地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (4): 726-740.doi: 10.12082/dqxxkx.2023.210769
王鹏洲1,2(), 赵志远1,2,3,*(
), 姚伟1,2, 吴升1,2,3, 汪艳霞4, 方莉娜1,2,3, 邬群勇1,2,3
收稿日期:
2021-12-01
修回日期:
2022-03-18
出版日期:
2023-04-25
发布日期:
2023-04-19
通讯作者:
*赵志远(1989—),男,安徽亳州人,博士,从事时空大数据分析与挖掘。E-mail: zyzhao@fzu.edu.cn作者简介:
王鹏洲(1997—),男,山西晋中人,硕士,从事时空轨迹数据挖掘。E-mail: pzwang_gis@163.com
基金资助:
WANG Pengzhou1,2(), ZHAO Zhiyuan1,2,3,*(
), YAO Wei1,2, WU Sheng1,2,3, WANG Yanxia4, FANG Lina1,2,3, WU Qunyong1,2,3
Received:
2021-12-01
Revised:
2022-03-18
Online:
2023-04-25
Published:
2023-04-19
Contact:
ZHAO Zhiyuan
Supported by:
摘要:
城市出租汽车是居民出行的重要方式之一,地理流空间理论为发掘人群出行特征,优化车辆运营效率提供了新视角。本文利用厦门市出租汽车轨迹数据,采用地理流空间分析理论,对人群出行的整体随机性质进行了分析,基于流相似性度量识别并分析了丛集、汇聚、发散和社区4种典型模式及混合模式的空间分布特征,对比了基于巡游车和网约车2种车辆的人群出行模式。结果表明流空间理论能够系统性发现人群出行典型模式及混合模式,主要体现在:① 基于2类车辆的人群出行流在空间中呈现出显著的非随机特征;② 巡游车和网约车的典型模式在空间分布上有明显差别,网约车的有关模式分布范围更广,在厦门岛外各区中心及岛内东部软件园等区域附近较为突出,且网约车由于其订单由用户需求驱动,更容易发现潜在的高出行需求区域,同时出行结构更容易形成社区模式,而巡游车主要分布在传统岛内知名城市地标附近;③ 同一区域内巡游车和网约车出行混合模式普遍存在,约占典型模式的四分之一左右,而且不同类型车辆的主要混合模式存在差异,综合考虑混合模式能够提高城市公共设施规划的精确性和科学性。本文结果可以为车辆调度优化和城市交通规划提供支持,也表明地理流空间理论能够更有效揭示地理流对象的空间模式特征。
王鹏洲, 赵志远, 姚伟, 吴升, 汪艳霞, 方莉娜, 邬群勇. 基于地理流空间的巡游车与网约车人群出行模式研究[J]. 地球信息科学学报, 2023, 25(4): 726-740.DOI:10.12082/dqxxkx.2023.210769
WANG Pengzhou, ZHAO Zhiyuan, YAO Wei, WU Sheng, WANG Yanxia, FANG Lina, WU Qunyong. Human Travel Patterns by E-hailing Cars and Traditional Taxis based on Geographic Flow Space[J]. Journal of Geo-information Science, 2023, 25(4): 726-740.DOI:10.12082/dqxxkx.2023.210769
表1
订单数据样例
车辆ID | 上车时间 | 上车经度/°E | 上车纬度/°N | 下车时间 | 下车经度/°E | 下车纬度/°N |
---|---|---|---|---|---|---|
87a1*****a | 2019-05-31 08:49:00 | 118.1****2 | 24.5****1 | 2019-05-31 09:17:00 | 118.1****3 | 24.4****6 |
f8bb*****2 | 2019-05-31 07:04:00 | 118.1****1 | 24.4****5 | 2019-05-31 07:11:00 | 118.1****8 | 24.4****3 |
ff50*****7 | 2019-05-31 07:48:00 | 118.0****5 | 24.4****3 | 2019-05-31 08:27:00 | 118.1****8 | 24.4****0 |
9b28*****f | 2019-05-31 07:51:00 | 118.1****8 | 24.5****6 | 2019-05-31 08:34:00 | 118.0****8 | 24.5****3 |
表3
Statistics on the number of grids in each mix pattern (个)
模式 | 巡游车 早高峰 | 巡游车 晚高峰 | 网约车 早高峰 | 网约车 晚高峰 |
---|---|---|---|---|
发散-社区 | 8 | 7 | 15 | 13 |
发散-汇聚 | 4 | 2 | 1 | 5 |
发散-丛集 | 1 | 2 | 4 | 1 |
社区-汇聚 | 11 | 4 | 30 | 19 |
社区-丛集 | 22 | 10 | 17 | 21 |
汇聚-丛集 | 1 | 0 | 11 | 6 |
汇聚-发散-社区 | 1 | 10 | 1 | 3 |
汇聚-发散-丛集 | 0 | 0 | 1 | 4 |
汇聚-社区-丛集 | 8 | 1 | 20 | 19 |
丛集-发散-社区 | 1 | 3 | 2 | 7 |
丛集-汇聚-发散-社区 | 3 | 8 | 0 | 4 |
混合模式总体占比/% | 28 | 24 | 27 | 25 |
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