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

 ORCID:

Huaqing Li

https://orcid.org/0000-0001-6310-8965

Dawen XIA

https://orcid.org/0000-0002-0151-9643

-   Go to

Article info.
Open peer comments

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

http://doi.org/10.1631/FITEE.2200621


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",
volume="24",
number="9",
pages="1316-1331",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200621"
}

%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

TY - JOUR
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


Abstract: 
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.

基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测

夏大文1,耿建1,黄瑞曦1,申冰琪1,胡杨2,李艳涛3,李华青4
1贵州民族大学数据科学与信息工程学院,中国贵阳市,550025
2贵州交通技师学院汽车工程系,中国贵阳市,550008
3重庆大学计算机科学学院,中国重庆市,400044
4西南大学电子与信息工程学院,中国重庆市,400715
摘要:针对出租车与乘客之间的供需不平衡问题,本文提出一种基于Spark的分布式归一化集合经验模态分解和面向空间注意力机制的双向门控循环单元(EEMDN-SABiGRU)模型,实现乘客热点的精准预测,旨在于降低盲目巡航开支、提高载客效率和实现收益最大化。首先,提出一种归一化的集合经验模态分解方法(EEMDN),处理网格中乘客热点数据,解决非平稳序列问题和数值差异过大造成的预测精度下降问题,避免EMD本征模态函数(IMF)存在的模态混叠现象。其次,构建一种基于乘客上下车热点的权重和乘客的空间规律性的空间注意力机制,捕捉每个网格中的乘客热点特征。再次,融合一种双向门控循环单元(GRU)算法,解决GRU仅能获取前向信息而忽略后向信息问题,提高特征提取的准确性。最后,在Spark并行计算框架下,采用真实的出租车GPS轨迹数据,基于EEMDN-SABiGRU模型实现了乘客热点的准确预测。实验结果表明,在00网格4个数据集上,与LSTM、EMDL-STM、EEMD-LSTM、GRU、EMD-GRU、EEMD-GRU、EMDN-GRU、CNN和BP相比,EEMDN-SABiGRU的平均绝对百分比误差、平均绝对误差、均方根误差和最大误差值分别降低了43.18%、44.91%、55.04%和39.33%。

关键词:乘客热点预测;集合经验模态分解(EEMD);空间注意力机制;双向门控循环单元(BiGRU);GPS轨迹;Spark

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

Reference

[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

<1>

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