{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:45:29Z","timestamp":1740138329562,"version":"3.37.3"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"07n08","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71661015"],"award-info":[{"award-number":["71661015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:p> Considering the functional attributes of public bicycle outlets, users\u2019 travel destinations and travel distances, this paper proposes a key node optimization scheme for urban public bicycle networks based on the combination of key nodes and wavefront theory. First analyze the net wave surface flow during peak hours to determine key nodes, then schedule or add nodes to achieve normal diversion in the area, and finally introduce betweenness indicators to evaluate the diversion effect. Through an example analysis of the operation data of a city\u2019s public bicycle system, the research results show that the optimization scheme can better meet the dynamic needs of users of the public bicycle system, improve the user\u2019s rental experience, increase user stickiness, and ensure maximum revenue and operating efficiency. It can provide a theoretical basis for the reasonable dispatch of public bicycles at the outlets. <\/jats:p>","DOI":"10.1142\/s0218213020400163","type":"journal-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T04:24:36Z","timestamp":1606710276000},"page":"2040016","source":"Crossref","is-referenced-by-count":3,"title":["A Key Node Optimization Scheme for Public Bicycles Based on Wavefront Theory"],"prefix":"10.1142","volume":"29","author":[{"given":"Yali","family":"Peng","sequence":"first","affiliation":[{"name":"Jiangxi Normal University, Ziyang Road, Nanchang 330022, China"}]},{"given":"Ting","family":"Liang","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Ziyang Road, Nanchang 330022, China"}]},{"given":"Yuxin","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Ziyang Road, Nanchang 330022, China"}]},{"given":"Hong","family":"Yin","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Ziyang Road, Nanchang 330022, China"}]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Ziyang Road, Nanchang 330022, China"}]},{"given":"Jiangang","family":"Deng","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University, Ziyang Road, Nanchang 330022, China"}]}],"member":"219","published-online":{"date-parts":[[2020,11,30]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213020400163","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T04:25:12Z","timestamp":1606710312000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213020400163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,30]]},"references-count":0,"journal-issue":{"issue":"07n08","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["10.1142\/S0218213020400163"],"URL":"https:\/\/doi.org\/10.1142\/s0218213020400163","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2020,11,30]]}}}