Where Should Traffic Sensors Go? Sensor Placement for Urban Flow Estimation

Institut

Lehrstuhl für Fahrzeugtechnik (TUM-ED)

Typ

Masterarbeit /

Inhalt

 

Beschreibung

• Traffic sensors observe only a small part of an urban road network. Their locations determine how
much these measurements reveal about traffic on unobserved roads.
• This thesis investigates where a limited number of sensors should be placed to improve network-
wide traffic-flow estimation, especially when historical measurements are unavailable.
• Available resources include OpenStreetMap road graphs, synthetic traffic flows, and an existing
pretrained graph neural network—a model that learns relationships between connected road
segments.
• The core study uses synthetic demand scenarios with known flows on every road segment. It
compares placement strategies with a fixed estimator and equal sensor budgets; real-data evaluation
is an optional extension.

 

Work Packages
• Review sensor-placement methods and reproduce random and graph-based baselines
• Develop a placement strategy using synthetic flows or information from the existing model
• Compare strategies across sensor budgets and road networks on held-out demand scenarios; keep
test flows hidden during sensor selection
• Analyse estimation errors on unobserved roads, robustness, and computational cost; document
reproducible results and limitations

Voraussetzungen

Prerequisites
• Programming skills in Python and basic knowledge of machine learning
• Interest in transport networks, graph methods, or optimisation
• Independent, structured work and critical interpretation of results

Möglicher Beginn

sofort

Kontakt

Joshua Mutter, M.Sc.
joshua.muttertum.de

Ausschreibung