在"长途"道路运输中分析道路粗度和驾驶员舒适度,使用随机森林方法
Olusola O Ajayi1, Anish M Kurien1, Karim Djouani1,2
1F'SATI, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
粗的道路大大降低了长途驾驶员的舒适度,并增加了安全风险. 通过维护改善道路表面对于驾驶员的福祉和运输效率至关重要.
科学领域:
- 运输工程 运输工程
- 机器学习应用 机器学习应用
- 驾驶过程中的人类因素
背景情况:
- 长途运输对全球贸易至关重要,但驾驶员的舒适性往往被忽视.
- 道路粗度会对驾驶员的舒适性产生负面影响,导致疲劳,分心和事故.
研究的目的:
- 通过机器学习研究道路粗度和驾驶员舒适度之间的关系.
- 确定道路状况对驾驶员福利和安全的影响.
主要方法:
- 使用随机森林回归,支持矢量回归和XGBoost模型.
- 通过MIRANDA移动应用程序收集了1,048,576个关于车辆加速和国际粗度指数 (IRI) 的数据点.
- 对比了不同机器学习模型的性能,以实时应用和准确性.
主要成果:
- 在增加道路粗度和降低驾驶员舒适度之间发现了显著的相关性.
- 更粗的道路与更高的垂直加速 (SD-0.73) 和更低的舒适度相关 (平均10.01,SD-0.64).
- 随机森林因为其实时部署能力和较低的内存使用量而被选中,尽管XGBoost的训练速度和准确性优越.
结论:
- 平滑的路面和有效的维护对于提高道路安全和减少驾驶员疲劳至关重要.
- 数据驱动的洞察力可以为交通当局和政策制定者提供信息,以改善道路状况和驾驶员福利.
- 优先考虑道路质量支持高效的运输和长途驾驶员的福祉.
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