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Spot Speed StudyEngineering 191HAutumn, 2011Frederic Carrier, Seat 36Erick Dommer, Seat 33Nathan Kidder, Seat 34Darren Nash, Seat 35Instructor: Brooke C. MorinClass Section: 9:30Lab Section: Wednesday, 9:30-11:18Date of Experiment: 10/5/11Date of Submission: 10/12/11IntroductionThe 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.Experimental MethodologyThis 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 is a 35 mph zone.timesFigure 1: Diagram showing the proper way to set up the experiment.Results and DescriptionsAll 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 feett sec*1 mile5280 feet*3600 sec1 hourEquation 1: Conversion of time into speedThe 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. Table 1: Key information about the data.Data InformationPace33.5-43.5 mphPercentage of vehicles in pace72%Median38 mphMode41 mphMean39.68 mph85th percentile43 mph15th percentile34 mphEstimated standard deviation4.5 mphCalculated standard deviation5.12 mphDiscussionOne 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.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 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. Summary and ConclusionsTo 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.Appendix AFigures and ChartsTable A1: Complete data table with speed groups, time groups and frequencyAppendix BEquations and Sample CalculationsMean Calculation*:x=nisiN(B1)ni=Frequency of observations in group isi=Middle speed of group i in mphN=Total number of observationsx=2*29+11*33+16*35+14*37+16*39+28*41+10*43+5*45+5*47+5*49+1*55+1*63114x=39.68 mphEstimated Standard Deviation**:sest=P85-P152(B2)P85=85th percentileP15=15th percentileSest=43-342Sest=4.5 mphCalculated Standard Deviation**:S=(xi-x)2N-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+11410(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)2S=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 ................
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