地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (2): 176-184.doi: 10.3724/SP.J.1047.2017.00176

• 地理空间分析综合应用 • 上一篇    下一篇

小城市居民出行行为时空动态及驱动机制研究

吴健生1,2(), 李博1,*(), 黄秀兰1   

  1. 1. 北京大学城市规划与设计学院 城市人居环境科学与技术重点实验室,深圳 518055
    2. 北京大学城市与环境学院 地表过程分析与模拟教育部重点实验室,北京 100871
  • 收稿日期:2016-02-24 修回日期:2016-04-15 出版日期:2017-02-28 发布日期:2017-02-17
  • 通讯作者: 李博 E-mail:wujs@szpku.edu.cn;alex_libo@pku.edu.cn
  • 作者简介:

    作者简介:吴健生(1965-),男,博士生导师,教授,研究方向为遥感与GIS、景观生态学与土地利用、数字城市与城市安全。E-mail: wujs@szpku.edu.cn

  • 基金资助:
    国家自然科学基金项目(41271101)

Spatio-temporal Dynamics and Driving Mechanisms of Resident Trip in Small Cities

WU Jiansheng1,2(), LI Bo1,*(), HUANG Xiulan1   

  1. 1. Key Laboratory for Urban Habitant Environmental Science and Technology, School of Urban Planning & Design, Peking University, Shenzhen 518055, China;
    2. Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
  • Received:2016-02-24 Revised:2016-04-15 Online:2017-02-28 Published:2017-02-17
  • Contact: LI Bo E-mail:wujs@szpku.edu.cn;alex_libo@pku.edu.cn

摘要:

相比于大城市,中小城市在新型城镇化中至关重要,具有独特的居民出行行为特征,但以往的研究并没有得到足够的关注。目前研究主要使用浮动车数据分析特大城市居民的出行行为,但考虑到小城市土地开发强度低、公共交通不发达、研究空间尺度精细等特点,这些研究方法不能完全适用于针对小城市的研究。因此,本文使用小城市出租车GPS轨迹数据识别上下客事件,沿道路生成随机样点采样得到了分时段的上下客密度,并对其时空动态进行描述和表达;筛选出显著影响上下客密度时空分布的9类设施,建立出租车上下客事件的地理加权回归模型;分析了小城市出租车上下客时空动态与各类城市设施的时空关系,发现在工作日与双休日和一天中不同时段中,不同城市设施对上下客事件的影响具有不同的分布规律及其驱动机制。研究结果可为小城市的城市规划和交通需求精细化管理提供参考。

关键词: 小城市, 出租车, 出行行为, 时空动态, 地理加权回归模型

Abstract:

Taxi is an indispensable urban traffic mode in small cities. However, there are limited efforts focusing on explaining traffic congestion or resident commuting from a perspective of land use in small cities. This study attempts to reveal the spatio-temporal dynamics of resident trip activities from the aspect of urban functional features. Based on the GPS taxi data, we build a set of temporal GWR models on an hourly basis, which indicates that urban facilities have various effects on the pick-up and drop-off events during different daytime periods. Nine facilities, including coach station, supermarket, restaurant, residential area, karaoke, hotel, hospital, bank and administrative center, have been observed to be the critical elements to explain the ridership variations. A spatio-temporal mechanism has been proposed based on the discovery that facilities with different urban functions have different impacts on resident trip demands. In contrast to the large cities, the trip activities of residents are spatially and temporally various in the small cities. The primary traffic demands are commuting activities, commerce, entertainment and intercity transfers. More rush hours, especially the “noon rush” and “midnight rush”, are revealed in small cities. The results provide valuable insights for quantitatively predicting the taxi demand as a function of the spatio-temporal variables, which may have implications on the traffic demand management and the urban planning of small cities.

Key words: small city, resident trip, taxi, GWR model, spatio-temporal dynamics