Coordination of IVI and transit signal priority on transit evacuations.

Details

• Creators:
  Zhang, Li ; Wen, Yi ; Wang, Feng ; Eksioglu, Sandra
• Corporate Creators:
  National Center for Intermodal Transportation for Economic Competitiveness
• Subject/TRT Terms:
  • Disasters and emergency operations
  • Evacuation
  • Bus transit
  • Traffic signal priority
  • Wireless communication systems
  • Adaptive control
  • Microsimulation
  • Travel time
  • Traffic delays
  • Routes and routing
  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  National Center for Intermodal Transportation
• Abstract:
  During an emergency evacuation, execution time is always critical to the evacuees who are ; transit dependent. Transit Signal Priority (TSP) can speed up the transit services by prioritizing ; the approaching bus at a signalized intersection. With the emergence of IntelliDrive (formerly ; known as IVI), which is a wireless communication technology used to transfer data among ; vehicles and infrastructures, a TSP system can obtain more accurate traffic data and react to ; the approaching bus in a wider area. This report proposes an adaptive TSP system to facilitate ; the transit-based emergency evacuation on the basis of the U.S. Department of Transportation ; (DOT)’s IntelliDrive initiative. ; The objective of this project is to study the TSP and IntelliDrive coordination and to evaluate ; the impacts of the proposed TSP strategies on the transit-based emergency evacuation. The ; emergency evacuation model consists of two optimization models: a TSP optimization model ; and a bus routing optimization model. The TSP optimization model includes bus travel time ; prediction and traffic signal optimization. The bus travel time prediction is used to estimate the ; bus arrival time at the intersection. The traffic signal optimization considers both the bus delay ; and the network-wide vehicle delay. It determines when and which TSP strategy will be applied. ; The principal inputs for the TSP optimization model are: bus speed, position, busload, queue ; length, and traffic signal status. The bus routing optimization model is proposed to optimize the ; transit vehicles allocation and routing. The Dijkstra Algorithm has been modified to find out the ; shortest paths among the pickup points and the shelters in the network. Additionally, a hybrid ; intelligence algorithm consisting of a Genetic Algorithm and a Hill Climbing Algorithm, which ; was developed under the sponsorship of a previous project, has been applied to solve the ; transit vehicle routing and allocation problem.
  More +
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/28373/dot_28373_DS1.pdf
• File Type:
  [PDF - 3.16 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:29ba57472fcba00b4f46b33844f6f548cfb0f9b0b932ace971770afee28bdee6be4ab054e41a319e17e20fe0974526ec9d03ed88a688e4c3bfee9811e11174e4