Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

摘要: With shared mobility services becoming increasingly popular and vehicle automation technology advancing fast, there is an increasing interest in analysing the impacts of large-scale deployment automated vehicles. In this study, a large fleet vehicles providing private rides to passengers introduced agent-based simulation model based on city Amsterdam, Netherlands. The dimensioned for sufficient service efficiency during peak-hours, meaning that off-peak hours substantial share idle, requiring relocation strategies. This study assesses performance zonal pro-active strategies on-demand passenger transport under constrained curbside parking capacity: (1) demand-anticipation, (2) even supply dispersion (3) balancing between demand are analysed regard (passenger waiting times, operational efficiency), externalities (driven mileage, usage) equity (spatial distribution provision). All outperformed by naive remain-at-drop off-location strategy scenario where capacity abundance. demand-anticipation heuristic leads highest average times due bunching at demand-hotspots which results uneven usage facilities. most favourable achieved with heuristics supply, costs higher driven mileage idle These open up opportunities municipalities accompany introduction fleets suitable management mitigate undesired effects.