Improved traffic operations through real-time data collection and control.

Details

• Creators:
  Boyles, Stephen ; Levin, Michael W.
• Corporate Creators:
  University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Intersections
  • Traffic congestion
  • Traffic signal phases
  • Real time control
  • Back pressure
  • Autonomous vehicles
  • reservation-based intersection control
  • dynamic traffic assignment
  • pressure-based policy
• Publication/ Report Number:
  D-STOP/2016/108
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Texas
• Corporate Publisher:
  University of Texas at Austin. Center for Transportation Research
• Abstract:
  Intersections are a major source of delay in urban networks, and reservation-based intersection control for ; autonomous vehicles has great potential to improve intersection throughput. However, despite the high ; flexibility in reservations, existing control policies are fairly limited. To increase reservation throughput, ; we adapt two pressure-based policies for reservations in dynamic traffic assignment. The backpressure ; policy is throughput optimal in communications networks, but communications networks are significantly ; different from traffic networks. We propose that congestion propagation can be introduced by modeling ; each cell in the cell transmission model as a link in a communications network. The finite-buffer limitation ; on the maximum pressure per cell can be overcome by including queue spillback to previous cells and ; links. However, a counterexample shows that local pressure-based policies such as backpressure cannot be ; throughput optimal under user equilibrium route choice. Therefore, we also adapt the P0 policy to ; reservations. Its adaptation is more straightforward, although dynamic traffic assignment also prevents ; proving that P0 is throughput optimal. Nevertheless, results on the downtown Austin network show that ; both backpressure and P0 performed significantly better than first-come-first-served, which has been used ; in most previous work on reservations. Therefore, although backpressure and P0 cannot be proven to be ; throughput optimal, they provide a better alternative to existing policies.
  More +
• Format:
  PDF
• Funding:
  DTRT13-G-UTC58
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31774/dot_31774_DS1.pdf
• File Type:
  [PDF - 350.39 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:1af2996411231fca726d1d6966838cbf0228367aff7c1fcb794c880fd9b648bd