Big Transportation Data Analytics
| 项目名称： | Big Transportation Data Analytics |
| 摘要： | The world of Big Data is moving fast. There are several private sector Big Data vendors using data from vehicle probes, and from Wi-Fi, cellular, and Bluetooth data traces to synthesize transportation information such as vehicle speeds and origin-destination travel flows. In addition, Utah Department of Transportation (UDOT)'s Traffic Operations Center recently just purchased HERE traffic probe data to assist traffic management and road network performance assessment. UDOT also possesses Big Data in the form of Performance Measurement System (PeMS), that has been archived since 2008. This historic data set should be mined to learn new things about the direct and proximate causes of traffic volume change and travel reliability. There is also an urging need to develop analytical approaches that leverage existing historic data to reveal methods for estimating traffic, with the potential to reduce the burden of the annual traffic count program. UDOT maintains an annual traffic count program which requires the acquisition of hundreds of short-duration counts each year. This traffic count effort represents a significant cost to UDOT, while also exposing UDOT staff to the dangers inherent to being exposed to traffic. The proposed research will seek to determine whether statistical modeling and/or machine learning methods might be employed to partially or fully supplant the short-duration traffic count program and, in doing so, reduce the effort, cost, and staff exposure of UDOT's traffic count program. The primary objective of this research is to determine whether statistical modeling and/or machine learning can be applied to for estimating/predicting traffic conditions (e.g. vehicle miles of travel (VMT) and reliability) when it is conflated with other available data sets, such as demographics, income, highway capacity, and highway improvement projects. The secondary objective is to develop analytical methods that can be used by UDOT in the future to estimate traffic volumes and reliability for short-duration traffic count sites, based on other available data, and quantitative relationships. A range of analytical methods will be attempted, from well-established methods (e.g. traffic engineering techniques) to more sophisticated methods, such as advanced statistical modeling and machine learning. |
| 状态： | Active |
| 资金： | 90000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Mountain-Plains Consortium |
| 项目负责人： | Kline, Robin |
| 执行机构： | University of Utah, Salt Lake City |
| 主要研究人员： | Liu, Xiaoyue Cathy |
| 开始时间： | 20171115 |
| 预计完成日期： | 20220731 |
| 实际结束时间： | 0 |
相关文献
- Application of Data Science and Big Data Analytics in Underground Transportation Infrastructure
- Big Data Analytics to Aid Developing Livable Communities
- Application of Big Data Approaches for Traffic Incident Management (TIM)
- Big Data Visualization and Spatiotemporal Modeling of Aggressive Driving
- Transportation Big Data: Networked Sensor Data Error Estimation
- Integrated analytics and visualization for multi-modality transportation data
检索历史
应用推荐
