Spot Speed Study 

Engineering 1281H

Autumn, 2015

 

 

 

 

Leo Steinkerchner, Seat 12

Sam Rankin, Seat 17

Casey Schomer, Seat 7

Zarius Shroff, Seat 11

 

B. Rice Thursday 8:00 AM

 

Date of Experiment: 09/03/15

Date of Submission: 09/24/15

1. Introduction

Any university must address the inherent issue of traffic flow. It is necessary for every

university to provide routes for students, staff, and guests that is not only both efficient and safe,

but also cost-effective. According to the Spot Speed Study Write-Up, “The Ohio State University

[had] received complaints from several pedestrians walking on stretches of Woody Hayes Drive

and Olentangy River Road [concerning] the speed of traffic in these areas” [2]. Therefore, the

purpose of this experiment was to use a spot speed study to measure the speed of traffic flowing

through these areas, then make a recommendation on whether or not additional safety

precautions are necessary.

Section 2 of this report covers the methods used to carry out the experiment. This

includes the equipment used, the setup down prior to testing, and actual procedure of the

experiment. Section 3 provides all of the data gathered during the experiment and statistical

analyses and representations of it. Section 4 discusses the significance of this data in the context

of this spot speed study. Finally, Section 5 summarizes the results of the experiment and

provides conclusive statements to address the problem presented above.

2. Experimental Methodology

2.1. Equipment

To measure the speed of the cars, only a stopwatch and pre-marked lines of orange paint

spaced 176 ft. apart on Olentangy River Road were used. The data were then recorded on the

Spot Speed Study – Field Sheet, shown in Appendix A.

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2.2. Setup

Prior to the experiment, a 176 ft. interval was marked with orange paint at Location I as

shown in Figure 1 below. As indicated by the figure, the speed limit at Location I is 35 mph. As

additional precautions, all data were taken from a safe distance away from the road, all traffic

laws concerning pedestrians were obeyed, and the experiment was designed so as to not disrupt

the drivers of the cars going past.

Also, each team member was assigned a role for the experiment, as described in Table 1

on the following page. Once roles were assigned, the group decided on a hand signal that the

Flagger could use to indicate to the timer when a car entered the measurement range (described

in Section 2.3 Experimental Processes, on next page).

Table 1: Description of roles for the Spot Speed Study [2].

Member Role Description

3

AB

DEF

G

H

I

C

N

S

E W

25 mph

35 mph

AB

DEF

G

H

I

C

N

S

E W

25 mph

35 mph

Figure 1: Location of Spot Speed Study [2].

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

the time determined by the timer.

Flagger Signal the timer when the vehicle passes the first marker.

TimerStart 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 the road.

2.3. Experimental Processes

Upon arriving at the location, the weather and road conditions, location, time, and posted

speed limit were all documented on the field sheet. At location I, the Flagger stood at the

beginning of the speed trap (on the South side of Figure 1), while the Timer and Recorder stood

at the end of the speed trap (on the North side of Figure 1). The Safety Engineer moved between

these two positions, often staying in the middle between them.

As per the agreed-upon signal, as a car approached the speed trap, the Flagger raised one

arm straight into the air, signaling the Timer to be ready to start the stopwatch. Then, precisely

when the car front edge passed the first orange line, the Flagger’s arm was brought swiftly back

down to their side. When this happened, the Timer started the stopwatch. If there was a group of

cars close together, the signal always referred to the car that was leading as it passed the Flagger.

When the car passed the second orange line, 176 ft. down the road, the Timer stopped the

stopwatch. The reading on the stop watch was told to the Recorder, then documented in the

appropriate bin of the field sheet. This process was repeated as often as possible for roughly 30

minutes.

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3. Results and Description

Testing began at 8:45 AM on September 3, 2015 with sunny weather and dry road

conditions. A total of 64 cars were measured during the time interval. The speed each was

travelling (shown below in Table 2) was determined by the time it took for each to pass through

the speed trap and the conversions listed on the Spot Speed Study – Field Sheet (Appendix B).

Also shown is the frequency of each speed group as a percentage of all the cars measured, as

well as the cumulative percent frequency, which represents the sum of all cars up to and

including that speed group. Because the data produced a bimodal distribution that was not

representative of the situation, the size of the speed groups was extended to span 4 mph, rather

than the original 2 mile-per-hour bins.

These data were then plotted on two graphs with same x-axis; one shows the percent

frequency of cars travelling at a particular speed (a), while the other shows the cumulative

percent frequency of these same cars (b) (See Appendix B). On graph (a), where frequencies

represent a single value for speed, data was plotted according to the middle value of each speed

range because this was the point that best represented each range (e.g. the 28-32 mph range was

plotted as 30 mph). Meanwhile, graph (b) represents data up to and including each speed group,

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Speed (mph)

Frequency

Percent Frequency

Cumulative Percent Frequency

28-32 2 3.13% 3.13%32-36 6 9.38% 12.50%36-40 27 42.19% 54.69%40-44 15 23.44% 78.13%44-48 10 15.63% 93.75%48-52 2 3.13% 96.88%52-56 2 3.13% 100.00%Total 64 100.00% 100.00%

Table 2: Frequency of cars travelling within various speed groups.

and so its values were plotted according to the top value of each speed range (e.g. the 28-32 mph

range was plotted as 32 mph).

Using these graphs, 6 significant values about the data were approximated. This

information, as well as the average speed of the cars and the calculated and estimated standard

deviation is summarized in Table 3, printed below.

From graph (a) the modal speed of traffic can be seen, as it was the point which

corresponded to the highest percent frequency. Also determined from graph (a) was the overall

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Table 3: Statistical data of the measured speed frequencies.

Graphical Approximations Calculations

Data Point Value Data Point Value

Mode 39 mph Average Speed 40.44 mph

10 mph Pace 34.5-44.5 mphEstimated Standard Deviation

4.00 mph

15th Percentile Speed

37 mphCalculated Standard Deviation

4.88 mph

85th Percentile Speed

45 mph --- ---

50th Percentile Speed

40 mph --- ---

Percent of vehicles in Pace

73% --- ---

pace of the cars, i.e. the 10 mph range at which the most cars were driving. To find this, 10 mph

(as per the x-axis of the graph) were marked on a straight edge, which was then moved down

from the top of the curve to find the point where the left and right sides of the curve were 10 mph

apart. To signify pace on the graphs, these points were marked, and vertical lines drawn through

across the entire figure—because the two graphs use the same x-axis, the pace can be marked the

same on both of them.

Using graph (b), the 15th and 85th percentiles were determined by finding the speed value

that reached 15% and 85% on the cumulative frequency curve, respectively (shown by solid lines

at these values. Similarly, the 50th percentile (which was also the median) corresponded to the

speed value that reached 50% on the curve. Finally, graph (b) was used to find the percent of cars

travelling within the pace. To find this value, we subtracted the value where the lower bound of

the pace meets the cumulative curve (9%) from the value where the upper bound of the pace

meets the cumulative curve (82%).

Illustrated by Equation 1 below, the average speed was found by dividing the sum of all

of the cars’ speeds by the total number of cars. The standard deviation was first estimated by

dividing the difference between the 85th percentile and the 15th percentile by 2 (Equation 2 on

next page), and then calculated using the accepted standard deviation equation for a sample of a

population, shown in Equation 3 on the next page. Sample calculations for each of these

equations can be found in Appendix C.

x = Σ (n∗s)c

(1)

Sest=P85−P15

2

(2)

7

S=√ Σ ( xi−x )2

c−1

(3)

4. Discussion

Central tendency is “the tendency of samples of a given measurement to cluster around

some central value” [3]. By observing the percent frequency curve, it can be noted that the data

was clustered around the modal value of 39 miles per hour. By definition, this meant the data

exhibited central tendency [3]. Meanwhile, the dispersion around this central point was fairly

small. This was because the standard deviation of 4.88 mph was only 20% of the entire range (24

mph). If there standard deviation were higher, it would suggest the data were further spread out

from the mean, and therefore that the dispersion was higher. The low dispersion was further

evidenced by the fact that the percent of vehicles in the pace was 73%, i.e. the majority of the

cars were travelling around the same speed.

These data, focused around 39 mph, suggested that the general flow of traffic exceeded

the posted speed limit of 35 mph. However, according to the Ohio Department of Public Safety

“Motor Vehicle Laws,” in 35 mph zones, travelling at less than 5 mph over the speed limit is

considered a minor speed infraction [4]. Therefore, with 40 mph being both 5 mph over the

speed limit and the median of the data, 50% of measured drivers were either obeying the posted

speed limit or only committing a minor speed infraction. It follows that 50% of measured drivers

were committing a major speed infraction. This data agreed with studies done by the National

Highway Traffic Safety Administration, which found that 40% of drivers admitted to sometimes

speeding, while 30% of drivers admitted to regularly speeding [5].

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The measured data referred specifically to traffic flow at this particular location, at this

particular time of day and day of the week, and at this time of year. If any of these factors were

changed, the results could be affected. For example, a study done in the middle of the night,

when there are many fewer cars on the road, might find that cars travel much faster than

measured here. Or, a study done at midday on an autumn Saturday in Columbus could find that

cars move well below the speed limit due to the sheer number of cars on the road. Meanwhile,

there were several sources of error inherent in our experimental design. For instance, the use of

the Flagger-to-Timer hand signals caused a delay in timing due to response time that

systematically lowered all values for time, increasing all values for speed. Also, due to the close

proximity of the cars (and an inability to time more than one car at a time) it was impossible to

record the speed of every single car. This created random variance in our data, potentially

skewing it in one direction or another, or changing the dispersion, as some of the cars that went

past without being measured were travelling at any of the various speeds represented.

5. Summary and Conclusions

The experiment aimed to measure the follow of traffic on a particular section of

Olentangy River Road, then recommend whether or not the area needed more safety precautions.

It found that drivers on Olentangy River Road around 8:30 AM on a sunny and dry weekday

tend to drive from about 34.4 mph to 44.4 mph where the speed limit is 35 mph. More drivers

travelled at 39 mph than any other speed, though the average speed of all of the cars was 40.44

mph. Furthermore, roughly half of the measured drivers in these conditions drive significantly

faster than the posted speed limit. In finding these results, the experiment succeeded in

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measuring the speed of traffic flow in this area. Given the data, the complaints from pedestrians

seem justified—many drivers are, in fact, driving well above the speed limit.

If the experiment were repeated, it could be improved in several ways to increase the

overall accuracy of the data. In order to outweigh the effects of reaction time on the Timer’s

actions, the length of the speed trap should be increased. Using a larger distance would create

larger values for time, which would then be less susceptible to error due to reaction time, but

doing so increases the chance that cars might change speed within this range. In order to measure

more of the cars going past, the experiment could be done with two Flagger-Timer pairs, perhaps

measuring the speed of cars in different lanes, or simply measuring the first and second cars

within every cluster of cars going past, rather than just the first. Doing so would increase the

accuracy of the results by excluding fewer data points. Also, while it is impossible to remove

human error from this experiment, if the experiment were performed for a longer period of time

(e.g. if cars were measured for 60 minutes or more rather than just 30 minutes) and therefore if a

larger number of data points was obtained, the random errors would become less significant,

increasing the experiment’s accuracy.

If the university provided $300 in funding, it is recommended that that money be spent on

radar guns (an individual radar gun suitable for this experiment can cost around $95, therefore 3

radar guns could be purchased) [6]. These radar guns could entirely eliminate the need for a

separate Flagger and Timer, thereby reducing the possibility for human error significantly. They

would also increase the possibility of measuring more of the cars within a given time frame as

we would no longer be restricted to measuring only the first car within a group of cars. Moving

forward, it is recommended that a study be done to determine what safety precautions would be

most effective and cost-efficient at protecting pedestrians in this area.

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References

[1] Spot Speed Study – Field Sheet. 2015, September 8. www.carmen.osu.edu.

[2] Spot Speed Study Write-Up. 2015, September 8. www.carmen.osu.edu.

[3] Dictionary.com. 2015, September 8. www.dictionary.reference.com.

[4] Digest of Ohio: Motor Vehicle Laws. 2015, September 8.

http://publicsafety.ohio.gov/links/hsy7607.pdf.

[5] National Survey of Speeding Attitudes and Behavior. 2015, September 8.

http://www.nhtsa.gov/About+NHTSA/Press+Releases/NHTSA+Finds+Nearly+Half+of+

All+Drivers+Believe+Speeding+is+a+Problem+on+U.S.+Roads.

[6] RadarGuns.com. 2015, September 8. http://www.radarguns.com/bushnell-velocity-speed-

sports-radar-gun.html.

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APPENDIX A

Spot Speed Study – Field Sheet

Figure A1: Field Sheet for the Spot Speed Study documents the frequency of vehicles moving at particular speeds as well as conditions of the road being sampled [1].

A2

APPENDIX B

Spot Speed Study Frequency Distribution Curves

Figure B2: Percent Frequency and Cumulative Percent Frequency graphs for the Spot Speed Study show the relative frequency of vehicles moving at particular speeds.

B2

APPENDIX C

Sample Calculations

Sample calculation for Equation 1:

x=Σ(n∗s)c

=(3∗35 )+(2∗40 )+(1∗45)

3+2+1=

2306

=38.33

Sample calculation for Equation 2:

Sest=P85−P15

2= 45mph−37mph

2=8mph

2=4mph

Sample calculation for Equation 3:

S=√ Σ ( xi−x )2

c−1=√¿¿¿

C2

¿√ 1+1+0+4+94

=√ 154

=1.94

APPENDIX D

Symbols

n Frequency of a particular speed range

s Medan speed of a particular speed range (mph)

c Total number of cars measured

P x Xth Percentile (mph)

S Standard Deviation (mph)

Sest

x i

x

Estimated Standard Deviation (mph)

Measured data point (mph)

Mean speed value (mph)

D2