Top-<i>k</i> hotspots recommendation algorithm based on real-time traffic

WU Tao; MAO Jia-li; XIE Qing-cheng; YANG Yan-qiu; WANG Jin

doi:10.3969/j.issn.1000-5641.2017.05.017

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Sep. 2017

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WU Tao, MAO Jia-li, XIE Qing-cheng, YANG Yan-qiu, WANG Jin. Top-k hotspots recommendation algorithm based on real-time traffic[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 186-200. doi: 10.3969/j.issn.1000-5641.2017.05.017

Citation:

WU Tao, MAO Jia-li, XIE Qing-cheng, YANG Yan-qiu, WANG Jin. Top-k hotspots recommendation algorithm based on real-time traffic[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 186-200. doi: 10.3969/j.issn.1000-5641.2017.05.017

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Top-k hotspots recommendation algorithm based on real-time traffic

doi: 10.3969/j.issn.1000-5641.2017.05.017

1.
College of Computer, China West Normal University, Nanchong Sichuan 637000, China
2.
Department of Electronic Technology, Officers College of PAP, Chengdu 610000, China

Received Date: 2017-06-19
Publish Date: 2017-09-25

Abstract

Abstract

To cut down the no-load rate of taxis and relieve the traffic pressure, an effective hotspot recommendation method of picking up passenger is necessitated. Aiming at the problem of lower recommendation precision of traditional recommendation technique due to ignoring the actual road situation, we propose a two-phase real-time hotspot recommendation approach for picking up passenger. In the phase of offline mining, timebased hotspots are extracted by mining the history taxi trajectory dataset. In the phase of online recommendation, according to the position and time of taxi requests, a potential no-passenger time cost evaluation function that based on real-time road situation is presented to evaluate and rank hotspots, and obtain top-k hotspots of picking up passenger.Experimental results on taxi trajectory data show that, our proposal ensure smaller potential no-load time overhead due to considering real-time traffic conditions, and hence has good effectiveness and robustness as compared to the traditional recommendation approached.
- potential no-passenger time cost function,
- real-time traffic,
- hotspot,
- recommendation

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References(14)

References

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