The transition to sustainable urban transport increasingly relies on bicycles, though their usage is significantly influenced by weather conditions, particularly high temperatures. To address this challenge, this research approach focuses on optimizing cycling conditions through strategic tree placement.
Inspired by the existing 3-30-300 urban green space rule coverage (3 large trees near every house, 30% tree shade in a neighbourhood, distance between any house and green area less than 300 m), a new guideline is proposed which states that 35 percent of cycling paths should be shaded by trees, 5% more than the baseline to actively encourage cycling.

For the municipality of Delft, a geospatial analysis was conducted using Geographic Information System (GIS) data from Cobra Groeninzicht and the municipal authorities. QGIS and Python were employed to analyse tree and cycling network information. The shadow simulation focused on a July day at 14:00 PM, representing the warmest conditions with the sun at its highest position.
Initial analysis revealed that, on this July day, this day Delft's cycling network was only 26.6 percent shaded by trees. To meet the 35 percent target, 223 "standard" trees were manually added in the GIS data, their location choice based on cycle paths which seemed to have insufficient shade. This purposeful tree addition increased shadow coverage to 37.5 percent, successfully satisfying the proposed shading rule.

A subsequent research phase developed an automated tool for optimizing tree placement. This software allows users to select different tree sizes while considering appropriate spacing between trees. The tool enables municipalities to quickly determine optimal tree locations for enhancing cycling conditions.
By quantitatively assessing and improving urban cycling infrastructure through intelligent tree placement, this method provides a practical, environmentally friendly approach to promoting sustainable urban transportation.