J4 ›› 2013, Vol. 26 ›› Issue (2): 98-104.doi: 10.3976/j.issn.1002-4026.2013.02.019

• 交通运输专栏 • 上一篇    下一篇

北京市交通承载力预测研究

李荣,吴建军   

  1. 1.北京交通大学交通运输学院,北京 100044; 2.北京交通大学轨道交通控制与安全国家重点实验室,北京 100044
  • 收稿日期:2012-12-21 出版日期:2013-04-20 发布日期:2013-04-20
  • 通信作者: 吴建军(1973-),男,副教授,研究方向为交通网络复杂性、轨道交通流特性。 E-mail:jjw1@bjtu.edu.cn
  • 作者简介:李荣(1988-),男,硕士研究生,研究方向为城市交通承载力。
  • 基金资助:

    国家自然科学基金(71271024);教育部新世纪优秀人才支持计划(NCET120764);北京市自然科学基金(8102029);教育部高等学校优秀博士学位论文资助项目(201170)。

Prediction of Beijing traffic carrying capacity

 LI Rong, WU Jian-Jun   

  1. 1.School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2.State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China
  • Received:2012-12-21 Online:2013-04-20 Published:2013-04-20

摘要:

造成大城市交通拥堵、环境污染和资源短缺等问题的根本原因在于机动车保有量增长过快,甚至超过交通承载力。本文应用多因素分析法分析了影响机动车保有量的关键因素,建立各因素的时间序列预测模型,并使用多元回归分析模型预测机动车保有量。在分析交通承载力的关键影响因素的基础上,建立交通承载力定量计算模型,通过关键因素的时间序列分析对交通承载力进行预测。根据现有数据计算,2010年以后北京市机动车保有量将超过交通承载力,且机动车保有量增长速度远远超过交通承载力增长速度,因此必须采取相关措施限制机动车保有量的快速增长。

关键词: 交通承载力, 时间序列分析, 多元回归分析

Abstract:

The essentials  that cause traffic congestion, environmental pollution and resource deficiency lie in too rapid vehicle increase which even exceeds traffic carrying capacity. We employ multivariate analysis to analyze the key factors impacting vehicle number. We also construct a time series prediction model for every key factor, and employ multiple regression analysis to predict vehicle number. We further establish a quantitative calculation model to determine urban traffic carrying capacity based on the analysis of key factors of traffic carrying capacity. The number of motor vehicle will exceed Beijing traffic carrying capacity after 2010 based on the calculation of the present data, and the vehicle growth rate will exceed that of traffic carrying capacity. Some relevant specifications therefore must be drawn to restrict the increase in vehicle number.

Key words: traffic carrying capacity, analysis of time series, multiple regression analysis

中图分类号: 

  • U491.1+4