On the joint network equilibrium of parking and travel choices under mixed traffic of shared and private autonomous vehicles

This paper investigates the joint network equilibrium of parking and travel route choices in the future mobility paradigm with mixed traffic of private and shared autonomous vehicles. Specifically, the authors consider that private autonomous vehicle (PAV) travelers need to make both route and parking choices though the vehicle can drive itself to parking after dropping off the traveler. The origin–destination-based shared autonomous vehicle (OD-SAV) ride service allows multiple travelers with a common origin and destination (OD) pair to share the same vehicle, while the routing of OD-SAVs is determined by the operator. In this context, a bi-level model is developed which optimizes the OD-SAV service fare and OD-SAV flow in the upper-level, and specifies the travel demands, route and parking choices, and network traffic equilibrium in the lower-level. In particular, PAV travelers choose their route and parking location to minimize their own travel time or cost, while the routing of OD-SAVs is subject to the decision of the operator. The OD-SAVs controlled by the operator may minimize each vehicle’s travel time (user equilibrium, ‘UE’) or minimize the total travel time of all OD-SAVs operated by the operator (Cournot-Nash equilibrium, ‘CN’). The joint equilibrium of travel and parking under either UE or CN routing for OD-SAVs can be modeled as a Variational Inequalities (VI) problem. The uniqueness/non-uniqueness properties of the joint network equilibrium are investigated. Moreover, the authors examine the OD-SAV service operator’s optimal operation decisions subject to the lower-level network equilibrium. Solution approaches are introduced to solve the joint equilibrium and the proposed bi-level model. Numerical studies are conducted to illustrate the model and analytical results, and also to provide further understanding.


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  • Accession Number: 01889953
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 14 2023 8:44AM