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首页> 外文期刊>Accident Analysis & Prevention >Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model
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Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model

机译：调查摩托车骑士的伤害严重程度：结合潜在类聚类分析和随机参数对数模型的两步法

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摘要

Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015-2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders' injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety.
机译：由于交通安全数据中异构性质的广泛存在，用于调查摩托车骑士伤害严重程度的传统方法始终导致掩盖某些潜在关系，这对于制定有效的安全对策可能至关重要。本研究提出了一种将潜在类聚类分析和随机参数对数模型相结合的两步方法，以探索影响伤害的因素，而不是像以前的研究那样将一个模型应用于整个数据集或关注于预先定义的碰撞类型。电单车的水平。隐性类聚类方法首先用于将摩托车碰撞分成相对均匀的类。然后为每个集群精心开发混合logit模型，以识别其独特的影响因素。该分析基于中国湖南省警方报告的碰撞数据集（2015-2017）。拟合优度指标和接收器工作特性曲线表明，该方法在对骑手的受伤严重程度进行建模时更为准确。在每个同质亚组中发现的异质性支持随机参数对数模型在研究中的应用。更重要的是，这些结果表明，将摩托车事故划分为相对同质的簇是一个初步步骤，有助于发现隐藏在整个数据模型中的一些重要影响因素。实践证明，该方法具有很好的解决异质性来源的潜力。在特定情况下确定的伤害危险因素可为交通工程师和政策制定者提供更可靠的信息，以改善摩托车交通安全。

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