Shared Autonomous Vehicle Modeling Considering System Optimization and Simulation
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS(2024)
摘要
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.
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关键词
Shared autonomous vehicles,Vehicle assignment,Optimization,Simulation
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