{'fraction_non_alphanumeric': 0.012539184952978056, 'fraction_numerical': 0.0, 'mean_word_length': 5.203883495145631, '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}}
Parking Occupancy Prediction and Pattern Analysis

Finding a parking space in San Francisco City Area is really a headache issue. We try to find a reliable way to give parking information by prediction. We reveals the effect of aggregation on prediction for parking occupancy in San Francisco. Different empirical aggregation levels are tested with several prediction models. Moreover it proposes a sufficient condition leading to prediction error decreasing. Due to the aggregation effect, we would like to explore patterns inside parking. Thus daily occupancy profiles are also investigated to understand travelers behavior in the city.