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Providing adequate yellow signal time is important and can reduce crashes

Part 1: This includes an Excel spreadsheet that performs specific calculations.

The Insurance Institute for Highway Safety, IIHSLinks to an external site., states on their site that “Providing adequate yellow signal time is important and can reduce crashes. Studies have shown that increasing yellow timing to values associated with guidelines published by the Institute of Transportation Engineers can significantly decrease the frequency of red light violations and reduce the risk of total crashes, injury crashes and right-angle crashes (Bonneson & Zimmerman, 2004Links to an external site.; Retting & Greene, 1997Links to an external site.; Van Der Horst, 1988Links to an external site.; McGee et al., 2012Links to an external site.)._

You will design an Excel spreadsheet that calculates the allowable yellow light interval before changing to red and the red clearance interval based on the Virginia Beach Signal Timing Information. Links to an external site.

The equations that you will implement in your Excel spreadsheet calculator are available on slide 5 of the Signal Timing information.

Your Excel Spreadsheet should show the following:

Posted Speed Limit
Adjusted Speed limit
Intersection width
Vehicle Length
Yellow Change Interval
Red Clearance Interval
Total interval to come to a stop

You will create charts showing the change in the Yellow change interval, Red clearance Interval, and the total Change time for each posted speed used. You will discuss in Part 2 of your project your conclusion based on those results.

You can investigate in your Excel spreadsheet with different reaction times, deceleration rates, and vehicle length.

Part 2: This includes the development of a PowerPoint presentation showcasing the project enhancement as well as the Excel spreadsheet you designed.

You will design a PowerPoint presentation that details the added functionality of the traffic light system you designed as well as the results of your Excel spreadsheet calculations and comparison charts completed. This portion of the course project is worth 30% of the total project grade.

PowerPoint Presentation Requirements

No more than five (5) slides.
Must have a title slide.
Must have transitions of your choice between slides.
Must include an animation of bulleted points

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Regression with neural networks

Introduction
The task is to implement a simple neural network that achieves an adequate performance on a real data set for predicting a real valued parameter. The dataset consists of chemical properties (representation) of superconductors, and the parameter value to be predicted is their critical temperature in Kelvins (?K).
Further information on the dataset is available at: http://archive.ics.uci.edu/ml/datasets/Superconductivty+Data
The task is utilize the training dataset consisting of the properties of superconductors and their critical temperatures in order to learn a regression model. Then this model should be applied on the test dataset to predict the critical temperature of the superconductors in that dataset.
Assignment
Implement a simple neural network and the backpropagation algorithm in Java or Python! Use the trained model to predict the critical temperature for each test sample.
2.1. Java
The code must contain a Main class, and within this, a main() function. It will receive all inputs on the standard input, and should output the solution to the standard output. Upload the zipped source code files of your application to the BME MIT HomeWork portal. (https://hf.mit.bme.hu).
2.2. Python
The code must be a single python file, that will be run and receives all inputs onto the standard input, and it should write the solution to the standard output. Upload the zipped single python file to the BME MIT Homework portal. Use Python3.x, and only standard libraries are available (e.g. no numpy!) (https://hf.mit.bme.hu).
2.3. Input
The program receives all inputs via the standard input. The input consists of the representation of training samples, the corresponding critical temperatures, and also
VIMIAC10 2019 3rd Major homework
the representation of test samples. The character ’ ’ is used as a line separator. The input is structured according to the following:
The first 17011 lines each contain a representation of a chemical compound, that is 81 parameters as real numbers separated by the ’ ’ character. These are the training samples.
These are followed by 17011 temperature values, i.e. the target value to be learned (i.e. a single temperature value in each row).
Lastly, 4252 test samples (chemical compound representations) for which the critical temperature has to be predicted. These are the test samples.
The solution should implement the backpropagation algorithm. The scaling/normalization of data is recommended before learning. Note that the available CPU time for the code is approximately 120 CPU secs.
2.4. Output
The output contains the predictions for the test samples, i.e. a predicted temperature for each sample. The output should be formatted such that each row contains only one prediction, the order corresponds to the order of test samples. Rows should be separated by the character, the output should be written to standard output.
Evaluation
The evaluation is based on RMSE (root mean squared error):
,
where ???? is the real value, and is the predicted value. A solution reaching a RMSE lower than 17.0 gets 12 points, however a solution above 23.0 gets 0 points. Between these two endpoints the evaluation is linear (the score is rounded to the nearest integer)