China’s traffic safety attracts increasing research interest.Official data show that crashes in the western region of China are more severe than those in the eastern region.However,research on crash severity in weste...China’s traffic safety attracts increasing research interest.Official data show that crashes in the western region of China are more severe than those in the eastern region.However,research on crash severity in western China is scarce.This study applied a hierarchical Bayesian logistic model to examine the significant factors related to crash and vehicle/driver levels and their heterogeneous impacts on the severity of drivers’ injury.Crash data were collected from Lintao,a rural mountainous county in western China.A variable was proposed to measure the relative difference between the crashworthiness of one vehicle and the aggressivity of the other vehicle in the mixed traffic flow.Results indicated that the majority of the total variance was induced by between-crash variance,showing the suitability of the utilized hierarchical modeling approach.One crash-level variable and six vehicle/driver-level variables,namely,road type,compatibility difference,age,vehicle type,drunk driving,driving unregistered vehicle,and driving years,significantly affected modeling drivers’ injury severities.Among these variables,road type(national and provincial),age(young and senior drivers),driving unregistered vehicle,and drunk driving tended to increase the odds of crash-related mortality.Driving years(new drivers with less than six years of driving experience) and vehicle type(heavy vehicle) were likely to decrease the probability of fatal outcomes.Compatibility difference was relatively significant,and the possibilities of mortality in single vehicle crashes were higher than those inmultivehicle and pedestrian-involved crashes.The developed methodology and estimation results provided insights into the internal mechanism of rural crashes and effective countermeasures to prevent rural crashes.展开更多
[目的]将现代计量学上普遍应用的自回归移动平均模型(autoregressive integrated moving average model,ARIMA)引入生态足迹分析,寻求动态预测结果。[方法]以山东省济宁市微山县为案例,对其1995—2010年的生态足迹和生态承载力进行估算...[目的]将现代计量学上普遍应用的自回归移动平均模型(autoregressive integrated moving average model,ARIMA)引入生态足迹分析,寻求动态预测结果。[方法]以山东省济宁市微山县为案例,对其1995—2010年的生态足迹和生态承载力进行估算,预测该县2010—2015年的生态足迹和生态承载力变化趋势。[结果]2011与2012年真实数据检验结果显示,ARIMA模拟模型的预测误差仅为6.12%和4.89%。[结论]基于ARIMA的生态足迹动态模拟模型具有较高的准确性和适用性。展开更多
In the Report of the 19 th National Congress of the Communist Party of China,it stated that we must put the ecological safety and green development in the first place.To achieve rural revitalization,ecological livabil...In the Report of the 19 th National Congress of the Communist Party of China,it stated that we must put the ecological safety and green development in the first place.To achieve rural revitalization,ecological livability is the most important factor.Taking Luquan Yi and Miao Autonomous County(national level poor county)in Jinsha River valley of Yunnan Province as an example,using DPSIR(drivers,pressures,states,impacts,responses)model,this paper established an evaluation indicator system.Besides,using the entropy method,it determined the indicator weight.In addition,it evaluated the land ecological security of Luquan County since the implementation of targeted poverty alleviation policy(2013-2017)using multi-factor comprehensive evaluation method.This study shows that Luquan County has improved its ecological security in the past five years.The ecological status has gone through four stages,from sensitive state to severe state to critical safety to excellent ecology,and the value of the comprehensive index of land ecological security shows a rising trend.Through analyzing the ecological security value of each criterion hierarchy,it obtained the main factors affecting the ecological security of Luquan County.On this basis,it came up with feasible measures and recommendations for studying the ecological security of land in the region,so as to guide the rational use of land and sustainable economic development,provide a reference for the implementation of rural revitalization strategies,and promote the construction of regional ecological civilization.展开更多
基金The research reported in this paper is part of the project supported by the National Natural Science Foundation of China (71871123)。
文摘China’s traffic safety attracts increasing research interest.Official data show that crashes in the western region of China are more severe than those in the eastern region.However,research on crash severity in western China is scarce.This study applied a hierarchical Bayesian logistic model to examine the significant factors related to crash and vehicle/driver levels and their heterogeneous impacts on the severity of drivers’ injury.Crash data were collected from Lintao,a rural mountainous county in western China.A variable was proposed to measure the relative difference between the crashworthiness of one vehicle and the aggressivity of the other vehicle in the mixed traffic flow.Results indicated that the majority of the total variance was induced by between-crash variance,showing the suitability of the utilized hierarchical modeling approach.One crash-level variable and six vehicle/driver-level variables,namely,road type,compatibility difference,age,vehicle type,drunk driving,driving unregistered vehicle,and driving years,significantly affected modeling drivers’ injury severities.Among these variables,road type(national and provincial),age(young and senior drivers),driving unregistered vehicle,and drunk driving tended to increase the odds of crash-related mortality.Driving years(new drivers with less than six years of driving experience) and vehicle type(heavy vehicle) were likely to decrease the probability of fatal outcomes.Compatibility difference was relatively significant,and the possibilities of mortality in single vehicle crashes were higher than those inmultivehicle and pedestrian-involved crashes.The developed methodology and estimation results provided insights into the internal mechanism of rural crashes and effective countermeasures to prevent rural crashes.
文摘[目的]将现代计量学上普遍应用的自回归移动平均模型(autoregressive integrated moving average model,ARIMA)引入生态足迹分析,寻求动态预测结果。[方法]以山东省济宁市微山县为案例,对其1995—2010年的生态足迹和生态承载力进行估算,预测该县2010—2015年的生态足迹和生态承载力变化趋势。[结果]2011与2012年真实数据检验结果显示,ARIMA模拟模型的预测误差仅为6.12%和4.89%。[结论]基于ARIMA的生态足迹动态模拟模型具有较高的准确性和适用性。
文摘In the Report of the 19 th National Congress of the Communist Party of China,it stated that we must put the ecological safety and green development in the first place.To achieve rural revitalization,ecological livability is the most important factor.Taking Luquan Yi and Miao Autonomous County(national level poor county)in Jinsha River valley of Yunnan Province as an example,using DPSIR(drivers,pressures,states,impacts,responses)model,this paper established an evaluation indicator system.Besides,using the entropy method,it determined the indicator weight.In addition,it evaluated the land ecological security of Luquan County since the implementation of targeted poverty alleviation policy(2013-2017)using multi-factor comprehensive evaluation method.This study shows that Luquan County has improved its ecological security in the past five years.The ecological status has gone through four stages,from sensitive state to severe state to critical safety to excellent ecology,and the value of the comprehensive index of land ecological security shows a rising trend.Through analyzing the ecological security value of each criterion hierarchy,it obtained the main factors affecting the ecological security of Luquan County.On this basis,it came up with feasible measures and recommendations for studying the ecological security of land in the region,so as to guide the rational use of land and sustainable economic development,provide a reference for the implementation of rural revitalization strategies,and promote the construction of regional ecological civilization.
