考虑车辆轨迹的驾驶行为特征和交通排放分析
Xuejiao Du1,2, Xiuyun Kang1, Yan Gao3
1College of Marxism, Northeast Normal University, Changchun, China.
Frontiers in psychology
|February 13, 2024
概括
这项研究将驾驶行为与交通排放联系起来,使用GPS数据和高级分析来识别减少排放的驾驶模式. 结果有助于预测可持续城市交通的驾驶员排放.
科学领域:
- 环境科学 环境科学
- 运输工程 运输工程
- 数据科学数据科学数据科学
背景情况:
- 对于节约资源,环保社会的日益增长的需求,需要可持续的城市交通.
- 汽车废气是城市污染的主要来源.
- 了解驾驶行为与排放之间的联系对于减排战略至关重要.
研究的目的:
- 分析驾驶行为与交通排放之间的关系.
- 开发限制驾驶员行为的方法,以减少污染物排放.
- 利用数据驱动的洞察力促进低碳城市交通.
主要方法:
- 使用Navicat预处理GPS数据 (集成,选,排序).
- 在SPSS.中使用盒和线图和线性插值的清洁速度数据.
- 应用主要组件分析 (PCA) 来减少指标的维度.
- 使用K-MEANS和K-MEDOIDS算法对驾驶员行为进行集群.
- 通过符号近似聚合分析驾驶状态.
- 利用MOVES交通排放模型和决策树进行分析和预测.
主要成果:
- 根据速度,加速度和其他指标确定了不同的驾驶员行为集群.
- 在特定的驾驶状态/模式和交通排放之间建立了可量化的关系.
- 开发了一个决策树模型,用于预测驾驶模式和估计相关排放.
结论:
- 司机行为显著影响交通排放.
- 数据驱动的集群和排放建模可以确定减少排放的驾驶策略.
- 这些发现支持开发针对可持续城市交通的有针对性的干预措施.
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