基于地铁刷卡数据的乘客特别出行行为研究——以武汉市地铁1、2、4号线为例
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摘要
为得出地铁乘客出行时空规律,挖掘特别出行乘客分布信息,基于武汉市2015年3月连续一周的地铁刷卡数据,根据乘客参加不同活动所产生的出行时空特征定义了4类人群:早出类人群、晚归类人群、长时间出行类人群和频繁出行类人群。并从时间和空间两个角度分析了这4类人群出行时空规律。研究结果表明,9%的地铁乘客具有早出行为,5%的地铁乘客具有晚归行为,85%的地铁乘客出行距离较长,1%的地铁乘客每天具有频繁出行行为。并且2号线在3条线路中的客流量最大,特殊人群多分布在1号线附近,4号线的使用率最低。
Based on the subway smartcard data gained from one week in a row in March 2015 of Wuhan Metro,this paper defines four types of special travel behaviors:early birds,night owls,tireless itinerants, and recurring itinerants and analyzes the spatio-temporal patterns of these four specialtravelbehaviors.The result shows that 9% of subway passengers travels significantly earlier than average passengers; 5% of subway passengers rides in unusual late hours; 85% of subway passengers commutes in excessively long distance; and 1% of subway passengers makes significantly more trips per day. In addition, the traffic volume of Line 2 is the largest among the three lines. Special travel behaviors are mostly located near line 1. The utilization rate of Line 3 is the lowest.
Based on the subway smartcard data gained from one week in a row in March 2015 of Wuhan Metro,this paper defines four types of special travel behaviors:early birds,night owls,tireless itinerants, and recurring itinerants and analyzes the spatio-temporal patterns of these four specialtravelbehaviors.The result shows that 9% of subway passengers travels significantly earlier than average passengers; 5% of subway passengers rides in unusual late hours; 85% of subway passengers commutes in excessively long distance; and 1% of subway passengers makes significantly more trips per day. In addition, the traffic volume of Line 2 is the largest among the three lines. Special travel behaviors are mostly located near line 1. The utilization rate of Line 3 is the lowest.
引文
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[2] 贾晓朋, 孟斌, 张媛媛. 北京市不同社区居民通勤行为分析[J]. 地域研究与开发, 2015, 34(1): 55-59
[3] 曹瑞, 涂伟, 巢佰崇,等. 基于智能卡数据的地铁周边职住用地识别与分析[J]. 测绘地理信息, 2016, 41(3):74-78
[4] Liu X, Song Y, Wu K, et al. Understanding Urban China with Open Data[J]. Cities, 2015, 47(47):53-61
[5] 龙瀛, 孙立君, 陶遂. 基于公共交通智能卡数据的城市研究综述[J]. 城市规划学刊, 2015(3): 70-77
[6] Tao S, Corcoran J, Mateo-Babiano I, et al. Exploring Bus Rapid Transit Passenger Travel Behaviour Using Big Data[J]. Applied Geography, 2014, 53: 90-104
[7] Tao S, Rohde D, Corcoran J. Examining the Spatial-Temporal Dynamics of Bus Passenger Travel Beha-viour Using Smart Card Data and the Flow-Comap[J]. Journal of Transport Geography, 2014, 41: 21-36
[8] 李军, 邓红平. 基于公交 IC 卡数据的乘客出行分类研究[J]. 重庆交通大学学报 (自然科学版), 2016, 35(6): 109-114
[9] Agard B, Morency C, Trépanier M. Mining Public Transport User Behaviour From Smart Card Data[J]. IFAC Proceedings Volumes, 2006, 39(3):399-404
[10] Mohamed K, C?me E, Baro J, et al. Understanding Passenger Patterns in Public Transit Through Smart Card and Socioeconomic Data[C]. UrbComp, Seattle, WA, USA, 2014
[11] 许园园, 塔娜, 李响. 基于地铁刷卡数据的城市通勤与就业中心吸引范围研究[J]. 人文地理, 2017,32(3):93-101
[12] Long Y, Liu X, Zhou J, et al. Early Birds, Night Owls, and Tireless/Recurring Itinerants: An Explor-atory Analysis of Extreme Transit Behaviors in Beijing, China[J]. Habitat International, 2016, 57:223-232
[13] 龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J]. 地理学报, 2012, 67(10):1 339-1 352
[14] Liu X, Wang M. How Polycentric Is Urban China and Why? A Case Study of 318 Cities[J]. Landscape and Urban Planning, 2016, 151:10-20