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Repo-docs-05-routing-and-scoring

zemion edited this page 2026-07-06 14:42:12 +02:00

Table of Contents

• Routing and Scoring Model
• Goal
• Route cost model
• Segment attributes
• Bikepacking profiles
• Loaded gravel
• Hardtail bikepacking
• Road touring
• E-bikepacking
• Beginner-safe
• Confidence model
• Hike-a-bike risk
• Critical gap detection
• Stage planning algorithm
• Example thresholds

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Mirrored from /mnt/DATA/git/pikebacker/docs/05_routing_and_scoring.md. Origin: repository. Active tasks and changing state belong in Gitea issues; this wiki page is durable project context.

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Routing and Scoring Model

Goal

Optimize for a loaded bikepacking trip, not a generic bike route.

Route cost model

A candidate route is scored by segment and by whole-trip logistics.

segment_cost =
  distance_cost
+ climb_cost
+ steepness_cost
+ poor_surface_cost
+ traffic_stress_cost
+ access_uncertainty_cost
+ hike_a_bike_risk_cost
+ remoteness_cost
+ weather_sensitivity_cost
- official_cycle_route_bonus
- confirmed_good_segment_bonus
- scenic_bonus

stage_cost =
  ride_effort_cost
+ sleep_risk_cost
+ water_gap_cost
+ food_gap_cost
+ daylight_risk_cost
+ weather_risk_cost
+ bailout_gap_cost
+ repair_gap_cost

Segment attributes

Each segment should carry:

• distance meters,
• ascent/descent,
• max grade,
• average grade,
• surface,
• smoothness,
• OSM highway class,
• bicycle/access tags,
• traffic stress proxy,
• cycle route membership,
• protected-area overlap,
• POI distance to next water/food/repair/sleep,
• community report score,
• confidence score.

Bikepacking profiles

Loaded gravel

• Prefer gravel/compact dirt and low-traffic roads.
• Penalize technical MTB trails.
• Penalize grades above 12% more heavily when loaded.
• Penalize unknown surfaces in remote areas.

Hardtail bikepacking

• Allow more rough surfaces.
• Allow singletrack where bicycle access and difficulty are acceptable.
• Penalize sustained paved highways and high traffic.

Road touring

• Prefer paved low-traffic roads and official cycle routes.
• Avoid rough surfaces, tracks, and paths with unknown rideability.

E-bikepacking

• Add range and charging constraints.
• Penalize long remote stretches without accommodation/charging.
• Respect local e-bike access distinctions where available.

Beginner-safe

• Strongly prefer known surfaces and legal certainty.
• Avoid high traffic, remote stretches, long dry gaps, and steep grades.

Confidence model

The app should show both recommendation and confidence.

Example route summary:

Distance: 382 km
Ascent: 5,420 m
Known surface: 88%
Unpaved: 42%
Longest water gap: 36 km
Longest food gap: 58 km
Access confidence: Medium-high
Sleep certainty: 4/5 nights confirmed options
Bailout gap: Max 71 km from train/ferry/bus
Warnings: 2 steep loaded-bike climbs, 1 uncertain access segment

Hike-a-bike risk

Estimate using:

• steep grade,
• poor smoothness,
• mtb:scale,
• sac_scale,
• surface type,
• trail width if available,
• rider profile,
• actual ride speed reports,
• user reports.

A route can include hike-a-bike if the profile allows it, but it must be visible.

Critical gap detection

Calculate gaps along the route:

• water gap,
• food gap,
• sleep gap,
• repair gap,
• bailout gap,
• charging gap for e-bike.

Warnings should be generated when a gap exceeds profile thresholds.

Stage planning algorithm

1. Generate candidate endpoints every 5–15 km around the daily target window.
2. Score each endpoint by sleep options, water/food, daylight fit, safety, and bailout.
3. Prefer endpoints with at least one high-confidence sleep option.
4. Add Plan B endpoints before and after Plan A.
5. Rebalance subsequent stages to avoid one impossible day.
6. Surface warnings for stages where no good endpoint exists.

Example thresholds

Profile	Target km/day	Max grade warning	Max water gap	Max food gap
Beginner-safe	40–70	10%	25 km	40 km
Loaded gravel	60–100	12%	40 km	70 km
Hardtail remote	50–90	15%	55 km	90 km
Road touring	70–130	10%	40 km	70 km
E-bikepacking	50–100	12%	40 km	60 km + charging

Thresholds should be configurable per user.