Full Text:   <556>

Summary:  <223>

Suppl. Mater.: 

CLC number: TP39

On-line Access: 2023-06-21

Received: 2022-12-05

Revision Accepted: 2023-09-21

Crosschecked: 2023-04-11

Cited: 0

Clicked: 1373

Citations:  Bibtex RefMan EndNote GB/T7714


Huaqing Li


Dawen XIA


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.9 P.1316-1331


A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

Author(s):  Dawen XIA, Jian GENG, Ruixi HUANG, Bingqi SHEN, Yang HU, Yantao LI, Huaqing LI

Affiliation(s):  College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China; more

Corresponding email(s):   dwxia@gzmu.edu.cn, huaqingli@swu.edu.cn

Key Words:  Passenger hotspot prediction, Ensemble empirical mode decomposition (EEMD), Spatial attention mechanism, Bi-directional gated recurrent unit (BiGRU), GPS trajectory, Spark

Dawen XIA, Jian GENG, Ruixi HUANG, Bingqi SHEN, Yang HU, Yantao LI, Huaqing LI. A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1316-1331.

@article{title="A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction",
author="Dawen XIA, Jian GENG, Ruixi HUANG, Bingqi SHEN, Yang HU, Yantao LI, Huaqing LI",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction
%A Dawen XIA
%A Jian GENG
%A Ruixi HUANG
%A Bingqi SHEN
%A Yang HU
%A Yantao LI
%A Huaqing LI
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 9
%P 1316-1331
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200621

T1 - A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction
A1 - Dawen XIA
A1 - Jian GENG
A1 - Ruixi HUANG
A1 - Bingqi SHEN
A1 - Yang HU
A1 - Yantao LI
A1 - Huaqing LI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 9
SP - 1316
EP - 1331
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200621

To address the imbalance problem between supply and demand for taxis and passengers, this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit (EEMDN-SABiGRU) model on spark for accurate passenger hotspot prediction. It focuses on reducing blind cruising costs, improving carrying efficiency, and maximizing incomes. Specifically, the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences, while dealing with the eigenmodal EMD. Next, a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid, taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the grid. Furthermore, the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information, to improve the accuracy of feature extraction. Finally, the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the spark parallel computing framework. The experimental results demonstrate that based on the four datasets in the 00-grid, compared with LSTM, EMD-LSTM, EEMD-LSTM, GRU, EMD-GRU, EEMD-GRU, EMDN-GRU, CNN, and BP, the mean absolute percentage error, mean absolute error, root mean square error, and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%, 44.91%, 55.04%, and 39.33%, respectively.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Ali A, Zhu YM, Zakarya M, 2021. A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multim Tool Appl, 80(20):31401-31433.

[2]Batty M, Axhausen KW, Giannotti F, et al., 2012. Smart cities of the future. Eur Phys J Spec Top, 214(1):481-518.

[3]Bi SB, Xu RZ, Liu AL, et al., 2021. Mining taxi pick-up hotspots based on grid information entropy clustering algorithm. J Adv Transp, 2021:5814879.

[4]Cao Y, Hou XL, Chen N, 2022. Short-term forecast of OD passenger flow based on ensemble empirical mode decomposition. Sustainability, 14(14):8562.

[5]Cheng X, Mao JD, Li J, et al., 2021. An EEMD-SVD-LWT algorithm for denoising a lidar signal. Measurement, 168:108405.

[6]Dong YH, Qian SY, Zhang K, et al., 2017. A novel passenger hotspots searching algorithm for taxis in urban area. Proc 18th IEEE/ACIS Int Conf on Software Engineering, Artificial Intelligence, Networking and Parallel/ Distributed Computing, p.175-180.

[7]Engelbrecht J, Booysen MJ, van Rooyen GJ, et al., 2015. Survey of smartphone-based sensing in vehicles for intelligent transportation system applications. IET Intell Transp Syst, 9(10):924-935.

[8]Gao HH, Liu C, Li YHZ, et al., 2020. V2VR: reliable hybrid-network-oriented V2V data transmission and routing considering RSUs and connectivity probability. IEEE Trans Intell Transp Syst, 22(6):3533-3546.

[9]Gong L, Liu X, Wu L, et al., 2016. Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartogr Geogr Inform Sci, 43(2):103-114.

[10]Huang ZH, Tang JY, Shan GX, et al., 2019. An efficient passenger-hunting recommendation framework with multitask deep learning. IEEE Int Things J, 6(5):7713-7721.

[11]Jamil MS, Akbar S, 2017. Taxi passenger hotspot prediction using automatic ARIMA model. Proc 3rd Int Conf on Science in Information Technology, p.23-28.

[12]Jiang XS, Zhang L, Chen XQ, 2014. Short-term forecasting of high-speed rail demand: a hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transp Res Part C Emerg Technol, 44:110-127.

[13]Kim T, Sharda S, Zhou XS, et al., 2020. A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): city-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service. Transp Res Part C Emerg Technol, 120:102786.

[14]Li ML, Yan M, He HW, et al., 2021. Data-driven predictive energy management and emission optimization for hybrid electric buses considering speed and passengers prediction. J Clean Prod, 304:127139.

[15]Li XF, Zhang Y, Du MY, et al., 2020. The forecasting of passenger demand under hybrid ridesharing service modes: a combined model based on WT-FCBF-LSTM. Sustain Cities Soc, 62:102419.

[16]Li XL, Pan G, Wu ZH, et al., 2012. Prediction of urban human mobility using large-scale taxi traces and its applications. Front Comput Sci, 6(1):111-121.

[17]Liu J, Wu NQ, Qiao Y, et al., 2020. Short-term traffic flow forecasting using ensemble approach based on deep belief networks. IEEE Trans Intell Transp Syst, 23(1):404-417.

[18]Liu XP, Zhang YQ, Zhang QC, 2022. Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption. J Hydroinf, 24(3):535-558.

[19]Luo HM, Cai JM, Zhang KP, et al., 2021. A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences. J Traffic Transp Eng Engl Ed, 8(1):83-94.

[20]Nie ZH, Shen F, Xu DJ, et al., 2020. An EMD-SVR model for short-term prediction of ship motion using mirror symmetry and SVR algorithms to eliminate EMD boundary effect. Ocean Eng, 217:107927.

[21]Niu XX, Ma JW, Wang YK, et al., 2021. A novel decomposition-ensemble learning model based on ensemble empirical mode decomposition and recurrent neural network for landslide displacement prediction. Appl Sci, 11(10):4684.

[22]Ou JJ, Sun JH, Zhu YC, et al., 2020. STP-TrellisNets: spatial-temporal parallel trellisnets for metro station passenger flow prediction., p.1185-1194.

[23]Qin QD, He HD, Li L, et al., 2020. A novel decomposition-ensemble based carbon price forecasting model integrated with local polynomial prediction. Comput Econ, 55(4):1249-1273.

[24]Qu BT, Yang WX, Cui G, et al., 2019. Profitable taxi travel route recommendation based on big taxi trajectory data. IEEE Trans Intell Transp Syst, 21(2):653-668.

[25]Rezaei H, Faaljou H, Mansourfar G, 2021. Stock price prediction using deep learning and frequency decomposition. Exp Syst Appl, 169:114332.

[26]Saadallah A, Moreira-Matias L, Sousa R, et al., 2020. BRIGHT—drift-aware demand predictions for taxi networks. IEEE Trans Knowl Data Eng, 32(2):234-245.

[27]Seng DW, Lv FS, Liang ZY, et al., 2021. Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit. Front Inform Technol Electron Eng, 22(9):1179-1193.

[28]Wang RK, Huang WJ, Hu BT, et al., 2022. Harmonic detection for active power filter based on two-step improved EEMD. IEEE Trans Instrum Meas, 71:9001510.

[29]Xia DW, Jiang SY, Yang N, et al., 2021a. Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data. Phys A Stat Mech Appl, 578:126056.

[30]Xia DW, Zhang MT, Yan XB, et al., 2021b. A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction. Neur Comput Appl, 33(7):2393-2410.

[31]Xia DW, Bai Y, Geng J, et al., 2022a. A distributed EMDN-GRU model on Spark for passenger waiting time forecasting. Neur Comput Appl, 34(21):19035-19050.

[32]Xia DW, Zheng YL, Bai Y, et al., 2022b. A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction. Multim Tool Appl, 81(19):27523-27549.

[33]Xu DW, Wang YD, Jia LM, et al., 2017. Real-time road traffic state prediction based on ARIMA and Kalman filter. Front Inform Technol Electron Eng, 18(2):287-302.

[34]Yang X, Xue QC, Yang XX, et al., 2021. A novel prediction model for the inbound passenger flow of urban rail transit. Inform Sci, 566:347-363.

[35]Yao XW, Wang FG, Zhang Y, 2016. A prediction model of security situation based on EMD-PSO-SVM. Proc Int Conf on Electrical and Information Technologies for Rail Transportation, p.355-363.

[36]Yu FH, Hao HBW, Li QL, 2021. An ensemble 3D convolutional neural network for spatiotemporal soil temperature forecasting. Sustainability, 13(16):9174.

[37]Zhang WY, Xia DW, Chang GY, et al., 2022. APFD: an effective approach to taxi route recommendation with mobile trajectory big data. Front Inform Technol Electron Eng, 23(10):1494-1510.

[38]Zhang XK, Zhang QW, Zhang G, et al., 2018. A novel hybrid data-driven model for daily land surface temperature forecasting using long short-term memory neural network based on ensemble empirical mode decomposition. Int J Environ Res Publ Health, 15(5):1032.

[39]Zheng LJ, Xia D, Zhao X, et al., 2018. Spatial-temporal travel pattern mining using massive taxi trajectory data. Phys A Stat Mech Appl, 501:24-41.

[40]Zheng Y, 2017. Urban computing: enabling urban intelligence with big data. Front Comput Sci, 11(1):1-3.

[41]Zheng Y, Capra L, Wolfson O, et al., 2014. Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol, 5(3):38.

[42]Zhou YR, Li J, Chen H, et al., 2020. A spatiotemporal attention mechanism-based model for multi-step citywide passenger demand prediction. Inform Sci, 513:372-385.

[43]Zhu L, Yu FR, Wang YG, et al., 2018. Big data analytics in intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst, 20(1):383-398.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE