地理科学进展 ›› 2020, Vol. 39 ›› Issue (1): 45-55.doi: 10.18306/dlkxjz.2020.01.005

• 研究论文 • 上一篇    下一篇

天气因素对城市地铁客流时空分布的影响——基于智能交通卡数据的实证研究

许熳灵, 付晓*(), 汤君友, 刘志远   

  1. 东南大学交通学院,南京 211189
  • 收稿日期:2019-02-11 修回日期:2019-07-09 出版日期:2020-01-28 发布日期:2020-03-28
  • 通讯作者: 付晓
  • 作者简介:许熳灵(1994— ),女,湖北宜昌人,硕士生,主要从事交通地理信息系统、交通大数据研究。E-mail: 522282287@qq.com
  • 基金资助:
    国家自然科学基金项目(71601045);江苏省社会科学基金项目(16GLC008);中央高校基本科研业务费项目(2242019K40203)

Effects of weather factors on the spatial and temporal distributions of metro passenger flows: An empirical study based on smart card data

XU Manling, FU Xiao*(), TANG Junyou, LIU Zhiyuan   

  1. School of Transportation, Southeast University, Nanjing 211189, China
  • Received:2019-02-11 Revised:2019-07-09 Online:2020-01-28 Published:2020-03-28
  • Contact: FU Xiao
  • Supported by:
    National Natural Science Foundation of China(71601045);Social Science Foundation of Jiangsu Province(16GLC008);Fundamental Research Funds for the Central Universities(2242019K40203)

摘要:

城市地理空间、气候环境及交通系统间存在复杂的相互联系、相互制约的关系,交通及地理时空数据为理解三者间关系带来了新的机遇。城市轨道交通是居民绿色出行、缓解中国大城市交通拥堵的重要交通方式。深入研究影响城市地铁客流时间和空间分布变化的因素,有利于制定合理的土地利用及交通需求管理政策,也可为实时响应特定天气条件下旅客出行需求的变化和优化公交服务运营提供理论依据。论文使用智能交通卡数据,以南京市为例,通过建立一种季节性差分自回归移动平均(seasonal autoregressive integrated moving average with explanatory variables, SARIMAX)模型,解释不同种类的天气因素(如降雨、气温、相对湿度、风速等)对地铁客流量时空分布的影响程度。研究发现:降雨类因素在高峰和周末时段对地铁客流量的影响较大;各天气因素对各地铁站点客流量的影响大致呈现出从城市中心区域向外围区域逐渐变小的渐变式规律,且地铁无规律出行者比有规律出行者更易受恶劣天气因素的影响。

关键词: 城市轨道交通, 智能交通卡数据, 客流时空分布, 天气因素, 南京市

Abstract:

Urban geographic space, climate, and transportation system are interrelated, and recently available traffic and spatial big data bring new opportunities for understanding the relationship among them. Urban rail transit is an important transport mode for residents to travel green and relieve traffic congestion in big cities in China. In-depth study of factors that affect the changes in the spatial and temporal distributions of metro passenger flows is conducive to the formulation of reasonable land use and traffic demand management policies, and can also provide a theoretical basis for real-time response to the changes in travel demand under specific weather conditions and optimization of transit service operation. To study the impact of weather conditions on metro usage in densely populated areas, in this research the influence of local weather factors (including temperature, humidity, rainfall and so on) on hourly metro passenger flows was investigated based on metro smart card data and weather data from Nanjing City, China. A time series model—seasonal autoregressive integrated moving average with explanatory variables (SARIMAX)—was developed to investigate the impact of weather conditions on metro passenger flows. It is found that some weather factors such as rainfall have significant influence on metro passenger flows. Except for some special sites (large residential areas and large transportation hubs), the influence of weather factors on metro passenger flows reduces gradually from the city center to suburban areas. The effects of weather conditions on regular metro passengers and irregular metro passengers were explicitly compared in this study. Irregular metro passengers are found more vulnerable to adverse weather conditions than regular metro passengers.

Key words: urban metro system, smart card data, passenger flow distribution, weather factors, Nanjing City