这项研究利用了一种新颖的深度学习模型,Inception-v3,使用从路易斯安那州收集的五年(2016-2021年)的数据来预测行人撞车的严重程度。最终的数据集包含了与行人属性相关的40个不同的变量,环境条件,和车辆细节。碰撞严重程度分为三类:致命,损伤,也没有受伤。Boruta算法用于确定变量的重要性,并调查行人碰撞严重程度的影响因素,揭示了几个相关的方面,包括行人性别,行人和驾驶员损伤,张贴的速度限制,酒精参与,行人年龄,能见度障碍,道路照明条件,以及行人和驾驶员的状况,包括分心和注意力不集中。为了解决数据不平衡,该研究采用随机欠采样(RUS)和合成少数群体过采样技术(SMOTE)。DeepInsight技术将数值数据转换为图像。随后,使用Inception-v3开发了五个碰撞严重性预测模型,考虑了各种情况,包括原创,欠采样,过采样,欠采样和过采样数据的组合,和前二十五个重要变量。结果表明,在几个性能指标方面,应用过采样和欠采样的模型优于基于其他数据平衡技术的模型。包括准确性,灵敏度,精度,特异性,假阴性率(FNR),假阳性率(FPR),和F1得分。该模型实现了93.5%的预测精度,77.5%,85.9%是致命的,损伤,没有伤害类别,分别。此外,基于几个性能指标和McNemar的测试表明,与传统的机器学习和统计模型相比,Inception-v3深度学习模型的预测性能在统计上更优越。这项研究的见解可以被安全专业人员有效地利用,紧急服务提供商,交通管理中心,和车辆制造商加强其安全措施和应用。
This research leverages a novel deep learning model, Inception-v3, to predict pedestrian crash severity using data collected over five years (2016-2021) from Louisiana. The final dataset incorporates forty different variables related to pedestrian attributes, environmental conditions, and vehicular specifics. Crash severity was classified into three categories: fatal, injury, and no injury. The Boruta algorithm was applied to determine the importance of variables and investigate contributing factors to pedestrian crash severity, revealing several associated aspects, including pedestrian gender, pedestrian and driver impairment, posted speed limits, alcohol involvement, pedestrian age, visibility obstruction, roadway lighting conditions, and both pedestrian and driver conditions, including distraction and inattentiveness. To address data imbalance, the study employed Random Under Sampling (RUS) and the Synthetic Minority Oversampling Technique (SMOTE). The DeepInsight technique transformed numeric data into images. Subsequently, five crash severity prediction models were developed with Inception-v3, considering various scenarios, including original, under-sampled, over-sampled, a combination of under and over-sampled data, and the top twenty-five important variables. Results indicated that the model applying both over and under sampling outperforms models based on other data balancing techniques in terms of several performance metrics, including accuracy, sensitivity, precision, specificity, false negative ratio (FNR), false positive ratio (FPR), and F1-score. This model achieved prediction accuracies of 93.5%, 77.5%, and 85.9% for fatal, injury, and no injury categories, respectively. Additionally, comparative analysis based on several performance metrics and McNemar\'s tests demonstrated that the predictive performance of the Inception-v3 deep learning model is statistically superior compared to traditional machine learning and statistical models. The insights from this research can be effectively harnessed by safety professionals, emergency service providers, traffic management centers, and vehicle manufacturers to enhance their safety measures and applications.