Spot Speed Report by Matt Inniger

8/11/2019 Spot Speed Report by Matt Inniger

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Spot Speed Study

Engineering 1281H

Autumn, 2014

Matthew Inniger, Seat 12

Scott Kanta, Seat 18

Quincy OMalley, Seat 21

Veronica Peterson, Seat 15

A.Theiss Thursday 12:40 P.M.

Date of Experiment: 9/11/14

Date of Submission: 9/18/14


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1. Introduction

Speeding can be very dangerous, and the possibility of serious injury increases

dramatically when traffic is exposed to the amount of pedestrians that travel beside and across

Woody Hayes Drive. The Ohio State University has heard the complaints of citizens, and

investigated the issue. The purpose of this study was to collect data and use statistical analysis to

determine if the situation is serious enough to warrant the use of resources to more strictly

enforce the speed limit.

The following sections break down the study in more detail. In Section 2 (Experimental

Methodology), the procedure of how the data was collected and analyzed is explained step by

step and discussed. Section 3 (Results and Description) displays the results of the data collection

and of the statistical analysis. In Sections 4 (Discussion), the trends of the experiment are

discussed. Also, the central tendency and dispersion of the data are discussed in this section as

well as the comparison of the results to the posted speed limits and any possible sources of error.

In Section 5 (Summary and Conclusions), the implications of those results are summarized,

solutions to possible sources of error are given, and suggestions for future work are included.

2.

Experimental Methodology

The team headed to a sample stretch of Woody Hayes Drive to test speed patterns in the

area. To do this, the team divided into four roles, the flagger, the timer, the recorder, and the

safety engineer. One team member was the flagger, who signaled when the front bumper of a

target vehicle passed him for the timer to begin timing. The team member who was timing would

start the stopwatch when the flagger signaled and stop the stopwatch when the front bumper of


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the target vehicle passed him. One of the remaining team members served as the recorder, and

recorded the time the target vehicles took to travel the predetermined distance. The time was

recorded on a pre-made sheet that gave the range of speeds for the range of time that the target

vehicle took to travel the predetermined distance. An example of the sheet the recorder used can

be found in Figure 1 below. The fourth team member served as the safety engineer, and was

responsible for the safety of the team as they operated around a busy road. For a table explaining

these roles, see Table A1 in Appendix A.

Figure 1:An example of the sheet used by the recorder [1].

The team set up at a predetermined site, shown as F in Figure 2 below. The flagger and

the timer were 176 feet away from each other on the south side of the road, timing eastbound

Figure 2: Spot Speed Study conducted at location Fat 1:30PM [1].

AB

DEF

G

H

I

C

N

S

EW

25mph

35mph

AB

DEF

G

H

I

C

N

S

EW

25mph

35mph


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traffic. The recorder wrote down the times of the target vehicles, as well as observations on the

weather, road conditions, posted speed limit, and time of day.

For this study, the flagger signaled to the timer by pointing at the timer when the front

bumper of the target car passed him. This signal was chosen because it is discrete, and the goal

was not to startle drivers or make them aware that they are part of a study.

3. Results and Description

Sixty-seven vehicles were timed during the experiment. Table 1 on the next page shows

the raw results of the data collection. Figure A2 in Appendix A is a representation of the

compiled data. In the figure, the uppermost plot shows the frequency, as a percent, of vehicles

driving certain speeds through the testing area. This was found by taking the number of vehicles

driving each speed and dividing that number by the total amount of vehicles timed, then

multiplying by one hundred. The lower plot shows the cumulative frequency, which is all of the

frequencies at that speed and slower speeds. So the cumulative frequency is simply the

percentage of vehicles that were recorded at a certain speed or slower. In the percent frequency

plot, the highest point on the curve is the mode of the data, in this case was 28 mph, and 17.91%

of the vehicles were traveling at this speed. Another analysis to draw from the percent frequency

plot is the pace. The pace of the data can be described as the ten mile per hour range within

which most cars were found driving [1]. The pace of the data set was 26-36 mph, and 66% of

the vehicles were driving in this pace. The pace itself was found graphically. A ruler was used to

determine how far 10 mph was along the x-axis of the frequency distribution plot. Then, the ruler

is pulled down from the top of the curve and stopped when the 10 mph distance touches both

sides of the curve. To determine the percent of vehicles in the pace, vertical lines were drawn on

the cumulative frequency plot at the least and greatest values of the pace. The percent frequency


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at the lowest speed in the pace was subtracted from the percent frequency at the greatest speed in

the pace. This difference is the percent of vehicles driving in the pace. A sample calculation for

this can be found in Equation B1 in Appendix B.

Table 1: The raw data that was collected at the testing site.

Speed Group

Min (mph)

Max

(mph) Frequency

Frequency

% Cumulative Frequency Cumulative frequency %

14 16 1 1.49% 1 1.49%

16 18 0 0.00% 1 1.49%

18 20 0 0.00% 1 1.49%

20 22 4 5.97% 5 7.46%

22 24 5 7.46% 10 14.93%

24 26 4 5.97% 14 20.90%

26 28 12 17.91% 26 38.81%

28 30 11 16.42% 37 55.22%

30 32 9 13.43% 46 68.66%

32 34 5 7.46% 51 76.12%

34 36 7 10.45% 58 86.57%

36 38 5 7.46% 63 94.03%

38 40 1 1.49% 64 95.52%

40 42 1 1.49% 65 97.01%

42 44 1 1.49% 66 98.51%

44 46 0 0.00% 66 98.51%

46 48 1 1.49% 67 100.00%

The cumulative frequency plot is useful for deriving several analyses as well. The median

of the data set, which is also the 50th percentile, was 30 mph, which is useful as a single

representative value of the data set. This value can be derived by either the traditional method, or

by drawing a line on the cumulative frequency plot at 50% on the y-axis and looking at what

speed along the x-axis that the line crosses the cumulative frequency curve. Another

representation of the data as a single value was the statistical average, or the mean, of the data.

The average speed of the vehicles tested was found to be 30.87 mph. The average speed was


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found by dividing the sum of all the data points, and dividing that sum by the total number of

data points. A sample calculation for this can be found in Equation 1 below

x xi

n (1)

The cumulative frequency plot is also necessary for the calculation of another analytical

value, standard deviation. The equation for a quick estimate of standard deviation is listed below

in Equation 2.

(2)This equation offers an estimate of standard deviation by simply subtracting the 85 thand

15thpercentile speeds, which can be derived from the cumulative frequency plot, and taking the

difference and dividing it by two. To derive the 85th and 15th percentile, a horizontal line is

drawn on the cumulative frequency plot at 85 percent frequency and 15 percent frequency.

Where the horizontal lines cross the cumulative frequency curve is the 85 th or 15th percentile

speeds, respectively. According to this method, the standard deviation of the data was 4.5 mph.

The formula for precisely calculating standard deviation is much more complex and is

shown on the next page in Equation 3.

(3)

To calculate the standard deviation precisely, the difference of each value and the mean

of the data is added together, and that sum is divided by the total number of data points minus


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one. For a sample calculation using this formula, see Appendix B. The exact standard deviation

of the data collected during the experiment was 5.7 mph.

A representation of the analyses derived from the data in table format can be found in

Table A3 in Appendix A.

4.

Discussion

Central tendency is often defined as the tendency of samples of a given measurement to

cluster around some central value.[1] The fact that the arithmetic mean, median, and the mode

are all very close to each other shows that the data exhibits central tendency. This occurs because

of the posted speed limit, which encourages drivers to drive similar speeds. In a study like this

one, where that similar speed is being measured, it only makes sense that the measurements

would cluster around a central value, which in this case is the posted speed limit.

Robert Niles describes standard deviation asa statistic that tells you how tightly all the

various examples are clustered around the mean in a set of data. When the examples are pretty

tightly bunched together and the bell-shaped curve is steep, the standard deviation is small.

When the examples are spread apart and the bell curve is relatively flat, that tells you you have a

relatively large standard deviation.[2] Standard deviation is a measure of dispersion. Measures

of dispersion express quantitatively the degree of variation or dispersion of values in a

population or in a sample. [3] The standard deviation of the data is this study was small, and

this is represented graphically by the bell curve in the percent frequency plot. The bell curve is

steep, which suggests a small standard deviation. This suggestion is reinforced by the calculated

standard deviation. So this data lacks dispersion, and this occurs because there is a central value


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(the posted speed limit) that drivers are supposed to be traveling at so the measurements do not

vary much.

From the data recorded and the analysis of that data, it can be concluded that the

disregard of the speed limit by vehicles on Woody Hayes Drive is serious enough to warrant

stricter enforcement. The average speed of a vehicle on Woody Hayes Drive is more than five

miles per hour over the speed limit. The entire pace of the data was located above the speed

limit, and the highest percentage of the vehicles tested were traveling three miles per hour or

more over the speed limit. In fact, only the vehicles under the 15 th percentile were actually

driving a legal speed. The standard deviation also supports this conclusion, because the data was

tightly clustered around the average speed of 30.87 mph, which is more than five miles per hour

over the speed limit.

These results are not unexpected. Research shows that Ohio is the number one state in

amount of driving citations [4], so vehicles driving over the speed limit is very common

throughout the state.

There were some possible limitations and sources of error in the experiment. For

instance, the sample stretch of the road was four lanes, which meant the flagger and timer had to

watch two lanes of traffic, which could have caused miscommunication and therefore a faulty

measurement. Another issue that could have arisen during data collection was the possibility of

pedestrians cutting off the line of sight between the flagger and timer. This would have also

caused miscommunication and a possible faulty measurement. Another limitation of the

experiment is the possibility of drivers realizing they are being recorded. If they noticed, and

took it upon themselves to affect the data by either dramatically increasing speed or dramatically


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decreasing speed, that would have serious implications on the validity of the study. In order to

avoid the data of this study being skewed, measures were taken to prevent these limitations from

skewing the data. The flagger and timer agreed to only watch the furthest outside lane that was

traveling eastbound. This way only one car at a time was a possible target. The flagger and timer

stayed far away from the road and out of the way of pedestrians in order to maintain a clear line

of sight, and they agreed on a discrete signal to avoid alerting drivers of the fact they were part of

a study. So it can be assumed none of these limitations skewed the data.

5. Summary and Conclusions

The purpose of this study was to determine the seriousness of speeding on Woody Hayes

Drive. The results of the experiment indicate that a majority of the vehicles traveling on Woody

Hayes Drive travel above the posted speed limits. This is dangerous and can lead to accidents

that can cost human lives. The experiment and the analysis of results have shown that it would be

worth the resources for The Ohio State University to more strictly enforce the speed limit on

Woody Hayes Drive.

When the time comes for the situation to be re-tested to see if the new enforcement

methods are effective, several modifications may be made to the experimental methodology. One

modification is that the flagger and timer could use cell phones to be in constant communication

about what vehicles are about to be targeted. This wouldnt replace the signal, as the time lag in

cell phone communication would skew the data if it did. However, this would eliminate

miscommunications surround which vehicle is the target vehicle. Also, the safety engineers role

should be expanded to include keeping pedestrians out the line of sight between the flagger and

timer. This would assure that the timing is pinpoint because the timer would see exactly when

the flagger signals. Lastly, the data point of any vehicle that is observed to drastically change


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speed when they enter the testing area should be thrown out, as it can be assumed that the driver

was aware of the study and tried to affect the results.

If The Ohio State University does decide to allocate more funds to the project, then the

purchase of a Bushnell Speedster III Multi-Sport Radar Gun Kit with Tripod [5] for $100.99

would make the measurement of vehicle speeds much more exact, and would drastically reduce

the possibility of error. The timer would simply point the device at the target vehicle and pull the

trigger, and the vehicles speed would be displayed for simple recording. This makes the timing

process much more accurate as it eliminates the communication between the flagger and timer,

because anywhere there is communication there is the possibility of miscommunication, this

device would eliminate that possibility.


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References

[1] Spot Speed Write Up. 2014, September 12. www.carmen.osu.edu

[2] Standard Deviation. 2014, September 12. www.robertniles.com

[3] Glossary of Statistical Terms. 2014, September 13. www.statistics.com

[4] Driving Citation Statistics. 2014, September 13. www.statisticbrain.com

[5] Bushnell Speedster III Multi-Sport Radar Gun. 2014, September 13. www.radarguns.com


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APPENDIX A

Tables and Figures


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A2

Table A1: Description of team member roles [1].

Member Role Description

RecorderMake a tally mark on the field sheet in the row that

corresponds to the time determined by the timer.

FlaggerSignal the timer when the vehicle passes the first

marker.

Timer

Start the stopwatch when he/she receives the signal

from the flagger and stop the stopwatch when the

vehicle passes the second marker.

Safety Engineer Keep all members of the team safe and out of theroad.


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A3

Figure A1:The percent frequency (top) and cumulative frequency (bottom) plots of the data


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A4

Table A3: Containing the important analytical values discussed in Section 3

Analysis Value

Mode (mph) 28

50th percentile speed (mph) 30

10 mph Pace

26 mph-

36 mph

Percent of vehicles in Pace (%) 66

15th percentile speed (mph) 26 mph

85th percentile Speed 35 mph

Average Speed (mph) 30.86567

Estimated standard deviation (mph) 4.5

Calculated standard deviation (mph) 5.701928


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APPENDIX B

Sample Calculations


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B2

Sample Calculation for Equation 1: Average Speed

x xin

16 mph22 mph24 mph26 mph44 mph48 mph67

3.86567 mph

Sample Calculation for Equation 2: Estimated Standard Deviation

sest8515

2

sest35 mph26 mph

24.5 mph

Sample Calculation for Equation 3: Calculated Standard Deviation

sxix21

(16-3.87)24(22-3.87)25(24-3.87)24(26-3.87)212(28-3.87)2(48-3.87)267-1


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APPENDIX C

Symbols


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C2

Estimated Standard Deviation (mph) Percentile (%) Calculated Standard Deviation (mph) Sum (mph)

Individual x values (mph)

Mean of x values (mph)

Number of data points

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