2. Table A1 shows the frequency of each speed group while Figure A1 and A2 show the
graphs of frequency distribution and the cumulative frequency respectively. Other information
such as mean, estimated standard deviation and calculated standard deviation were calculated
using Equation B1, B2 B3 in Appendix B and charted in Table 1.
B1
Table 1: Key information about the data.
Data Information
33.5-43.5
Pace
mph
Percentage of vehicles in pace
72%
Median
38 mph
Mode
41 mph
Mean
39.68 mph
85th percentile
43 mph
15th percentile
34 mph
Estimated standard deviation
4.5 mph
Calculated standard deviation
5.12 mph
4. Discussion
One thing that the data clearly shows is that most drivers did not care about the speed limit
that morning. This is easily seen when one looks at the mean, mode and median, all of which
are higher than the 35 mph speed limit. Although the mean of the data was higher than the
speed limit, the data still followed a somewhat normal distribution with a little skew to the left.
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The data followed a normal pattern with 72% of its points located in the pace between 33.5
mph and 43.5 mph. One can also see from the cumulative frequency graph that only about 16%
of drivers respected the 35 mph speed limit that morning. The data does not show much
dispersion except a few outliers. Although the speeds would be much higher, one could expect
around the same amount of dispersion doing the same experiment with race cars at the Indy
500 in normal green flag conditions, meaning that most car speeds would cluster around the
mode of the data with minimal dispersion and few outliers. This is due to the fact that in both
cases there was not anything near such as traffic, stoplights or pedestrians that would require
drivers to stop causing more dispersion. If the same experiment had been conducted on a
random Saturday down High Street with average traffic, pedestrians and many stop lights, one
could assume that there would be much more dispersion and inconsistency in the data.
Although the experiment gathered some good data, it could have been much more
accurate if human error would have been taken out of it. If the experiment had some kind of
sensor instead of a flagger and a timer armed with a stop watch, the data could be much more
accurate and it would rid itself of error due to human error and reaction time. Another way to
get more accurate data would be to make the data gathering process a little bit more discreet
as to not let the drivers know they are being timed. Some drivers either accelerated or slowed
down when they saw that they we being timed throwing off our data in the process. One way
to fix this would be once again using small sensor or spreading out the groups and the group
members to make it less obvious to the driver that they are being timed. The way this
experiment was carried out gave good data but not complete data. Since it was conducted
under fair weather and the road was dry when the experiment was done, we only have data for
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fair days with dry roads. Also, we only have data for the hour between 10 AM and 11 AM.
People’s driving tendencies might be affected a lot by different things such as the road
condition, the time of day and the weather. In order to get a very complete and accurate set of
data, the experiment would need to be carried out a few more times under different road
conditions, weather conditions and at different times of the day.
5. Summary and Conclusions
To see if current speed enforcement was enough to keep people driving the speed limit, a spot
speed experiment was conducted. It was done by creating a speed trap and recording how long
it took cars to go through the speed trap. Using some calculations, the times were then
converted into speeds and graphed to allow for easier analysis. Looking at the data, it is easy to
conclude that current law enforcement is not enough to get people to obey the speed limit on
Olentangy River Road. 50% of the drivers who were timed were driving more than 3 mph above
the 35 mph limit. Even if the experiment yielded some pretty conclusive data, it should be
repeated under different road and weather conditions using something more accurate than a
stop watch and something not as noticeable as a group of people standing on the sidewalk in
order to get more accurate data.
B1
Appendix A
Figures and Charts
B1
Table A1: Complete data table with speed groups, time groups and frequency
Speed Group
62
60
58
56
54
52
50
48
46
44
42
40
38
36
34
32
30
28
64
62
60
58
56
54
52
50
48
46
44
42
40
38
36
34
32
30
Time Group
Number of Vehicles
1.88
1.93
1
1.94
1.99
0
2.00
2.06
0
2.07
2.13
0
2.14
2.21
1
2.22
2.30
0
2.31
2.39
0
2.40
2.49
5
2.50
2.60
5
2.61
2.72
5
2.73
2.85
10
2.86
2.99
28
3.00
3.15
16
3.16
3.32
14
3.33
3.52
16
3.53
3.74
11
3.75
3.99
0
4.00
4.28
2
Total
114
B1
Appendix B
Equations and Sample Calculations
B1
Mean Calculation*:
𝑥̅ =
∑ 𝑛𝑖 𝑠𝑖
(B1)
𝑁
𝑛𝑖 = 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠 𝑖𝑛 𝑔𝑟𝑜𝑢𝑝 𝑖
𝑠𝑖 = 𝑀𝑖𝑑𝑑𝑙𝑒 𝑠𝑝𝑒𝑒𝑑 𝑜𝑓 𝑔𝑟𝑜𝑢𝑝 𝑖 𝑖𝑛 𝑚𝑝ℎ
𝑁 = 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠
𝑥̅ =
2 ∗ 29 + 11 ∗ 33 + 16 ∗ 35 + 14 ∗ 37 + 16 ∗ 39 + 28 ∗ 41 + 10 ∗ 43 + 5 ∗ 45 + 5 ∗ 47 + 5 ∗ 49 + 1 ∗ 55 + 1 ∗ 63
114
𝑥̅ = 39.68 𝑚𝑝ℎ
Estimated Standard Deviation**:
𝑃85 −𝑃15
𝑠𝑒𝑠𝑡 =
2
(B2)
𝑃85 = 85𝑡ℎ 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒
𝑃15 = 15𝑡ℎ 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒
𝑆𝑒𝑠𝑡 =
43 − 34
2
𝑆𝑒𝑠𝑡 = 4.5 𝑚𝑝ℎ
Calculated Standard Deviation**:
∑(𝑥𝑖 −𝑥̅ )2
𝑆=√
𝑁−1
(B3)
𝑆
=√
2(29 − 39.7)2 + 11(33 − 39.7)2 + 16(35 − 39.7)2 + 14(37 − 39.7)2 + 16(39 − 39.7)2 + 28(41 − 39.7)2 +
114
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
10(43 − 39.7)2 + 5(45 − 39.7)2 + 5(47 − 39.7)2 + 5(49 − 39.7)2 + (55 − 39.7)2 + (63 − 39.7)2
𝑆 = 5.12 𝑚𝑝ℎ
*: Equation was taken from Analysis Write Up under the 191H Course Materials at
Carmen.osu.edu
**:Equation was taken from Spot Speed Lecture Slides under the 191H Course Materials at
Carmen.osu.edu
B1