{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T09:14:29Z","timestamp":1773306869154,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Research Project of Hebei Education Department","award":["QN2023109"],"award-info":[{"award-number":["QN2023109"]}]},{"name":"Fundamental Research Funds for the Hebei University of Water Resources and Electric Engineering","award":["SYKY2306"],"award-info":[{"award-number":["SYKY2306"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Quantitative evaluation of public transit networks (PTNs) with complex-network models informs route optimization and operational adjustments. Prior studies emphasize large cities and pay limited attention to small-sized urban systems. This study examines the bus network of Cangzhou City, Hebei Province, China, to broaden the empirical scope and characterize PTNs in smaller cities. The dataset for this study comprises route and stop records, passenger boarding logs, and bus GPS traces. We develop a general workflow for bus data cleaning and completion. To characterize the dynamic bus network and compare it with the static network, we construct a static network and Directed Weighted Dynamic Network I (DWDN I) using the L-space method, and we construct Directed Weighted Dynamic Network II (DWDN II) using the P-space method. We calculated network metrics including degree, weighted degree, clustering coefficient, path length, network diameter, network efficiency, and small-world coefficient. The principal results show that: (1) at the macroscopic level, the dynamic PTN tracks passenger demand, as the average degree, weighted average degree, and clustering coefficient fluctuate in concert with passenger flows; (2) key stations concentrate in the urban core, and stations with high weighted degree display pronounced spatial autocorrelation; (3) the exponential form of the weighted-degree distribution indicates that the examined bus network is not scale-free, while the dynamic network\u2019s small-world coefficient exceeds that of the static network across time periods, reflecting stronger small-world characteristics. This study integrates network and spatial attributes of the PTN to offer an exploratory case for investigating public transit networks in third-tier cities. The findings can inform comparable studies and offer practical guidance for bus operators.<\/jats:p>","DOI":"10.3390\/fi18030144","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T14:43:18Z","timestamp":1773240198000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analyzing Characteristics of Public Transport Complex Networks Based on Multi-Source Big Data Fusion: A Case Study of Cangzhou, China"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0531-9553","authenticated-orcid":false,"given":"Linfang","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Transportation Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou 061001, China"},{"name":"Hebei Higher Institute of Transportation Infrastructure Research and Development Center for Digital and Intelligent Technology Application, Cangzhou 061001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanics and Aeronautics, Inner Mongolia University of Technology, Hohhot 010051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongpu","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Transportation Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou 061001, China"},{"name":"Hebei Higher Institute of Transportation Infrastructure Research and Development Center for Digital and Intelligent Technology Application, Cangzhou 061001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Lan","sequence":"additional","affiliation":[{"name":"Department of Transportation Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou 061001, China"},{"name":"Hebei Higher Institute of Transportation Infrastructure Research and Development Center for Digital and Intelligent Technology Application, Cangzhou 061001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, B., and Liu, J. 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