Metadata-Version: 2.1
Name: ratpmetro
Version: 0.0.1
Summary: Incident analysis of Paris RATP metro lines
Home-page: https://github.com/tianyikillua/ratpmetro
Author: Tianyi Li
Author-email: tianyikillua@gmail.com
License: License :: OSI Approved :: MIT License
Description: # Statistical analysis of incident probability and causes on RATP metro lines
        
        *Trafic perturbé*...again?! This library and this [notebook ](https://github.com/tianyikillua/ratpmetro/blob/master/examples/notebook.ipynb) provides a statistical point of view for all these daily incidents occurred on the Paris RATP metro lines.
        
        <img width="450" src="https://user-images.githubusercontent.com/4027283/52844997-46dd8c00-3106-11e9-8e6d-3f812c8140c3.png">
        
        Using tweets coming from official RATP Twitter accounts ([@Ligne1_RATP](https://twitter.com/Ligne1_RATP) for line 1 for example), we will see
        
        1. What is the probability of encountering some operational incidents on a particular line?
        2. Which lines are more likely to cause everybody unhappy?
        3. What are the main causes of these problems?
        4. Should I be considered lucky if I never take metros during rush hours?
        5. Are there less problems during weekends?
        6. Instead of going on vacation, is there any reason to be happy if I still work in August?
        
        ### Some examples in the year 2018
        
        The fist two examples below refer to the RATP metro line 2, which I take everyday for work.
        
        #### I should leave work before 17:00 on Wednesday (or after 21:00)
        
        In the figure below you can see the probability of catching an operational incident (*trafic perturbé*, *interrompu*...) at a given hour (in fact in the next following hour) and a specific weekday. The maximum value (nearly 9%) can be found on Wednesday at the evening rush hours.
        
        <img width="500" src="https://user-images.githubusercontent.com/4027283/52820966-c55a0f80-30ad-11e9-867d-c927fb74ca37.png">
        
        #### It's us the main responsible for these incidents
        
        Nearly half (48%) of the incidents come from us. Also, 9% is due to unattended bags...
        
        <img width="450" src="https://user-images.githubusercontent.com/4027283/52821080-166a0380-30ae-11e9-8244-ad7e087cff89.png">
        
        #### Line 13 should be jealous of line 4
        
        Every Parisian knows that the line 13 is bad. But in the year 2018 it was beaten by the line 4, which records an average probability of incidents larger than 4%.
        
        <img width="500" src="https://user-images.githubusercontent.com/4027283/52821104-2681e300-30ae-11e9-8ef3-7cb166656605.png">
        
        ### License
        
        The code is licensed under the terms of the MIT license. The analysis results (text, figures, tables) are licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
        
        ### Author
        
        [Tianyi Li](https://www.linkedin.com/in/tianyikillua) ([tianyikillua@gmail.com](mailto:tianyikillua@gmail.com))
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
