A General Equilibrium Model for Integrated CAV Ridesourcing and Transit Services for the Morning Commute

Details

• Creators:
  Fan, Rong ; MacCabe, Dan ; Ban, Xuegang (Jeff)
• Corporate Creators:
  University of Washington. Department of Civil and Environmental Engineering
• 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 Vehicles Connected Vehicles General Equilibrium Model Multimodal Transportation Optimization Integrated Transit Public Transit Ridesourcing Traffic Equilibrium
• Resource Type:
  Manuscript
• Geographical Coverage:
  United States
• Corporate Publisher:
  University of Washington. Department of Civil and Environmental Engineering
• Abstract:
  Commuting congestion increases alongside the prosperity of urban cities. With the rapid development of ride sourcing services and the advances of the connected and automated vehicles (CAV), researchers are seeking innovative approaches to alleviate commuting congestion by integrating CAV-based ride sourcing and transit services. We propose a general equilibrium model for an integrated, multimodal CAV ride sourcing and transit system. Our model captures the economic behaviors and interactions of the major players (i.e. the ride sourcing company and customers) in the commuting problem by optimizing the profit of the ride sourcing company and the utility of customers, as well as considering the network congestion. Results show that the demand for shared rides and transit are affected by the relative costs of different types of travel modes of the integrated system. While transit uses generally reduces congestion, ridesharing alone may still cause higher congestion compared with solo driving because of the deadhead miles. Our model can systematically investigate the mode choices of customers and measure the resulting congestion effect in a multimodal network, which helps bring valuable insights to transportation planners, transit agencies, and ride sourcing companies.
• Format:
  PDF
• Funding:
  69A3551747124
• Collection(s):
  University Transportation Centers Autonomous Vehicles
• Main Document Checksum:
  urn:sha-512:259ba106f57d5aa06ca922ea9d0105e48163fa98235d8a660baf6a035075abd181a42c27de6f9d41ddada32e47d5eaef3e1978b52d2eb043250a8897791160f6
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/61341/dot_61341_DS1.pdf
• File Type:
  [PDF - 1.27 MB ]