自行车数据驱动的应用框架:一个荷兰的案例研究,基于机器学习的自行车延迟估计在使用全国稀缺的GPS数据信号交叉路口全国稀缺的GPS数据
Yufei Yuan1, Kaiyi Wang2, Dorine Duives1
1Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
估计十字路口的自行车延误对于运输性能至关重要. 机器学习模型有效地使用稀疏的GPS数据和外部因素来预测这些延迟,为交通管理和政策提供信息.
科学领域:
- 运输工程 运输工程
- 数据科学数据科学数据科学
- 城市规划 城市规划
背景情况:
- 各国越来越多地采用数据驱动的方法来评估运输系统.
- 荷兰已经制定了自行车流量计数和GPS数据收集的协议.
- 准确估计在信号交叉点的自行车延误对于性能评估至关重要.
研究的目的:
- 开发一个通用框架来分析循环数据.
- 通过使用机器学习,估计信号交叉点的平均自行车延误.
- 评估使用稀疏的GPS数据用于延迟估计的可行性.
主要方法:
- 利用荷兰每年100万次骑自行车的数据集.
- 应用了各种机器学习模型:随机森林,k-最近邻居,支持向量回归,极端梯度增强和神经网络.
- 集成稀疏的GPS循环数据与外部信息,如天气和交叉路口的复杂性.
主要成果:
- 证明了用不完整的GPS数据估计自行车延误的可行性.
- 机器学习模型通过结合补充数据源成功预测了延迟.
- 展示了将稀疏的传感器数据与上下文信息相结合的价值.
结论:
- 数据驱动的方法,特别是机器学习,可以有效地估计自行车延误.
- 稀少的GPS数据,当与外部信息增强时,是用于运输分析的可行资源.
- 调查结果支持信息化的交通管理,自行车政策制定和基础设施规划.
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