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On the opportunistic topology of taxi networks in urban mobility environment

On the opportunistic topology of taxi networks in urban mobility environment
On the opportunistic topology of taxi networks in urban mobility environment
Understanding and characterizing the connectivity of vehicular networks has become increasingly important because of their wide applications and fast development. To address the dynamical links in vehicular networks, time-varying graph (TVG) is one of the most important models. Nowadays, due to the fact that lots of vehicular applications can tolerate a small amount of latency in communication, opportunistic reachability graph (ORG) characterizes the connectivity better by introducing delay tolerance to the model. However, people still do not have a high-level summarization, i.e. the topology, of the vehicular network on how nodes are clustered and isolated. In this paper, based on ORG model, we analyze the opportunistic topology of taxi networks in urban mobility environment by mainly focusing on the number, location and evolution of connected components and the size of the largest components to reveal the unique properties of the taxi networks instead of just links and hops. Our analysis is based on the real taxi traces of big cities and reflects the real urban mobility environment. We find that the opportunistic topology of the networks with delay tolerance is substantially different from the instantaneous topology without considering the delay. Moreover, we unveil the fundamental relationships and trade-offs between the dynamical topology and the key network parameters related to mobility, e.g., delay tolerance, transmission distance, etc. To the best of our knowledge, our study is the first work to reveal the characteristics of opportunistic topology models in the large-scale urban mobility environment with real traces.
2332-7790
171-188
Xu, Ran
9f76b69c-a388-4c9a-8a0b-207d40227661
Li, Yong
ac705db5-b891-4d14-ac43-a87acd05cdd7
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Xu, Ran
9f76b69c-a388-4c9a-8a0b-207d40227661
Li, Yong
ac705db5-b891-4d14-ac43-a87acd05cdd7
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Xu, Ran, Li, Yong and Chen, Sheng (2020) On the opportunistic topology of taxi networks in urban mobility environment. IEEE Transactions on Big Data, 8 (1), 171-188. (doi:10.1109/TBDATA.2018.2878577).

Record type: Article

Abstract

Understanding and characterizing the connectivity of vehicular networks has become increasingly important because of their wide applications and fast development. To address the dynamical links in vehicular networks, time-varying graph (TVG) is one of the most important models. Nowadays, due to the fact that lots of vehicular applications can tolerate a small amount of latency in communication, opportunistic reachability graph (ORG) characterizes the connectivity better by introducing delay tolerance to the model. However, people still do not have a high-level summarization, i.e. the topology, of the vehicular network on how nodes are clustered and isolated. In this paper, based on ORG model, we analyze the opportunistic topology of taxi networks in urban mobility environment by mainly focusing on the number, location and evolution of connected components and the size of the largest components to reveal the unique properties of the taxi networks instead of just links and hops. Our analysis is based on the real taxi traces of big cities and reflects the real urban mobility environment. We find that the opportunistic topology of the networks with delay tolerance is substantially different from the instantaneous topology without considering the delay. Moreover, we unveil the fundamental relationships and trade-offs between the dynamical topology and the key network parameters related to mobility, e.g., delay tolerance, transmission distance, etc. To the best of our knowledge, our study is the first work to reveal the characteristics of opportunistic topology models in the large-scale urban mobility environment with real traces.

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Accepted/In Press date: 9 November 2018
Published date: 28 February 2020

Identifiers

Local EPrints ID: 436725
URI: http://eprints.soton.ac.uk/id/eprint/436725
ISSN: 2332-7790
PURE UUID: 219bbbb5-9d05-4174-b23f-3feb0aef7916

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Date deposited: 03 Jan 2020 11:03
Last modified: 06 Oct 2020 19:22

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