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Users cannot consistently compare the energy, emissions, and monetary cost of route alternatives or choose an environmentally preferable route. A trustworthy implementation needs vehicle context and sourced assumptions; generic distance multipliers are not sufficient for ranking routes.
Current state
Driving cards can show a generic estimate based on 170 g CO₂/km.
Entur transit results can expose provider CO₂ data and the UI can identify the lowest result.
The EV planner models consumption and supports presets plus one locally stored custom EV profile.
There is no unified cost/impact contract, multi-vehicle garage, toll/fuel/electricity model, or eco-aware routing preference.
Proposed solution
First build a provenance-aware route impact/cost comparison layer. Use a selected vehicle plus route attributes to estimate energy, operational emissions, tolls, and fuel/electricity cost. Only expose an eco/cost routing preference after evaluation shows alternative ranking is materially reliable.
Acceptance criteria
A shared route-impact contract distinguishes provider-reported, calculated, defaulted, and unknown values.
Vehicle inputs cover relevant EV, combustion, hybrid, motorcycle, and bicycle parameters without requiring excessive setup.
Emission/energy factors are versioned, region-aware where necessary, cited, and display their assumptions.
Fuel/electricity prices and currency have an explicit source/timestamp or user override.
Tolls, fares, parking, and other components remain separately identifiable rather than hidden in one number.
Calculations account for distance, speed/elevation when available, vehicle efficiency, occupancy, and uncertainty.
Comparisons never equate operational emissions with full lifecycle emissions unless lifecycle data is explicitly modeled.
Unknown inputs degrade to ranges or “not available,” not false precision.
A benchmark compares calculated results with provider/known-route references and defines when ranking is allowed.
Route cards explain why an alternative is cheaper/lower impact and allow users to change assumptions.
Caching, localization, units, currency conversion, and privacy are tested.
Eco/cost preference remains behind a quality gate and always offers the normal routing result.
Alternatives considered
A universal grams-per-kilometre number is simple but can mis-rank EVs, hybrids, traffic conditions, and transit. Delegating everything to providers produces inconsistent or missing values.
Area
Routing, EV planning, personal vehicles, route comparison UI.
Dependencies
Coordinate the user-facing vehicle model with the personal-vehicles issue (#305).
Problem
Users cannot consistently compare the energy, emissions, and monetary cost of route alternatives or choose an environmentally preferable route. A trustworthy implementation needs vehicle context and sourced assumptions; generic distance multipliers are not sufficient for ranking routes.
Current state
Proposed solution
First build a provenance-aware route impact/cost comparison layer. Use a selected vehicle plus route attributes to estimate energy, operational emissions, tolls, and fuel/electricity cost. Only expose an eco/cost routing preference after evaluation shows alternative ranking is materially reliable.
Acceptance criteria
Alternatives considered
A universal grams-per-kilometre number is simple but can mis-rank EVs, hybrids, traffic conditions, and transit. Delegating everything to providers produces inconsistent or missing values.
Area
Routing, EV planning, personal vehicles, route comparison UI.
Dependencies
Coordinate the user-facing vehicle model with the personal-vehicles issue (#305).