{'fraction_non_alphanumeric': 0.020654044750430294, 'fraction_numerical': 0.0, 'mean_word_length': 5.761627906976744, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Validation of Runway Capacity Models

Many runway capacity estimation models are available today, and developers usually claim that their models have been validated. However, information about the validation process is often limited, and different models are validated at differing levels of complexity. This paper proposes two validation methodologies for use in testing model predictions against reality. It demonstrates the two methodologies on two models [the airfield capacity model (ACM) and the runway simulator (rS)] and two airports (San Francisco International and Los Angeles International in California). The results indicate that when arrivals and departures are considered separately, both ACM and rS tend to overpredict capacities under good visibility conditions and to predict larger ranges of capacity values than are seen empirically. However, when considering total operations (arrivals and departures together), the results show that both models failed to provide good estimates of total throughput at both airports. Overall, arrival and departure capacity estimates from rS typically reflect empirical capacities better than those from ACM.