Classification of Anomalies in Shared Mobility: Linking Causes and Types

Institut

Lehrstuhl für Fahrzeugtechnik

Typ

Bachelorarbeit / Semesterarbeit / Masterarbeit /

Inhalt

 

Beschreibung

Background

In shared mobility systems, anomalies represent unusual deviations from expected demand and usage patterns. While anomaly detection identifies when and where such irregularities occur, classification provides the crucial next step: understanding what type of anomaly it is and why it happened. Possible causes include external events (weather, concerts, strikes), operational disruptions (vehicle stockouts, rebalancing issues), or structural shifts (policy changes, long-term demand trends). A robust anomaly classification framework allows operators not only to detect but also to interpret anomalies, supporting proactive decision-making and revenue optimization.

Your Role

• Literature research: Review of anomaly classification methods in time series, mobility, and related domains.
• Taxonomy development: Define relevant classes of anomalies (e.g., point vs. collective, shortterm vs. long-term, event-driven vs. structural).
• Data enrichment: Integrate external datasets (e.g., weather, event calendars, strike reports) to support classification.
• Model implementation: Apply and evaluate algorithms for anomaly classification (statistical, machine learning, or causal methods).
• Evaluation & visualization: Validate classification results on realworld shared mobility data from Munich and present findings in intuitive visualizations (maps, timelines, dashboards).

Voraussetzungen

What should you bring along?

• Strong interest and motivation in mobility data science
• Initiative & independent way of working
• Basic programming skills (Python)

Language

English/German

If you are interseted write an email and attach a CV and a grade sheet to your application.

Svetlana Zubareva, s.zubarevatum.de

Tags

FTM Studienarbeit, FTM SM, FTM Zubareva

Möglicher Beginn

sofort

Kontakt

Svetlana Zubareva, M.Sc.
Raum: MW3505
Tel.: 017660388031
s.zubarevatum.de

Ausschreibung