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A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction

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Abstract

Predicting passenger hotspots helps drivers quickly pick up travelers, reduces cruise expenses, and maximizes revenue per unit time in intelligent transportation systems. To improve the accuracy and robustness of passenger hotspot prediction (PHP), this paper proposes a parallel Grid-Search-based Support Vector Machine (GS-SVM) optimization algorithm on Spark, which provides an efficient methodology to search for passengers in a complex urban traffic network quickly. Specifically, to effectively locate passenger hotspots, an urban road network is gridded on the Spark parallel distributed computing platform. Moreover, to enhance the accuracy of PHP, the grid search (GS) approach is employed to optimize the radial basis function (RBF) of the support vector machine (SVM), and the cross-validation method is utilized to find out the global optimal parameter combination. Finally, the SVM optimization algorithm is implemented on Spark to improve the robustness of PHP. In particular, the proposed GS-SVM algorithm is applied to successfully predict passenger hotspots. By analyzing seven groups of data sets and comparing with serval state-of-the-art algorithms including autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), and convolutional neural network (CNN), the results of an empirical study indicate that the MAPE value of our GS-SVM algorithm is lower than that of comparative algorithms at least 78.4%.

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Acknowledgments

This work described in this paper was supported in part by the National Natural Science Foundation of China (Grant nos. 61762020, 62162012, 61773321, 62072061, and 62173278), the Science and Technology Talents Fund for Excellent Young of Guizhou (Grant no. QKHPTRC20195669), the Science and Technology Support Program of Guizhou (Grant no. QKHZC2021YB531), and the Scientific Research Platform Project of Guizhou Minzu University (Grant no. GZ-MUSYS[2021]04).

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Authors and Affiliations

  1. College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, China

    Dawen Xia, Yongling Zheng, Yu Bai & Xiaobo Yan

  2. Department of Automotive Engineering, Guizhou Traffic Technician and Transportation College, Guiyang, 550008, China

    Yang Hu

  3. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Yantao Li

  4. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Huaqing Li

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  1. Dawen Xia
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  2. Yongling Zheng
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Correspondence to Dawen Xia or Huaqing Li.

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Xia, D., Zheng, Y., Bai, Y. et al. A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction. Multimed Tools Appl 81, 27523–27549 (2022). https://doi.org/10.1007/s11042-022-12077-x

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