地理学报 ›› 2012, Vol. 67 ›› Issue (12): 1657-1665.doi: 10.11821/xb201212007

• 城市与人口研究 • 上一篇    下一篇

基于手机基站数据的城市交通流量模拟

吴健生1, 黄力1, 刘瑜2, 彭建3, 李卫锋4, 高松2, 康朝贵2   

  1. 1. 北京大学深圳研究生院城市人居环境科学与技术重点实验室, 深圳 518055;
    2. 北京大学遥感与地理信息系统研究所, 北京 100871;
    3. 北京大学城市与环境学院, 北京 100871;
    4. 香港大学城市规划与设计系, 香港
  • 收稿日期:2012-05-11 修回日期:2012-10-09 出版日期:2012-12-20 发布日期:2013-02-07
  • 通讯作者: 黄力,E-mail:huangli.ghost@gmail.com E-mail:huangli.ghost@gmail.com
  • 作者简介:吴健生(1965-),男,副教授,主要研究方向为城市景观生态和GIS。E-mail:wujs@pkusz.edu.cn
  • 基金资助:

    国家自然科学基金项目(41271101)

Traffic Flow Simulation Based on Call Detail Records

WU Jiansheng1, HUANG Li1, LIU Yu2, PENG Jian3, LI Weifeng4, GAO Song2, KANG Chaogui2   

  1. 1. Shenzhen Graduate School of Peking University, The Key Laboratory for Environmental and Urban Sciences, Shenzhen 518055, Guangdong, China;
    2. Institute of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China;
    3. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China;
    4. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
  • Received:2012-05-11 Revised:2012-10-09 Online:2012-12-20 Published:2013-02-07
  • Supported by:

    National Natural Science Foundation of China, No.41271101

摘要: 基于移动定位数据的城市内社会经济活动特征分析是人类移动性的重要研究内容,而交通流量更是这些特征的基本反映。为还原城市道路网络的使用情况并分析其分布特征,本文从产生交通流量的个体出发,对包含基站位置的手机话单数据进行系统抽样,利用蒙特卡洛方法产生个体的出行起止点,并结合当地道路交通网络求得最短路径,最后估算出一天内道路交通网络上的流量分布。通过分析发现:城市内大部分道路的流量小,使用率低,大部分交通流量集中在小部分主干道路;进一步统计分析可知,当地道路交通流量符合20/80规律,即大约20%的道路承担着80%的交通流量;而对不同类型的道路,流量分布也反映出其在城市道路网络中的地位和作用。此研究对于历史交通流量分布的重现、城市道路交通模式的研究以及基于此的道路网络规划情景模拟都有着重要意义。

关键词: 人类移动性, 手机基站数据, 城市交通流量模拟

Abstract: Urban social and economic activity analysis based on mobile location data is a magnificent context for human mobility research and the traffic flow is one of the most basic activities. In order to restore the use of urban transportation network and examine its distribution, we apply a novel approach to draw a traffic flow distribution map of local road network based on a large number of individual cellphone detailed records. We reconstruct details of individual user's mobility and generate its traffic flow step by step: 1. Sampling cellphone records from local operator;2. simulating the random start point and end point for each individual by Monte Carlo;3. working out its route through the shortest path. After sampling and simulating thousands of records in one day, we finally draw a traffic flow distribution map of local road network, in which we uncover that a large portion of roads contains a small portion of flows and vice versa. In further statistical analysis, we reach the 20/80 principle of traffic flow: 20% of the top roads accommodate 80% of traffic flow. And flow distribution of different road types reflects the function of urban transportation networks. This research make a contribution to the reconstructed historical traffic flow distribution, studies on urban road network pattern and scenario simulation of transportation planning.

Key words: human mobility, mobile phone data, traffic flow simulation