FA-85546 / Ride-hailing fare and surge pricing / Open access

A single rider with overlap gets the shared discount · case 01

When only one rider meets the overlap threshold, that rider still receives the matched discount.

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

ROOT CAUSE

The two-rider requirement for a match is missing.

VERIFIED REPAIR

Clear the matched set when fewer than two riders qualify.

Unsuccessful approach: Counting all riders instead of qualifying riders still leaves a lone qualifier matched.

Case contract

In a shared ride, a rider counts as matched if they shared at least min_overlap_s seconds with another rider; a match needs at least two such riders, otherwise nobody is matched. Matched riders pay solo x (100 - discount_pct)% and unmatched riders pay solo x 95%, both floored to the cent in the rider's favor. Return id -> price.

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

N = 1
observations = []
def solve(riders, discount_pct, min_overlap_s):
    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s]
    out = {}
    for r in riders:
        pct = discount_pct if r['id'] in matched else 5
        out[r['id']] = r['solo'] * (100 - pct) // 100
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 3, 'solo': 1999}],
    25, 180],
   {'r0': 1899, 'r1': 1899, 'r2': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    25, 180],
   {'r0': 2379, 'r1': 833, 'r2': 833}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}], 30, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}, {'id': 'r1', 'overlap_s': 179, 'solo': 1234}], 40, 180],
   {'r0': 2379, 'r1': 1172}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    25, 180],
   {'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 877},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}],
    40, 180],
   {'r0': 1899, 'r1': 526, 'r2': 1199}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234}], 30, 180],
   {'r0': 613, 'r1': 863})],
 [('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 1999}], 25, 180],
   {'r0': 1899, 'r1': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 3, 'solo': 1234}, {'id': 'r3', 'overlap_s': 0, 'solo': 877}],
    30, 180],
   {'r0': 1899, 'r1': 2379, 'r2': 1172, 'r3': 833}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 30, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 877}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    40, 180],
   {'r0': 526, 'r1': 833, 'r2': 833, 'r3': 740}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1999}],
    25, 180],
   {'r0': 925, 'r1': 657, 'r2': 1499}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 877}], 25, 180],
   {'r0': 833, 'r1': 833}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}], 40, 180], {'r0': 1899})],
 [('regression: lone matched rider', [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}], 30, 180], {'r0': 833}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 3, 'solo': 2505}], 30, 180],
   {'r0': 2379, 'r1': 2379}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999}], 30, 180],
   {'r0': 1172, 'r1': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999}], 40, 180],
   {'r0': 1899, 'r1': 1899}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 877}], 25, 180],
   {'r0': 1878, 'r1': 657}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 877}],
    25, 180],
   {'r0': 925, 'r1': 1878, 'r2': 1878, 'r3': 657}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}], 30, 180], {'r0': 1172})],
 [('regression: lone matched rider', [[{'id': 'r0', 'overlap_s': 600, 'solo': 1999}], 30, 180], {'r0': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 2505}], 25, 180],
   {'r0': 1172, 'r1': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 25, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 877}], 25, 180],
   {'r0': 657, 'r1': 657}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 1234}],
    25, 180],
   {'r0': 925, 'r1': 1878, 'r2': 1499, 'r3': 1172}),
  ('normal control 3', [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}], 40, 180], {'r0': 833}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 2505}],
    40, 180],
   {'r0': 740, 'r1': 526, 'r2': 2379})],
 [('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999}], 25, 180],
   {'r0': 1172, 'r1': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 179, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 179, 'solo': 2505}],
    30, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],
    25, 180],
   {'r0': 657, 'r1': 925, 'r2': 657}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234}], 25, 180],
   {'r0': 1878, 'r1': 925}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505}], 25, 180],
   {'r0': 1899, 'r1': 2379}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999}], 25, 180],
   {'r0': 833, 'r1': 1899})]]
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 fixture	Actual	Expected	Outcome
regression: lone matched rider	`{'r0': 1499, 'r1': 1899, 'r2': 1899}`	`{'r0': 1899, 'r1': 1899, 'r2': 1899}`	Failed
partial repair probe: lone matched rider	`{'r0': 2379, 'r1': 657, 'r2': 833}`	`{'r0': 2379, 'r1': 833, 'r2': 833}`	Failed
second regression	`{'r0': 1399}`	`{'r0': 1899}`	Failed
normal control 1	`{'r0': 2379, 'r1': 1172}`	`{'r0': 2379, 'r1': 1172}`	Passed
normal control 2	`{'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}`	`{'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}`	Passed
normal control 3	`{'r0': 1899, 'r1': 526, 'r2': 1199}`	`{'r0': 1899, 'r1': 526, 'r2': 1199}`	Passed
normal control 4	`{'r0': 613, 'r1': 863}`	`{'r0': 613, 'r1': 863}`	Passed

SHA-256 / 5e63ccbcd794dedc12d8755112b32e67770658b301c6b04d8a4227606c0904fe

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(riders, discount_pct, min_overlap_s):
    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s]
    if len(riders) < 2:
        matched = []
    out = {}
    for r in riders:
        pct = discount_pct if r['id'] in matched else 5
        out[r['id']] = r['solo'] * (100 - pct) // 100
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 3, 'solo': 1999}],
    25, 180],
   {'r0': 1899, 'r1': 1899, 'r2': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    25, 180],
   {'r0': 2379, 'r1': 833, 'r2': 833}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}], 30, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}, {'id': 'r1', 'overlap_s': 179, 'solo': 1234}], 40, 180],
   {'r0': 2379, 'r1': 1172}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    25, 180],
   {'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 877},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}],
    40, 180],
   {'r0': 1899, 'r1': 526, 'r2': 1199}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234}], 30, 180],
   {'r0': 613, 'r1': 863})],
 [('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 1999}], 25, 180],
   {'r0': 1899, 'r1': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 3, 'solo': 1234}, {'id': 'r3', 'overlap_s': 0, 'solo': 877}],
    30, 180],
   {'r0': 1899, 'r1': 2379, 'r2': 1172, 'r3': 833}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 30, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 877}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    40, 180],
   {'r0': 526, 'r1': 833, 'r2': 833, 'r3': 740}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1999}],
    25, 180],
   {'r0': 925, 'r1': 657, 'r2': 1499}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 877}], 25, 180],
   {'r0': 833, 'r1': 833}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}], 40, 180], {'r0': 1899})],
 [('regression: lone matched rider', [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}], 30, 180], {'r0': 833}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 3, 'solo': 2505}], 30, 180],
   {'r0': 2379, 'r1': 2379}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999}], 30, 180],
   {'r0': 1172, 'r1': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999}], 40, 180],
   {'r0': 1899, 'r1': 1899}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 877}], 25, 180],
   {'r0': 1878, 'r1': 657}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 877}],
    25, 180],
   {'r0': 925, 'r1': 1878, 'r2': 1878, 'r3': 657}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}], 30, 180], {'r0': 1172})],
 [('regression: lone matched rider', [[{'id': 'r0', 'overlap_s': 600, 'solo': 1999}], 30, 180], {'r0': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 2505}], 25, 180],
   {'r0': 1172, 'r1': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 25, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 877}], 25, 180],
   {'r0': 657, 'r1': 657}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 1234}],
    25, 180],
   {'r0': 925, 'r1': 1878, 'r2': 1499, 'r3': 1172}),
  ('normal control 3', [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}], 40, 180], {'r0': 833}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 2505}],
    40, 180],
   {'r0': 740, 'r1': 526, 'r2': 2379})],
 [('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999}], 25, 180],
   {'r0': 1172, 'r1': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 179, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 179, 'solo': 2505}],
    30, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],
    25, 180],
   {'r0': 657, 'r1': 925, 'r2': 657}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234}], 25, 180],
   {'r0': 1878, 'r1': 925}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505}], 25, 180],
   {'r0': 1899, 'r1': 2379}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999}], 25, 180],
   {'r0': 833, 'r1': 1899})]]
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 fixture	Actual	Expected	Outcome
regression: lone matched rider	`{'r0': 1499, 'r1': 1899, 'r2': 1899}`	`{'r0': 1899, 'r1': 1899, 'r2': 1899}`	Failed
partial repair probe: lone matched rider	`{'r0': 2379, 'r1': 657, 'r2': 833}`	`{'r0': 2379, 'r1': 833, 'r2': 833}`	Failed
second regression	`{'r0': 1899}`	`{'r0': 1899}`	Passed
normal control 1	`{'r0': 2379, 'r1': 1172}`	`{'r0': 2379, 'r1': 1172}`	Passed
normal control 2	`{'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}`	`{'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}`	Passed
normal control 3	`{'r0': 1899, 'r1': 526, 'r2': 1199}`	`{'r0': 1899, 'r1': 526, 'r2': 1199}`	Passed
normal control 4	`{'r0': 613, 'r1': 863}`	`{'r0': 613, 'r1': 863}`	Passed

SHA-256 / 3db611d20bb30d34fbaab9fced7962e23d2d544c1a6351dfabc3031b355ebde6

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(riders, discount_pct, min_overlap_s):
    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s]
    if len(matched) < 2:
        matched = []
    out = {}
    for r in riders:
        pct = discount_pct if r['id'] in matched else 5
        out[r['id']] = r['solo'] * (100 - pct) // 100
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999},
     {'id': 'r2', 'overlap_s': 3, 'solo': 1999}],
    25, 180],
   {'r0': 1899, 'r1': 1899, 'r2': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],
    25, 180],
   {'r0': 2379, 'r1': 833, 'r2': 833}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}], 30, 180], {'r0': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}, {'id': 'r1', 'overlap_s': 179, 'solo': 1234}], 40, 180],
   {'r0': 2379, 'r1': 1172}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    25, 180],
   {'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 877},
     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}],
    40, 180],
   {'r0': 1899, 'r1': 526, 'r2': 1199}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234}], 30, 180],
   {'r0': 613, 'r1': 863})],
 [('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 1999}], 25, 180],
   {'r0': 1899, 'r1': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 3, 'solo': 1234}, {'id': 'r3', 'overlap_s': 0, 'solo': 877}],
    30, 180],
   {'r0': 1899, 'r1': 2379, 'r2': 1172, 'r3': 833}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 30, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 877}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],
    40, 180],
   {'r0': 526, 'r1': 833, 'r2': 833, 'r3': 740}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 180, 'solo': 1999}],
    25, 180],
   {'r0': 925, 'r1': 657, 'r2': 1499}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 877}], 25, 180],
   {'r0': 833, 'r1': 833}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}], 40, 180], {'r0': 1899})],
 [('regression: lone matched rider', [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}], 30, 180], {'r0': 833}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 2505}, {'id': 'r1', 'overlap_s': 3, 'solo': 2505}], 30, 180],
   {'r0': 2379, 'r1': 2379}),
  ('second regression',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999}], 30, 180],
   {'r0': 1172, 'r1': 1899}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999}], 40, 180],
   {'r0': 1899, 'r1': 1899}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 877}], 25, 180],
   {'r0': 1878, 'r1': 657}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 2505}, {'id': 'r3', 'overlap_s': 180, 'solo': 877}],
    25, 180],
   {'r0': 925, 'r1': 1878, 'r2': 1878, 'r3': 657}),
  ('normal control 4', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}], 30, 180], {'r0': 1172})],
 [('regression: lone matched rider', [[{'id': 'r0', 'overlap_s': 600, 'solo': 1999}], 30, 180], {'r0': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 2505}], 25, 180],
   {'r0': 1172, 'r1': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 25, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 877}], 25, 180],
   {'r0': 657, 'r1': 657}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},
     {'id': 'r2', 'overlap_s': 600, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 1234}],
    25, 180],
   {'r0': 925, 'r1': 1878, 'r2': 1499, 'r3': 1172}),
  ('normal control 3', [[{'id': 'r0', 'overlap_s': 0, 'solo': 877}], 40, 180], {'r0': 833}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 877},
     {'id': 'r2', 'overlap_s': 179, 'solo': 2505}],
    40, 180],
   {'r0': 740, 'r1': 526, 'r2': 2379})],
 [('regression: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999}], 25, 180],
   {'r0': 1172, 'r1': 1899}),
  ('partial repair probe: lone matched rider',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 179, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 179, 'solo': 2505}],
    30, 180],
   {'r0': 833, 'r1': 1172, 'r2': 2379}),
  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 40, 180], {'r0': 1172}),
  ('normal control 1',
   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 1234},
     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],
    25, 180],
   {'r0': 657, 'r1': 925, 'r2': 657}),
  ('normal control 2',
   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234}], 25, 180],
   {'r0': 1878, 'r1': 925}),
  ('normal control 3',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505}], 25, 180],
   {'r0': 1899, 'r1': 2379}),
  ('normal control 4',
   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999}], 25, 180],
   {'r0': 833, 'r1': 1899})]]
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 fixture	Actual	Expected	Outcome
regression: lone matched rider	`{'r0': 1899, 'r1': 1899, 'r2': 1899}`	`{'r0': 1899, 'r1': 1899, 'r2': 1899}`	Passed
partial repair probe: lone matched rider	`{'r0': 2379, 'r1': 833, 'r2': 833}`	`{'r0': 2379, 'r1': 833, 'r2': 833}`	Passed
second regression	`{'r0': 1899}`	`{'r0': 1899}`	Passed
normal control 1	`{'r0': 2379, 'r1': 1172}`	`{'r0': 2379, 'r1': 1172}`	Passed
normal control 2	`{'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}`	`{'r0': 1878, 'r1': 1878, 'r2': 2379, 'r3': 925}`	Passed
normal control 3	`{'r0': 1899, 'r1': 526, 'r2': 1199}`	`{'r0': 1899, 'r1': 526, 'r2': 1199}`	Passed
normal control 4	`{'r0': 613, 'r1': 863}`	`{'r0': 613, 'r1': 863}`	Passed

SHA-256 / 247bc17989f8c276eb351aa8d5e5c78e498ab186d47c043b0af10c43ceedb5e4

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 2026-09-29T14:50:41.325524+00:00.

Case digest / f5411530cd4db93f291ef31afaffb07fdef8f60d5eba2eb6a0c5da1faef2016e