CS-433 - Machine Learning project 2

Introduction

The aim of this project is to learn to use the concepts of machine learning presented in the lectures and practiced in the labs on a real-world dataset. For this project we chose to collaborate with an EPFL lab TRANSP-OR who provided a historical traffic dataset of a bridge in Switzerland, in order to generate discrete traffic data using ML methods.

Organisation

This project is organized as follows :

• the repository data that contains a small dataset extract.txt
• the repository report that contains the LaTeX template of our final written report.
• the repository src that includes:
  • 0-sumo.ipynb that briefly explains how to simulate traffic using "Simulation of Urban MObility" (SUMO) of the extract.txt dataset.
  • 1-data-exploration.ipynb were the data exploration of our datasat is made.
  • 2-forecasting-model-selection.ipynb that selects a model to predict the hourly number of cars per week.
  • 3-sampling-interval-selection.ipynb that finds the most appropriate sampling interval for which to predict the number of cars.
  • 4-rate-prediction.ipynb that predicts the number of vehicules, speed and weight per hour that we called "rate".
  • 5-discrete-event-generation.ipynb that converts the previous rate into discrete events.
  • utils.py that contains the pipeline and helper functions.
  • several .xml files that helps to define the road to generate the traffic on SUMO.
  • the repository sumo-files that contains all the files used to setup the simulation. You can find more infos on each file in the notebook 0-sumo.ipynb
• the file ML-Project-2.pdf which is our report that provides a full explanation of our ML system and our findings.

How to use our project

• Just make sure to have the libraries mentioned below installed on your environment before running the cells in the jupyter notebook.
• To reproduce our setup, please run the notebooks in a successive way (from 1 to 5).
• Don't forget to put the dataset in the repository "data" at the same level of the repository "src". You can find our dataset named "405.txt" on this link.
• To run SUMO, open XQuartz (if you use MacOS), go to repository src and type "sumo-gui -c sumo-files/hello.sumocfg" in the terminal.

Libraries

In this project we used these libraries :

• matplotlib
• seaborn
• minidom
• os
• datetime
• numpy
• pandas
• tensorflow
• scipy
• script
• IPython
• pickle
• statsmodels
• collections
• sumo
• XQuartz if using OS X

Members of group brr

• Luca Bataillard - 282152
• Julian Blackwell - 289803
• Changling Li - 282440