Research Group Mobility Justice
Goal

The research group aims to create awareness, assess and addresses mobility injustices. The main methodology focuses on mixed methods approaches combining spatial analysis and machine learning with qualitative techniques.
To make our mobility just and including different needs and abilities, we propose as a first step that you feel what others feel. In other words, create awareness and empathy. The next step is to assess fairness, that is, who is privileged in terms of mobility opportunities and who is not. Finally, when you are aware and know the real situation, it is time to address it.
Events
Teaching
Lectures
- Mixed-Methods procedures for mobility research
- Accessibility planning
- Visualization of Transport data
- Basis transport concepts
- Transportation Policies and Project Design
(more information can be found here)
PhD Courses
- Interpreting statistics for mobility research
Study Group: "Justice League: Mobility Hubs"
Six master students are meeting every 3 weeks for one and half hours to discuss the master thesis, get feedback from each other, and tips on how to write the master thesis. In the group, various topics are included, and the methods go from qualitative to quantitative.
The main research questions are:
- Who are the potential users of mobility hubs?
- How to make mobility hubs more inclusive?
- What elements should be included in a mobility hub?
Awareness
Fernanda Navarro: A serious board game for spreading awareness and empathy towards vulnerable-to-exclusion users of mobility hubs
Assessment
Sana Jafaar: Can personal values help use predict transport behaivor?
Joelean Hall: Exploring Advantages and Challenges of Bike-sharing in a Low-Income Resident Area in Houston, TX
Jan Wajerski : Mobility Hub User Analysis
Giulia Peaucellier: Influence of Perceived Safety on Mode Choices in Munich
Addressing
Alex Preis: How stakeholder participation tools can contribute to the success of mobility hubs: A guideline for citizen-centered, stakeholder-oriented planning of mobility hubs.
Korbinian: Can best practice bicycle infrastructure improve cycle-ability in Munich? A mixed-methods research
New topics for Master's Thesis and Study projects
Awareness:
- Mapping parties as a tool for creating awareness in stakeholdes (M.Sc. Hector Ochoa)
- Serious board games as a tool for creating awareness
Assessment:
- Social media analysis on mobility justice
- Qualitative assessment of perceived urban mobility justice in Munich (M.Sc. Sindi Haxhija)
- Quantitative analysis on mobility justice in “Berg am Laim”
- Who is allowed and who is forced to be immobile?
Further interesting information:







Research group leader
Expert on Accessibility and Equity
Dr. Maria Teresa Baquero Larriva
Expert on Older People Mobility
Expert on Inclusive Urban Mobility
Public participation and Evaluation
Data analysis
Gender expert
2022
Pfertner, M., Büttner, B., Duran-Rodas, D., & Wulfhorst, G. (2022). Workplace relocation and its association with car availability and commuting mode choice. Journal of Transport Geography, 98, 103264.
Büttner, B., Baguet, J., Duran-Rodas, D., Hall, J., Navarro-Ávalos, F., Nichols, A., Susilo, Y. (2022). Deliverable D 3.1 Guidelines for the integration of mobility hubs into the urban space. SmartHubs.
2021
Durán-Rodas, D. (2021). Efficiency and/or equity? Understanding and planning bike-sharing based on spatial fairness (Doctoral dissertation, Technische Universität München).
Fuchs, S., Durán-Rodas, D., Stöckle, M., & Pfertner, M. (2021, June). Who uses shared microbility? Exploring users’ social characteristics beyond sociodemographics. In 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (pp. 1-6). IEEE.
Duran-Rodas, D., Wright, B., Pereira, F. C., & Wulfhorst, G. (2021). Demand And/oR Equity (DARE) method for planning bike-sharing. Transportation Research Part D: Transport and Environment.
2020
Duran-Rodas, D., Villeneuve, D., & Wulfhorst, G. (2020). Bike-sharing: the good, the bad, and the future-an analysis of the public discussion on Twitter. European Journal of Transport and Infrastructure Research, 20(4), 38-58.
Duran-Rodas, D., Villeneuve, D., Pereira, F. C., & Wulfhorst, G. (2020). How fair is the allocation of bike-sharing infrastructure? Framework for a qualitative and quantitative spatial fairness assessment. Transportation Research Part A: Policy and Practice, 140, 299-319.
Duran-Rodas, D., Chaniotakis, E., Wulfhorst, G., and Antoniou, C. (2020) Open source data–driven method to identify most influencing spatiotemporal factors. An example of station–based bike sharing. In Konstadinos G. Goulias, Adam W. Davis (Eds.), Mapping the Travel Behavior Genome. Elsevier, Pages 503-526.
2022
Michel Geipel (2022): Factors of the built and social environments associated with the allocation of mobility hubs: A systematic literature review
Samuel Juhasz-Aba (2022): Mixed methods approach to assess equity in potential expansions of public transport by rail (RPT)
Pauline Klanke (2022): What are the Needs and Expectations Towards a Smart Mobility Hub? A Mixed-Methods Case Study in Munich
Korbinian Kreutzarek (2022): Literature research on best practice elements of bicycle infrastructure
Sana Jafaar (2022): Where to allocate mobility hubs? Identifying common built environment factors associated with the usage of multiple shared mobility options in Chicago
German Santiago Linares Ramirez (2022): Are essential services fairly distributed in Milan? Spatial Fairness Assessment with an Inclusive Accessibility by Proximity Index
Fernanda Navarro (2022): Integrating sustainable criteria through an AHP-GIS method to allocate mobility hubs using open data sources
2021
Leonel Guerrero (2021): Exploring social media strategies for transport planning
2020
Sophia Fuchs (2020): Vergleich der Nutzerprofile von Bikesharing und Shared E-Scooter Angeboten in München
Matthias Langer (2020): Quantitative assessment of car dependence. An application in the public transport area of Munich, Germany
Michael Stöckle (2020): Bike Sharing Systems in Munich: A (non)users’ behavioural, socio–demographic and psychographic analysis