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Aggregate location recommendation in dynamic transportation networks

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Abstract

Travel planning and location recommendation are increasingly important in recent years. In this light, we propose and study a novel aggregate location recommendation query (ALRQ) of discovering aggregate locations for multiple travelers and planning the corresponding travel routes in dynamic transportation networks. Assuming the scenario that multiple travelers target the same destination, given a set of travelers’ locations Q, a set of potential aggregate location O, and a departure time t, the ALRQ finds an aggregate location o ∈ O that has the minimum global travel time \({\sum }_{q \in Q} T(q,o,t)\), where T(q,o,t) is the travel time between o and q with departure time t. The ALRQ problem is challenging due to three reasons: (1) how to model the dynamic transportation networks practically, and (2) how to compute ALRQ efficiently. We take two types of dynamic transportation networks into account, and we define a pair of upper and lower bounds to prune the search space effectively. Moreover, a heuristic scheduling strategy is adopted to schedule multiple query sources. Finally, we conducted extensive experiments on real and synthetic spatial data to verify the performance of the developed algorithms.

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Acknowledgements

This paper is partly supported by Natural Science Foundation of P.R.China (No. 61373147, and No. 61672442), Fujian Province Science and Technology Plan Project (No. 2016Y0079), the Open Research Fund Program of Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University, and the Open Research Fund Program of Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

    Jianmin Li, Yan Wang, Ying Zhong & Shunzhi Zhu

  2. Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University, 3688 Nanhai Ave, Nanshan Qu, Shenzhen Shi, 518060, Guangdong Sheng, China

    Danhuai Guo

  3. Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University, 3688 Nanhai Ave, Nanshan Qu, Shenzhen Shi, 518060, Guangdong Sheng, China

    Danhuai Guo

  4. CNIC, Chinese Academy of Sciences, Beijing, China

    Danhuai Guo

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  1. Jianmin Li
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Correspondence to Shunzhi Zhu.

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This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

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Li, J., Wang, Y., Zhong, Y. et al. Aggregate location recommendation in dynamic transportation networks. World Wide Web 21, 1637–1653 (2018). https://doi.org/10.1007/s11280-017-0496-3

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