Uncovering urban human mobility from large scale taxi GPS data
Uncovering urban human mobility from large scale taxi GPS data
复制标题
从大规模出租车 GPS 数据中揭示城市人员流动性
DOI：
10.1016/j.physa.2015.06.032
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发表时间：
2015-11-15
影响因子：
3.3
通讯作者：
Wang, Hua
中科院分区：
文献类型：
--
作者：
Tang, Jinjun;Liu, Fang;Wang, Hua
出租车GPS轨迹数据包含了大量的城市人类活动和流动的时空信息。出租车作为移动的传感器，从出租车出行中获得的信息有利于城市和交通规划。研究的原始数据来自哈尔滨市1100多名出租车司机。首先将城市区域划分为400个不同的交通区域，分析了工作日和周末城市区域内的始发地和目的地分布。基于密度的空间聚类算法（DBSCAN）用于聚类上车和下车位置。在此基础上，以哈尔滨市某购物中心为例，对四种空间交互模型进行了标定和比较，研究了上车地点搜索行为。从GPS数据中提取出租车出行信息，利用占用和非占用状态下的出行距离、时间和平均速度来研究人员的移动性。最后，以哈尔滨市中心区实测OD矩阵为例，基于熵最大化方法对交通分布模式进行建模，并通过实例验证了该方法的有效性。(C)2015 Elsevier B.V.版权所有。
Taxi GPS trajectories data contain massive spatial and temporal information of urban human activity and mobility. Taking taxi as mobile sensors, the information derived from taxi trips benefits the city and transportation planning. The original data used in study are collected from more than 1100 taxi drivers in Harbin city. We firstly divide the city area into 400 different transportation districts and analyze the origin and destination distribution in urban area on weekday and weekend. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to cluster pick-up and drop-off locations. Furthermore, four spatial interaction models are calibrated and compared based on trajectories in shopping center of Harbin city to study the pick-up location searching behavior. By extracting taxi trips from GPS data, travel distance, time and average speed in occupied and non-occupied status are then used to investigate human mobility. Finally, we use observed OD matrix of center area in Harbin city to model the traffic distribution patterns based on entropy-maximizing method, and the estimation performance verify its effectiveness in case study. (C) 2015 Elsevier B.V. All rights reserved.