Shared Autonomous Vehicle Modeling Considering System Optimization and Simulation

This paper optimizes the assignment of shared automated vehicles under users’ uncertain departure times. Automated vehicles can drive themselves, so no staff are needed to relocate vehicles in the one-way carsharing system. To optimize fleet placement and use, a two-phase solution method was established. Phase 1 strategically distributes vehicles across stations using a system optimization approach, while Phase 2 tracks vehicle movements via an agent-based simulation model. Phase 1 solutions serve as inputs to Phase 2 simulations. Using a fleet size of roughly 10,000 vehicles, case study applications were run across the Austin, Texas region's six-county network. In the base case setting, results suggest that system profits are optimized when vehicle rental is priced at $1.28/km ($0.8/mi). The number of proactive vehicle relocations falls 9.8% if the relocation operation cost rises from $0.096/km ($0.06/mi) to $0.32/km ($0.2/mi). Average per-trip profit is $10.60 when using high-cost vehicles, and $11.60 when using low-cost vehicles. Results from a 3-h simulation show an average person-trip length of 25 km (15.6 mi), with 29.6 min of average driving time. When a 24-h day was simulated, the vehicle-occupied time and vehicle-distance traveled were 4 h and 200 km (125 mi) per vehicle-day, respectively. The low coefficient of variation of satisfied demand across 30 demand scenarios suggests the robustness of the two-phase solution method.