Predicting Travel Time on Freeway Corridors: Machine Learning Approach
| 项目名称： | Predicting Travel Time on Freeway Corridors: Machine Learning Approach |
| 摘要： | Estimating the travel time of any segments on freeways is of great importance to route planning, traffic monitoring, and bottleneck identification. Many researchers have conducted numerous studies on estimating travel time. However, travel time forecasting is still a very challenging problem since it can be affected by diverse complex factors, including spatial correlations, temporal dependencies, and external conditions (e.g., weather). The purpose of this project is to develop machine learning approach that incorporates the stochastic characteristics of segments to model the travel time on a freeway corridor. Segment travel time correlations will be analyzed and examined using an advanced model (e.g., pattern recognition model and neural network model) based on historical travel time data. To evaluate the quality of such model, other models (including time-series models, and linear regression models) which may not explicitly consider spatial-temporal correlations between segment travel times will also be developed. The proposed approach will be developed, used and tested to analyze and predict the travel time on several freeway corridors in Charlotte, North Carolina using vehicle probe data. The advantages and disadvantages of each model will also be identified and compared. |
| 状态： | Active |
| 资金： | 90007 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | University of North Carolina, Charlotte |
| 项目负责人： | Fan, Wei (David) |
| 执行机构： | Center for Advanced Multimodal Mobility Solutions and Education |
| 主要研究人员： | Fan, Wei (David) |
| 开始时间： | 20181001 |
| 预计完成日期： | 20200930 |
| 实际结束时间： | 0 |
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