原文传递
Ubiquitous Traffic Volume Estimation through Machine-Learning Procedure
| 题名： | Ubiquitous Traffic Volume Estimation through Machine-Learning Procedure |
| 作者： | Hou, Yi (ORCID:0000000281730923)##Garikapati, Venu (ORCID:0000000316031883) |
| 关键词： | traffic volume estimation##machine learning |
| 摘要： | Traffic volume data is one of the most important metrics for accurate assessment of the performance of a transportation system. Quality volume data is required to effectively assess extent of delay and congestion, detect real-time perturbations to the network, and understand traffic patterns during major weather events. Traffic volume on freeways are typically collected through continuous count stations installed by state DOTs, while there is lack of traffic volume observability on off-freeway roads. The National Renewable Energy Laboratory (NREL), in Collaboration with the I-95 Corridor Coalition and the University of Maryland, extended its research into estimating volumes anywhere anytime from industry probe based data for off-freeway roads. NREL combined vehicle probe count data with several other data sets (speed, whether, roadway geometry, time-of-day, day-of-week, etc.) to estimate hourly volumes as well as AADTs. The research validated and demonstrated the machine learning model, namely XGBoost, using data collected from Pennsylvania, North Carolina, and Tennessee. |
| 报告类型： | 科技报告 |
相关文献
- Leveraging Machine Learning To Enhance Network Security.
- Developing an Interactive Machine-Learning-based Approach for Sidewalk Digitalization.
- Traffic Flow Theory, 2006. Transportation Research Record. Journal of the Transportation Research Board, No. 1965.
- Frequency-Based Feature Extraction for Malware Classification.
- Statistical Inference Using Stochastic Gradient Descent: Volumes 1 and 2.
- URBAN RAIL CORRIDOR CONTROL THROUGH MACHINE LEARNING: AN INTELLIGENT VEHICLE-HIGHWAY SYSTEM APPROACH.
检索历史
应用推荐
