Developmental differences in children's adaptation to vehicle distance and speed in street-crossing decision-making.

INTRODUCTION: Young children cannot effectively adapt their behaviors to vehicles at varied distances and speeds, which is a critical cause of road accidents. However, the impact of this crucial ability on children's street-crossing decision-making and the age at which they acquire it remain unclear. METHOD: This study examined the crossing decision-making behavior of children at 6, 8, and 11 years of age in facing 51 different videotaped traffic scenarios with varying vehicle distances and speeds. Sixty Chinese elementary school students, with 20 children evenly distributed into each of the three age groups (6 years, 8 years, and 11 years old), participated in a simulated street-crossing task using video projections. Hierarchical logistic regression models were used to analyze how age moderated the effects of vehicular motion factors (vehicle-pedestrian distance, vehicle speed) on children's crossing safety, including dangerous crossing and crossing decision-making. RESULTS: The results showed that when either vehicle-pedestrian distance decreased or vehicle speed increased all age groups tended to cross less frequently but probability of dangerous crossing increased. Compared to 8-year-old and 11-year-old children, 6-year-old children showed a less pronounced tendency toward both of these crossing decision-making behaviors, and had more dangerous crossing outcomes. CONCLUSIONS: These findings suggest that inadequate adaptation to vehicle-pedestrian distance and vehicle speed may partly contribute to the inferior safety of street-crossing behavior in 6-year-olds compared to 8-year-olds. No significant differences were observed between 8- and 11-year-old children, suggesting the turning point for this ability might occur between 6 and 8 years of age. PRACTICAL APPLICATIONS: Preventive measures aimed to reduce crossing risks for children should consider children's developmental stages.

A multi-level analysis on the causes of train-pedestrian collisions in Southwest China 2011-2020.

Collisions between trains and pedestrians are the primary cause of railway casualties. However, there remains a lack of comprehensive understanding regarding the underlying causes of this phenomenon. This study employs a multi-level approach to investigate the factors associated with the occurrence and severity of train-pedestrian collisions. The investigation is based on 2160 independent cases that occurred in southwest China from 2011 to 2020. Multiple contributing factors related to the victim, train, track, and socio-economic status of the surrounding district were examined, utilizing information from various sources. At the county level, several risk factors were identified in predicting the occurrence rate. These factors include higher population density and a greater number of normal-speed stations. However, the presence of high-speed train stations did not exhibit any significant impact. Additionally, the study found that regulations pertaining to protective fences were highly effective in reducing the occurrence rate. Regarding the prediction of collision severity, certain factors were found to increase the death rate. These factors include young men as victims, engaging in lying down or crossing behaviors, higher train speeds, gentle downhill slopes, lower education levels, and a higher proportion of the labor force. These findings emphasize the necessity of adopting a comprehensive perspective when examining the causes of train-pedestrian collisions. Furthermore, it underscores the significance of considering the notable differences between rapidly developing countries such as China and developed countries. Based on our findings, we also provide corresponding policy suggestions.

Directional dependence between major cities in China based on copula regression on air pollution measurements.

Air pollution is well-known as a major risk to public health, causing various diseases including pulmonary and cardiovascular diseases. As social concern increases, the amount of air pollution data is increasing rapidly. The purpose of this study is to statistically characterize dependence between major cities in China based on a measure of directional dependence estimated from PM2.5 measurements. As a measure of the directional dependence, we propose the so-called copula directional dependence (CDD) using beta regression models. An advantage of the CDD is that it does not rely on strict assumptions of specific probability distributions or linearity. We used hourly PM2.5 measurement data collected at four major cities in China: Beijing, Chengdu, Guangzhou, and Shanghai, from 2013 to 2017. After accounting for autocorrelation in the PM2.5 time series via nonlinear autoregressive models, CDDs between the four cities were estimated to produce directed network structures of statistical dependence. In addition, a statistical method was proposed to test the directionality of dependence between each pair of cities. From the PM2.5 data, we could discover that Chengdu and Guangzhou are the most closely related cities and that the directionality between them has changed once during 2013 to 2017, which implies a major economic or environmental change in these Chinese regions.