CASE STUDY PRESENTATION

AI Fleet Optimisation Cuts Aarhus Home-Care Cars by 30%

Aarhus used GPS data and FleetOptimiser AI to right-size home-care vehicles before new leases, cutting cars, costs and CO2.

Case Study
MitigationTransportationImplementation & operationsPhysical/technical solutionsResource efficiencyEconomic development

Use real GPS driving data to reveal fleet overcapacity and lease only the vehicles needed, keeping service patterns while cutting cost and CO2.

The big idea
  • 30% fewer home-care cars
  • 49.4 tons CO2 avoided
  • DKK 3m saved over 5 years
So what?
1

The Challenge

Aarhus Municipality aims for a fossil-free vehicle fleet by 2025, requiring ongoing replacement and new leasing agreements. In spring 2022, the home-care service had to renew contracts for part of a 200-car fleet and estimated a need for 43 leased cars. The challenge was to avoid locking in unnecessary vehicles, costs and emissions while preserving the same service-driving patterns.

2

The Plan

The plan was to base fleet decisions on observed driving behaviour rather than departmental estimates. FleetOptimiser used GPS data to calculate actual capacity needs and propose a smaller vehicle fleet that could still support the same transport patterns.

  1. Step 1

    Aarhus joined other municipalities and regions to develop FleetOptimiser, an AI tool for assessing real fleet needs.

  2. Step 2

    The home-care fleet was selected as a practical test area because new leasing agreements were due.

  3. Step 3

    GPS driving data was loaded and used to map current routes and vehicle use.

  4. Step 4

    FleetOptimiser analysed patterns to identify overcapacity and test whether routes could still be served with fewer cars.

  5. Step 5

    The municipality planned to lease 30 cars rather than 43 and explore broader fleet sharing and modal mix optimisation.

3

The Results

30%

Reduction in the home-care fleet.

49.4 tons CO2

Emissions avoided from 13 cars over five years.

25%

Cost saving for leasing and operation.

DKK 3m

Savings from leasing 13 fewer cars over five years.

The analysis found that the home-care service could lease 30 cars instead of 43 while maintaining existing driving patterns. Avoiding 13 cars reduced both fleet expenses and climate impact over a five-year period.

4

Key Lessons

Real driving data can expose unused capacity before new leasing contracts lock in costs and emissions. The case also shows that organisational buy-in matters, because changing fleet ownership can meet resistance even when service levels are maintained.

  • Leasing based on estimates
  • Cars managed decentrally
  • Overcapacity hidden
Before
  • GPS data guided leasing
  • Same routes with fewer cars
  • Fleet sharing potential
After

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