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

Dawen XIA; Jian GENG; Ruixi HUANG; Bingqi SHEN; Yang HU; Yantao LI; Huaqing LI

doi:10.1631/FITEE.2200621

Your Location：

Home >

Browse articles >

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

Regular Papers | Updated：2023-09-21

- A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction
- 基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 9, Pages: 1316-1331(2023)
- Affiliations：
  
  1.College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China
  2.Department of Automotive Engineering, Guizhou Traffic Technician and Transportation College, Guiyang 550008, China
  3.College of Computer Science, Chongqing University, Chongqing 400044, China
  4.College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
- Author bio：
  
  ‡Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2200621
  CLC： TP39
- Received：05 December 2022，
  
  Accepted：11 April 2023，
  
  Published：0 September 2023
- Accepted：
Scan QR Code
Dawen XIA, Jian GENG, Ruixi HUANG, et al. A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1316-1331.
DOI：

Dawen XIA, Jian GENG, Ruixi HUANG, et al. A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1316-1331. DOI： 10.1631/FITEE.2200621.

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%。

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.

关键词

Keywords

references

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 . doi: 10.1007/s11042-020-10486-4 http://doi.org/10.1007/s11042-020-10486-4

Batty M , Axhausen KW , Giannotti F , et al. , 2012 . Smart cities of the future . Eur Phys J Spec Top , 214 （ 1 ）: 481 - 518 . doi: 10.1140/epjst/e2012-01703-3 http://doi.org/10.1140/epjst/e2012-01703-3

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 . doi: 10.1155/2021/5814879 http://doi.org/10.1155/2021/5814879

Cao Y , Hou XL , Chen N , 2022 . Short-term forecast of OD passenger flow based on ensemble empirical mode decomposition . Sustainability , 14 （ 14 ）: 8562 . doi: 10.3390/su14148562 http://doi.org/10.3390/su14148562

Cheng X , Mao JD , Li J , et al. , 2021 . An EEMD-SVD-LWT algorithm for denoising a lidar signal . Measurement , 168 : 108405 . doi: 10.1016/j.measurement.2020.108405 http://doi.org/10.1016/j.measurement.2020.108405

Dong YH , Qian SY , Zhang K , et al. , 2017 . A novel passenger hotspots searching algorithm for taxis in urban area . Proc 18 th IEEE/ACIS Int Conf on Software Engineering, Artificial Intelligence, Networking and Parallel/ Distributed Computing , p. 175 - 180 . doi: 10.1109/SNPD.2017.8022719 http://doi.org/10.1109/SNPD.2017.8022719

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 . doi: 10.1049/iet-its.2014.0248 http://doi.org/10.1049/iet-its.2014.0248

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 . doi: 10.1109/tits.2020.2983835 http://doi.org/10.1109/tits.2020.2983835

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 . doi: 10.1080/15230406.2015.1014424 http://doi.org/10.1080/15230406.2015.1014424

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 . doi: 10.1109/JIOT.2019.2901759 http://doi.org/10.1109/JIOT.2019.2901759

Jamil MS , Akbar S , 2017 . Taxi passenger hotspot prediction using automatic ARIMA model . Proc 3 rd Int Conf on Science in Information Technology , p. 23 - 28 . doi: 10.1109/ICSITech.2017.8257080 http://doi.org/10.1109/ICSITech.2017.8257080

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 . doi: 10.1016/j.trc.2014.03.016 http://doi.org/10.1016/j.trc.2014.03.016

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 . doi: 10.1016/j.trc.2020.102786 http://doi.org/10.1016/j.trc.2020.102786

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 . doi: 10.1016/j.jclepro.2021.127139 http://doi.org/10.1016/j.jclepro.2021.127139

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 . doi: 10.1016/j.scs.2020.102419 http://doi.org/10.1016/j.scs.2020.102419

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 . doi: 10.1007/s11704-011-1192-6 http://doi.org/10.1007/s11704-011-1192-6

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 . doi: 10.1109/TITS.2020.3011700 http://doi.org/10.1109/TITS.2020.3011700

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 . doi: 10.2166/hydro.2022.146 http://doi.org/10.2166/hydro.2022.146

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 . doi: 10.1016/j.jtte.2019.07.002 http://doi.org/10.1016/j.jtte.2019.07.002

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 . doi: 10.1016/j.oceaneng.2020.107927 http://doi.org/10.1016/j.oceaneng.2020.107927

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 . doi: 10.3390/app11104684 http://doi.org/10.3390/app11104684

Ou JJ , Sun JH , Zhu YC , et al. , 2020 . STP-TrellisNets: spatial-temporal parallel trellisnets for metro station passenger flow prediction. , p. 1185 - 1194 . doi: 10.1145/3340531.3411874 http://doi.org/10.1145/3340531.3411874

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 . doi: 10.1007/s10614-018-9862-1 http://doi.org/10.1007/s10614-018-9862-1

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 . doi: 10.1109/TITS.2019.2897776 http://doi.org/10.1109/TITS.2019.2897776

Rezaei H , Faaljou H , Mansourfar G , 2021 . Stock price prediction using deep learning and frequency decomposition . Exp Syst Appl , 169 : 114332 . doi: 10.1016/j.eswa.2020.114332 http://doi.org/10.1016/j.eswa.2020.114332

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 . doi: 10.1109/TKDE.2018.2883616 http://doi.org/10.1109/TKDE.2018.2883616

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 . doi: 10.1631/FITEE.2000243 http://doi.org/10.1631/FITEE.2000243

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 . doi: 10.1109/TIM.2022.3146913 http://doi.org/10.1109/TIM.2022.3146913

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 . doi: 10.1016/j.physa.2021.126056 http://doi.org/10.1016/j.physa.2021.126056

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 . doi: 10.1007/s00521-020-05076-2 http://doi.org/10.1007/s00521-020-05076-2

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 . doi: 10.1007/s00521-022-07482-0 http://doi.org/10.1007/s00521-022-07482-0

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 . doi: 10.1007/s11042-022-12077-x http://doi.org/10.1007/s11042-022-12077-x

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 . doi: 10.1631/FITEE.1500381 http://doi.org/10.1631/FITEE.1500381

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 . doi: 10.1016/j.ins.2021.02.036 http://doi.org/10.1016/j.ins.2021.02.036

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 . doi: 10.1007/978-3-662-49370-0_37 http://doi.org/10.1007/978-3-662-49370-0_37

Yu FH , Hao HBW , Li QL , 2021 . An ensemble 3D convolutional neural network for spatiotemporal soil temperature forecasting . Sustainability , 13 （ 16 ）: 9174 . doi: 10.3390/su13169174 http://doi.org/10.3390/su13169174

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 . doi: 10.1631/FITEE.2100530 http://doi.org/10.1631/FITEE.2100530

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 . doi: 10.3390/ijerph15051032 http://doi.org/10.3390/ijerph15051032

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 . doi: 10.1016/j.physa.2018.02.064 http://doi.org/10.1016/j.physa.2018.02.064

Zheng Y , 2017 . Urban computing: enabling urban intelligence with big data . Front Comput Sci , 11 （ 1 ）: 1 - 3 . doi: 10.1007/s11704-016-6907-2 http://doi.org/10.1007/s11704-016-6907-2

Zheng Y , Capra L , Wolfson O , et al. , 2014 . Urban computing: concepts, methodologies, and applications . ACM Trans Intell Syst Technol , 5 （ 3 ）: 38 . doi: 10.1145/2629592 http://doi.org/10.1145/2629592

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 . doi: 10.1016/j.ins.2019.10.071 http://doi.org/10.1016/j.ins.2019.10.071

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 . doi: 10.1109/TITS.2018.2815678 http://doi.org/10.1109/TITS.2018.2815678

Views

202

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰