通过抽取美国印第安纳州2013-2015年1947起摩托车单车事故，分别建立Nested Logit与Random Parameters Logit模型以分析摩托车事故伤害程度的影响因素，模型的参数分别采用全信息最大似然估计法与蒙特卡洛模拟方法进行估计。两个模型估计结果均表明女性、年龄、使用头盔、酒驾、甩出车外、超速、冲出道路、载人、车龄>10年、路面潮湿、曲线坡度、交叉口、限速值>50mph、4、7月份、夜间无灯光、郊区、事故碰撞物（防护栏、树、墙、路缘、电线杆、涵洞）与摩托车事故伤害程度显著相关。通过对比Nested Logit与Random Parameters Logit模型的AIC与BIC准则值，表明Random Parameters Logit模型对事故数据的拟合优度更高，能够得到更好的参数估计结果。文中结果能为后期国内系统的摩托车事故伤害分析提供指导与依据。

To analyze the factors affecting the injury severity of motorcycle crashes, Nested Logit and Random Parameters Logit models were developed. Motorcycle crashes data from 2013 to 2015 in Indian were collected with a total of 1947 single-vehicle motorcycle crashes occurring during that time period. The parameters of Nested logit and Random Parameters Logit models were estimated under full information maximum likelihood (FIML) method and Monte Carlo method separately. Two model estimation results all show that female, age, helmet use, alcohol use, speeding, run off of road, passenger on vehicle, vehicle age over 10 years old, wet pavement, horizontal curve with slope, intersection, speed limits over 50mph, months of April and July, nights without street lights, rural area, fixed collision objections (guardrail, tree, wall, curb, pole, culvert) are significantly related to motorcycle injury severity. By compared with the AIC value and BIC value of Nested logit and Random Parameters Logit models, showing that the overall model fit of Random Parameters Logit model is better than Nested logit model. The paper can provide a reference and guidance for further analysis of crash injury severity in domestic motorcycle crashes.