The Stop-and-Go City: Buses in Edinburgh
Edinburgh buses are great but slow. I analyzed the data and found the issue - bus stops are too frequent, averaging just 323 meters apart!
Blog - data
by Martin Lellep
Latest posts
Data Science Project Failing After 1,600 Days
I dedicated over 1,600 days to a data science project, only to see it fail. In this article, I share a new checklist I developed to help prevent similar setbacks in the future.
Edinburgh Pothole Logger
I made a mobile app using no-code to capture potholes in Edinburgh to help future cyclists.
rC3 2021 Supplementary Materials
Supplementary materials for my rC3 2021 contribution Optimising public transport: A data-driven bike-sharing study in Marburg .
Cycling in Marburg: Interactive transition matrix figures
Interactive versions of the Nextbike transition matrix figure from previous blog articles.
Parking in Marburg: Corona update
A quantitative update of the parking demand in Marburg during the Corona pandemic 2020.
Cycling in Marburg (4/4): Machine learning predictions
In this article, I use machine learning to predict the number of parked bikes in two ways.
Cycling in Marburg (3/4): For the city council
I use quantitative analyses to derive social and environmental benefits of the Nextbike system in Marburg. Also, I investigate which routes in Marburg are popular among Nextbike users. Hence, I offer quantitative arguments as to why bikes are good for Marburg and how to improve the biking experience in Marburg even further.
Cycling in Marburg (2/4): For Nextbike users
In this article, I draw quantitative conclusions for cyclists who use Nextbikes in Marburg. For these quantitative conclusions, I use Nextbike data that I previously scraped.
Cycling in Marburg (1/4): Project introduction and data source
I collected Nextbike data in Marburg and introduce my plan to evaluate the data in this article. Also, I present what the data is made up of and how it is obtained in detail. Lastly, a first temporal analysis of the bike usage in Marburg is presented.
Parking in Marburg: a quantitative study
The parking demand in Marburg is analysed quantiatively based on publicly available data. In addition, Gaussian Processes are used for spatial predictions.