公路交通事故与气象条件关系及其气象预警模型

罗慧; 李良序; 胡胜; JohnNairn; 刘宇; 郑磊

公路交通事故与气象条件关系及其气象预警模型

罗慧¹,
李良序¹,
胡胜²,
JohnNairn³,
刘宇¹,
郑磊⁴

1.
陕西省气象局, 西安 710015
2.
广州中心气象台, 广州 110049
3.
South Australia Regional Office, Kent Town, Adelaide, Australia 5067
4.
陕西省公安厅, 西安 710049

资助项目: 陕西省气象局2005年重点项目“陕西省交通气象预报服务系统”资助

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出版历程
- 收稿日期: 2006-04-28
- 修回日期: 2006-12-31
- 刊出日期: 2007-06-30

The Relationship Between Road Traffic Crashes and Meteorological Condition with Construction of Its Road Weather Warning Model

1.
Shaanxi Provincial Meteorological Bureau, Xi'an 710015
2.
Guangzhou Central Meteorological Office, Guangzhou 110049
3.
South Australia Regional Office, Kent Town, Adelaide, Australia 5067
4.
Shaanxi Provincial Public Security Department, Xi'an 710049

摘要

摘要: 以西安地区为例, 分析该地区2002年1月—2004年12月连续3年逐日公路交通事故资料1096个样本, 以及该地区、对应的逐日13个气象要素资料, 并将样本数据划分为春夏和秋冬两个半年, 来考虑气象要素在不同时段的不同影响, 建立合理有效的公路气象预警模型。通过2005年3月—2006年4月交通事故发生起数共365个测试样本的检验, 发现这个模型具有较高的预测准确性, 运用该地区气象要素建立公路交通事故的预警模型是可行而有效的。研究表明:西安地区气象要素中包含着影响和可以预测当地公路交通的信息, 按照在logistic方程中的显著性大小和因子负载的大小, 在春夏半年 (4—9月), 影响西安地区公路交通事故相关因素依次为:能见度因子、相对湿度因子和降水因子; 而在秋冬半年 (10月—次年3月), 依次为温度因子、能见度因子、降水因子。
- 公路气象;
- 因子分析;
- 多重共线性;
- logistic回归分析
Abstract: In order to construct a valid road weather warning model, the effect of weather on the daily number of all reported successive traffic accidents day by day from January 2002 to December 2004 (1096 valid samples) in the area of Xi'an city is examined. Thirteen meteorological elements sampled over the same area day by day are applied for the corresponding three years. The 1096 sample days are divided into two half-year parts given different seasonal regimes of the meteorological elements. Data are processed using statistical product and service solution (SPSS) version 12.0.Factor analysis methods are utilized to summarize four public factors, which are F_i(i=1, …, 4) and H_i(i=1, …, 4), integrated by temperature, visibility, relative humidity and rainfall in spring-summer half-year period and temperature, visibility, rainfall and pressure respectively in autumn-winter half-year period. Multi-collinearity is effectively overcome by this process and the meteorological variables are reduced from thirteen elements to four public factors.Logistic regression analysis based on these four public factors is applied. Initially the model is calculated, the number in the set that occurs most frequently in all traffic crashes data is obtained. Then it is supposed that if the raw crash data are greater than the mode value 1 is set to this variable and targets the high crashes frequency; if the raw data are smaller than the mode value 0 is applied to this variable and targets low crashes frequency. Binary logistic by entrance option is applied to get the road weather warning model over the two half-year periods. But F₁ (temperature factor in spring-summer period) and H₄(pressure factor in autumn-winter period) are shown to be insignificant, so they are abandoned when the logistic regression equations are constructed.The model is evaluated for preferable prediction accuracy through samples tests. The results show that the meteorological factors used to indicate their influences and forecast traffic crashes in Xi'an city result in a suitable and effective road weather warning model.In summary, according to significance level in the logistic regression equation and the value of factor loadings, the factors are respectively integrated by visibility, relative humidity and rainfall in spring-summer half-year period and temperature, visibility and rainfall in autumn-winter half-year period.
- road weather;
- factor analysis;
- multi-collinearity;
- logistic regression

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图 1 2002—2004年西安地区公路交通事故起数分月同比

Fig. 1 Comparison of traffic crashes data of each month in Xi'an area from 2002 to 2004

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图 2 西安地区2001年1月—2004年12月交通事故众数直方图

(a) 春夏半年, (b) 秋冬半年

Fig. 2 Mode of traffic crashes data in spring-summer half-year (a) and autumn-winter half-year (b) in Xi'an area from Janugry, 2001 to December, 2004

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表 1 春夏半年各因子的方差贡献率 (单位:%)

Table 1 Total variance explained of each factor (unit:%)

表 2 春夏半年旋转后因子负载矩阵

Table 2 Rotated component matrix of each factor in spring-summer half-year

表 3 秋冬半年旋转后因子负载矩阵

Table 3 Rotated component matrix of each factor in autumn-winter half-year

表 4 春夏半年因子得分系数矩阵

Table 4 Component score coefficient matrix of each factor in spring-summer half-year

表 5 秋冬半年因子得分系数矩阵

Table 5 Component score coefficient matrix of each factor in autumn-winter half-year

表 6 进入logistic回归方程的变量

Table 6 Variables in the logistic equation

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