Reservation Admission Under Overstay Risk in Smart Parking Systems

In the context of smart cities, reservation-based parking systems may accept more requests than they can ultimately honor when users stay longer than declared. This paper introduces a risk-aware admission-control framework that explicitly accounts for overstay uncertainty while preserving high facility utilization. For each future time slot affected by a request, admission is based on an upper bound on the probability that too few occupied stalls will be released. We derive three alternatives: a dependence-robust Markov bound, a sharper Chernoff bound under conditional independence, and a Cantelli bound incorporating common exogenous impediments. We also compare uniform and aggregate allocations of a reservation-level risk budget and establish an incremental per-acceptance reliability guarantee. Simulation experiments under saturated demand, different overstay regimes, correlated disruptions, and four operational contexts show that nominal admission and static buffering may accept many reservations that cannot be honored. By contrast, risk-aware policies substantially reduce or eliminate no-park events while maintaining high utilization. Uniform Chernoff admission achieves the highest service rate in most scenarios, serving more users than the deterministic baselines despite accepting fewer reservations. The experiments also reveal that uniform slot-level budget allocation implicitly favors shorter stays, an effect that can markedly increase throughput when parking durations are heterogeneous.