HighlightsLinear program of routing and passenger matching for shared autonomous vehicles considering congestion.System optimal traffic assignment formulation with the link transmission model.Formulation may be useful for congestion-aware, real-time routing heuristics.Shared autonomous vehicle scenarios are compared with a system optimal dynamic traffic assignment benchmark.Peak hour travel patterns significantly increase congestion and vehicle miles traveled as compared with off-peak scenarios.
AbstractWe study the shared autonomous vehicle (SAV) routing problem while considering congestion. SAVs essentially provide a dial-a-ride service to travelers, but the large number of vehicles involved (tens of thousands of SAVs to replace personal vehicles) results in SAV routing causing significant congestion. We combine the dial-a-ride service constraints with the linear program for system optimal dynamic traffic assignment, resulting in a congestion-aware formulation of the SAV routing problem. Traffic flow is modeled through the link transmission model, an approximate solution to the kinematic wave theory of traffic flow. SAVs interact with travelers at origins and destinations. Due to the large number of vehicles involved, we use a continuous approximation of flow to formulate a linear program. Optimal solutions demonstrate that peak hour demand is likely to have greater waiting and in-vehicle travel times than off-peak demand due to congestion. SAV travel times were only slightly greater than system optimal personal vehicle route choice. In addition, solutions can determine the optimal fleet size to minimize congestion or maximize service.
Congestion-aware system optimal route choice for shared autonomous vehicles
Tramsportation Research, Part C: Emerging Technologies ; 82 ; 229-247
2017-06-26
19 pages
Article (Journal)
Electronic Resource
English
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