In the context of rapid urbanization in Tashkent and ongoing Smart City reforms, deterministic transport-planning approaches may inadequately capture non-linear shifts in recorded passenger demand under structural change. This study proposes a planning-oriented scenario framework combining a multilayer perceptron (MLP) regressor with an externally specified logistic guardrail to support public transport capacity analysis through 2030. The empirical basis comprises anonymized annual operational reports from eight bus depots for 2014-2023 and rolling-stock inventory data as of 01.06.2025. A linear pre-shock trend is retained only as a conservative baseline illustrating the limits of simple extrapolation after disruption. Given the very small annual sample and the single hold-out check for 2023, the framework is not presented as evidence of stable forecasting performance. Instead, it serves as a decision-support instrument mapping exogenously specified digital trip-capture assumptions into conditional demand trajectories under a conservative normative upper bound and linking them to rolling-stock planning implications for long-horizon public transport planning needs.
Keywords
Passenger DemandPublic Transport PlanningScenario AnalysisMLP RegressorConditional Hold-Out CheckSustainable Urban Mobility
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