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基于检测和聚类框架的群众来源轨迹的城市道路表面状况传感
Haiyang Lyu1, Qiqi Zhong1, Yu Huang1
1Department of Geographic Information Science, School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究介绍了一种使用智能手机传感器来检测路面状况的框架,该框架可以从众包数据中检测路面状况. 它通过绘制道路质量图表,使城市交通规划和维护更好.
科学领域:
- 城市规划和交通工程.
- 地理空间数据分析.
- 信号处理和机器学习
背景情况:
- 道路表面状况对城市交通流量和交通基础设施管理有重大影响.
- 准确和实时的道路状况数据对于有效的城市维护和规划至关重要.
- 现有的道路状况评估方法可能昂贵且耗费大量劳动力.
研究的目的:
- 开发一种新的框架,利用众筹智能手机数据来感知道路表面状况.
- 利用智能手机传感器 (GPS,加速度计,定向传感器) 来检测道路损坏和特征.
- 为城市道路维护和规划提供可扩展和具有成本效益的解决方案.
主要方法:
- 使用来自智能手机的众筹轨迹,配备GPS,定向传感器和加速度计.
- 将空间转换应用于加速度计数据,并计算方向角以获取运动信息.
- 采用波纹散射转换用于特征编码和LSTM神经网络来提取道路表面的碰撞特征 (BFRS).
- 集成BFRS使用两阶段集群方法,考虑距离和方向进行表示.
主要成果:
- 成功地从智能手机传感器数据中提取了道路表面的碰撞特征 (BFRS).
- 证明了框架处理众筹路径以评估道路状况的能力.
- 在地图上可视化计算的道路表面状况,提供实用的见解.
结论:
- 拟议的检测和聚类框架为评估城市道路表面状况提供了一种实用和有效的方法.
- 通过先进的信号处理和机器学习处理的众包智能手机数据,可以有效地告知城市道路维护和规划.
- 这种方法在改善城市交通网络的管理和安全方面具有重大潜力.
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