Introduction

Careful consideration of the factors that result in the traffic collision occurrence is essential from the public health point of view. This allows us to propose prevention measures based on a multidisciplinary focus, including the health, education, labor, transportation areas, and others. Accordingly, the present study pursues a twofold objective: a) investigating how socio-economic characteristics of a specific area can influence the level of traffic safety frequency in that area; b) evaluating and comparing the capabilities of different modeling approaches in pursuit of the former goal.

Methods

A set of 365 cities in the State of California with their socio-economic characteristics (such as age, educational level, median household income, and among others) in hand were considered in predicting the rate of traffic injuries and fatalities based on the regional population. Furthermore, the capability of Principal Component Analysis (PCA) in enhancing the accuracy of the results obtained from the developed Artificial Neural Network (ANN) has been studied using SPSS software.

Results

According to the results, the male population percentage has the most significant impact on the modeling process, followed by the unemployment rate and the education level. College and university enrollment is another significant contributing factor that has been ignored in the previous studies, which shows how students’ behavior would affect the overall traffic safety condition of a given city.

Conclusions

Generally, it can be concluded that the evaluation of the correlation between socio-economic factors and traffic collision occurrence can provide decision-makers with valuable insight into the preventive policies in pursuing the goal of improving the overall level of traffic safety.

中文翻译：

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评估加利福尼亚州城市的社会经济影响因素对其交通安全状况的影响

介绍

从公共卫生的角度出发，仔细考虑导致交通事故发生的因素是至关重要的。这使我们能够基于多学科的重点提出预防措施，包括卫生，教育，劳动，交通领域等。因此，本研究追求双重目标：a）研究特定区域的社会经济特征如何影响该区域的交通安全频率水平；b）为实现先前的目标而评估和比较不同建模方法的能力。

方法

在根据地区人口预测交通伤害和死亡人数时，考虑了加利福尼亚州的365个城市，这些城市具有社会经济特征（例如年龄，教育水平，家庭收入中位数等） 。此外，已经使用SPSS软件研究了主成分分析（PCA）增强从已开发的人工神经网络（ANN）获得的结果的准确性的能力。

结果

根据结果​​，男性人口百分比对建模过程的影响最大，其次是失业率和教育程度。高校入学率是另一个在先前的研究中被忽略的重要因素，它表明学生的行为将如何影响特定城市的整体交通安全状况。

结论

通常，可以得出结论，对社会经济因素与交通冲突发生之间的相关性进行评估可以为决策者提供预防性政策的宝贵见解，以实现提高交通安全总体水平的目标。