Development of Dynamic Network Traffic Simulator for Mixed Traffic Flow Under Connected and Autonomous Vehicle Technologies

Details

• Creators:
  Wang, Chaojie ; Wang, Yu ; Peeta, Srinivas
• Corporate Creators:
  Center for Connected and Automated Transportation (CCAT) ; Purdue University ; Georgia Institute of Technology
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Autonomous Land Vehicles Autonomous Vehicles Connected Vehicles Dynamics Human Factors Intelligent Vehicles Mixed Traffic Mobile Applications Stability Analysis Traffic Flow Traffic Network Traffic Safety Traffic Simulation Vehicle Mix

  More +
• Publication/ Report Number:
  CCAT Report #22
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report 8/15/2017 -04/30/2021
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  Connected and autonomous vehicles (CAVs) will generate a revolution in the transportation system, with great potential to improve traffic safety, efficiency, and environmental sustainability. However, the transition to CAVs will occur over time and, during it, CAVs will coexist with human-driven vehicles (HDVs) in the traffic flow. While several studies have examined the potential impact of CAVs on the driving environment, there is a key need for modeling approaches that can characterize network level evolution of information flow propagation and traffic flow dynamics and their impacts on stability under mixed traffic streams. There is the need for a comprehensive traffic flow modeling framework that incorporates different levels of connectivity and automation as well as different market penetration rates. This study first develops a multiclass information flow propagation model to describe and control the interactions between information flow dynamics and traffic flow dynamics. A two-layer model is developed to characterize the information flow propagation wave (IFPW) under the designed queuing strategy with solution methods under homogenous and heterogeneous conditions. This study also proposes a multiclass traffic assignment model, where HDV users and CAV users follow different route choice principles, characterized by the cross-nested logit (CNL) model and user equilibrium (UE) model, respectively. Combining two models, traffic managers can control the propagation of information of different information classes under V2V communications and design effective planning and operational strategies to leverage the advantages of CAVs and manage traffic congestion under mixed traffic flows.
• Format:
  PDF
• Funding:
  69A3551747105
• Collection(s):
  University Transportation Centers Autonomous Vehicles
• Main Document Checksum:
  urn:sha-512:e8960922587dc3ef6d3a6901b55be6127353281c23bc8c4eeb122dd5b121b3150b2d0c53c1a5b4c696ab7c81011288b80de7113f43bd8f412f7cd2f4862ec1d7
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/72911/dot_72911_DS1.pdf
• File Type:
  [PDF - 3.23 MB ]