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A distributed EMDN-GRU model on Spark for passenger waiting time forecasting

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Abstract

It is hard to forecast waiting time from mobile trajectory big data on the traditional centralized mining platform, and especially the taxi driving direction cannot be clearly distinguished by the GPS trajectories of taxicabs in intelligent transportation systems. To this end, we propose a direction identification method (named CA-D) combined with Coordinate Axis and GPS Direction to distinguish taxi driving direction and then establish a distributed model (named EMDN-GRU) on Spark to forecast passenger waiting time based on an Empirical Mode Decomposition (EMD) algorithm with Normalization and a Gated Recurrent Unit (GRU) model. Specifically, in the process of waiting time forecasting, the CA-D method is used to differentiate directions to reduce the data interference caused by different taxi driving directions. Furthermore, the EMD algorithm with normalization is utilized to process large-scale GPS trajectory data. Finally, the GRU model with adjusted parameters is employed to forecast the time-series data obtained in the previous step, and the forecasting results are denormalized and superimposed to produce waiting time for passengers. Compared with LSTM, GRU, EMD-LSTM, EMD-GRU, CNN, and BP, the experimental results from a case study indicate that EMDN-GRU is significantly superior to others. In particular, from three data sets of weekday, weekend, and one week, the MAPE values of EMDN-GRU are reduced by 92.48%, 95.01%, and 90.47% at most.

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Acknowledgements

This work described in this paper was supported in part by the National Natural Science Foundation of China (Grant Nos. 62162012, 62173278, and 62072061), the Science and Technology Support Program of Guizhou (Grant No. QKHZC2021YB531), the Youth Science and Technology Talents Development Project of Colleges and Universities in Guizhou (Grant No. QJHKY2022175), and the Scientific Research Platform Project of Guizhou Minzu University (Grant No. GZMUSYS[2021]04). Yu Bai and Jian Geng contributed equally to this work and should be considered as co-second authors.

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Authors and Affiliations

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

    Dawen Xia, Yu Bai, Jian Geng & Wenyong Zhang

  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. Yu Bai
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Xia, D., Bai, Y., Geng, J. et al. A distributed EMDN-GRU model on Spark for passenger waiting time forecasting. Neural Comput & Applic 34, 19035–19050 (2022). https://doi.org/10.1007/s00521-022-07482-0

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