Abstract:
This study presents a multi-objective optimization approach for determining
optimal aisle widths in underground parking facilities, balancing vehicle maneuverability against parking capacity. The research methodology integrates geometric modeling,
computational simulations, and empirical validation to establish evidence-based recommendations for aisle width design. Through systematic testing of aisle widths ranging from
4.5 to 6.0 m across various vehicle types, the study identifies 5.0–5.5 m as the optimal range
that maximizes both objectives for modern vehicle fleets. Geometric modeling establishes
theoretical minimum widths based on vehicle turning radii, while software simulations
quantify maneuverability metrics including parking success rates, time requirements, and
collision probabilities. Physical testing in operational underground parking facilities validates these findings through controlled experiments with drivers of varying experience
levels. The research demonstrates that aisle widths below 5.0 m significantly compromise
maneuverability, particularly for larger vehicles, while widths exceeding 5.5 m provide negligible additional benefits while reducing capacity. A case study application in Kazakhstan,
examining regional vehicle distributions and regulatory frameworks, confirms the model’s
practical utility. The findings suggest that current parking standards in some regions may
require revision to accommodate changing vehicle dimensions. This optimization framework provides urban planners, architects and engineers with a data-driven methodology
for designing underground parking facilities that enhance both user experience and space
utilization efficiency.
