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A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data
| 题名： | A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data |
| 正文语种： | 英文 |
| 作者： | Nadia Bathaee; Alireza Mohseni; SeJoon Park; J. David Porter; David S. Kim |
| 作者单位： | School of Mechanical, Industrial and Manufacturing Engineering; |
| 关键词： | Bluetooth; cluster analysis; traffic mode differentiation |
| 摘要： | A variety of automatic data collection technologies have been used to gather road and highway system data. The majority of these automatic data collection technologies are designed to collect vehicle-based data and either do not have the capability to collect other travel mode data (e.g., bicycles and pedestrians), or may need to be deployed differently to support this capability. One type of wireless-based data collection system that has been deployed recently is based on Bluetooth technology. A key feature of Bluetooth-based data collection systems that makes travel mode identification feasible is that the Bluetooth-enabled devices within vehicles are also present on bicyclists and pedestrians. This research explores the effectiveness of applying cluster analysis methods when processing data collected via Bluetooth technology from vehicles, bicyclists, and pedestrians to automatically identify the associated travel modes. The results of several experiments utilizing multiple Bluetooth-based data collection units arranged linearly and in relatively close proximity on a simulated intersection demonstrate the potential of cluster analysis to accurately differentiate transportation modes from the collected data. |
| 出版年： | 2018 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 22 |
| 期： | 4 |
| 页码： | 353-364 |
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