Active Bottleneck Management on Freeways through Connected Vehicles

This study is focused on bottleneck formations on freeways and how utilization of connected vehicles could improve the traffic flow. Bottlenecks are restrictions points along a freeway that have lower capacities than their upstream segments. In this study, the focus is on sag curves and tunnels. Using the Hampton Roads Bridge Tunnel (HRBT) in Norfolk as the study site, the team developed several models to capture traffic behavior through a tunnel. An extensive literature review is conducted to document previous findings on traffic flow and control at sag curves and tunnels. It also included relevant active traffic management strategies, the use of connected vehicles to estimate the state of system, calibration of microsimulation models, and the traffic management practices for tunnels. A number of studies has been completed in the past on various technologies to manage the congestion within tunnels and strategies to estimate and improve the traffic conditions within a tunnel. Managing the length of queues and the duration of bottlenecks in the context of tunnel traffic have been investigated as well. In this project, traffic data from fixed sensors and probe vehicle data along the HRBT corridor are collected and analyzed to characterize the pertinent traffic flow phenomena. Evolution of congestion patterns and how they impact the throughput are investigated. The data are then used to calibrate a microscopic simulation model developed in VISSIM. After calibrating the model created for the HRBT, the importance of the downstream observability in absorbing shockwaves and preventing bottleneck formation within the tunnel are investigated. Reduced downstream observability (i.e., ability of the driver to observe additional vehicles ahead) due to restricted tunnel geometry is regarded as an important factor in shockwave and phantom jam generation within a tunnel which lead to bottleneck formations and a lower throughput. For testing the effect of higher downstream observability in reducing the bottleneck formation, simulation scenarios are implemented, where a percentage of cars within the traffic is assumed to have higher downstream observability. These cars are assumed to be probe cars which have higher downstream observability because of connectivity to other probe cars and the infrastructure. The results of the tests show significant travel time reductions and capacity increase at the tunnel. The research team also investigated traffic control options that could improve the throughput and delays in a typical sag curve. In particular, a control strategy based on the variable speed limit (VSL) principles is developed and simulated using an advanced car following model. Traffic flow along a sag curve is simulated using the intelligent driver model (IDM), a time-continuous car-following model. A feedback control algorithm is developed for adjusting the approach speeds of connected vehicles (CVs) so that the throughput of the sag curve is maximized. Depending on the traffic density at the sag curve, adjustments are made to the speeds of the CVs. A simulation-based optimization method using a meta-heuristic algorithm is employed to determine the critical control parameters. Various market penetration rates for CVs are also considered in the simulations. Even at relatively low market penetration rates (e.g., 5-10 %), significant improvements in travel times and throughput are observed. Additional details of this model are presented in the report.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Photos; References; Tables;
  • Pagination: 57p

Subject/Index Terms

Filing Info

  • Accession Number: 01664828
  • Record Type: Publication
  • Contract Numbers: NTC2016-MU-R-04
  • Created Date: Mar 13 2018 10:14AM