酒精可获得性对酒驾交通事故的空间效应

    Spatial Effects of Alcohol Availability on Drunk Driving Traffic Accidents

    • 摘要: 运用空间计量模型对酒精销售场所对酒后驾驶交通事故的空间效应进行实证研究.基于2 114个交通分析小区,提取3 356起酒后驾驶交通事故、社会人口和酒精消费相关兴趣点等数据.在空间自相关性分析后,构建传统回归模型、空间滞后模型、空间误差模型和空间杜宾模型,基于拉格朗日乘数、对数似然函数值和似然比率检验进行模型比较,最后量化分析解释变量的直接效应、溢出效应和总效应.结果表明:市区酒驾交通事故不具有空间自相关特征,近郊区和远郊区的酒驾交通事故具有空间自相关特征.增量空间自相关结果显示近郊区和远郊区空间自相关性最显著的距离分别为1.54、7.95 km.模型对比结果表明空间杜宾模型拟合度和解释力最优.近郊区和远郊区解释变量的空间效应特征各不相同,但总体而言,零售店密度均具有正向直接效应和溢出效应,宾馆酒店密度和公司企业密度均具有负向的直接效应.该结论可为交通执法、交通管理、土地利用等提供重要依据.

       

      Abstract: The spatial econometric model was used to conduct an empirical study of the spatial effects of alcohol outlets on drunk driving accidents. 3 356 cases data of alcohol-related crashes, social population and alcohol consumption related points of interest (POI) were collected based on the 2 114 traffic analysis zones (TAZ). After applying exploratory spatial data analysis, the traditional ordinary least square model, the spatial lag model, spatial error model and spatial durbin model were developed. The models were compared based on Lagrange multiplier, log likelihood and likelihood ratio test. Finally, the direct effect, spillover effect and total effect of explanatory variables were quantitatively analyzed. Results show that there is no spatial autocorrelation for the alcohol related crashes in urban district, but spatial autocorrelation in inner suburban district and outer suburban district. The incremental spatial autocorrelation shows that the most significant distance of alcohol-related road crashes is 1.54 km in inner suburban district and 7.95 km in outer suburban district, respectively. Compared with different spatial models, the SDM model obtains the best model fit and explanatory power. The spatial effects of explanatory variables in inner suburban district and outer suburban district are different; however, overall, retails density has positive direct effect and spillover effect, and the density of hotels and companies has negative direct effect. The conclusion can provide an important basis for traffic law enforcement, traffic management, land use, and so on.

       

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