Pick-Up Point Recommendation Using Users’ Historical Ride-Hailing Orders

Wireless Algorithms, Systems, and Applications (WASA 2022)

Lingyu Zhang ^1,2    Zhijie He ²    Xiao Wang²    Ying Zhang²    Jian Liang²    Guobin Wu²    Ziqiang Yu³    Penghui Zhang ⁴    Minghao Ji⁴    Pengfei Xu⁴    Yunhai Wang<sup>1   

1</sup>School of Computer Science and Technology, Shandong University, Qingdao, China   
²Didi Chuxing, Beijing, China   
³Yantai University, Yantai, China   
⁴School of Information Science and Technology, Northwest University, Kirkland, USA   

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Abstract

The ride-hailing app must provide users with appropriate pick-up points when they submit their travel demands and their locations are recognized, efficiently reducing users’ operation complexity and optimizing the software performance. Most apps currently try to search for locations near users’ current GPS locations as the Points of Interest (POIs), which is an efficient method of locating, but seriously ignores personal preferences. In this paper, we deeply analyze the historical ride-hailing orders of users on Didi Chuxing platform (http:// www.didiglobal.com). We explore the given dataset, get the general regularity of users’ commuting, and propose a Pick-Up Points Recommendation Model (PPRM) based on the clustering algorithm. We cluster users’ historical orders using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) according to orders’ spatial information. In this way, the candidate outputs closest to the user’s current environment/feature can be found in a specific category. The linear addition of the candidate outputs severs as the final pick-up point provided. Therefore, our model can offer recommendations of the best pick-up points. In addition, experimental results based on real-world datasets indicate that our model can efficiently and accurately provide users with optimal points.

Paper: [PDF]        

Bibtex

  @inproceedings{zhang2022pick,
  title={Pick-Up Point Recommendation Using Users’ Historical Ride-Hailing Orders},
  author={Zhang, Lingyu and He, Zhijie and Wang, Xiao and Zhang, Ying and Liang, Jian and Wu, Guobin and Yu, Ziqiang and Zhang, Penghui and Ji, Minghao and Xu, Pengfei and others},
  booktitle={International Conference on Wireless Algorithms, Systems, and Applications},
  pages={393--405},
  year={2022},
  organization={Springer}
}