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Using connected vehicle data to inform pricing of future toll roads

A client wanted to better understand pricing for tolled roads across major Australian cities to inform project pricing.

This case study uses custom Data Science services.

A client wanted to better understand pricing for tolled roads across Melbourne, Sydney and Brisbane to inform their project pricing. Specifically, they wanted to create price models based on travel time savings compared to non-tolled roads. Understanding driver experiences and the reliability of current toll assets across Australia helped determine an appropriate pricing structure. 

Previously, the client had used Google Maps data, which only provided basic travel time estimations and was not disaggregated enough for detailed analysis. The Compass Data Science team was able to provide more granular data exports, including analysis of different percentiles focusing only on workdays (excluding weekends, public holidays and school breaks), and see data for specific trip segments.

To inform their analysis, the client used two different methodologies with Compass data:

  • Travel time via speed packets. This approach estimates travel time by understanding the associated speed packets for road segments along a route of interest. The travel times for each segment are then added together to provide an overall travel time for the entire route of interest.
  • Raw vehicle trajectories. This method determines true travel time by calculating the difference in timestamps for trips that fully traverse the entire route of interest.

Both methodologies provide insight into nuanced driver behaviours, such as queueing. However, speed packets are an estimation of overall travel time, whereas raw trajectories provide a true overall travel time. The raw trajectories methodology is used where there are trips that have traversed the entire route of interest from its designated start and endpoint.

The client also examined freight movements using Compass’ freight origin-destination data to understand how freight vehicles moved through different Local Government Areas (LGAs). This dataset was crucial for pricing models since freight vehicles have distinct travel patterns, occupy more space and have a greater impact on the road surface than private vehicles.

All Compass users can query toll assets at a national level and are not bound by jurisdiction.