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Frontiers of Information Technology & Electronic Engineering
ISSN 2095-9184 (print), ISSN 2095-9230 (online)
2023 Vol.24 No.9 P.1316-1331
A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction
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.
Key words: Passenger hotspot prediction; Ensemble empirical mode decomposition (EEMD); Spatial attention mechanism; Bi-directional gated recurrent unit (BiGRU); GPS trajectory; Spark
机构：1浙江大学，海洋学院，中国舟山，316000；2浙江大学海南研究院，中国三亚，572000；3教育部海洋传感技术与装备工程研究中心，中国舟山，316021
目的：1.以水下轻量化水下电动机械臂为研究对象，探索水下电动机械臂和自主水下航行器（AUV）的集成技术，设计一套全新的适用于500m以上水深的水下机械臂。2.提高AUV的现场操作干预能力和自主作业能力，为海底探测取样作业提供更加有效、经济、方便、快速的手段，在海洋资源探测中发挥更大的作用。
创新点：1.以水下轻量化水下电动机械臂为研究对象，探索水下电动机械臂和AUV的集成技术，设计了一款全新的适应AUV搭载的水下电动机械手。2.该水下电动机械手的密封方式借鉴了深海液压系统的工作原理，采用压力补偿的方式提升电动机械手本身的耐压性和防水性，提升了电动机械手的适用水深和水下工作的可靠性。
方法：1.基于机器人运动学与动力学理论，进行仿真验证，并搭建水下电动机械手实验平台。2.进行陆上和水下的实验，完成轨迹的跟踪实验，并对水上水下和仿真实验的数据进行对比分析，得到水下电动机械手的轨迹跟踪精度，以验证该机械手的运行精度。
结论：1.在匀加速/减速过程中，机械手关节的运行更加稳定；在从匀减速到停止的过渡阶段有微量的过冲；在匀速运动过程中，关节角度跟踪不稳定，从波动幅度来看，误差范围约为0.01 rad。2.通过进一步分析机械手致动器的运动轨迹误差可以得出，机械手在空气中的绝对跟踪误差峰值约为18 mm，而在水下约为14 mm；机械手在水下的末端运动精度比在水中高，匀速时产生的振动幅度也比在空气中小得多。3.要提高机械臂系统的性能，需要设计更精确的控制系统；进行流体力学分析，还需要搭载配备视觉系统的AUV，以便在水下环境和实际海洋环境中进行下一步的自主操作实验。
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DOI:
10.1631/FITEE.2200621
CLC number:
TP39
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On-line Access:
2023-06-21
Received:
2022-12-05
Revision Accepted:
2023-09-21
Crosschecked:
2023-04-11