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FA-68016 / Traffic signal timing plans / Open access

Volume-density added initial and gap reduction: the reduction is quantised to whole reduction periods · case 01

Volume-density added initial and gap reduction returns a wrong result when the reduction is quantised to whole reduction periods.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Integer division happens before multiplying by the gap range, so the allowed gap stays at the initial gap for the whole reduction period.

VERIFIED REPAIR

Restore the linear reduction step rule so that the step reads `gap = g0 - (g0 - gm) * done // x['time_to_reduce']`.

Unsuccessful approach: Interpolating the gap itself and flooring it truncates the gap instead of the reduction amount, reducing one tenth too much.

Case contract

Input {min_green, added_per_act_tenths, max_initial, red_actuations, initial_gap_tenths, min_gap_tenths, time_before_reduction, time_to_reduce, call, t}. Variable initial = min(max_initial, max(min_green, ceil(actuations*added/10))) seconds. Gap reduction is timed from the conflicting call (a call before green start counts from 0): until time_before_reduction elapses the allowed gap is the initial gap; during time_to_reduce it falls linearly with the reduction amount truncated to whole tenths; afterwards it is the minimum gap. Return {initial, gap_tenths}.

Why this case matters

Signal timing arithmetic is exact and integer or rational; a wrong rule silently produces unsafe or inefficient timing plans.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    m = x['min_green']
    initial = min(x['max_initial'], max(m, -(-x['red_actuations'] * x['added_per_act_tenths'] // 10)))
    g0, gm = x['initial_gap_tenths'], x['min_gap_tenths']
    el = x['t'] - max(x['call'], 0)
    if el <= x['time_before_reduction']:
        gap = g0
    elif el >= x['time_before_reduction'] + x['time_to_reduce']:
        gap = gm
    else:
        done = el - x['time_before_reduction']
        gap = g0 - (g0 - gm) * (done // x['time_to_reduce'])
    return {'initial': initial, 'gap_tenths': gap}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 10, 'red_actuations': 15, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 13, 'time_to_reduce': 19, 'call': 12, 't': 11}, {'initial': 10, 'gap_tenths': 50}), ({'min_green': 5, 'added_per_act_tenths': 25, 'max_initial': 11, 'red_actuations': 17, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 9, 'time_to_reduce': 29, 'call': -4, 't': 36}, {'initial': 11, 'gap_tenths': 17}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 16, 'red_actuations': 13, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 2, 'time_to_reduce': 24, 'call': 14, 't': 46}, {'initial': 16, 'gap_tenths': 10}), ({'min_green': 7, 'added_per_act_tenths': 25, 'max_initial': 30, 'red_actuations': 5, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 5, 'time_to_reduce': 10, 'call': 11, 't': 58}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 13, 'red_actuations': 16, 'initial_gap_tenths': 30, 'min_gap_tenths': 10, 'time_before_reduction': 11, 'time_to_reduce': 12, 'call': 14, 't': 43}, {'initial': 13, 'gap_tenths': 10})], [({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 9, 'added_per_act_tenths': 10, 'max_initial': 22, 'red_actuations': 0, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 8, 'time_to_reduce': 22, 'call': 6, 't': 7}, {'initial': 9, 'gap_tenths': 50}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 25, 'red_actuations': 1, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 8, 'time_to_reduce': 5, 'call': -1, 't': 37}, {'initial': 5, 'gap_tenths': 15}), ({'min_green': 6, 'added_per_act_tenths': 15, 'max_initial': 10, 'red_actuations': 4, 'initial_gap_tenths': 40, 'min_gap_tenths': 10, 'time_before_reduction': 0, 'time_to_reduce': 28, 'call': 9, 't': 30}, {'initial': 6, 'gap_tenths': 18}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 28, 'red_actuations': 8, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 1, 'time_to_reduce': 26, 'call': -2, 't': 29}, {'initial': 16, 'gap_tenths': 10}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 6, 'added_per_act_tenths': 15, 'max_initial': 26, 'red_actuations': 6, 'initial_gap_tenths': 30, 'min_gap_tenths': 15, 'time_before_reduction': 15, 'time_to_reduce': 8, 'call': -2, 't': 34}, {'initial': 9, 'gap_tenths': 15}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20})], [({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 26, 'red_actuations': 16, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 4, 'time_to_reduce': 18, 'call': 14, 't': 8}, {'initial': 24, 'gap_tenths': 35}), ({'min_green': 5, 'added_per_act_tenths': 10, 'max_initial': 11, 'red_actuations': 8, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 1, 'time_to_reduce': 10, 'call': -4, 't': 19}, {'initial': 8, 'gap_tenths': 15}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 14, 'red_actuations': 13, 'initial_gap_tenths': 50, 'min_gap_tenths': 10, 'time_before_reduction': 6, 'time_to_reduce': 6, 'call': 4, 't': 0}, {'initial': 14, 'gap_tenths': 50}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 29, 'red_actuations': 17, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 6, 'time_to_reduce': 15, 'call': 12, 't': 36}, {'initial': 29, 'gap_tenths': 10}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 5, 'added_per_act_tenths': 10, 'max_initial': 18, 'red_actuations': 16, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 11, 'time_to_reduce': 25, 'call': 5, 't': 28}, {'initial': 16, 'gap_tenths': 26})], [({'min_green': 8, 'added_per_act_tenths': 15, 'max_initial': 29, 'red_actuations': 15, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 3, 'time_to_reduce': 23, 'call': 0, 't': 2}, {'initial': 23, 'gap_tenths': 40}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 9, 'added_per_act_tenths': 10, 'max_initial': 10, 'red_actuations': 1, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 9, 'time_to_reduce': 30, 'call': 8, 't': 26}, {'initial': 9, 'gap_tenths': 40}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 4, 'added_per_act_tenths': 25, 'max_initial': 17, 'red_actuations': 5, 'initial_gap_tenths': 35, 'min_gap_tenths': 20, 'time_before_reduction': 6, 'time_to_reduce': 6, 'call': -1, 't': 21}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 14, 'red_actuations': 17, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 5, 'call': 9, 't': 47}, {'initial': 14, 'gap_tenths': 15}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 22, 'red_actuations': 2, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 5, 'call': -4, 't': 3}, {'initial': 10, 'gap_tenths': 50})], [({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 4, 'added_per_act_tenths': 10, 'max_initial': 24, 'red_actuations': 15, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 1, 'time_to_reduce': 27, 'call': 13, 't': 35}, {'initial': 15, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 7, 'added_per_act_tenths': 15, 'max_initial': 12, 'red_actuations': 3, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 3, 'time_to_reduce': 6, 'call': 0, 't': 40}, {'initial': 7, 'gap_tenths': 20}), ({'min_green': 10, 'added_per_act_tenths': 20, 'max_initial': 30, 'red_actuations': 18, 'initial_gap_tenths': 35, 'min_gap_tenths': 20, 'time_before_reduction': 0, 'time_to_reduce': 26, 'call': 0, 't': 33}, {'initial': 30, 'gap_tenths': 20}), ({'min_green': 4, 'added_per_act_tenths': 20, 'max_initial': 13, 'red_actuations': 8, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 6, 'time_to_reduce': 23, 'call': 0, 't': 55}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 15, 'max_initial': 28, 'red_actuations': 0, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 15, 'time_to_reduce': 10, 'call': 5, 't': 20}, {'initial': 8, 'gap_tenths': 50})]]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check('timing oracle' + ' %d' % i, 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
timing oracle 0{'gap_tenths': 35, 'initial': 12}{'gap_tenths': 28, 'initial': 12}Failed
timing oracle 1{'gap_tenths': 40, 'initial': 11}{'gap_tenths': 31, 'initial': 11}Failed
timing oracle 2{'gap_tenths': 50, 'initial': 10}{'gap_tenths': 50, 'initial': 10}Passed
timing oracle 3{'gap_tenths': 40, 'initial': 11}{'gap_tenths': 17, 'initial': 11}Failed
timing oracle 4{'gap_tenths': 20, 'initial': 8}{'gap_tenths': 20, 'initial': 8}Passed
timing oracle 5{'gap_tenths': 10, 'initial': 16}{'gap_tenths': 10, 'initial': 16}Passed
timing oracle 6{'gap_tenths': 20, 'initial': 13}{'gap_tenths': 20, 'initial': 13}Passed
timing oracle 7{'gap_tenths': 10, 'initial': 13}{'gap_tenths': 10, 'initial': 13}Passed

SHA-256 / 404063a7b9c0972ae2a5229df60b8c403ca1c4cde6d1006c52f269fb8105593b

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    m = x['min_green']
    initial = min(x['max_initial'], max(m, -(-x['red_actuations'] * x['added_per_act_tenths'] // 10)))
    g0, gm = x['initial_gap_tenths'], x['min_gap_tenths']
    el = x['t'] - max(x['call'], 0)
    if el <= x['time_before_reduction']:
        gap = g0
    elif el >= x['time_before_reduction'] + x['time_to_reduce']:
        gap = gm
    else:
        done = el - x['time_before_reduction']
        gap = (g0 * (x['time_to_reduce'] - done) + gm * done) // x['time_to_reduce']
    return {'initial': initial, 'gap_tenths': gap}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 10, 'red_actuations': 15, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 13, 'time_to_reduce': 19, 'call': 12, 't': 11}, {'initial': 10, 'gap_tenths': 50}), ({'min_green': 5, 'added_per_act_tenths': 25, 'max_initial': 11, 'red_actuations': 17, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 9, 'time_to_reduce': 29, 'call': -4, 't': 36}, {'initial': 11, 'gap_tenths': 17}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 16, 'red_actuations': 13, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 2, 'time_to_reduce': 24, 'call': 14, 't': 46}, {'initial': 16, 'gap_tenths': 10}), ({'min_green': 7, 'added_per_act_tenths': 25, 'max_initial': 30, 'red_actuations': 5, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 5, 'time_to_reduce': 10, 'call': 11, 't': 58}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 13, 'red_actuations': 16, 'initial_gap_tenths': 30, 'min_gap_tenths': 10, 'time_before_reduction': 11, 'time_to_reduce': 12, 'call': 14, 't': 43}, {'initial': 13, 'gap_tenths': 10})], [({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 9, 'added_per_act_tenths': 10, 'max_initial': 22, 'red_actuations': 0, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 8, 'time_to_reduce': 22, 'call': 6, 't': 7}, {'initial': 9, 'gap_tenths': 50}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 25, 'red_actuations': 1, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 8, 'time_to_reduce': 5, 'call': -1, 't': 37}, {'initial': 5, 'gap_tenths': 15}), ({'min_green': 6, 'added_per_act_tenths': 15, 'max_initial': 10, 'red_actuations': 4, 'initial_gap_tenths': 40, 'min_gap_tenths': 10, 'time_before_reduction': 0, 'time_to_reduce': 28, 'call': 9, 't': 30}, {'initial': 6, 'gap_tenths': 18}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 28, 'red_actuations': 8, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 1, 'time_to_reduce': 26, 'call': -2, 't': 29}, {'initial': 16, 'gap_tenths': 10}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 6, 'added_per_act_tenths': 15, 'max_initial': 26, 'red_actuations': 6, 'initial_gap_tenths': 30, 'min_gap_tenths': 15, 'time_before_reduction': 15, 'time_to_reduce': 8, 'call': -2, 't': 34}, {'initial': 9, 'gap_tenths': 15}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20})], [({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 26, 'red_actuations': 16, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 4, 'time_to_reduce': 18, 'call': 14, 't': 8}, {'initial': 24, 'gap_tenths': 35}), ({'min_green': 5, 'added_per_act_tenths': 10, 'max_initial': 11, 'red_actuations': 8, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 1, 'time_to_reduce': 10, 'call': -4, 't': 19}, {'initial': 8, 'gap_tenths': 15}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 14, 'red_actuations': 13, 'initial_gap_tenths': 50, 'min_gap_tenths': 10, 'time_before_reduction': 6, 'time_to_reduce': 6, 'call': 4, 't': 0}, {'initial': 14, 'gap_tenths': 50}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 29, 'red_actuations': 17, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 6, 'time_to_reduce': 15, 'call': 12, 't': 36}, {'initial': 29, 'gap_tenths': 10}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 5, 'added_per_act_tenths': 10, 'max_initial': 18, 'red_actuations': 16, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 11, 'time_to_reduce': 25, 'call': 5, 't': 28}, {'initial': 16, 'gap_tenths': 26})], [({'min_green': 8, 'added_per_act_tenths': 15, 'max_initial': 29, 'red_actuations': 15, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 3, 'time_to_reduce': 23, 'call': 0, 't': 2}, {'initial': 23, 'gap_tenths': 40}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 9, 'added_per_act_tenths': 10, 'max_initial': 10, 'red_actuations': 1, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 9, 'time_to_reduce': 30, 'call': 8, 't': 26}, {'initial': 9, 'gap_tenths': 40}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 4, 'added_per_act_tenths': 25, 'max_initial': 17, 'red_actuations': 5, 'initial_gap_tenths': 35, 'min_gap_tenths': 20, 'time_before_reduction': 6, 'time_to_reduce': 6, 'call': -1, 't': 21}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 14, 'red_actuations': 17, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 5, 'call': 9, 't': 47}, {'initial': 14, 'gap_tenths': 15}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 22, 'red_actuations': 2, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 5, 'call': -4, 't': 3}, {'initial': 10, 'gap_tenths': 50})], [({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 4, 'added_per_act_tenths': 10, 'max_initial': 24, 'red_actuations': 15, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 1, 'time_to_reduce': 27, 'call': 13, 't': 35}, {'initial': 15, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 7, 'added_per_act_tenths': 15, 'max_initial': 12, 'red_actuations': 3, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 3, 'time_to_reduce': 6, 'call': 0, 't': 40}, {'initial': 7, 'gap_tenths': 20}), ({'min_green': 10, 'added_per_act_tenths': 20, 'max_initial': 30, 'red_actuations': 18, 'initial_gap_tenths': 35, 'min_gap_tenths': 20, 'time_before_reduction': 0, 'time_to_reduce': 26, 'call': 0, 't': 33}, {'initial': 30, 'gap_tenths': 20}), ({'min_green': 4, 'added_per_act_tenths': 20, 'max_initial': 13, 'red_actuations': 8, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 6, 'time_to_reduce': 23, 'call': 0, 't': 55}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 15, 'max_initial': 28, 'red_actuations': 0, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 15, 'time_to_reduce': 10, 'call': 5, 't': 20}, {'initial': 8, 'gap_tenths': 50})]]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check('timing oracle' + ' %d' % i, 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
timing oracle 0{'gap_tenths': 28, 'initial': 12}{'gap_tenths': 28, 'initial': 12}Passed
timing oracle 1{'gap_tenths': 30, 'initial': 11}{'gap_tenths': 31, 'initial': 11}Failed
timing oracle 2{'gap_tenths': 50, 'initial': 10}{'gap_tenths': 50, 'initial': 10}Passed
timing oracle 3{'gap_tenths': 16, 'initial': 11}{'gap_tenths': 17, 'initial': 11}Failed
timing oracle 4{'gap_tenths': 20, 'initial': 8}{'gap_tenths': 20, 'initial': 8}Passed
timing oracle 5{'gap_tenths': 10, 'initial': 16}{'gap_tenths': 10, 'initial': 16}Passed
timing oracle 6{'gap_tenths': 20, 'initial': 13}{'gap_tenths': 20, 'initial': 13}Passed
timing oracle 7{'gap_tenths': 10, 'initial': 13}{'gap_tenths': 10, 'initial': 13}Passed

SHA-256 / 3826bd65eca5a57872304f8c1c7f1081aef5ba35f7b2e777db9b1422773315a2

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    m = x['min_green']
    initial = min(x['max_initial'], max(m, -(-x['red_actuations'] * x['added_per_act_tenths'] // 10)))
    g0, gm = x['initial_gap_tenths'], x['min_gap_tenths']
    el = x['t'] - max(x['call'], 0)
    if el <= x['time_before_reduction']:
        gap = g0
    elif el >= x['time_before_reduction'] + x['time_to_reduce']:
        gap = gm
    else:
        done = el - x['time_before_reduction']
        gap = g0 - (g0 - gm) * done // x['time_to_reduce']
    return {'initial': initial, 'gap_tenths': gap}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 10, 'red_actuations': 15, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 13, 'time_to_reduce': 19, 'call': 12, 't': 11}, {'initial': 10, 'gap_tenths': 50}), ({'min_green': 5, 'added_per_act_tenths': 25, 'max_initial': 11, 'red_actuations': 17, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 9, 'time_to_reduce': 29, 'call': -4, 't': 36}, {'initial': 11, 'gap_tenths': 17}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 16, 'red_actuations': 13, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 2, 'time_to_reduce': 24, 'call': 14, 't': 46}, {'initial': 16, 'gap_tenths': 10}), ({'min_green': 7, 'added_per_act_tenths': 25, 'max_initial': 30, 'red_actuations': 5, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 5, 'time_to_reduce': 10, 'call': 11, 't': 58}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 13, 'red_actuations': 16, 'initial_gap_tenths': 30, 'min_gap_tenths': 10, 'time_before_reduction': 11, 'time_to_reduce': 12, 'call': 14, 't': 43}, {'initial': 13, 'gap_tenths': 10})], [({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 9, 'added_per_act_tenths': 10, 'max_initial': 22, 'red_actuations': 0, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 8, 'time_to_reduce': 22, 'call': 6, 't': 7}, {'initial': 9, 'gap_tenths': 50}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 25, 'red_actuations': 1, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 8, 'time_to_reduce': 5, 'call': -1, 't': 37}, {'initial': 5, 'gap_tenths': 15}), ({'min_green': 6, 'added_per_act_tenths': 15, 'max_initial': 10, 'red_actuations': 4, 'initial_gap_tenths': 40, 'min_gap_tenths': 10, 'time_before_reduction': 0, 'time_to_reduce': 28, 'call': 9, 't': 30}, {'initial': 6, 'gap_tenths': 18}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 28, 'red_actuations': 8, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 1, 'time_to_reduce': 26, 'call': -2, 't': 29}, {'initial': 16, 'gap_tenths': 10}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 6, 'added_per_act_tenths': 15, 'max_initial': 26, 'red_actuations': 6, 'initial_gap_tenths': 30, 'min_gap_tenths': 15, 'time_before_reduction': 15, 'time_to_reduce': 8, 'call': -2, 't': 34}, {'initial': 9, 'gap_tenths': 15}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20})], [({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 26, 'red_actuations': 16, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 4, 'time_to_reduce': 18, 'call': 14, 't': 8}, {'initial': 24, 'gap_tenths': 35}), ({'min_green': 5, 'added_per_act_tenths': 10, 'max_initial': 11, 'red_actuations': 8, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 1, 'time_to_reduce': 10, 'call': -4, 't': 19}, {'initial': 8, 'gap_tenths': 15}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 14, 'red_actuations': 13, 'initial_gap_tenths': 50, 'min_gap_tenths': 10, 'time_before_reduction': 6, 'time_to_reduce': 6, 'call': 4, 't': 0}, {'initial': 14, 'gap_tenths': 50}), ({'min_green': 6, 'added_per_act_tenths': 20, 'max_initial': 29, 'red_actuations': 17, 'initial_gap_tenths': 35, 'min_gap_tenths': 10, 'time_before_reduction': 6, 'time_to_reduce': 15, 'call': 12, 't': 36}, {'initial': 29, 'gap_tenths': 10}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 5, 'added_per_act_tenths': 10, 'max_initial': 18, 'red_actuations': 16, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 11, 'time_to_reduce': 25, 'call': 5, 't': 28}, {'initial': 16, 'gap_tenths': 26})], [({'min_green': 8, 'added_per_act_tenths': 15, 'max_initial': 29, 'red_actuations': 15, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 3, 'time_to_reduce': 23, 'call': 0, 't': 2}, {'initial': 23, 'gap_tenths': 40}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 9, 'added_per_act_tenths': 10, 'max_initial': 10, 'red_actuations': 1, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 9, 'time_to_reduce': 30, 'call': 8, 't': 26}, {'initial': 9, 'gap_tenths': 40}), ({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 4, 'added_per_act_tenths': 25, 'max_initial': 17, 'red_actuations': 5, 'initial_gap_tenths': 35, 'min_gap_tenths': 20, 'time_before_reduction': 6, 'time_to_reduce': 6, 'call': -1, 't': 21}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 14, 'red_actuations': 17, 'initial_gap_tenths': 40, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 5, 'call': 9, 't': 47}, {'initial': 14, 'gap_tenths': 15}), ({'min_green': 10, 'added_per_act_tenths': 15, 'max_initial': 22, 'red_actuations': 2, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 5, 'call': -4, 't': 3}, {'initial': 10, 'gap_tenths': 50})], [({'min_green': 5, 'added_per_act_tenths': 15, 'max_initial': 20, 'red_actuations': 7, 'initial_gap_tenths': 40, 'min_gap_tenths': 20, 'time_before_reduction': 10, 'time_to_reduce': 15, 'call': 3, 't': 20}, {'initial': 11, 'gap_tenths': 31}), ({'min_green': 4, 'added_per_act_tenths': 10, 'max_initial': 24, 'red_actuations': 15, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 1, 'time_to_reduce': 27, 'call': 13, 't': 35}, {'initial': 15, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 20, 'max_initial': 12, 'red_actuations': 9, 'initial_gap_tenths': 35, 'min_gap_tenths': 15, 'time_before_reduction': 5, 'time_to_reduce': 20, 'call': -4, 't': 12}, {'initial': 12, 'gap_tenths': 28}), ({'min_green': 7, 'added_per_act_tenths': 15, 'max_initial': 12, 'red_actuations': 3, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 3, 'time_to_reduce': 6, 'call': 0, 't': 40}, {'initial': 7, 'gap_tenths': 20}), ({'min_green': 10, 'added_per_act_tenths': 20, 'max_initial': 30, 'red_actuations': 18, 'initial_gap_tenths': 35, 'min_gap_tenths': 20, 'time_before_reduction': 0, 'time_to_reduce': 26, 'call': 0, 't': 33}, {'initial': 30, 'gap_tenths': 20}), ({'min_green': 4, 'added_per_act_tenths': 20, 'max_initial': 13, 'red_actuations': 8, 'initial_gap_tenths': 30, 'min_gap_tenths': 20, 'time_before_reduction': 6, 'time_to_reduce': 23, 'call': 0, 't': 55}, {'initial': 13, 'gap_tenths': 20}), ({'min_green': 6, 'added_per_act_tenths': 25, 'max_initial': 25, 'red_actuations': 3, 'initial_gap_tenths': 50, 'min_gap_tenths': 20, 'time_before_reduction': 8, 'time_to_reduce': 12, 'call': 0, 't': 40}, {'initial': 8, 'gap_tenths': 20}), ({'min_green': 8, 'added_per_act_tenths': 15, 'max_initial': 28, 'red_actuations': 0, 'initial_gap_tenths': 50, 'min_gap_tenths': 15, 'time_before_reduction': 15, 'time_to_reduce': 10, 'call': 5, 't': 20}, {'initial': 8, 'gap_tenths': 50})]]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check('timing oracle' + ' %d' % i, 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
timing oracle 0{'gap_tenths': 28, 'initial': 12}{'gap_tenths': 28, 'initial': 12}Passed
timing oracle 1{'gap_tenths': 31, 'initial': 11}{'gap_tenths': 31, 'initial': 11}Passed
timing oracle 2{'gap_tenths': 50, 'initial': 10}{'gap_tenths': 50, 'initial': 10}Passed
timing oracle 3{'gap_tenths': 17, 'initial': 11}{'gap_tenths': 17, 'initial': 11}Passed
timing oracle 4{'gap_tenths': 20, 'initial': 8}{'gap_tenths': 20, 'initial': 8}Passed
timing oracle 5{'gap_tenths': 10, 'initial': 16}{'gap_tenths': 10, 'initial': 16}Passed
timing oracle 6{'gap_tenths': 20, 'initial': 13}{'gap_tenths': 20, 'initial': 13}Passed
timing oracle 7{'gap_tenths': 10, 'initial': 13}{'gap_tenths': 10, 'initial': 13}Passed

SHA-256 / af8e8834b56196d5f7fa23867373725f243ca692c450c121fc008eee7016c15e

Verification & scope

A deterministic, bounded toy model with a stipulated contract; it makes no claim of conformance to any agency manual or standard. 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 2026-09-29T14:47:58.301228+00:00.

Case digest / fcff270a91ceef400325268b64695d67b7b22b3730e80ca1f06f826c989a7483