地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (7): 1185-1195.doi: 10.12082/dqxxkx.2021.200334

• 地球信息科学理论与方法 • 上一篇    下一篇

大数据城市通勤交通模型的构建与模拟应用

刘云舒1,2,3(), 赵鹏军1,2,3,*(), 吕迪1,2,3   

  1. 1.北京大学深圳研究生院,深圳 518055
    2.北京大学城市与环境学院,北京 100871
    3.北京大学地表过程分析与模拟教育部重点实验室, 北京 100871
  • 收稿日期:2020-06-29 修回日期:2020-07-26 出版日期:2021-07-25 发布日期:2021-09-25
  • 通讯作者: 赵鹏军
  • 作者简介:刘云舒(1995— ),男,河南焦作人,硕士生,主要从事地理空间大数据研究。E-mail: yunshu.liu@pku.edu.cn
  • 基金资助:
    国家自然科学基金项目(41925003)

Big-data Oriented Commuting Distribution Model and Application in Large Cities

LIU Yunshu1,2,3(), ZHAO Pengjun1,2,3,*(), LV Di1,2,3   

  1. 1. Shenzhen Graduate School, Peking University, Shenzhen 518055, China
    2. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
    3. Laboratory for Earth Surface Processes(LESP) Ministry of Education, Peking University, Beijing 100871, China
  • Received:2020-06-29 Revised:2020-07-26 Online:2021-07-25 Published:2021-09-25
  • Contact: ZHAO Pengjun
  • Supported by:
    National Natural Science Foundation of China(41925003)

摘要:

近年来大数据在交通分析中被广泛关注,但目前多以可视化展示和现象空间分析描述为主,缺乏基于大数据的交通数量模型和模拟预测研究,成为大数据技术在交通规划中应用的主要技术障碍。本文基于交通与土地利用之间的交互作用关系,构建区位空间依赖因子((Location-space Dependence Indicator, LSDI),对传统重力模型进行改进,提出大数据城市通勤分布模型。以北京市为例,采用某运营商2017年9月的手机信令大数据,进行模型的应用和校验。模拟结果显示,在出行产生预测中,通勤人口与常住人口表现出良好的线性关系;在出行分布预测中,基于区位空间依赖因子的修正重力模型综合表现最优,在通勤OD分布中实现了低估现象的优化,在OD数量发生率中拟合优度达到0.85。本研究为大数据城市交通预测模型研发提供了新的技术方法,对于推动大数据在交通规划中的应用具有一定价值。

关键词: 大数据模型, 通勤交通模型, 出行分布, 区位空间依赖, 手机信令数据, 交通规划, 通勤OD, 模型模拟

Abstract:

In recent years, big data has been widely applied in traffic analysis. However, they are mostly used for data visualization and phenomenon description. There is a lack of big-data oriented transport modeling, which leads to limited application of big-data in transportation planning. In this study, we propose a Location-Space Dependent Indicator (LSDI) based on the time-space interaction between transportation and land use. Based on this indicator, the urban commuting distribution model is developed, which improves the traditional gravity model. Taking Beijing as a study case, the developed model is applied and verified using mobile phone signaling big data derived from the communication service of an operator in September 2017. Travel generation and distribution models are constructed and verified respectively. Our results show that: (1) For the travel generation model simulations, commuter population and resident population show a good linear relationship. This model generates a significant prediction with a goodness of fit of 0.84; (2) For the travel distribution model simulations, a comparison analysis is conducted between gravity model, radiation model, and modified model with LSDI. The gravity model corrected by real commuting data performs best in regression analysis with a goodness of fit of 0.94. But large errors occur in the probability density distribution. The radiation model performs normal in regression analysis with a goodness of fit of 0.37. It has a better accuracy in the probability density distribution. The modified gravity model with LSDI has the best overall performance. The underestimation phenomenon is optimized in the commuter population distribution with a highest goodness of fit (0.85). Our findings provide new insights in developing big-data oriented transport prediction models and contribute to promote the application of big data in transport planning.

Key words: big-data oriented model, commuting transport model, trip distribution, location-space dependence indicator, mobile phone signaling data, transport planning, commuting OD, model simulation