交通运输系统工程与信息 ›› 2015, Vol. 15 ›› Issue (1): 218-225.

• 案例分析 • 上一篇    下一篇

降雨对城市道路行程速度的影响

龚大鹏1,宋国华1,黎明1,高永2,于雷*1,3   

  1. 1. 北京交通大学城市交通复杂系统理论与技术教育部重点实验室,北京100044; 2. 北京交通发展研究中心,北京100161;3. 德克萨斯南方大学,休斯敦77004,美国
  • 收稿日期:2014-07-07 修回日期:2014-08-02 出版日期:2015-02-25 发布日期:2016-02-25
  • 作者简介:龚大鹏(1992-),男,安徽人,硕士生.
  • 基金资助:

    国家重点基础研究发展计划(973计划)(2012CB725403);典型交通事件下区域交通拥堵演变规律分析(T14JB00180).

Impact of Rainfalls on Travel Speed on Urban Roads

GONG Da-peng1, SONG Guo-hua1, LI Ming1, GAO Yong2, YU Lei1,3   

  1. 1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China; 2. Beijing Transportation Research Center, Beijing 100161, China; 3. Department of Transportation Studies, Texas Southern University, Houston, TX 77004, USA
  • Received:2014-07-07 Revised:2014-08-02 Online:2015-02-25 Published:2016-02-25

摘要:

以北京市实时天气数据和基于浮动车的城市道路行程速度、交通运行指数数据为基础,对比分析降雨天气和正常天气的行程速度、指数、降雨量等数据指标.然后从降雨强度、时间段、拥堵等级等角度展开对城市道路运行参数的分析,建立降雨天气速度预测修正模型,并进行模型验证.研究得出,在夜间降雨强度达到中雨及以上时,快速路、主干路、次支路的速度下降百分比分别为:8.8%、4.8%、5.9%,分别得出高峰和平峰时降雨强度与行程速度下降之间的关系;得出在全路网不同拥堵等级下降雨强度与行程速度下降之间的关系.最后对速度预测模型进行实测验证.结果表明,该模型可以对降雨天气条件下的行程速度进行有效预测,预测的平均误差在5%以内.

关键词: 城市交通, 降雨强度, 行程速度, 道路等级, 速度预测

Abstract:

By utilizing Beijing real-time weather data, travel speed on urban roads and traffic performance index (TPI) based on floating car data (FCD), this paper analyzes and compares travel speed, TPI and rainfalls under rainfall weather and normal weather. Then, the operating parameters of urban roads are analyzed from the perspectives of precipitation intensity, time periods and congestion levels. Further, the correction model for speed prediction is developed and validated. The study shows that travel speeds on the expressway, major arterial and collector decrease by 8.8% , 4.0% and 5.9% respectively when the precipitation intensity reaches the moderate rain level or above at night, and further derives the relationships between the precipitation intensity and the travel speed reduction during the peak and non- peak hours. In addition, the relationships between the precipitation intensity and the travel speed reduction under different congestion levels are also obtained. Finally, application examples are presented to validate the proposed model, which shows that the model can effectively predict the travel speed under the rainfall weather, and the average prediction error is less than 5%.

Key words: urban traffic, precipitation intensity, travel speed, road classes, speed prediction

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