Beyond Walking and Biking: Expanding the 15-Minute City Area through Public Transport [Data and Code]

Abstract

The concept of the "15-minute city" has recently attracted notable attention and is being widely discussed in urban planning and policymaking. The original idea focuses solely on active modes, thus walking and biking, without considering the role of public transport, which is, in fact, essential for accessing amenities of daily needs in urban areas. Additionally, most studies exploring this concept model walking and biking with constant average speeds. While this simplification is considered reasonable in flat urban environments, it may result in inaccurate estimations for cities on more hilly terrain. This study aims to address these two drawbacks by integrating public transport into the 15-minute concept and incorporating speed as a function of street inclination. The results for the case study of Vienna indicate only small differences in average accessibility when modelling walking speed in a slope-dependent manner. In contrast, for biking the difference is notable. Secondly, incorporating public transport as a valid mode option decreases the average duration to access all daily needs from 23.30 minutes (walking only) to 16.88 minutes and the median duration from 15.07 minutes to 13.28 minutes. The main finding of this work is that adding public transport extends the 15-minute city area rather than optimizing travel times within the existing walkable area. Furthermore, the presented analyses provide the means to uncover categories that limit the area of the 15-minute city.

How to use?

The provided material includes data and scripts which were used for the analysis in the paper entitled "Beyond Walking and Biking: Expanding the 15-Minute City Area through Public Transport", accepted for AGILE Conference 2025 (Association of Geographic Information Laboratories in Europe).

It comprises three folders within the zip file:

1. code: Includes script files essential for conducting the analysis. The scripts are written in Python.
2. data: Contains the datasets for the analysis.
3. results: Includes the outcomes showcased in the associated paper.

Further information

Programming Language: Python (3.10 tested)

For reproducibility read the README.txt; for necessary libraries refer to the requirements.txt. Both files are included in the zip folder.

All data files are licensed under CC BY 4.0, all software files are licensed under MIT License.