Prati Gabriele, De Angelis Marco, Marín Puchades Víctor, Fraboni Federico, Pietrantoni Luca
Department of Psychology, University of Bologna, Bologna, Italy.
PLoS One. 2017 Feb 3;12(2):e0171484. doi: 10.1371/journal.pone.0171484. eCollection 2017.
The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist's maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types.
与自行车碰撞严重程度相关的因素可能因不同的自行车碰撞模式而有所不同。因此，识别具有同质属性的不同自行车碰撞模式非常重要。当前的研究旨在识别意大利自行车碰撞的亚组，并分别分析不同的自行车碰撞类型。本研究聚焦于2011年至2013年期间在意大利发生的自行车碰撞事故。我们分析了与基础设施特征（道路类型、道路标志和地点类型）、道路使用者（即对方车辆和骑车人的机动动作、碰撞类型、骑车人的年龄和性别）、车辆（对方车辆类型）以及环境和时间段变量（一天中的时间、一周中的日期、季节、路面状况和天气）相对应的分类指标。为了识别自行车碰撞的同质亚组，我们使用了潜在类别分析。通过潜在类别分析，自行车碰撞数据集被划分为19个类别，代表19种不同的自行车碰撞类型。逻辑回归分析用于识别类别归属与自行车碰撞严重程度之间的关联。最后，对每个潜在类别进行关联规则分析，以揭示与严重程度增加可能性相关的因素。关联规则突出了与19种自行车碰撞类型中每种类型严重程度增加可能性相关的不同碰撞特征。
