• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Promet-traffic & transportation >PREDICTION OF FATAL AND MAJOR INJURIES OF DRIVERS, CYCLISTS, AND PEDESTRIANS IN COLLISIONS
【24h】

PREDICTION OF FATAL AND MAJOR INJURIES OF DRIVERS, CYCLISTS, AND PEDESTRIANS IN COLLISIONS

机译：预测碰撞中司机，骑自行车者和行人的致命和重大伤害

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns; providing additional data-driven decision support for strategic planning. A detailed exploratoty analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.
机译：交通相关的死亡和严重伤害可能会影响道路上的每个人，无论是驾驶，骑自行车还是散步。多伦多是加拿大最大的城市和北美第四大的城市，旨在消除与城市街道的交通相关的死亡和严重伤害。本研究的目的是使用从过去的模式学习的数据分析和机器学习技术来构建预测模型;为战略规划提供额外的数据驱动决策支持。提出了详细的探索性分析，研究了影响多伦多碰撞的变量与因素之间的关系。提出了一种基于学习的模型，以通过比较两种预测模型来预测交通碰撞中的死亡和严重伤害：套索回归和随机林。探索性数据分析结果揭示了时空和行为模式，例如交叉口碰撞中的普遍性，春季和夏季和司机中的侵略性驾驶和疏忽行为。预测结果表明，对于司机，骑自行车者和行人的伤害严重程度的最佳预测因子是随机森林，精度分别为0.80,0.89和0.80。所提出的方法展示了机器学习应用于流量和碰撞数据的有效性，用于探索性和预测分析。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号