主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

Journal of Meteorology and Environment ›› 2018, Vol. 34 ›› Issue (3): 106-111.doi: 10.3969/j.issn.1673-503X.2018.03.013

Previous Articles    

Effects of meteorological conditions on highway traffic safety in Liaoning province

LIN Yi1, LI Qian2, ZHANG Kai1, LI Lan1, QI Xin1, LIN Zhong-guan1, LIN Song3, ZHANG Yun-fu1   

  1. 1. Liaoning Province Public Meteorological Service Center, Shenyang 110166, China;
    2. Shenyang Regional Climate Center, Shenyang 110166, China;
    3. Heilongjiang Province Public Meteorological Service Center, Harbin 150030, China
  • Received:2017-12-06 Revised:2018-01-19 Online:2018-06-30 Published:2018-06-30

Abstract: Based on highway traffic accident data from 2010 to 2015 in Liaoning province,including geographic position,level of accident and environmental condition,highway traffic accident index was quantified based on the level and the number of accidents using order relation analysis method.By matching the accident location with the nearest meteorological observation station,correlation relationship between meteorological factors and traffic accident number was analyzed.A stepwise regression analysis was proposed to estimate highway traffic accident index based on meteorological factors.The results show that the years with high frequency of highway traffic accidents are characterized by more precipitation and less sunshine hours.The number of accidents caused by weather and average highway traffic accident index are the largest in winter.The correlation relationship between highway traffic accident index and relative humidity (or precipitation) is better than that with other meteorological factors.The stepwise regression method is used for simulating highway accident index based on meteorological factors.It suggests that the prediction accurate rates of major accidents in winter and autumn are higher than those in other seasons,and they are 62.5% and 51.3%,respectively.

Key words: Highway traffic accident, Meteorological conditions, Highway traffic accident index, Order relation andysis method, Regression model

CLC Number: