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首页> 外文期刊>Research in Transportation Business and Management >Introducing drivers' collaboration network: A two-layers social network perspective in road transportation system analysis
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Introducing drivers' collaboration network: A two-layers social network perspective in road transportation system analysis

机译:介绍司机协作网络:道路运输系统分析中的两层社会网络视角

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摘要

Drivers can make significant impacts on transportation systems. They can leave important information due to their social behaviors. But, the role of drivers has been overlooked yet. In this paper, for the first time, drivers' collaboration network is introduced. The network is considered in a heterogenous form, because of existence multiple relationships between drivers in the real-world situation. Since drivers do not belong to only one community, the overlapping of communities is considered and a new overlapping community detection algorithm is developed to discover the hidden structure of the network. Also, we present a new overlapping score to improve the community detection algorithm, using the adjacencies among non-memeber neighbor nodes and communities. Solving the algorithm will lead to discovering dense communities of drivers that have meaningful relationships with each other. This will result in a better understanding of the transportation network and also improving the overall performance of the system. A comparison of the developed algorithm with the others demonstrates the effectiveness of the algorithm. To evaluate the applicability of the algorithm, a real drivers' collaboration network is presented and the developed algorithm is applied to derive insights.
机译:司机可以对运输系统产生重大影响。由于他们的社会行为,他们可以留下重要信息。但是,司机的作用尚未被忽视。在本文中,介绍了驱动程序的协作网络。该网络以异形形式被认为是因为现实世界局势中的驱动程序之间存在多种关系。由于驱动程序不属于一个社区,因此考虑了社区的重叠,并且开发了一种新的重叠群落检测算法来发现网络的隐藏结构。此外,我们介绍了一种新的重叠分数来改善社区检测算法,使用非Memeber邻居节点和社区之间的邻接。解决该算法将导致发现彼此有意义的关系的驱动程序的密集社区。这将导致更好地了解运输网络,并提高了系统的整体性能。与其他算法的比较展示了算法的有效性。为了评估算法的适用性,提出了一个真实的驱动程序的协作网络,并应用了发达的算法来获得洞察力。

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