The goal was to strengthen the Tauranga Transport Model by integrating real-world GPS trip data, providing an evidence-based understanding of freight and general vehicle movements.
Our Approach:
- Trip Identification & Classification – Processed GPS records to detect vehicle movements in and out of Tauranga, assigning them to the Council’s internal Traffic Analysis Zones (TAZs).
- Spatial Density Clustering – Applied clustering to identify the origins of trips from outside the city, providing a clearer picture of inter-regional movements.
- Data Anonymity – Implemented privacy safeguards to ensure all data was anonymised, protecting individuals and companies while preserving analytical value.
- Integration with the Tauranga Transport Model – Delivered the classified, anonymised dataset for direct integration, replacing modelled assumptions with real operational data.
Key Findings:
- Detailed Movement Patterns – Produced a clear picture of heavy and light vehicle flows, identifying high-volume corridors and congestion hotspots.
- External Origin Insights – Revealed where incoming trips were most likely to start, enhancing the understanding of Tauranga’s freight and commuter links.
- Improved Model Accuracy – Replacing assumptions with GPS-based observations resulted in a transport model that more closely reflects actual network behaviour.
Impact:
- Better Infrastructure Planning – Supported more precise prioritisation of road investments and upgrades.
- Enhanced Congestion Management – Equipped the Council with data to inform targeted interventions on high-impact routes.
- Data-Driven Decision-Making – Strengthened the evidence base for long-term transport strategies and funding proposals.
