# Portfolio - Stopping Distances

Extracts from this document...

Introduction

Stopping Distances

When a driver stops her car, she must first think to apply the brakes. Then the brakes must actually stop the vehicle.

The table below lists the average times for these processes at various speeds.

Table 1. average times for these processes at various speeds

Speed (kmh-1) | Thinking distance (m) | Braking distance (m) | Stopping distance (m) |

32 | 6 | 6 | 12 |

48 | 9 | 14 | 23 |

64 | 12 | 24 | 36 |

80 | 15 | 38 | 53 |

96 | 18 | 55 | 73 |

112 | 21 | 75 | 96 |

Using this table we can graph two data plots: (i) Speed versus Thinking distance, (ii) Speed versus Braking distance, and (iiI) Speed versus Stopping distance

- Speed versus Thinking distance

Table 2. Speed versus Thinking distance

Speed (kmh-1) | Thinking distance (km) |

32 | 0.006 |

48 | 0.009 |

64 | 0.012 |

80 | 0.015 |

96 | 0.018 |

112 | 0.021 |

Graph 1. Speed versus Thinking distance

This is clearly a linear graph since we can see a straight line. This shows us that the correlation between speed and thinking distance is directly proportional, meaning that as speed increases the thinking distance will also increase. In other words, as the speed of a car increases it takes a longer time for the driver to think about applying the breaks.

Since this graph is linear we can develop a model to fit the data using the equation y= mx+b where m stands for gradient and b stands for the y-intercept.

Steps taken:

- First we find the gradient m

Middle

Graph 4. Quadratic model for Speed versus Braking distance

However, because it is a quadratic we have to evaluate whether the negatives will be a good fit to represent the data. Below is a graph showing the same graph as above but with an extended window frame.

Graph 5. Quadratic model for Speed versus Braking distance with enlarged window frame

Here we can see that the plots match well on the right side. However since we cannot have negative speed the model is not a good fit despite that it is a good fit to represent the data on the right.

Having that said, the other option is the power function and it was chosen because it is polynomial and we can eliminate all negative values since the domain is within positive values.

Steps taken to develop power model using GDC:

- Insert data into GDC table

L1 – Speed

L2 – Braking distance

- Use implemented Power Regression for variables L1 and L2

- Insert the information into STAT PLOT

- Plot data from table

Graph 6. Speed versus Braking distance

- Implement power function into the graph

Graph 7. Power model for Speed versus Braking distance

In the end we get a function:

Conclusion

Now that we have a good model to represent the data, it is time to test whether this model would fit further data.

Table 5. New data plots

Speed (kmh-1) | Stopping distance (km) |

10 | 0.0025 |

40 | 0.017 |

90 | 0.065 |

160 | 0.18 |

Table 6. Speed versus Stopping distance with new data

Speed (kmh-1) | Stopping distance (km) |

10 | 0.0025 |

32 | 0.012 |

40 | 0.017 |

48 | 0.023 |

64 | 0.036 |

80 | 0.053 |

90 | 0.065 |

96 | 0.073 |

112 | 0.096 |

160 | 0.18 |

Graph 21. Speed versus Stopping time using Model B

As we can see the model is a very good fit to represent the data since the function passes through all data plots. However, a more accurate function could be developed by using more data plots.

We also have to consider anomaly results since some things are not accounted for. For example, the friction between the tires and the road, and weather conditions affecting driver’s reaction time which may not fit my model accurately.

With more data plots we can make modifications to the function since all the current data plots fit the function.

This student written piece of work is one of many that can be found in our AS and A Level Core & Pure Mathematics section.

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