Car Stopping Distances

Briefly describe the problem being investigated?

With the data that has been collected from a new study on stopping distances we can begin to answer questions about road safety. In particular we can examine speed signs and how vehicles react to different conditions. The key problem is that speed signs don’t change regardless of weather conditions which can be dangerous in wet circumstances. To find the best speed limit for these conditions would enable effective travel times without compromising on safety.

Investigation Question:

When traveling at 50kp/h, to what extent is the total stopping distance in wet and dry conditions different? Is it enough to prompt road rules changes and implement new technology to stop serious road crashes?

What do you think the answer to your question about stopping distances is?

That the differences in weather conditions will not be enough to consider any changes to the speed limit system and car tires should have enough grip to brake.

From the dataset provided, what data will you use?

I will use the dry and wet conditions in my variables and will use 50 km/h only in my tests because 50km/h is closer to the speed used most on highways and is more likely to show safety concerns. I will be looking at the total stopping distance of the car. This distance is made up of the braking distance and the reaction time. While these two bits of data may vary, they relate to the investigation because reaction time might also be slower in certain conditions.

Does your stopping distance data need cleaning? Justify your answer.

There appears to be a few outliers in the data under wet conditions – with a spear of 5 meters in the stopping distance. I have decided to keep the data as the spread seems to be inline with other bits of data in the test. I have tested is reliability with the dot plot and it seems the results for the wet conditions are inconsistent so it is possible to have an outlier for this data.

What graphs do you plan to produce to display your stopping distance data?

• Box Plot
• Dot Plot
• Percentile

Summary Statistics

	Dry	Wet
Minimum	19.8m	23
Maximum	35.3m	48.9
Median	24.15m	35.85
Upper Quartile	25.7m	39.1
Lower Quartile	22m	32.3
IQR	3.7m	6.8
Mean	24.5733m	35.47m

Comparison:

If we were to not count the data that fathom has suggested is outlying in the box plot then we can make the statement that top 75% of the tests in wet conditions, there were longer stopping distances than 100% of the tests in dry condition. IF we were to include the data fathom has said is outlying then only the top 50% of the test tests were further than 100% of the dry test.

The median of the wet tests is greater then 100% of the dry tests showing us a clear difference in results.

The means of the wet and dry tests at 50km/h had a 10.90m (2dp) difference showing clear results that the car took longer to stop.

The dry data has a slight positive skew distribution whereas the wet tests have a slight negative skew distribution.

The results appear to be less consistent in the wet tests as the data is more spread out where as in the dry tests there is more stacking.

In the dry car stopping tests there is clusters from the bottom of the data set on the dot plot to the middle. On the wet stopping tests there are clusters in the middle of the data set.

By looking at the percentile graph we can see that the wet results are more evenly distributed.

Conclusion:

Question:

Based on a 120 random samples, when traveling at 50kp/h, to what extent is the total stopping distance in wet and dry conditions different, Is it enough to prompt road rules changes and implement new technology to stop serious road crashes?

Answer:

The stopping distance between wet and dry is large and the 10.9metres between means shows this well. It show that if you are traveling in wet condition you are likely to stop around 10 metres further than in dry conditions at 50km/h. Other clear evidence pointing towards the big differences in stopping distance is the top 75% of wet test being greater then 100% of dry tests.

We can also see that the data is significantly different because the mediams are outside the middle 50% in the box plots.

Describe what you would expect to see if the random sampling process was repeated from the same population. Comment on what you would expect to see in your summary statistics/graphs and whether you think you would be able to reach the same conclusion.

If you were to repeat the investigation and runs tests again you would likely see the same results and possible some outliers. If you were to test more you could perhaps get rid of these outliers bit the results would be much the same as the data is relatively consistent. You would most likely reach the same conclusion.

Comment on how reasonable you think your results are based on your knowledge of stopping distances.

I think these results are reasonable because they show a consistent spread of results. They could however be influenced by the drive pushing the break with different tensions.

Explain why your friends who are learning to drive might be interested in these results.

They might be interested because they want to be safe on the road and want to learn how to drive safely in wet and dry conditions so they would value this information. They would not want to be in a crash so this would be helpful to them. It suggests they would need to lower their speed when it is wet and make sure it is suitable for the condition.

Explain why other drivers might be interested in these results.

Other drivers would be interested for the same reasons as they would want to learn to drive slower so they don’t crash. The stopping distance could be the difference between life and death and knowing about different weather conditions is important.

Which organizations or other groups of people might be interested in these results, and why?

The New Zealand transport authority might be interested in this data so they can decide if they want to buy electronic road speed signs which they could change in bad weather to prevent crashes. The NZ police might also be interested so that they can better protect people on the roads.

General drivers and passengers in New Zealand are also likely to be interested as it affects most people.

What other questions has this investigation generated?

I now have questions like…

• Does the total breaking distance go up in a linear form depending on the speed of the vehicle or does it peak and are there certain speeds were it is most dangerous in bad conditions. I would need to test different speeds in different conditions to answer this question. The NZTA, the Police and General public would be interested in this investigation.

• Do electronic signs help the New Zealand public?

Tim Armstrong