E-viri
Recenzirano
Odprti dostop
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Gong, Shiwen; Liu, Dandan; Bhuiyan, Md Zakirul Alam; Niu, Xiaoguang
Connection science, 12/2022, Letnik: 34, Številka: 1Journal Article
In this paper, we deal with the problem of similarity search about crowdedness for participatory-sensing buses for urban transportation. Similarity search is usually applied for measuring similarities in heterogeneous information networks. However, many models implement similarity search in a global setting, without taking object attributes into consideration. OCP, a novel OLAP-based crowdedness perception, is an attribute-enriched and meta-path-based model with machine learning to capture similarity based on the object connectivity, visibility and features. A set of common crowdedness attribute dimensions are defined across different types of objects, which can be obtained from the participatory passenger's sensor data through deep-neural-network-based posture recognition. Accordingly, an object can be described as a series of node vectors from different dimensions. In such framework, OLAP is applied in analysing multiple resolutions and improving efficiency of similarity search. In addition, our data sources are based on participatory-sensing instead of using vehicle GPS systems. As more data be collected through participatory-sensing, more accurate crowdedness for a bus can be estimated. The experiment results further demonstrate the efficiency of our analytical approaches.
Vnos na polico
Trajna povezava
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Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Vir: Osebne bibliografije
in: SICRIS
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