地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (6): 1047-1060.doi: 10.12082/dqxxkx.2022.210691
收稿日期:
2021-10-30
修回日期:
2021-12-25
出版日期:
2022-06-25
发布日期:
2022-08-25
通讯作者:
*李 梅(1978— ),女,陕西西安人,博士,副教授,主要从事地学信息可视化,实时GIS与应急研究。 E-mail: mli@pku.edu.cn作者简介:
姜 晓(1992— ),男,江苏徐州人,硕士生,主要从事地学信息可视化与数据挖掘研究。E-mail: jiangxiao@stu.pku.edu.cn
基金资助:
JIANG Xiao(), BAI Lubin, LOU Xiayin, LI Mei*(
), LIU Hui
Received:
2021-10-30
Revised:
2021-12-25
Online:
2022-06-25
Published:
2022-08-25
Supported by:
摘要:
当前,我国政府和单车企业多以划定电子围栏停车点的方式进行共享单车的规范化管理,由于单个电子围栏内部单车流入流出的随机性和不确定性较大,以单个围栏为单位进行单车管理的工作量大且不具现实意义。因此,有必要对电子围栏停车点进行聚类划分,实行区域化的管理与调度。基于此,本文提出一种基于时空约束的网络图聚类算法,该算法综合考虑空间因素(地理位置、地理环境特征)和时间因素(历史订单),只需通过距离阈值设定即可实现电子围栏的多尺度聚类划分,实验分别在3000 m和700 m距离阈值条件下对厦门岛和乌石浦地区电子围栏进行聚类,结果显示该算法不仅能够将具有相似时空特征的电子围栏聚到同一社区簇内,而且能够使得单车流动主要集中在划分后的社区内部;随后,在社区划分基础上进行单车潮汐特征挖掘,能够有效识别和定位单车使用的热点地区;最后,利用长短时记忆神经网络(Long-Short Time Memory network, LSTM)进行单车订单需求预测,结果显示有84%以上社区的预测准确率在85%以上,平均预测准确率为91.301%,预测效果较好,可有效满足单车调度需求。本文研究成果可服务于电子围栏停车点规划与共享单车的区域化管理与调度工作。
姜晓, 白璐斌, 楼夏寅, 李梅, 刘晖. 基于多尺度时空聚类的共享单车潮汐特征挖掘与需求预测研究[J]. 地球信息科学学报, 2022, 24(6): 1047-1060.DOI:10.12082/dqxxkx.2022.210691
JIANG Xiao, BAI Lubin, LOU Xiayin, LI Mei, LIU Hui. Usage Patterns Identification and Flow Prediction of Bike-sharing System based on Multiscale Spatiotemporal Clustering[J]. Journal of Geo-information Science, 2022, 24(6): 1047-1060.DOI:10.12082/dqxxkx.2022.210691
表1
实验数据清单
数据名称 | 数据时间 | 数据规模 | 数据描述 | |
---|---|---|---|---|
字段名称 | 字段含义 | |||
厦门岛共享单车订单数据 | 2020年12月21—25日6:00 am—10:00 am | 58万条左右 | BICYCLE_ID | 加密后的单车ID号 |
LATITDUE | 纬度/° | |||
LONGITUDE | 经度/° | |||
LOCK_STATUS | 锁状态 | |||
UPDATE_TIME | 锁状态更新的时间 | |||
厦门岛共享单车电子围栏数据 | 2020年12月 | 1.4万个左右 | FENCE_ID | 电子围栏唯一编号 |
FENCE_LOC | 电子围栏位置坐标串 | |||
厦门岛POI数据 | 2021年1月 | 8000条左右 | POI_TYPE | POI地物类别 |
LATITDUE | 纬度/° | |||
LONGITUDE | 经度/° | |||
厦门岛路网数据 | 2021年1月 | 8000条道路 | Length | 道路长度/m |
name | 道路名称 |
表5
LSTM模型预测社区单车需求结果评价
社区 | 评价指标 | |||
---|---|---|---|---|
MAE | RMSE | PEARSON/% | AcR/% | |
0 | 12.394 | 26.682 | 84.651 | 86.888 |
1 | 27.065 | 37.996 | 97.572 | 94.228 |
2 | 21.540 | 38.921 | 96.790 | 96.674 |
3 | 60.711 | 96.166 | 92.481 | 86.226 |
4 | 48.237 | 66.411 | 98.163 | 97.235 |
5 | 11.158 | 16.725 | 98.011 | 95.098 |
6 | 6.461 | 8.509 | 97.987 | 95.845 |
7 | 33.671 | 42.844 | 99.099 | 95.559 |
8 | 5.448 | 10.145 | 75.370 | 91.557 |
9 | 44.250 | 64.302 | 97.784 | 97.264 |
10 | 25.329 | 55.762 | 98.099 | 95.295 |
11 | 19.842 | 29.365 | 99.111 | 97.393 |
12 | 35.355 | 50.229 | 99.335 | 95.543 |
13 | 2.250 | 3.806 | 54.333 | 78.498 |
14 | 7.0785 | 9.752 | 95.637 | 93.854 |
15 | 7.211 | 10.692 | 92.511 | 93.463 |
16 | 9.106 | 17.276 | 78.129 | 89.304 |
17 | 9.171 | 19.647 | 87.105 | 92.711 |
18 | 59.250 | 108.202 | 95.537 | 87.758 |
19 | 45.013 | 70.014 | 97.275 | 96.156 |
20 | 15.644 | 29.260 | 98.862 | 97.189 |
21 | 33.316 | 47.215 | 99.282 | 95.957 |
22 | 49.750 | 79.096 | 90.956 | 76.763 |
23 | 11.092 | 22.382 | 68.255 | 83.070 |
24 | 1.671 | 2.315 | 82.898 | 73.008 |
均值 | 24.080 | 38.548 | 91.010 | 91.301 |
[1] | 邵鹏, 王齐, 赵超. 共享单车绿色使用行为与意愿的影响因素研究[J]. 干旱区资源与环境, 2020, 34(3):64-68. |
[ Shao P, Wang Q, Zhao C. Research on the factors influencing shared bicycle green use behavior and intention[J]. Journal of Arid Land Resources and Environment, 2020, 34(3):64-68. ] DOI: 10.13448/j.cnki.jalre.2020.67
doi: 10.13448/j.cnki.jalre.2020.67 |
|
[2] |
Zhang Y P, Lin D, Mi Z F. Electric fence planning for dockless bike-sharing services[J]. Journal of Cleaner Production, 2019, 206:383-393. DOI: 10.1016/j.jclepro.2018.09.215
doi: 10.1016/j.jclepro.2018.09.215 |
[3] | Singla A, Santoni M, Bartok G, et al. Incentivizing users for balancing bike sharing systems[C]. Proceedings of the Twenty-Ninth Aaai Conference on Artificial Intelligence, 2015. |
[4] |
Rokach L, Maimon O. Clustering methods[M]. Boston: Springer US. 2005. DOI: 10.1007/0-387-25465-X_15
doi: 10.1007/0-387-25465-X_15 |
[5] | 章永来, 周耀鉴. 聚类算法综述[J]. 计算机应用, 2019, 39(7):1869-1882. |
[ Zhang Y L, Zhou Y J. Review of clustering algorithms[J]. Journal of Computer Applications, 2019, 39(7):1869-1882. ] DOI: 10.11772/j.issn.1001-9081.2019010174
doi: 10.11772/j.issn.1001-9081.2019010174 |
|
[6] |
高楹, 宋辞, 郭思慧,等. 接驳地铁站的共享单车源汇时空特征及其影响因素[J]. 地球信息科学学报, 2021, 23(1):155-170.
doi: 10.12082/dqxxkx.2021.200351 |
[ Gao Y, Song C, Guo S H, et al. Spatial-temporal characteristics and influencing factors of source and sink of dockless sharing bicycles connected to subway stations[J]. Journal of Geo-information Science, 2021, 23(1):155-170. ] DOI: 10.12082/dqxxkx.2021.200351
doi: 10.12082/dqxxkx.2021.200351 |
|
[7] | 靳爽, 庞明宝. 基于K-means的城市轨道交通社区接驳共享单车停靠点规划[J]. 科学技术与工程, 2019, 19(30):343-347. |
[ Jin S, Pang M B. Planning of shared bicycle stop for urban rail transit community connection based on K-means[J]. Science Technology and Engineering, 2019, 19(30):343-347. ] | |
[8] |
Yu J J, Ji Y J, Yi C Y, et al. Estimating model for urban carrying capacity on bike-sharing[J]. Journal of Central South University, 2021, 28(6):1775-1785. DOI: 10.1007/s11771-021-4661-6
doi: 10.1007/s11771-021-4661-6 |
[9] |
Hua M Z, Chen X W, Zheng S J, et al. Estimating the parking demand of free-floating bike sharing: A journey-data-based study of Nanjing, China[J]. Journal of Cleaner Production, 2020, 244:1-11. DOI: 10.1016/j.jclepro.2019.118764
doi: 10.1016/j.jclepro.2019.118764 |
[10] |
Jia W Z, Tan Y Y, Liu L, et al. Hierarchical prediction based on two-level Gaussian mixture model clustering for bike-sharing system[J]. Knowledge-Based Systems, 2019, 178:84-97. DOI: 10.1016/j.knosys.2019.04.020
doi: 10.1016/j.knosys.2019.04.020 |
[11] |
Du Y C, Deng F W, Liao F X. A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system[J]. Transportation Research Part C-Emerging Technologies, 2019, 103:39-55. DOI: 1 0.1016/j.trc.2019.04.006
doi: 1 0.1016/j.trc.2019.04.006 |
[12] | 刘畅. 共享单车需求预测及调度研究[D]. 武汉: 武汉理工大学, 2018. |
[ Liu C. Research on the demand forecast and scheduling of bike-sharing[D]. Wuhan: Wuhan University of Technology, 2018. ] | |
[13] |
Jia W Z, Tan Y Y, Li J. Hierarchical prediction based on two-level affinity propagation clustering for bike-sharing system[J]. IEEE Access, 2018, 6:45875-45885. DOI: 10.1 109/ACCESS.2018.2865658
doi: 10.1 109/ACCESS.2018.2865658 |
[14] | Ciancia V, Latella D, Massink M, et al. Exploring spatio-temporal properties of bike-sharing systems[C]. 2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW). IEEE, 2015. DOI: 10.1109/SASOW.2015.17 |
[15] | 数字中国建设峰会. 2021数字中国创新大赛之大数据赛道-城市管理大数据专题[DB/OL].(2021-1-25)[2021-1-31].https://dcic.datafountain.cn/competitions/10 015 . |
[ Digital China Summit.Digital China innovation contest-the big data of urban management, DCIC 2021. [DB/OL]. (2021-1-28)[2021-1-28].https://dcic.datafountain.cn/competitions/10 015 . ] | |
[16] | 高德地图. 厦门岛地区POI数据与城市路网数据[DB/OL].(2021-1-31)[2021-1-31]. https://ditu.amap.com/ . |
[ AmapThe POI data and urban road network data of Xiamen island[DB/OL]. (2021-1-31)[2021-1-31]. https://ditu.amap.com/ . ] | |
[17] |
张景奇, 史文宝, 修春亮. POI数据在中国城市研究中的应用[J]. 地理科学, 2021, 41(1):140-148.
doi: 10.13249/j.cnki.sgs.2021.01.015 |
[ Zhang J Q, Shi W B, Xiu C L. Urban research using points of interest data in China[J]. Scientia Geographica Sinica, 2021, 41(1):140-148. ] DOI: 10.13249/j.cnki.sgs.2021.01.015
doi: 10.13249/j.cnki.sgs.2021.01.015 |
|
[18] |
Xing Y Y, Wang K, Lu J J. Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China[J]. Journal of Transport Geography, 2020, 87:1-15. DOI: 10.1016/j.jtrangeo.2020.102787
doi: 10.1016/j.jtrangeo.2020.102787 |
[19] |
Wang L Z, Bao X G, Chen H M, et al. Effective lossless condensed representation and discovery of spatial co-location patterns[J]. Information Sciences, 2018, 436:197-213. DOI: 10.1016/j.ins.2018.01.011
doi: 10.1016/j.ins.2018.01.011 |
[20] |
Alcorn L G, Jiao J. Bike-sharing station usage and the surrounding built environments in major Texas cities[J]. Journal of Planning Education and Research, 2019, 3:1-14. DOI: 10.1177/0739456X19862854
doi: 10.1177/0739456X19862854 |
[21] | 禹文豪, 艾廷华. 核密度估计法支持下的网络空间POI点可视化与分析[J]. 测绘学报, 2015, 44(1):82-90. |
[ Yu Y H, AI T H, The visualization and analysis of POI features under network space supported by kernel density estimation[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(1):82-90 ] DOI: 10.11947/j.AGCS.2015.20130538
doi: 10.11947/j.AGCS.2015.20130538 |
|
[22] |
Zhang S C. Cost-sensitive KNN classification[J]. Neurocomputing, 2020, 391:234-242. DOI: 10.1016/j.neucom.2018.11.101
doi: 10.1016/j.neucom.2018.11.101 |
[23] |
Kapuku C, Kho S Y, Kim D K, et al. Modeling the competitiveness of a bike-sharing system using bicycle GPS and transit smartcard data[J]. Transportation Letters-the International Journal of Transportation Research, 2020,1-5. DOI: 10.1080/19427867.2020.1758389
doi: 10.1080/19427867.2020.1758389 |
[24] |
Fortunato S. Community detection in graphs[J]. Physics Reports, 2010, 486(3-5):75-174. DOI: 10.1016/j.physrep.2009.11.002
doi: 10.1016/j.physrep.2009.11.002 |
[25] |
Liu X, Murata T. Advanced modularity-specialized label propagation algorithm for detecting communities in networks[J]. Physica a-Statistical Mechanics and Its Applications, 2010, 389(7):1493-1500. DOI: 10.1016/j.physa.2009.12.019
doi: 10.1016/j.physa.2009.12.019 |
[26] | Chen L B, Zhang D Q, Wang L Y, et al. Dynamic cluster-based over-demand prediction in bike sharing systems[C]. Ubicomp'16: Proceedings of the 2016 Acm International Joint Conference on Pervasive and Ubiquitous Computing, 2016. DOI: 10.1145/2971648.2971652 |
[27] |
Sherstinsky A. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network[J]. Physica D: Nonlinear Phenomena, 2020, 404(8):132306. DOI: 10.1016/j.physd.2019.132306
doi: 10.1016/j.physd.2019.132306 |
[28] |
Zhang C, Zhang L N, Liu Y D, et al. Short-term prediction of bike-sharing usage considering public transport: a LSTM approach[C]. IEEE International Conference on Intelligent Transportation Systems-ITSC, 2018. DOI: 10. 109/ITSC.2018.8569726
doi: 10. 109/ITSC.2018.8569726 |
[29] | 付俐哲. 基于时空聚类与LSTM神经网络的共享单车需求预测模型[D]. 兰州: 西北师范大学, 2021. |
[ Fu L Z, Spatiotemporal clustering and LSTM based prediction model of bicycle sharing[D]. Lanzhou: Northwest Normal University, 2021 ]DOI: 10.27410/d.cnki.gxbfu.2021.001767
doi: 10.27410/d.cnki.gxbfu.2021.001767 |
|
[30] | 曹旦旦, 范书瑞, 张艳,等. 基于长短期记忆神经网络模型的共享单车短时需求量预测[J]. 科学技术与工程, 2020, 20(20):8344-8349. |
[ Cao D D, Fan S R, Zhang Y, et al. Short-term demand forecasting of shared bicycles based on long short-term memory neural net-work model[J]. Science Technology and Engineering, 2020, 20(20):8344-8349. ] | |
[31] |
Wang W J, Lu Y M. Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model[C]. International Conference on Mechanical Engineering, 2018, 324(1). DOI: 10.1088/1757-899X/324/1/012049
doi: 10.1088/1757-899X/324/1/012049 |
[32] | 万敏. 基于数据的共享单车需求预测和调度研究[D]. 南京: 南京大学, 2020. |
[ Wan M, Research on forecasting and scheduling of shared bicycle demand based on data[D]. Nanjing: Nanjing University, 2020. ] DOI: 10.27235/d.cnki.gnjiu.2020.000120
doi: 10.27235/d.cnki.gnjiu.2020.000120 |
|
[33] |
Adler J, Parmryd I. Quantifying colocalization by correlation: The pearson correlation coefficient is superior to the mander's overlap coefficient[J]. Cytometry Part A, 2010, 77a(8):733-742. DOI: 10.1002/cyto.a.20896
doi: 10.1002/cyto.a.20896 |
[1] | 张晗, 邬群勇. 基于LDA和优化蚁群的OD流向时空语义聚类算法[J]. 地球信息科学学报, 2022, 24(5): 837-850. |
[2] | 李军, 刘举庆, 游林, 董恒, 俞艳, 张晓盼, 钟文军, 杨典华. 多源大数据支持的土地储备智能决策模型集研究[J]. 地球信息科学学报, 2022, 24(2): 299-309. |
[3] | 陈伟杰, 赵楠, 张婕姝, 宋炳良. AIS数据在集装箱港口服务效率的应用研究[J]. 地球信息科学学报, 2022, 24(1): 153-164. |
[4] | 胡添, 刘涛, 杜萍, 余贝贝, 张萌生. 空间同位模式支持下城市服务业关联发现及特征分析[J]. 地球信息科学学报, 2021, 23(6): 969-978. |
[5] | 丁威, 邬群勇. 基于轨迹偏移算法的居民就医时空特征与空间格局分析[J]. 地球信息科学学报, 2021, 23(6): 979-991. |
[6] | 李雨欣, 王瑛, 马庆媛, 刘天雪, 司丽丽, 俞海洋. 基于DTW与K-means算法的河北场雨及雨型分区特征研究[J]. 地球信息科学学报, 2021, 23(5): 860-868. |
[7] | 谢聪慧, 吴世新, 张晨, 孙文涛, 何海芳, 裴韬, 罗格平. 基于谱系聚类的全球各国新冠疫情时间序列特征分析[J]. 地球信息科学学报, 2021, 23(2): 236-245. |
[8] | 尹凌, 刘康, 张浩, 奚桂锴, 李璇, 李子垠, 薛建章. 耦合人群移动的COVID-19传染病模型研究进展[J]. 地球信息科学学报, 2021, 23(11): 1894-1909. |
[9] | 高楹, 宋辞, 郭思慧, 裴韬. 接驳地铁站的共享单车源汇时空特征及其影响因素[J]. 地球信息科学学报, 2021, 23(1): 155-170. |
[10] | 陈芳淼, 黄慧萍, 贾坤. 时空大数据在城市群建设与管理中的应用研究进展[J]. 地球信息科学学报, 2020, 22(6): 1307-1319. |
[11] | 胡最. 传统聚落景观基因的地理信息特征及其理解[J]. 地球信息科学学报, 2020, 22(5): 1083-1094. |
[12] | 柯新利, 肖邦勇, 郑伟伟, 马艳春, 李红艳. 城镇-农业-生态空间划定的多情景模拟[J]. 地球信息科学学报, 2020, 22(3): 580-591. |
[13] | 潘淼鑫, 林甲祥, 陈崇成, 叶晓燕. 基于C-SOM和Spark的并行空间离群挖掘方法及应用[J]. 地球信息科学学报, 2019, 21(1): 128-136. |
[14] | 王陆一, 吴健生, 李卫锋. 中小城市公共自行车出行模式与驱动机制研究[J]. 地球信息科学学报, 2019, 21(1): 25-35. |
[15] | 高楹, 宋辞, 舒华, 裴韬. 北京市摩拜共享单车源汇时空特征分析及空间调度[J]. 地球信息科学学报, 2018, 20(8): 1123-1138. |
|