Autonomous Transit Impact Assessment (ATIA) tool
Public transport authorities and operators are exploring autonomous transit as a response to growing operational pressures, but knowing where and how to start remains a challenge. The Autonomous Transit Impact Assessment (ATIA) tool supports evidence-based decision-making by providing a fast, structured overview of the potential impacts of automation across routes and networks, helping organizations move from uncertainty to informed action.
The challenge
Public transport operators face mounting pressure from staff shortages, rising costs, and increasing service expectations. While automation offers potential, uncertainty remains around the business case — particularly during intermediate implementation phases. A realistic assessment must account for the upfront cost of equipping vehicles with autonomous driving systems (ADS) even before autonomy is achieved, as poorly designed or prolonged transitions risk increasing costs rather than reducing them.
The approach
Rebel offers the ATIA tool and service bundle, providing public transport operators (PTOs) and authorities (PTAs) with rapid assessment of service, cost, and FTE impacts of implementing autonomous transit. It analyzes route-specific impacts, charts changes in cost and staffing structures per service hour, and assesses network-wide annual potential.
The analytics are tailored to fleet, cost, and operational structures, as well as route and service characteristics. This includes network-wide rapid assessment of corridor and operational design domain (ODD) complexity for driverless operations. The tool models three implementation phases against a traditional baseline:
- Baseline: Current service delivery without autonomous technology — the reference point for all cost comparisons
- Phase 1: Full driver operation with ADS hardware installed — capturing the initial investment in autonomous-ready vehicles before any driver reduction
- Phase 2: Partial autonomous operation with transitional staffing effects, where the share of autonomous driving depends on corridor type
- Phase 3: Fully autonomous service delivery
This phased approach reflects real-world deployment: operators invest in ADS-equipped vehicles from day one, then progressively reduce driver dependency as technology and regulations mature. The tool quantifies the cost impact at each stage, including the critical question of when ADS investment begins to pay off.
Rebel offers a free introductory version featuring configuration of initial parameters, route entry, GTFS upload, and detailed analysis of service, staffing, and cost impacts across all phases.
Impact
Enabling data-driven decisions on autonomous transit by identifying where and when automation delivers the greatest operational, staffing, and cost benefits — from the first ADS-equipped vehicle to full autonomy.
The experience of a Rebel
