FAILURE MAP
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FA-85776 / Ride-hailing fare and surge pricing / Open access

Meter rounds every segment to a cent · case 01

Trips with many short segments drift from the exact tariff.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Each segment charge is rounded before accumulation.

THE FAILURE

Each segment charge is rounded before accumulation.

Unsuccessful approach: Rounding only distance segments still accumulates rounding drift.

Case contract

A single-tariff taximeter bills each segment [seconds, meters] by distance when its speed is at least v m/s (meters >= v*seconds, so a zero-duration movement bills distance) and by time otherwise, never both. Charges accumulate exactly (per_km per 1000 m, per_hour per 3600 s) and the total is rounded half up to a cent once, then the flag fall is added.

Why this case matters

Ride-hailing prices are computed per trip at scale; ordering, unit and boundary slips become systematic over- or under-charging.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(segments, rate):
    acc = Fraction(0)
    for dt, dm in segments:
        if dm >= rate['v'] * dt:
            acc += (dm * rate['per_km'] * 2 + 1000) // 2000
        else:
            acc += (dt * rate['per_hour'] * 2 + 3600) // 7200
    cents = int(acc * 2 + 1) // 2
    return rate['flag'] + cents
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: per-segment rounding',
   [[[10, 7], [5, 10]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 317),
  ('partial repair probe: per-segment rounding',
   [[[0, 3], [5, 25], [10, 53], [10, 110]], {'flag': 300, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 338),
  ('second regression', [[[1, 8], [10, 49]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 403),
  ('normal control 1', [[[10, 20]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 310),
  ('normal control 2', [[[30, 330], [10, 0], [5, 0]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}],
   381),
  ('normal control 3', [[[10, 0], [0, 0], [1, 7]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}],
   403),
  ('normal control 4', [[[0, 0], [10, 110]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 412)],
 [('regression: per-segment rounding',
   [[[10, 7], [10, 50], [1, 0], [5, 7], [10, 110]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}],
   355),
  ('partial repair probe: per-segment rounding',
   [[[1, 7], [1, 7], [30, 149]], {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 423),
  ('second regression',
   [[[0, 0], [10, 49], [10, 53], [30, 60], [30, 150]],
    {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}],
   486),
  ('normal control 1', [[[0, 0], [5, 10], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}],
   396),
  ('normal control 2',
   [[[10, 0], [0, 3], [1, 11], [10, 7]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 323),
  ('normal control 3', [[[0, 0], [0, 0]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 300),
  ('normal control 4', [[[1, 5], [10, 49]], {'flag': 300, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 311)],
 [('regression: per-segment rounding',
   [[[30, 149], [1, 8], [30, 150], [30, 7], [30, 149]],
    {'flag': 300, 'per_hour': 4150, 'per_km': 199, 'v': 5}],
   435),
  ('partial repair probe: per-segment rounding',
   [[[10, 20], [1, 2], [30, 149], [10, 110]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 463),
  ('second regression',
   [[[0, 7], [5, 0], [10, 49], [5, 7], [5, 24]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}],
   421),
  ('normal control 1',
   [[[30, 150], [0, 0], [30, 0], [5, 7], [30, 149], [10, 0]],
    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],
   495),
  ('normal control 2', [[[30, 0], [10, 49]], {'flag': 390, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 430),
  ('normal control 3', [[[1, 7], [1, 8], [1, 4]], {'flag': 300, 'per_hour': 3900, 'per_km': 230, 'v': 5}],
   305),
  ('normal control 4', [[[10, 49]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 401)],
 [('regression: per-segment rounding',
   [[[5, 10], [10, 49], [10, 50], [1, 0], [30, 7], [1, 5]],
    {'flag': 300, 'per_hour': 3900, 'per_km': 230, 'v': 5}],
   362),
  ('partial repair probe: per-segment rounding',
   [[[1, 8], [0, 0], [10, 50], [1, 7]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}], 405),
  ('second regression',
   [[[10, 7], [1, 4], [0, 7], [10, 50], [5, 24]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}],
   420),
  ('normal control 1', [[[30, 60], [10, 0], [10, 7]], {'flag': 390, 'per_hour': 3600, 'per_km': 230, 'v': 5}],
   440),
  ('normal control 2', [[[10, 7]], {'flag': 390, 'per_hour': 3600, 'per_km': 230, 'v': 5}], 400),
  ('normal control 3',
   [[[10, 20], [30, 7], [0, 0], [30, 330], [1, 4]], {'flag': 390, 'per_hour': 3600, 'per_km': 245, 'v': 5}],
   512),
  ('normal control 4',
   [[[10, 0], [10, 53], [1, 0], [1, 2], [0, 0]], {'flag': 390, 'per_hour': 3600, 'per_km': 245, 'v': 5}],
   415)],
 [('regression: per-segment rounding',
   [[[1, 5], [30, 330], [5, 7], [1, 4], [5, 28], [30, 0]],
    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],
   498),
  ('partial repair probe: per-segment rounding',
   [[[1, 11], [30, 149], [5, 25], [0, 0], [10, 110]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}],
   456),
  ('second regression',
   [[[10, 0], [30, 7], [30, 60], [30, 330]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 457),
  ('normal control 1', [[[1, 8]], {'flag': 390, 'per_hour': 3900, 'per_km': 245, 'v': 5}], 392),
  ('normal control 2', [[[10, 0], [0, 0]], {'flag': 390, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 400),
  ('normal control 3', [[[10, 20]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 402),
  ('normal control 4',
   [[[0, 0], [10, 53], [30, 149], [10, 50], [10, 50], [1, 7]],
    {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}],
   372)]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression: per-segment rounding318317Failed
partial repair probe: per-segment rounding339338Failed
second regression404403Failed
normal control 1310310Passed
normal control 2381381Passed
normal control 3403403Passed
normal control 4412412Passed

SHA-256 / 1b3ec7d6d1880638f7d3354507f9e1177680a19954438c4b80b835cb1d094e4e

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(segments, rate):
    acc = Fraction(0)
    for dt, dm in segments:
        if dm >= rate['v'] * dt:
            acc += (dm * rate['per_km'] * 2 + 1000) // 2000
        else:
            acc += Fraction(dt * rate['per_hour'], 3600)
    cents = int(acc * 2 + 1) // 2
    return rate['flag'] + cents
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: per-segment rounding',
   [[[10, 7], [5, 10]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 317),
  ('partial repair probe: per-segment rounding',
   [[[0, 3], [5, 25], [10, 53], [10, 110]], {'flag': 300, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 338),
  ('second regression', [[[1, 8], [10, 49]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 403),
  ('normal control 1', [[[10, 20]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 310),
  ('normal control 2', [[[30, 330], [10, 0], [5, 0]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}],
   381),
  ('normal control 3', [[[10, 0], [0, 0], [1, 7]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}],
   403),
  ('normal control 4', [[[0, 0], [10, 110]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 412)],
 [('regression: per-segment rounding',
   [[[10, 7], [10, 50], [1, 0], [5, 7], [10, 110]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}],
   355),
  ('partial repair probe: per-segment rounding',
   [[[1, 7], [1, 7], [30, 149]], {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 423),
  ('second regression',
   [[[0, 0], [10, 49], [10, 53], [30, 60], [30, 150]],
    {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}],
   486),
  ('normal control 1', [[[0, 0], [5, 10], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}],
   396),
  ('normal control 2',
   [[[10, 0], [0, 3], [1, 11], [10, 7]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 323),
  ('normal control 3', [[[0, 0], [0, 0]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 300),
  ('normal control 4', [[[1, 5], [10, 49]], {'flag': 300, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 311)],
 [('regression: per-segment rounding',
   [[[30, 149], [1, 8], [30, 150], [30, 7], [30, 149]],
    {'flag': 300, 'per_hour': 4150, 'per_km': 199, 'v': 5}],
   435),
  ('partial repair probe: per-segment rounding',
   [[[10, 20], [1, 2], [30, 149], [10, 110]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 463),
  ('second regression',
   [[[0, 7], [5, 0], [10, 49], [5, 7], [5, 24]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}],
   421),
  ('normal control 1',
   [[[30, 150], [0, 0], [30, 0], [5, 7], [30, 149], [10, 0]],
    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],
   495),
  ('normal control 2', [[[30, 0], [10, 49]], {'flag': 390, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 430),
  ('normal control 3', [[[1, 7], [1, 8], [1, 4]], {'flag': 300, 'per_hour': 3900, 'per_km': 230, 'v': 5}],
   305),
  ('normal control 4', [[[10, 49]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 401)],
 [('regression: per-segment rounding',
   [[[5, 10], [10, 49], [10, 50], [1, 0], [30, 7], [1, 5]],
    {'flag': 300, 'per_hour': 3900, 'per_km': 230, 'v': 5}],
   362),
  ('partial repair probe: per-segment rounding',
   [[[1, 8], [0, 0], [10, 50], [1, 7]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}], 405),
  ('second regression',
   [[[10, 7], [1, 4], [0, 7], [10, 50], [5, 24]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}],
   420),
  ('normal control 1', [[[30, 60], [10, 0], [10, 7]], {'flag': 390, 'per_hour': 3600, 'per_km': 230, 'v': 5}],
   440),
  ('normal control 2', [[[10, 7]], {'flag': 390, 'per_hour': 3600, 'per_km': 230, 'v': 5}], 400),
  ('normal control 3',
   [[[10, 20], [30, 7], [0, 0], [30, 330], [1, 4]], {'flag': 390, 'per_hour': 3600, 'per_km': 245, 'v': 5}],
   512),
  ('normal control 4',
   [[[10, 0], [10, 53], [1, 0], [1, 2], [0, 0]], {'flag': 390, 'per_hour': 3600, 'per_km': 245, 'v': 5}],
   415)],
 [('regression: per-segment rounding',
   [[[1, 5], [30, 330], [5, 7], [1, 4], [5, 28], [30, 0]],
    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],
   498),
  ('partial repair probe: per-segment rounding',
   [[[1, 11], [30, 149], [5, 25], [0, 0], [10, 110]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}],
   456),
  ('second regression',
   [[[10, 0], [30, 7], [30, 60], [30, 330]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 457),
  ('normal control 1', [[[1, 8]], {'flag': 390, 'per_hour': 3900, 'per_km': 245, 'v': 5}], 392),
  ('normal control 2', [[[10, 0], [0, 0]], {'flag': 390, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 400),
  ('normal control 3', [[[10, 20]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 402),
  ('normal control 4',
   [[[0, 0], [10, 53], [30, 149], [10, 50], [10, 50], [1, 7]],
    {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}],
   372)]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression: per-segment rounding317317Passed
partial repair probe: per-segment rounding339338Failed
second regression404403Failed
normal control 1310310Passed
normal control 2381381Passed
normal control 3403403Passed
normal control 4412412Passed

SHA-256 / d72d68ccc4c29fbba44bedfdb376bb14530051142dcca5b081f1874a65009779

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 7 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

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Verification & scope

A deterministic toy pricing contract stipulated for this example; it does not reproduce the pricing of any real ride-hailing operator or regulator. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:50:43.518971+00:00.

Case digest / 09b2cc5783dce5e4437fdb37b6fd6b21c6e1cc858ea4d242d81c7e86a32e2529