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Field Data Based Data Fusion Methodologies to Estimate Dynamic Origin-Destination Demand Matrices from Multiple Sensing and Tracking Technologies
| 题名： | Field Data Based Data Fusion Methodologies to Estimate Dynamic Origin-Destination Demand Matrices from Multiple Sensing and Tracking Technologies |
| 作者： | Zhu, S.; Guo, Y.; Zheng, H.; Peeta, S.; Ramadurai, G.; Wang, J. |
| 关键词： | Data fusion##Matrices##Sensing technologies##Tracking technologies##Sensors##Traffic data##Vehicles##Vehicle identification## |
| 摘要： | Modern technologies can use various types of sensors to collect traffic data; these include GPS, blue tooth, video, automatic vehicle identification (AVI), plate scanning, etc. Based on the characteristic of data collected by sensors, sensors can be categorized as follows. (a) Counting sensors: these sensors can count vehicles on a single lane or a set of lanes in the network. (b) Image/video sensors: these sensors can take images or videos of moving flows. (c) Vehicle-ID sensors: these sensors can be used to identify vehicle IDs in the network. Those sensors/technologies can get variant traffic data including link counts, intersection turning movements, flows and travel time on links and partial paths. This research seeks to propose a Bayesian method and tries to synthesize these multiple sources of data together to estimate dynamic O-D demand, thereby filling a key gap in the current dynamic O-D demand estimation literature. |
| 总页数： | 65 |
| 报告类型： | 科技报告 |
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