Evaluation of the Development Level of Smart Urban Rail Transit

Xiang, Yinghui; Wang, Jiatian; Yu, Zhao

doi:10.1109/ccdc55256.2022.10033952

Cited by 2 publications

(2 citation statements)

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“…The strategy mainly considers the distance factor, which affects the time delay in data transmission. There are 3 types of distances: the distance between the node and the next hop, as shown in Equation (2); the distance between a node and the other nodes in the cluster, as shown in Equation (3); and the distance between a node and the training node, as shown in Equation (4).…”

Section: Cluster Head Selection Strategymentioning

confidence: 99%

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A Service-Driven Routing Algorithm for Ad Hoc Networks in Urban Rail Transit

Cai,

Liu

et al. 2023

Computers

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Due to increased traffic pressure, traditional urban rail vehicle–ground communication systems are no longer able to meet the increasing communication requirements. In this paper, ad hoc networks are applied to urban rail transit vehicle–ground communication systems to improve link reliability and reduce transmission delay. In the proposed network, a service-driven routing algorithm is proposed, which considers the distance factor for cluster head selection and optimizes the routing transmission delay by service priority and congestion level. An auxiliary node-based routing maintenance mechanism is also proposed to avoid the problem of frequent breakage of communication links due to the high-speed movement of trains. Through the simulation, the proposed algorithm can effectively reduce the packet loss rate, end-to-end delay, and routing overhead of vehicle–ground communication compared with the traditional routing algorithm, which is more conducive to meeting the next generation of urban rail transit vehicle–ground communication requirements.

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Section: Cluster Head Selection Strategymentioning

confidence: 99%

“…According to the statistical data, over 50% of people choose urban rail transit as their primary trip mode. With the deep integration of urban rail transit with various emerging technologies such as mobile edge computing and artificial intelligence, urban rail transportation's intelligence level has increased significantly [2].…”

Section: Introductionmentioning

confidence: 99%