{'fraction_non_alphanumeric': 0.01629863301787592, 'fraction_numerical': 0.0, 'mean_word_length': 5.67719298245614, '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}}
Algorithm for Mining Network-Constrained Movement Patterns between Zones from Spatiotemporal OD Flows

In the era of big data, rich information about the movement behaviors of residents can be easily obtained. The movement patterns of residents can be mined from these data, which is essential for researching the correlation among zones in cities, thereby shedding light upon the understanding of the travel needs of residents. Many clustering algorithms are proposed to mine meaningful movement patterns between zones from the massive amount of origin–destination flows. However, most existing flow clustering algorithms focus only on the spatial information of the flow, but ignore the time attributes, precluding the identification of when the movement pattern occurs and how long it lasts. Nevertheless, for most existing algorithms that separately consider time attributes and cluster time and space, large amounts of redundant calculations result in high time complexity. Furthermore, only a few studies have considered flow clustering; most of people-related flows in the city are restricted by the road network, thereby causing the analyzed movement pattern between zones to not be consistent with reality. To address these problems, the present paper proposes a novel spatiotemporal similarity measure algorithm for flows. The algorithm uses flows as basic units, considers the spatiotemporal attributes, and introduces the road network constraints to make the results further realistic, in a bid to mine network-constrained movement patterns featured by spatiotemporal characteristics. To verify the feasibility and effectiveness of the proposed algorithm, we conduct experiments using artificial and real-world datasets of commuting data in Shanghai. Experiment results show that the algorithm can quickly and effectively mine movement patterns between zones with spatiotemporal attributes.