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实时跟踪数据和机器学习方法用于绘制行人行走行为的地图:莫拉图瓦大学的一项案例研究
Harini Sawandi1, Amila Jayasinghe1, Guenther Retscher2
1Department of Town & Country Planning, University of Moratuwa, Moratuwa 10400, Sri Lanka.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究利用手机数据绘制行人行为的地图,以识别高流量行走区域. 目标是为创建更适合行人使用的城市环境提供信息.
科学领域:
- 城市规划 城市规划
- 运输工程 运输工程
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 随着城市化和交通拥堵的增加,步行者友好的环境变得越来越必要.
- 一个重大挑战是缺乏关于行人运动的数据,以告知城市设计.
- 识别高行人度区域对于改善可行性至关重要.
研究的目的:
- 开发和介绍一种用于绘制行人行走行为的定量方法.
- 利用实时移动电话传感器数据进行城市分析.
- 在大学校园环境中识别关键的行人路线和位置.
主要方法:
- 基于位置服务 (LBS) 的定位数据与无监督机器学习技术的整合.
- 应用K-means集群来识别622个不同的速度集群.
- 使用新型信号处理分析行人步行速度,时间和方向.
主要成果:
- 使用移动GPS信号和机器学习成功地绘制了行人行走行为的地图.
- 识别特定的集群,代表不同步行模式在高峰时间.
- 在案例研究区域内的关键站点和路线上的行人活动量化.
结论:
- 开发的方法提供了一种定量方法,以了解城市地区的行人运动.
- 这项研究为通过数据驱动的洞察力改善城市步行能力提供了基础.
- 这些发现可以指导城市规划者在创造更有效的行人友好的环境.
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