| 智能卡和出租车轨迹数据中蕴含城市人群活动模式的差异性分析 |
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| 引用本文： | 郑晓琳,刘启亮,刘文凯,吴智慧.智能卡和出租车轨迹数据中蕴含城市人群活动模式的差异性分析[J].地球信息科学,2020,22(6):1268-1281. |
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| 作者姓名： | 郑晓琳 刘启亮 刘文凯 吴智慧 |
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| 作者单位： | 中南大学地理信息系,长沙 410083 |
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| 基金项目： | 国家重点研发计划项目(2017YFB0503601);国家自然科学基金项目(41971353);国家自然科学基金项目(41601410);中南大学创新驱动计划项目(2018CX015) |
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| 摘 要： | 当前采用交通流数据量化城市人群活动模式研究已经取得了丰硕的研究成果,但是对于同一区域、同一时段不同类型交通流数据反映城市人群活动模式的共性与差异性仍然知之甚少,直接影响了城市人群活动模式挖掘结果的可解释性与实际应用效果。为此,本文旨在对目前广泛采用的智能卡数据(公交和地铁刷卡)和出租车轨迹数据2种重要的交通流数据,从时空分布模式的差异性、行程距离及距离衰减效应的差异性、空间社团结构的差异性3个方面,探索二者反映城市人群活动模式的差异性:①采用北京市六环以内区域2016年5月9日至15日的智能卡和出租车轨迹数据进行实验分析,研究发现:①2种交通流反映出行需求的空间分布呈现出高度相关性,但是在同一空间单元上,2种交通流反映出行需求的时间相关性较低;②2种交通流的使用率在不同空间位置存在明显差异,仅在城市中心区域使用率较为均衡;③2种交通流反映人群行程距离的空间分布、距离衰减效应存在明显差异,公共交通对于促进长距离出行更为重要;④从2种交通流发现的空间社团结构都显示了城市的多中心结构特征,但是二者发现社团结构存在的差异性表明两种交通方式对城市空间交互起着不同的作用。本研究有助于深入理解多源交通流反映城市人群活动的内在机理,提升城市人群活动模式在城市规划、交通管理等领域的应用效果。
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| 关 键 词： | 城市人群活动 智能卡数据 出租车轨迹数据 时空模式 行程距离 距离衰减 空间社团 对比分析 |
| 收稿时间： | 2019-06-18 |
Discovery of Urban Human Mobility Patterns from Smart Card Transactions and Taxi GPS Trajectories: A Comparative Study |
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| ZHENG Xiaolin,LIU Qiliang,LIU Wenkai,WU Zhihui.Discovery of Urban Human Mobility Patterns from Smart Card Transactions and Taxi GPS Trajectories: A Comparative Study[J].Geo-information Science,2020,22(6):1268-1281. |
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| Authors: | ZHENG Xiaolin LIU Qiliang LIU Wenkai WU Zhihui |
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| Institution: | Department of Geo-informatics, Central South University, Changsha 410083, China |
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| Abstract: | In the era of big data, traffic flows play an important role in understanding our socioeconomic environment. In recent years, two types of traffic flows, smart card transactions and taxi GPS trajectories, have been widely but usually separately utilized to understand human mobility in big cities. To date, although numerous research achievements have been made, the relationship between these two types of traffic flows that occur in the same area and in the same time period is still poorly understood. Thus, the pattern of urban human mobility may be biased by using a single type of traffic flow. In this study, we aimed to compare the urban human mobility patterns derived from the two traffic flows, i.e., smart card transactions and taxi GPS trajectories. Taking the area within the Sixth Ring Road of Beijing as study area, we collected the smart card transactions and taxi GPS trajectories data from May 9th to May 15th in 2016. Specifically, we compared: ① the spatio-temporal distributions of public transit and taxi usage; ② the travel distance of public transition and taxi usage and the distance-decay effects, and ③ the spatial community structures discovered from the two traffic flows. Our results show that: ① the spatial distributions of travel demand revealed by two traffic flows are highly correlated. However, the correlations between origin and destination time series extracted from the two traffic flows were very weak; ② the usages of public transit and taxi had spatial heterogeneity, and the spatial difference between the usages of public transit and taxi cannot be fully explained by the number of intersections, the number of points of interest, and the average distance to the nearest city center, shopping center, hospital and subway; ③ the travel distances extracted from taxi GPS trajectories decayed faster than that extracted from public transit, indicating that public transit was more important in facilitating long-distance travel, and ④ spatial communities discovered from the two traffic flows both reflected the polycentric spatial structure of the city. However, the differences in spatial community structures indicated that the two traffic flows played different roles in spatial interactions in the city. The quantitative comparison between smart card transactions and taxi GPS trajectories could improve our understanding of human mobility in Beijing, which also demonstrates the potential biases by using a single traffic flow to study urban system dynamic. Our results suggest integrating multi-source traffic flows to understand urban human mobility patterns in future. |
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| Keywords: | human mobility smart card transactions taxi GPS trajectories spatio-temporal pattern travel distance distance decay spatial community comparative analysis |
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