R7 — Journey alignment (boundaries and correction UX)

Status: Wave 1 research recommendation
Owns methodology / contracts: tunes
Owns capture and correction UX: tunes-ios
Consumes alignment outcomes: tunes-web (validation, map association, quality flags)

Problem

Acoustic metrics without a trustworthy station-to-station section assignment are not comparable. Professional interior surveys report levels per section with duration (see acoustic-survey-methodology.md, ISO 3381 practice). Crowdsourced phone recordings must produce the same comparison unit — honestly — despite delays, branch ambiguity, late starts, early stops, transfers, and walking inside stations.

Alignment is therefore a scientific boundary problem and a product UX problem. Predictions may suggest cuts; only user-confirmed (or campaign-protocol) section maps should enter public comparison layers.

Working assumptions this doc inherits

Canonical public unit = station-to-station section with duration (scope-statement.md; provisional decision D-SEC).
Privacy default = local record → infer → review/correct → consent → derived upload (01-assumptions-and-open-questions.md).
Underground GPS is not trustworthy as a primary clock; topology snap and motion/acoustic cues matter more (acoustic-survey-methodology.md §3.2).
Schema stays generic railway with London as instances (ADR-001/002; R6).

What “aligned” means

A recording session is aligned when each retained time interval is assigned to exactly one of:

Interval type	Public comparison?	Notes
In-motion section	Yes (primary)	Between consecutive station stops on a declared service path
Station dwell	Optional / flagged	Platform or doors-open standing; often excluded from section Leq unless campaign says otherwise
Held between stations	Flag / exclude	Mid-section stop; disclose and usually exclude from section averages (pro practice excludes non-representative holds)
Walking / interchange	No (or separate layer)	Must not inflate or dilute section metrics
Unassigned / trimmed	No	User deleted or left unmapped; never silently filled

Every published section metric must carry duration T and an alignment confidence dimension separate from acoustic confidence.

Signal stack (prefer multi-cue, never one oracle)

Alignment should fuse prior (what the user said they would ride) with posterior cues (what sensors observed). No single cue is sufficient underground.

1. User-selected route (required prior)

Before or at recording start, the contributor selects:

Network / system (e.g. London Underground instance)
Line and direction (or terminus-facing direction)
Origin station (boarding)
Intended destination (or last intended stop)
Optional: known branch choice where topology forks

This prior constrains the search space to a path on the network graph. Without it, automatic section IDs are not credible for v1 public data.

Repo: tunes-ios UX; path validity against network definitions owned by tunes.

2. Timetable / expected run expectations (soft prior)

Where open timetable or headway data is available and licence-compatible:

Expected section transit durations (ranges, not point estimates)
Typical dwell bands
Known skip-stop / express patterns for that service type

Use as priors and plausibility checks, not as ground truth. Delays are normal; a model that forces timetable clocks will mis-cut late trains.

Do not imply operator endorsement by displaying branded “official” journey assurances. Open operational data ≠ affiliation (project-charter.md).

3. Station dwell patterns

Dwells often appear as:

Near-zero along-track progress (motion)
Lower or differently textured acoustic envelope vs running
Door/chime events at edges
Possible GPS / Wi-Fi / cell context change when near surface or large stations (opportunistic only)

Variable dwell is the main reason fixed-duration slicing fails. Alignment must allow stretch/shrink of both motion and dwell intervals.

4. Door sounds and acoustic envelope

Useful edge hints, not speech recognition by default:

Door close / open energy bursts
Sudden envelope shifts at stop ↔ go
Wheel-rail / traction texture changes when moving vs standing

Announcement recognition (station name ASR) is optional later research: high privacy cost, brittle accent/PA quality, and conflicts with derived-only defaults if audio snippets are retained. Prefer envelope + motion first; treat ASR as experiment-gated, campaign-only if ever.

5. Acceleration, braking, accelerometer / vibration

Phone IMU (when available and consented for motion capture):

Longitudinal accel/brake patterns between stops
Vibration floor differences: platform walk vs train run
Possible curve/squeal correlation as secondary texture (not identity)

Motion helps separate walking in stations from train motion better than audio alone. Document sensor gaps (backgrounding, watch-only, user denying motion) as quality flags — do not invent motion.

6. Optional GPS / location

Above ground / Overground / some subsurface: may refine absolute time ↔ place when accuracy is reported good.
Deep Tube: treat GPS as weak or absent; snap to topology + user path, not raw track following.
Prefer storing snapped section IDs and relative timeline, not high-rate raw traces, in derived uploads (location minimisation; R5).

7. Network topology

The graph supplies:

Legal next stations given line + direction + branch
Branch ambiguity sets (e.g. Northern line forks)
Transfer edges (out-of-vehicle)
Impossible transitions for fraud / error flags

Topology is authoritative for which section IDs exist; sensors only propose when cuts fall and whether the trip stayed on the declared path.

Post-recording editor (subtitle / chapter metaphor)

Primary UX owner: tunes-ios.

After recording (and preferably after local feature extraction), show a timeline editor analogous to subtitle or chapter markers:

Waveform or level envelope strip (derived display; not a requirement to upload audio).
Proposed section blocks labelled with from→to station names.
User can drag boundaries, stretch/compress blocks, merge/split, reassign station labels from the legal topology set, mark dwell/hold/walk, trim start/end.
Live duration per section updates as boundaries move (required for honest Leq,T).
Explicit confirm before the contribution enters the upload preview.

Predictions are suggestions. Unconfirmed auto-alignment must not ship into public map aggregates without a documented campaign protocol that accepts auto-confirm under stated conditions (not default for informal contributors).

Accessibility and commute constraints

One-handed correction where practical
Large hit targets for boundary handles
Screen-off recording must not require watching the journey
Correction should be understandable without acoustic training
Colour alone must not encode confidence

Numeric UX targets (time-to-correct, error rates) belong in user tests — not invented here.

Failure modes to design for (evaluation matrix)

Failure	Risk if ignored	Design response
Delays / long dwell	Boundaries drift; wrong station pairing	Soft timetable priors; dwell as first-class interval; user stretch
Branch ambiguity	Wrong section IDs on fork	Force branch choice when topology requires; post-edit reassignment
Train held between stations	Inflated section T; non-representative Leq	Detect long near-stationary mid-section; flag/exclude with disclosure
Late start (joined mid-journey)	Partial first section	Allow first block = “partial section”; quality flag; optional exclude from pairwise stats
Early stop (left before destination)	Trailing empty path	Trim unused planned stations; do not fabricate intervals
Missed / skipped stop	Extra or missing cuts	Topology + user correction; skip-stop service profiles when known
Transfers	Mixing lines in one section metric	Require explicit transfer break; separate sessions or marked interchange intervals
Walking in stations	Footfall / PA / retail noise in “section”	IMU + envelope classifiers → walk intervals; exclude from section Leq
Sensor loss / interruption	Silent gaps	Quality flags; do not interpolate fake motion; allow user mark gap
Background audio route change	Envelope artefacts at boundaries	Flag; avoid treating artefacts as station edges

Metrics to measure in experiments (no targets yet)

Define experiment protocols for:

Section ID correctness (vs contributor ground truth diary)
Boundary timing error distribution (seconds), reported with method
Branch selection accuracy where forks exist
Correction completion time and correction error rate (user test)
Calibration of alignment confidence vs observed error (does “high” mean high?)

Publish method and sample description; do not claim accuracy percentages until measured.

Confidence dimensions (alignment ≠ loudness)

Keep separate confidence axes (programme rule against one universal score):

Journey assignment confidence — path and section IDs
Boundary timing confidence — cut placement
Acoustic confidence — device / clipping / placement (R3/R4)
Metadata confidence — carriage etc. (R8)
Subjective completeness — survey present or not (R9)

tunes-web must be able to filter map layers by journey-assignment confidence independently of acoustic tier.

Suggested flags (names illustrative; final enum in schema ADR):

alignment_unconfirmed
partial_first_section / partial_last_section
held_between_stations
walk_or_interchange_mixed
branch_ambiguous
route_mismatch
implausible_section_duration
sensor_gap
gps_weak_or_absent (informational underground)

Repo split

Concern	Repo
Section unit definition, topology schema, flag enums, confidence dims	`tunes`
Recording, IMU/audio capture, inference, subtitle editor, confirm UX	`tunes-ios`
Reject impossible journeys, aggregate only confirmed sections, map honesty	`tunes-web`

Current disposition

The following research recommendations remain the alignment contract; changing their scientific meaning requires an ADR:

User route prior required for contributions eligible for public section comparison.
Human confirm default for section maps; auto-accept only under explicit campaign protocol.
Dwell / hold / walk are first-class interval types, not silent padding inside section Leq.
GPS optional and minimised; topology + motion + envelope primary underground.
Announcement ASR deferred; experiment-gated, not v1 dependency.
Alignment UX and offline correction owned by tunes-ios; science contracts in tunes.

Open questions (do not invent answers)

Exact fusion algorithm (HMM / DTW / rule-based) — spike in Phase 3.
Whether surface GPS should ever raise tier alone (likely no).
Minimum usable partial-section policy for map display.
How aggressively to prompt branch choice vs infer-then-correct.

Recommendation

Treat journey alignment as a constrained path labelling problem: user-selected route + topology define legal sections; dwell, doors, accel/brake, IMU, envelope, and optional GPS propose cuts; a subtitle-style post-recording editor in tunes-ios makes the passenger the final arbiter before derived upload. Design explicitly for delays, branches, late start, early stop, transfers, and in-station walking — and keep alignment confidence separate from acoustic metrics. Prefer excluding ambiguous intervals over publishing false station pairs.

Confidence: High for the signal stack shape, editor metaphor, and repo ownership; Medium for automatic cut quality until Phase 3 journey experiments; Low for any numeric accuracy claim until measured.

Depends on experiment/legal/user-test? Yes — real-journey boundary experiments; user tests of correction UX; privacy review if announcement audio features are ever proposed; licence check for any timetable data source.