A Deep Learning Model for Off-Ramp Hourly Flow Estimation

This paper aims to study the traffic volume of freeway off-ramps. Freeways are the main corridors in a transportation network serving a large portion of the traffic volume. Generally, this traffic passes into the lower level roads through off-ramps. Therefore, the traffic condition of the off-ramps is an essential factor affecting the transportation network operation. Among various traffic measures, traffic volume is the most challenging one as the continuous collection of volume data is impractical. Therefore, this study aims to estimate the ramps' hourly traffic volume using a deep learning method and explore the impacts of the input feature-space and ground-truth data collection strategies on the models' performance. The focus in this study is on traffic volume estimation of off-ramps since on-ramps and off-ramps function distinctly and should be analyzed separately. The primary data sources used in this study are volume counts, probe speeds, and infrastructure characteristics of the road segments. Through training and testing several neural network models, it became evident that the incorporation of traffic flow characteristics and infrastructure attributes of the lower-level road connected to the freeway significantly improves the off-ramp's traffic volume estimation accuracy. Further, analysis illustrated that the model could capture the temporal relationships between traffic volumes of a segment at different times; however, it was not able to establish the spatial relations between the traffic volumes of different ramps across the network.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 16p

Subject/Index Terms

Filing Info

  • Accession Number: 01764179
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-01585
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:21AM